Note : Les descriptions sont présentées dans la langue officielle dans laquelle elles ont été soumises.
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Method and system for object recognition via a computer vision
application
The present invention refers to a method and a system for object recognition
via a computer vision application.
Computer vision is a field in rapid development due to abundant use of
electronic devices capable of collecting information about their surroundings
via
sensors such as cameras, distance sensors such as LIDAR or radar, and depth
camera systems based on structured light or stereo vision to name a few.
These electronic devices provide raw image data to be processed by a
computer processing unit and consequently develop an understanding of an
environment or a scene using artificial intelligence and/or computer
assistance
algorithms. There are multiple ways how this understanding of the environment
can be developed. In general, 2D or 3D images and/or maps are formed, and
these images and/or maps are analyzed for developing an understanding of the
scene and the objects in that scene. One prospect for improving computer
vision is to measure the components of the chemical makeup of objects in the
scene. While shape and appearance of objects in the environment acquired as
2D or 3D images can be used to develop an understanding of the environment,
these techniques have some shortcomings.
One challenge in computer vision field is being able to identify as many
objects
as possible within each scene with high accuracy and low latency using a
minimum amount of resources in sensors, computing capacity, light probe etc.
The object identification process has been termed remote sensing, object
identification, classification, authentication or recognition over the years.
In the
scope of the present disclosure, the capability of a computer vision system to
identify an object in a scene is termed as "object recognition". For example,
a
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computer analyzing a picture and identifying/labelling a ball in that picture,
sometimes with even further information such as the type of a ball
(basketball,
soccer ball, baseball), brand, the context, etc. fall under the term "object
recognition".
Generally, techniques utilized for recognition of an object in computer vision
systems can be classified as follows:
Technique 1: Physical tags (image based): Barcodes, OR codes, serial
numbers, text, patterns, holograms etc.
Technique 2: Physical tags (scan/close contact based): Viewing angle
dependent pigments, upconversion pigments, metachromics, colors
(red/green), luminescent materials.
Technique 3: Electronic tags (passive): RFID tags, etc. Devices attached to
objects of interest without power, not necessarily visible but can operate at
other frequencies (radio for example).
Technique 4: Electronic tags (active): wireless communications, light, radio,
vehicle to vehicle, vehicle to anything (X), etc. Powered devices on objects
of
interest that emit information in various forms.
Technique 5: Feature detection (image based): Image analysis and
identification, i.e. two wheels at certain distance for a car from side view;
two
eyes, a nose and mouth (in that order) for face recognition etc. This relies
on
known geometries/shapes.
Technique 6: Deep learning/CNN based (image based): Training of a
computer with many of pictures of labeled images of cars, faces etc. and the
computer determining the features to detect and predicting if the objects of
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interest are present in new areas. Repeating of the training procedure for
each
class of object to be identified is required.
Technique 7: Object tracking methods: Organizing items in a scene in a
particular order and labeling the ordered objects at the beginning. Thereafter
following the object in the scene with known color/geometry/3D coordinates. If
the object leaves the scene and re-enters, the "recognition" is lost.
In the following, some shortcomings of the above-mentioned techniques are
presented.
Technique 1: When an object in the image is occluded or only a small portion
of the object is in the view, the barcodes, logos etc. may not be readable.
Furthermore, the barcodes etc. on flexible items may be distorted, limiting
visibility. All sides of an object would have to carry large barcodes to be
visible
from a distance otherwise the object can only be recognized in close range and
with the right orientation only. This could be a problem for example when a
barcode on an object on the shelf at a store is to be scanned. When operating
over a whole scene, technique 1 relies on ambient lighting that may vary.
Technique 2: Upconversion pigments have limitations in viewing distances
because of the low level of emitted light due to their small quantum yields.
They
require strong light probes. They are usually opaque and large particles
limiting
options for coatings. Further complicating their use is the fact that compared
to
fluorescence and light reflection, the upconversion response is slower. While
some applications take advantage of this unique response time depending on
the compound used, this is only possible when the time of flight distance for
that sensor/object system is known in advance. This is rarely the case in
computer vision applications. For these reasons, anti-counterfeiting sensors
have covered/dark sections for reading, class 1 or 2 lasers as probes and a
fixed and limited distance to the object of interest for accuracy.
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Similarly viewing angle dependent pigment systems only work in close range
and require viewing at multiple angles. Also, the color is not uniform for
visually
pleasant effects. The spectrum of incident light must be managed to get
correct
measurements. Within a single image/scene, an object that has angle
dependent color coating will have multiple colors visible to the camera along
the
sample dimensions.
Color-based recognitions are difficult because the measured color depends
partly on the ambient lighting conditions. Therefore, there is a need for
reference samples and/or controlled lighting conditions for each scene.
Different sensors will also have different capabilities to distinguish
different
colors, and will differ from one sensor type/maker to another, necessitating
calibration files for each sensor.
Luminescence based recognition under ambient lighting is a challenging task,
as the reflective and luminescent components of the object are added together.
Typically luminescence based recognition will instead utilize a dark
measurement condition and a priori knowledge of the excitation region of the
luminescent material so the correct light probe/source can be used.
Technique 3: Electronic tags such as RFID tags require the attachment of a
circuit, power collector, and antenna to the item/object of interest, adding
cost
and complication to the design. RFID tags provide present or not type
information but not precise location information unless many sensors over the
scene are used.
Technique 4: These active methods require the object of interest to be
connected to a power source, which is cost-prohibitive for simple items like a
soccer ball, a shirt, or a box of pasta and are therefore not practical.
Technique 5: The prediction accuracy depends largely on the quality of the
image and the position of the camera within the scene, as occlusions,
different
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viewing angles, and the like can easily change the results. Logo type images
can be present in multiple places within the scene (i.e., a logo can be on a
ball,
a T-shirt, a hat, or a coffee mug) and the object recognition is by inference.
The
visual parameters of the object must be converted to mathematical parameters
at great effort. Flexible objects that can change their shape are problematic
as
each possible shape must be included in the database. There is always
inherent ambiguity as similarly shaped objects may be misidentified as the
object of interest.
Technique 6: The quality of the training data set determines the success of
the
method. For each object to be recognized/classified many training images are
needed. The same occlusion and flexible object shape limitations as for
Technique 5 apply. There is a need to train each class of material with
thousands or more of images.
Technique 7: This technique works when the scene is pre-organized, but this
is rarely practical. If the object of interest leaves the scene or is
completely
occluded the object could not be recognized unless combined with other
techniques above.
Apart from the above-mentioned shortcomings of the already existing
techniques, there are some other challenges worth mentioning. The ability to
see a long distance, the ability to see small objects or the ability to see
objects
with enough detail all require high resolution imaging systems, i.e. high-
resolution camera, lidar, radar etc. The high-resolution needs increase the
associated sensor costs and increase the amount of data to be processed.
For applications that require instant responses like autonomous driving or
security, the latency is another important aspect. The amount of data that
needs to be processed determines if edge or cloud computing is appropriate for
the application, the latter being only possible if data loads are small. When
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edge computing is used with heavy processing, the devices operating the
systems get bulkier and limit ease of use and therefore implementation.
Since ambient lighting conditions as well as active light probes/sources are
important parts of the data collected in image analysis for object
recognition, it
was an object of the present invention to provide a possibility to combine the
need for specialized light probes associated with an imaging sensor device
with
the need for visually pleasant ambient lighting into a single lighting device.
Furthermore, this design lowers the sensitivity of the computer vision
application to ambient lighting in general, as now the specialized light probe
intensity is directly related to ambient lighting intensity and, in some
cases,
enable chemistry-/physics-based recognition techniques.
Thus, a need exists for systems and methods that are suitable for simplifying
requirements for object recognition via a computer vision application.
Summary of the invention
The above-mentioned objects are solved by the system and the method with
the features of the respective independent claims. Further embodiments are
presented by the following description and the respective dependent claims.
In the first aspect, embodiments of the invention provide a system for object
recognition via a computer vision application. The proposed system comprises
at least the following components:
- an object to be recognized, the object having object-specific reflectance
and
luminescence spectral patterns,
- a light source which is composed of at least two illuminants and configured
to give a specific spectral response on demand and to illuminate a scene
including the object to be recognized by switching between the at least two
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illuminants, wherein at least one of the at least two illuminants is based on
at least one solid-state system,
- a sensor which is configured to measure radiance data of the scene
including the object when the scene is illuminated by the light source,
- a data storage unit which stores and provides luminescence spectral
patterns together with appropriately assigned respective objects,
- a data processing unit which is configured to extract/estimate the object-
specific luminescence spectral pattern of the object to be recognized out of
the radiance data of the scene and to compare/match the
estimated/extracted object-specific luminescence spectral pattern with the
luminescence spectral patterns stored in the data storage unit, and to
identify a best matching luminescence spectral pattern and, thus, the object
assigned to the identified best matching luminescence spectral pattern.
The at least one solid-state system may be chosen from the group of solid-
state
systems comprising semiconductor light-emitting diodes (LEDs), organic light-
emitting diodes (OLEDs), or polymer light-emitting diodes (PLEDs).
In some embodiments, it may be advantageous to switch between the
illuminants of the light source at a rate faster than the human eye can
detect. It
may be preferred to use fast switching LEDs with broad emission bands, or
still
more ideally, narrow bands.
By designing unique luminescence spectral patterns and forming a database of
luminescence spectral patterns of objects/articles, it is possible to
recognize an
object displaying one of that luminescence spectral patterns using the
proposed
system. The proposed system allows to identify discrete luminescence spectral
patterns in spectral dimension of an image which is taken by the sensor. It is
to
be stated that the number of spectral characters is independent of the shape
of
the object to be recognized. This enables the proposed system to not be
limited
in number of classifications to geometry/shape of objects. Objects with the
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same shape and even the same color can be distinguished by analyzing the
chemical (luminescence) information extracted by the data processing unit.
According to a possible embodiment of the system, the system further
comprises a display unit which is configured to display at least the
identified
object which is assigned to the identified best matching luminescence spectral
pattern.
According to a further embodiment, the object to be recognized, is imparted,
e. g. coated, with predefined surface luminescent materials (particularly
luminescent dyes) whose luminescent chemistry, i.e. luminescence spectral
pattern, is known and used as a tag. By using luminescent chemistry of the
object as a tag, object recognition is possible irrespective of the shape of
the
object or partial occlusions.
Luminescence is the property of light being emitted from a material without
heat. A variety of luminescence mechanisms, such as chemiluminescence,
mechanoluminescence, and electroluminescence are known.
Photoluminescence is the emission of light/photons due to the absorption of
other photons. Photoluminescence includes fluorescence, phosphorescence,
upconversion, and Raman scattering. Photoluminescence, fluorescence and
phosphorescence are able to change the color appearance of an object under
ordinary light conditions. While there is a difference between the chemical
mechanisms and time scales of fluorescence and phosphorescence, for most
computer vision systems they will appear identical. Within the scope of the
present disclosure the terms "fluorescence" and "fluorescent" are mostly used
(exemplarily and as placeholder), however, a variety of luminescent
mechanisms are applicable to the invention.
The object can be imparted, i. e. provided with fluorescent materials in a
variety
of methods. Fluorescent materials may be dispersed in a coating that may be
applied through methods such as spray coating, dip coating, coil coating, roll-
to-
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roll coating, and others. The fluorescent material may be printed onto the
object. The fluorescent material may be dispersed into the object and
extruded,
molded, or cast. Some materials and objects are naturally fluorescent and may
be recognized with the proposed system and/or method. Some biological
materials (vegetables, fruits, bacteria, tissue, proteins, etc.) may be
genetically
engineered to be fluorescent. Some objects may be made fluroescent by the
addition of fluorescent proteins in any of the ways mentioned herein.
A vast array of fluorescent materials is commercially available.
Theoretically,
any fluorescent material should be suitable for the computer vision
application,
as the fluorescent spectral pattern of the object to be identified is measured
after production. The main limitations are durability of the fluorescent
materials
and compatibility with the host material (of the object to be recognized). One
example of suitable fluorescent materials are the BASF Lumogen F series of
dyes, such as, for example, yellow 170, orange 240, pink 285, red 305, a
combination of yellow 170 and orange 240 or any other combination thereof.
Another example of suitable fluorescent materials are Clariant Hostasol
fluorescent dyes Red GG, Red 5B, and Yellow 3G. Optical brighteners are a
class of fluorescent materials that are often included in object formulations
to
reduce the yellow color of many organic polymers. They function by fluorescing
invisible ultraviolet light into visible blue light, thus making the produced
object
appear whiter. Many optical brighteners are commercially available, including
BASF Tinopal SFP and Tinopal NFW and Clariant Telalux KSI and
Telalux 061.
According to still a further embodiment of the proposed system, the data
processing unit is configured to identify the best matching fluorescence
spectral
pattern by using any number of matching algorithms between the
extracted/estimated object-specific fluorescence spectral pattern and the
stored
fluorescence spectral patterns, the matching algorithms being chosen from the
group comprising but not limited to: lowest root mean squared error, lowest
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mean absolute error, highest coefficient of determination, matching of
maximum wavelength value.
The processing unit is further configured to estimate/calculate, using the
measured radiance data under the at least two illuminants, the fluorescence
spectral pattern of the object and, afterwards, to match the
estimated/calculated
fluorescence spectral pattern to the known database of a plurality of
fluorescence spectral patterns. According to an embodiment of the claimed
system, the processing unit is configured to estimate, using the measured
radiance data under the at least two illuminants, the luminescence spectral
pattern and the reflective spectral pattern of the object in a multistep
optimization process.
The sensor is generally an optical sensor with photon counting capabilities.
More specifically, it may a monochrome camera, or an RGB camera, or a
multispectral camera, or a hyperspectral camera. The sensor may be a
combination of any of the above, or the combination of any of the above with a
tuneable or selectable filter set, such as, for example, a monochrome sensor
with specific filters. The sensor may measure a single pixel of the scene, or
measure many pixels at once. The optical sensor may be configured to count
photons in a specific range of spectrum, particularly in more than three
bands.
It may be a camera with multiple pixels for a large field of view,
particularly
simultaneously reading all bands or different bands at different times.
A multispectral camera captures image data within specific wavelength ranges
across the electromagnetic spectrum. The wavelengths may be separated by
filters or by the use of instruments that are sensitive to particular
wavelengths,
including light from frequencies beyond the visible light range, i.e. infrared
and
ultra-violet. Spectral imaging can allow extraction of additional information
the
human eye fails to capture with its receptors for red, green and blue. A
multispectral camera measures light in a small number (typically 3 to 15) of
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spectral bands. A hyperspectral camera is a special case of spectral camera
where often hundreds of contiguous spectral bands are available.
The light source is preferably chosen as being capable of switching between at
least two different illuminants. Three or more illuminants may be required for
some methods. The total combination of illuminants is refered to as the light
source. One method of doing this is to create illuminants from different
wavelength light emitting diodes (LEDs). LEDs may be rapidly switched on and
off, allowing for fast switching between illuminants. Fluorescent light
sources
with different emissions may also be used. Incandescent light sources with
different filters may also be used. The light source may be switched between
illuminants at a rate that is not visible to the human eye. Sinusoidal like
illuminants may also be created with LEDs or other light sources, which is
useful for some of the proposed computer vision algorithms.
The sensor which is configured to measure the radiance data of the scene is
linked and synchronized with the switching of the light source between
illuminants. It may be configured to only capture information during the time
period one illuminant is active. It may be configured to capture/measure
information during one or more illuminants being active and use various
algorithms to calculate and issue the radiance for a subset of the
illuminants. It
may be configured to capture the scene radiance at a particular period before,
after or during the activation of the light source and may last longer or
shorter
than the light pulse. That means that the sensor is linked to the switching,
but it
does not necessarily need to capture radiance data during the time period only
one illuminant is active. This procedure could be advantageous in some
systems to reduce noise, or due to sensor timing limitations.
It is possible that the sensor is synchronized to the light source and that
the
sensor tracks the illuminants' status during the sensor integration time. The
spectral changes of the light source are managed by a control unit via a
network, working in sync with the sensor's integration times. Multiple light
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sources connected to the network can be synced to have the same temporal
and spectral change frequencies amplifying the effect.
In another aspect, embodiments of the invention are directed to a method for
object recognition via a computer vision application. The proposed method
comprises at least the following method steps:
- providing an object with object specific reflectance and fluorescence
spectral patterns, the object is to be recognized,
- illuminating a scene including the object with a light source which is
composed of at least two illuminants by switching between the at least two
illuminants, wherein at least one of the at least two illuminants is based on
at least one solid-state system,
- measuring, by means of a sensor, radiance data of the scene including the
object to be recognized when the scene is illuminated by the light source,
- providing a data storage unit such as a database with fluorescence spectral
patterns linked with appropriately assigned respective objects,
- estimating, by a data processing unit, the object-specific fluorescence
spectral pattern of the object to be recognized out of the radiance data of
the scene, and
- comparing/matching, by the data processing unit, the estimated object-
specific fluorescence spectral pattern of the object to be recognized with the
fluorescence spectral patterns stored in the data storage unit, and
- identifying, by the data processing unit, a best matching fluorescence
spectral pattern and, thus, the object assigned to the best matching
fluorescence spectral pattern.
The step of providing an object with an object specific reflectance and
fluorescence spectral pattern comprises, according to a possible embodiment,
in the case of an artificial object, for example, imparting fluorescence to
the
object to be recognized with a fluorescence material.
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In the case of a natural object as the object to be recognized, it is possible
that
the object intrinsically already has object specific reflectance and
fluorescence
spectral patterns.
The step of imparting fluorescence to the object may be realized by coating
the
object with the fluorescence material or otherwise imparting fluorescence to
the
surface of the object. In the latter case fluorescence may be distributed
throughout the whole object, and may thus be detectable at the surface as
well.
According to a possible embodiment of the proposed method, the method
further comprises the step of displaying via a display device at least the
identified object which is assigned to the identified best matching
fluorescence
spectral pattern.
The matching step of the proposed method particularly comprises to identify
the
best matching fluorescence spectral pattern by using any number of matching
algorithms between the estimated object-specific fluorescence spectral pattern
and the stored fluorescence spectral patterns, the matching algorithms being
chosen from the group comprising but not limited to lowest root mean squared
error, lowest mean absolute error, highest coefficient of determination,
matching of maximum wavelength value. Generally, the matching algorithms
are arbitrary.
The estimating step of the proposed method particularly comprises to estimate,
using the measured radiance data under the at least two illuminants, the
fluorescent spectra and the reflective spectra of the object.
The step of providing the data storage unit comprises to form a database of
fluorescence chemistry information of objects, i.e. of fluorescence spectral
patterns of objects/articles by designing multiple fluorescent formulations,
each
fluorescent formulation being applied and, thus, assigned to an object such
that
the object obtains and displays an object-specific fluorescence spectral
pattern
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when being illuminated by the light source. This can be achieved by using
specific mixtures of fluorescent chemicals with different emission profiles in
specific ratios to achieve different unique spectral signatures/fluorescence
spectral patterns.
Particularly, the light source is chosen as a switchable light source with two
illuminants and with a short switchover time between the two illuminants, i.e.
the two illuminants change rapidly among one another. It is further possible
that
the two illuminants are two sinusoidal-like illuminants of opposite phases.
The
two illuminants can be chosen as LED illuminants.
The step of capturing the radiance data of the scene is particularly performed
by a sensor which is linked and synchronized to the switching of the light
source
between the at least two illuminants. It is possible to only capture
information
during the time period one illuminant is active. Alternatively, it is also
possible to
capture information during the time period one or more illuminants are active
and to use various algorithms to calculate the radiance for a subset of the
illuminants.
In another aspect, embodiments of the invention provide a computer program
product having instructions that are executable by a computer, the computer
program product comprising instructions to realize/perform/execute any one of
the embodiments of the proposed method.
The present invention refers to a system and a method where fluorescence
spectra of an object to be recognized are characterized under controlled and
temporal lighting conditions and at spectral bands/lines of interest while the
spectral signature of the fluorescent formulation, which is applied to the
object
to be recognized, is used for object recognition in computer vision
applications.
The proposed system and method enable recognition of an object irrespective
of its shape, ambient lighting, and partial occlusions by using fluorescence
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chemistry, i.e. the fluorescence spectral pattern, of the object to be
recognized
as a tag.
In order to provide the data storage unit of the proposed system, unique
fluorescence spectral patterns measured for specific objects/articles and
accordingly linked with those objects are used in order to form a database of
fluorescence chemistry information of specific objects. The fluorescence is
either applied as an additive, coating, paint etc. or it is part of the
biological
material (i.e. fruit, vegetable) or it naturally exists (not artificially
placed) but can
be detected. The data storage unit provides a tool of unique fluorescence
spectral patterns, each being linked uniquely with a specific object. By means
of
the data storage unit the proposed system is enabled to recognize objects
displaying a specific fluorescence chemistry using the proposed system by
first
illuminating a respective object by the light source, sensing by the sensor
radiance data of the object and estimating by the data processing unit the
object-specific fluorescence spectral pattern out of the radiance data and
comparing the estimated object-specific fluorescence spectral pattern with the
fluorescent spectral patterns stored in the data storage unit.
It is known in the art that having engineered features that can be easily
defined
and detected is the most computationally efficient way of identifying objects
visually in comparison to other techniques. For example, by the scanning of a
barcode, a system immediately connects to a database to identify the object
being scanned. Similarly, the proposed system is even more efficient due to
its
ability to identify discrete fluorescence spectral patterns in the spectral
dimension of an image like a barcode reader operates in the spatial dimension.
The number of spectral characters is independent of the shape of the object of
interest. This enables the proposed system and method to not be limited in
number of classifications to geometry/shape of objects. Objects with the same
shape and even same color can be distinguished by analyzing the fluorescence
chemistry extracted by the computer vision system.
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One of the key aspects of the invention is that ambient lighting elements can
be
utilized as light probes for the proposed system and method. Indoor conditions
usually require a controlled and uniform lighting environment to be present to
facilitate computer vision applications. However, the proposed system and
method capitalize on the differences of lighting conditions to recognize
objects
instead. Furthermore, one unique aspect of the system is that it utilizes a
rapid
change of ambient lighting from a LED light source or comparable
arrangements to extract fluorescence chemistry information from a respective
object. The rapid changing of such lighting conditions is not visible to human
eyes and the spectral changes of lighting sources can be managed by the
system through a network, working in sync with the sensor's integration times.
Multiple light sources connected to the network can be synced to have the
same temporal and spectral change frequencies amplifying the effect.
Another unique aspect of the invention is that the fluorescence (or chemistry)
information of the objects can be coupled to information about that object,
i.e.
type of material, price, manuals, etc. and information held at the dynamic
(live)
database, i.e. the data storage unit that tracks and updates the information
in
3D maps. By dynamically tracking the objects in 3D space using the proposed
system and potentially in combination with other methods, the proposed system
would enable the ability to distinguish two identical objects by 3D location
of
those objects for as long as the object locations are dynamically updated, and
the objects are in field of view of the sensor of the proposed system.
The above-mentioned examples highlighting the unique utility of the proposed
system are not complete and it is not intended to be limited to those specific
applications. Further applications can be based on platforms using cameras of
various types, including monochrome, RGB type, multispectral or hyperspectral
sensors of light.
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According to one embodiment of the proposed method the object to be
recognized is provided with a luminescence material which is chosen from the
group comprising at least:
Any material with fluorescence (Stokes) characteristics in the UV, VIS, NIR
and/or IR, any material with upconversion (anti-Stokes) characteristics in VIS
and/or NIR, biologicals designed with fluorescence effects, biologicals
designed
with natural fluorescence effects, and/or food colorants.
The technique for imparting the object to be recognized with a luminescence
material can be chosen as one or a combination of the following techniques:
spraying, rolling, drawing down, deposition (PVC, CVD, etc.), extrusion, film
application/adhesion, glass formation, molding techniques, printing such as
inks, all types of gravure, inkjet, additive manufacturing, fabric/textile
treatments
(dye or printing processes), dye/pigment absorption, drawings (hand/other),
imparting stickers, imparting labels, imparting tags, chemical surface
grafting,
dry imparting, wet imparting, providing mixtures into solids, providing
reactive/nonreactive dyes.
The sensor to measure the radiance data of the scene can be chosen of the
group comprising at least: photodiodes of all types, sensors covering
wavelengths from 250 nm and above, sensors covering wavelengths up to
1.800 nm, sensors having dynamic or static filters, prism based or comparable
spatially wavelength separated systems, multiple cameras, stereocameras,
hyperspectral sensors 10
bands), multispectral sensors (> 3 bands), RGB
sensors (3 bands), sensors covering all bands or only selected bands, sensors
covering all frame rates, other sensors responsive to photons and/or
electromagnetic radiation (250 nm to 1.800 nm), sensors comprising polarized
filters (circular, linear, etc.), sensors having nonpolarized filters.
The database can be stored on an edge computing system or it can be stored
on a cloud. The data can be stored with or without additional information
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concerning the respective objects attached, ads, price, owner, SDS, calorie
values, recipes. Further data can be provided with expiration date, date of
manufacture, name, shelf life, ingredients list, location, time stamp of the
respective objects. Further the data can be provided with use instructions,
manufacturer, place of origin, recycling directions, manuals, ratings, reviews
concerning the respective objects. Further the data can be provided with
information about traffic signage information, data about type of material
such
as textile, clothing, dog leash, bicycle, car etc. concerning the respective
objects. Further, it can be provided with data about usage levels, remaining
amounts, weight, volume, alcohol content, alcohol consumed of the respective
objects.
The identified object which is assigned to the best matching luminescence
spectral pattern can be displayed via one or a combination of the following
devices: smart glasses, smart phones, smart watches, other wearables such as
chest cameras, spy cams, shoes, shirts, buttons, contact lenses, security
cameras, vehicles, drones, robotics, home assistants, laptops, tablets,
traffic
monitor cameras, indoor and outdoor systems, mobile or stationary systems,
TVs, toys, portable scanners, stationary scanners, coffee machines, home
appliances, industrial machinery, production equipment/plants,
recycling/sorting
equipment, smart trash bins, smart recycling bins, pens.
The proposed method has many application areas. Thus, it can be used for
example in: object recognition, object tracking, classification of objects,
object
identification, object locating, inventory management, automated orders,
retail,
online store, accident prevention autonomous vehicles, anti-counterfeiting,
augmented reality or mixed reality applications, advertising, fitness/health
management, warehousing, manufacturing, assembly, counting, learning,
sports, instructions, manuals, advice, cooking, and artificial intelligence
support.
The invention is further defined in the following examples. It should be
understood that these examples, by indicating preferred embodiments of the
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invention, are given by way of illustration only. From the above discussion
and
the examples, one skilled in the art can ascertain the essential
characteristics
of this invention and without departing from the spirit and scope thereof, can
make various changes and modifications of the invention to adapt it to various
uses and conditions.
Figure 1 shows schematically embodiments of the proposed system.
Figure 2 shows measured radiances of three broadband light sources used in
example 1.
Figure 3 shows an example comparison of measured and calculated emission
spectral patterns for one material from example 1.
Figure 4 shows measured (top) and calculated (bottom) emission spectra
(spectral patterns) for all materials for example 1.
Figure 5 shows in different tables different comparing/matching algorithms for
example 1.
Figure 6 shows a diagram of example illuminant spectrums and measured
radiances under a LED light source used in an embodiment of the proposed
system.
Figure 7 shows a diagram of an example comparison of measured and
calculated emission spectra (spectral patterns).
Figure 8 shows calculated emission spectra (spectral patterns) (left) and
measured emission spectra (spectral patterns) (right).
Figure 9 shows in different tables different comparing/matching algorithms
which can be used for example 2.
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Figure la and Figure lb show schematically embodiments of the proposed
system 100. The system 100 includes at least one object 130 to be recognized.
Further the system 100 includes a sensor 120 which can be realized by an
imager, such as a camera, particularly a multispectral or a hyperspectral
camera. The system 100 further includes a light source 110. The light source
110 is composed of different individual illuminants, the number of which and
nature thereof depend on the method used. For example 1 as indicated in
Figure la, three illuminants are provided and the three illuminants are
commonly available incandescent 111, compact fluorescent 112, and white
light LED 113 bulbs. The light source may also be composed of two illuminants
as shown in Figure lb. For example 2, only two illuminants are provided, the
two illuminants are custom LED illuminants 114 and 115. Illuminant 114
consists of three LEDs operating at 5 V. One LED is a 400 nm LED from VCC
(VAOL-5GUVOT4), with an inline resistor of 3300 ohms. The second LED is a
500 nm LED from Lumex (SSL-LX5093UEGC), with an inline resistor of 3300
ohms. The third LED is a 610 nm LED from Lumex (SSL-LX5094SOC), with
an inline resistor of 680 ohms. Illuminant 115 consists of three LEDs
operating
at 5V. One LED is a 470 nm LED from Cree, Inc. (C503B-BCS-CV0Z0461),
with an inline resistor of 5000 ohms. The second LED is a 574 nm LED from
Kingbright (WP7113CGCK), with an inline resistor of 100 ohms. The third LED
is a 643 nm LED from VCC (VAOL-5GAE4), with an inline resistor of 47 ohms.
The light source may be configured to illuminate a scene including the object
130 to be recognized by rapidly switching between the different illuminants
(111, 112 and 113 in Figure la, or 114 and 115 in Figure lb). The system 100
further comprises a data processing unit, i.e. CPU 140 which is configured to
estimate an object specific reflectance and/or fluorescence spectral pattern
with
reflectance and/or fluorescence spectral patterns stored in a data storage
unit
150 which is wirelessly or over a wire connected with the CPU 140 and to
identify a best matching reflectance and/or fluorescence spectral pattern and,
thus, an object which is assigned to the best matching reflectance and/or
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fluorescence spectral pattern. The system 100 further includes a display unit
160 which is configured to display at least the identified object which is
assigned to the identified best matching fluorescence spectral pattern. The
system 100 can comprise more than one sensor, more than one light source
and/or more than one object wherein all components of the system form a
network. Using the system 100 it is possible to perform an embodiment of the
proposed method for object recognition. The object 130 to be recognized is
imparted with a fluorescent material, thus providing the object with an object
specific reflectance and fluorescence spectral pattern. To create materials
with
unique fluorescent spectral patterns/signatures, BASF Lumogen F series dyes
dispersed into a one-component automotive clear coat were used. Four
different dyes, including Yellow 170, Orange 240, Pink 285, or Red 305,
labelled A, B, C, and D, and a combination of Yellow 170 and Orange 240,
labelled E, was used to create materials for examples 1 and 2. The tinted
clear
coats were drawn down onto white steel panels at 200 micron thickness and
cured. Other methods of applying fluorescence to an object may be used.
The coated object 130 is illuminated with the light source 110 which is
composed of multiple illuminants. The illuminants may be rapidly switched at a
rate that is not visible to the human eyes and the illuminant changes managed
by the proposed system through the network, working in sync with the
integration times of the sensor 120. Generally, it is possible that multiple
light
sources connected to the network can be synced to have the same temporal
and spectral change frequencies amplifying the effect. When the scene
including the object 130 is illuminated by the light source 110 radiance data
of
the scene including the object 130 are captured/measured by the sensor 120.
The data processing unit 140 estimates the object specific reflectance and/or
fluorescence spectral pattern out of the radiance data of the scene by first
separating fluorescence and reflectance spectra of the object.
Multiple methods of separating fluorescence from reflectance are known. The
method used in example 1 is described in Yinqiang Zheng, lmari Sato, and
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Yoichi Sato, "Spectra Estimation of Fluorescent and Reflective Scenes by
Using Ordinary Illuminates", ECCV 2014, Part V, LNCS 8693, pp. 188-202,
2014. The method described therein images a fluorescent material under three
different broadband illuminants with a hyperspectral camera. This paper in
incorporated by reference in full.
According to the present invention, using the measured radiance data under
three different illuminants 111, 112, and 113 as shown in Figure 1a, the
reflectance and fluorescence spectral patterns are calculated in a multistep
optimization process. The calculated fluorescence spectral pattern for the
object 130 is compared by the processing unit 140 to the known and measured
(using a fluorometer) spectral pattern from a library of materials stored in
the
database 150. The database 150 includes multiple fluorescence spectral
patterns linked with specific objects, respectively. To form such a database
it is
possible to design different fluorescent formulations and applying those
fluorescent formulations on respective different objects so that each object
is
uniquely linked with an object specific fluorescence spectral pattern. The
fluorescent formulations can be designed by using specific mixtures of
fluorescent chemicals with different emission profiles, in specific ratios to
achieve unique spectral signatures, respectively. The fluorescent material
applied to the object 130 can then be identified by any number of matching
algorithms between the calculated object specific fluorescence spectral
pattern
and the known material spectral patterns stored in the database 150, for
example, by lowest root mean squared error, lowest mean absolute error,
highest coefficient of determination, or matching of maximum emission
wavelength value. Identification of the fluorescent material then allows for
the
identification of object 130 using the database information.
Finally, the data processing unit 140 matches the estimated fluorescence
spectral pattern with object-specific fluorescence spectral patterns stored in
the
data storage unit 150 and identifies the best matching fluorescence spectral
pattern. Finally, the data processing unit 140 can read out from the data
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storage unit 150 by means of the identified best matching fluorescence
spectral
pattern the object which is linked to this best matching fluorescence spectral
pattern and can display the object together with the fluorescence spectral
pattern on the display unit 160.
The imager 120 can be a hyperspectral camera or a multispectral sensor.
Instead of the two dozen or more individual sensor bands in a hyperspectral
sensor, a multispectral sensor has approximately 4 to 20 sensor bands.
Multispectral sensors can operate in snapshot mode, capturing an entire scene
during a single exposure. In contrast, hyperspectral sensors typically operate
in
line scanning mode, meaning they cannot image the entire scene at one time.
Additionally, multispectral sensors are much more economical than
hyperspectral cameras. Multispectral sensors do not have the same spectral
resolution as hyperspectral cameras, but they are sufficient to predict the
material identification using the proposed method with appropriate matching
algorithms. The sensor may also operate in a monochrome manner, with a
mechanism to change the spectral region measured through time. The sensor
may operate with narrow-band filters. This may be useful in outdoor conditions
or other conditions with a solar lighting component when the narrow-band
filters
correspond to Fraunhofer lines, which are wavelengths missing from the solar
spectrum due to elemental absorption within the sun. In this manner, the solar
radiation, which may be overpowering compared to the artificial light source,
can largely be excluded, allowing for the separation of reflectance and
fluorescence and therefore object identification.
The fluorescent object 130 was imaged under the different illuminants, 111,
112, and 113 for example 1 as indicated in Figure 1a, or the LED illuminants
114 and 115 for example 2 as indicated in Figure lb. The sensor 120 used is a
Resonon Pika L hyperspectral imager for examples 1 and 2, consisting of 300
wavelength bands between approximately 384 nm and 1024 nm and positioned
roughly 0,5 metres from the object 130. For example 2, the resulting radiances
where rebinned to 10 nm intervals between 420 nm and 740 nm.
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The method used to separate fluorescence from reflectance used in example 2
is in the paper of Fu et al. "Separating Reflective and Fluorescent Compenents
Using High Frequency Illumination in the Spectral Domain", ICCV 2013. As
applied in their paper, the method requires customizable light source (Nikon
ELS-VIS) capable of outputting a sinusoidal-like spectrum. The customizable
light source is low powered and expensive, preventing widespread use or use in
typically sized scenes. Surprisingly, it has been found here that the light
source
can be replaced with inexpensive and high-powered LEDs despite current LED
technology being unable to create as narrow of emission bands as the Nikon
ELS-VIS. The hyperspectral images were recorded in the same manner as
Example 1 and rebinned to 10 nm intervals. Wavelengths at which both LED
illuminants 114, 115 have similar radiances are omitted due to the nature of
the
calculation. The calculated/estimated emission results were compared with the
fluorescence emission measured for each material using a fluorescence
spectrophotometer. To facilitate easy comparison, the measured emission
spectrum was also rebinned to the same 10 nm intervals and the same
wavelengths omitted.
For achieving the calculated/estimated emission results, a simple algorithm is
applied to the measured radiance data at each wavelength under each
illuminant of the two LED illuminants 114, 115 and thus allows for separation
of
the reflectance and fluorescence emission spectra to be captured.
Since reflection and fluorescence have different physical behaviours, they
need
to be described by different models. The radiance of a reflected surface
depends on the incident light and its reflectance. Using the nomenclature of
the
above mentioned paper Fu et al., the observed radiance of an ordinary
reflected surface at a wavelength X is computed as
/Ara = Vs) = r (A) (1)
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where I(X) is the spectrum of the incident light at wavelength X and r(X) is
the
spectral reflectance of the surface at wavelength X.
The observed radiance of a pure fluorescent surface depends on the incident
light, the material's absorption spectrum, and its emission spectrum.
Fluorescence typically absorbs light at some wavelengths and emits them at
longer wavelengths. The surface's absorption spectrum will determine how
much of the light is absorbed. Some of the absorbed energy is then released in
the form of an emission spectrum at longer wavelengths than the incident
light.
The remainder of the absorbed energy is released as heat. The observed
spectrum of pure fluorescent surface at wavelength X is described in terms of
its absorption and emission spectra as
pf = (.1 if ) 2.')d Ale(A) (2)
where a(X)and e(X) represent the absorption and emission spectrum. With k =
if I(X')a(X')d X'), pf(X) can be written as pf(X) = ke(X) which means that the
shape
or the distribution of the emitted spectrum is constant but the scale k of the
emitted spectrum changes under different illuminations. Namely, the radiance
of the fluorescent emission changes under different illuminations, but its
colour
stays the same regardless of illumination colour. Finally, the reflective and
fluorescent surface shows a radiance according to:
pr, = 1(A) = r k (..7.) (3)
When using, as proposed according to the proposed method, high frequency
sinusoidal illuminance in the spectral domain, the radiance of the object
under
these two sinusoidal illuminants can be described as:
¨ (')i)+ k()
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(4)
pz (A) = 1_(2) (A) + (A)
Therefrom, the reflectance r(X) and the fluorescent emission ke(X) can be
recovered as
¨ ¨
i-()) -
(A) ¨ (5)
¨
ke(l) = r A-)
- ) (6)
By means of the above described equations it is possible to calculate from the
radiance data p(X) and the intensity I(X) from the illuminants the reflectance
r(X)
and the fluorescent emission e(X) of the object which has been illuminated by
the light source. Thereby, the fluorescent emission corresponds to the object
specific fluorescence spectral pattern of the object. The calculated object
specific fluorescence spectral pattern is then compared with the fluorescence
spectral patterns which are stored in the database and linked with respective
specific objects.
Figure 2 shows example illuminant spectra 230, 240, and 250. The diagram
200 shows a horizontal axis 210 along which the wavelength is plotted and a
vertical axis 220 shows the intensity of the illumination. The curve 230 shows
the illumination of a first illuminant, namely a CFL (Compact Fluorescent
Lamp)
with at least three pronounced maxima, namely at 435,15 nm, at 546,47 nm
and the highest maximum at 611,45 nm. The curve 240 shows the illuminant
spectrum of a second illuminant, namely an incandescent illuminant with a
light
increase in intensity with increasing wavelength. The curve 250 shows the
illuminant spectrum of a third illuminant, namely a LED with two pronounced
maxima, namely at 453,54 nm and at 603,02 nm.
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Figure 3 shows a comparison of calculated emission results (calculated
fluorescence spectral patterns) for example 1 with fluorescent emissions
measured for material A using a fluorometer. The diagram 300 is spanned over
a horizontal axis 310 showing the wavelength and a vertical axis 320 showing
the normalized emission intensity. As can be seen from the curves 330 and 340
wherein the curve 330 shows the calculated emission with a maximum at
565,26 nm and the curve 340 shows the measured emission with a maximum
at 568 nm, a good accordance is visible.
Figure 4 shows on the top portion measured emission spectra (measured
fluorescence spectral patterns) and on the bottom portion respective
calculated
emission spectra (calculated fluorescence spectral patterns) for different
materials A, B, C, D, E, for example 1. In each diagram for each different
material A, B, C, D, E a different curve is plotted as indicated.
Figure 5 shows the results of quantitative comparisons between the calculated
and measured fluorescence emission spectra for example 1. The mean
absolute error (Figure 5a), spectral angle (Figure 5b) and Euclidean distance
(Figure Sc) were calculated for every calculated spectrum in relation to the
spectra for each measured material. The mean absolute error is a common
method of comparing the error of a calculated value to the ground truth value,
lower mean absolute error values indicate a better match between the
calculated and ground truth values. Spectral angle mapping (Figure 5b) is a
concept used in spectral imaging to classify objects to a known database of
spectra. For spectral angle mapping, a lower value is indicative of a closer
match between the unknown object and the measured object. Euclidean
distance (Figure Sc) is another concept used in spectral imaging in the same
manner as spectral angle. Again, lower values indicate a better match for
Euclidean distance. For the materials A, C, D and E the mean absolute error,
spectral angle, and Euclidean distance calculation results correctly identify
the
unknown material, with the exception of material B, as can be seen from the
tables shown in Figure 5a, Figure 5b and Figure Sc, respectively.
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Figure 6 shows example illuminant spectrums and measured radiances under
each illuminant for example 2. The diagram 600 shows a horizontal axis 610
along which the wavelength is plotted and a vertical axis 620 that shows the
intensity of the illumination. The curve 640 shows the illumination of a first
illuminant and the curve 641 shows the respective radiance data of the object
illuminated by the first illuminant. The curve 630 shows the illuminant
spectrum
of a second illuminant and the curve 631 shows the respective radiance data of
the object when illuminated by the second illuminant. The effect of
fluorescence
emission is obvious in the range from ":--, 530 - 650 nm.
Figure 7 shows a comparison of calculated emission results (calculated
fluorescence spectral patterns) for example 2 with fluorescence emissions
measured for material A using a fluorometer. The diagram 700 is spanned over
a horizontal axis 710 showing the wavelength and a vertical axis 720 showing
the normalized emission intensity. As can be seen from the curves 730 and 740
wherein the curve 730 shows the calculated emission and the curve 740 shows
the measured emission, a good accordance is visible.
Figure 8 shows on the left side calculated emission spectra (calculated
fluorescence spectral patterns) for example 2 and on the right side respective
measured emission spectra for different materials A, B, C, D, E. In each
diagram for each different material A, B, C, D, E a different curve is plotted
as
indicated.
Figure 9 shows the results of quantitative comparisons between the calculated
and measured fluorescence emission spectrum for example 2. The mean
absolute error (Figure 9a), spectral angle (Figure 9b) and Euclidean distance
(Figure 9c) were calculated for every calculated spectrum in relation to the
spectra for each measured material. For each of the materials A, B, C, D and E
the mean absolute error, spectral angle, and Euclidean distance calculation
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results correctly identify the unknown material as can be seen from the tables
shown in Figure 9a, Figure 9b and Figure 9c.
