Abstract: Systems and methods of improving alignment in dense prediction neural networks are disclosed. A method includes identifying, at a computing system, an input data set and a label data set with one or more first parts of the input data set corresponding to a label. The computing system processes the input data set using a neural network to generate a predicted label data set that identifies one or more second parts of the input data set predicted to correspond to the label. The computing system determines an alignment result using the predicted label data set and the label data set and a transformation of the one or more first parts, including a shift, rotation, scaling, and/or deformation, based on the alignment result. The computing system computes a loss score using the transformation, label data and the predicted label data set and updates the neural network based on the loss score.

Abstract: Apparatus and method for determining a quality score for virtual video cameras. For example, one embodiment comprises: a region of interest (ROI) detector to detect regions of interest within a first image generated from a first physical camera (PCAM) positioned at first coordinates; virtual camera circuitry and/or logic to generate a second image positioned at the first coordinates; image comparison circuitry and/or logic to establish pixel-to-pixel correspondence between the first image and the second image; an image quality evaluator to determine a quality value for the second image by evaluating the second image in view of the first image.

Abstract: Disclosed herein are an apparatus and method for monitoring a user based on multi-view face images. The apparatus includes memory in which at least one program is recorded and a processor for executing the program. The program may include a face detection unit for extracting face area images from respective user images captured from two or more different viewpoints, a down-conversion unit for generating at least one attribute-specific 2D image by mapping information about at least one attribute in the 3D space of the face area images onto a 2D UV space, and an analysis unit for generating user monitoring information by analyzing the at least one attribute-specific 2D image.

Abstract: A thermal image processing method includes determining a blending ratio of a first equalization algorithm to a second equalization algorithm that are different from each other, based on a result of analyzing an original histogram of an original thermal image; generating a first corrected thermal image obtained by applying the first equalization algorithm and the second equalization algorithm to the original thermal image according to the blending ratio; adjusting the blending ratio by comparing a corrected histogram of the first corrected thermal image with the original histogram; and generating a second corrected thermal image obtained by applying the first equalization algorithm and the second equalization algorithm to the original thermal image according to the adjusted blending ratio.

Abstract: A head-counter device (100) comprising a digital camera (1) adapted to provide a first digital image (IM1) representative of a counting zone (STR) of persons, the first image defining a first horizontal dimension (N) and a first vertical dimension (M), and a cropping module (7) configured for: analyzing the first image (IM1) and identifying a noise area (PCR) according to at least one of the following features: pixel light intensity, pixel color and/or presence of predefined patterns, cropping the noise area (PCR) from the first image (IM1) to obtain a second image (IM2) without the noise area, the noise area (PCR) being a peripheral portion of the first image having said first horizontal dimension and having a second vertical dimension (M?DSK+SM) shorter than the first vertical dimension.

Abstract: An object of the present invention is to extract an area of a foreground object with high accuracy. The present invention is an image processing apparatus including: a target image acquisition unit configured to acquire a target image that is a target of extraction of a foreground area; a reference image acquisition unit configured to acquire a plurality of reference images including an image whose viewpoint is different from that of the target image; a conversion unit configured to convert a plurality of reference images acquired by the reference image acquisition unit based on a viewpoint corresponding to the target image; and an extraction unit configured to extract a foreground area of the target image by using data relating to a degree of coincidence of a plurality of reference images converted by the conversion unit.

Abstract: A method of determining ripeness of a growing mushroom involves obtaining a sequence of images of a growing mushroom over a period of time and, from the sequence of images, measuring one or more features of the growing mushroom to obtain temporal measurements for the one or more features. The temporal measurements indicate rates at which the one or more features are changing. The temporal measurements are compared to pre-determined ripeness functions to determine the ripeness of the mushroom. The mushroom is picked if the comparison indicates that the mushroom is ready to be harvested.

Abstract: A system and a method of performing an image matting on an image are provided. The method includes detecting, by an image processing system, one or more objects in the image; determining, by the image processing system, a confidence map associated with the image using one or more image segmentation techniques for each of the one or more objects; and generating, by the image processing system, a tri-map for each of the one or more objects in the image from the confidence map based on at least one of a size of each of the one or more objects in the image and a distance between a first pixel in the image and a second pixel in at least one of the one or more objects in the image, wherein the tri-map is used to perform the image matting.

Abstract: Embodiments of the present disclosure discloses a method and apparatus for rendering an image, an electronic device and a computer readable storage medium. An example implementation of the method includes: acquiring a real color parameter and a face skin color parameter in a real user image including a face image area, determining lighting parameter information based on the real color parameter and the face skin color parameter, acquiring a to-be-rendered image obtained based on the real user image, and generating a texture parameter based on the lighting parameter information, and using the texture parameter to perform lighting rendering on the to-be-rendered image to obtain a rendered image.

Abstract: Disclosed is a method for simulating the rendering of a make-up product on the face of a subject, using a database of reference images including, for each reference individual, images of the face with and without the makeup product. The method includes: acquiring an image of the subject face without makeup; processing the image to extract, for each spatial area of each spatial frequency range of the image, first color feature values of the spatial area; determining, among the database of reference images, reference individuals having, when wearing no makeup, color feature values similar to the first color feature values of the subject; determining, from the first color feature values of the subject, and from color feature values of the reference individuals with and without the makeup product, second color feature values; and generating a modified image of the subject face based on the second color feature values.

Abstract: A processing apparatus combines a plurality of images based on a plurality of object images formed on an imaging plane of an image sensor by a plurality of lens units and to generate a combined image, and includes at least one processor or circuit that serves as an acquisition task configured to acquire information on a center position of each of the plurality of object images on the imaging plane, information on a correspondence relationship between the center position and positions of the plurality of images in the combined image, and conversion information for converting a first coordinate system in the imaging plane into a second coordinate system in the combined image, the conversion information being generated based on a correction function for correcting the plurality of object images, and a processing task configured to generate the combined image using the conversion information.

Abstract: An apparatus for image conversion includes a processor configured to classify a reference region representing a predetermined feature into a shadowed region and an unshadowed region, the reference region being in an aerial image represented in RGB color space; determine a tone correction factor so that a difference between an average luminance of the shadowed region and an average luminance of the unshadowed region in the aerial image represented in predetermined color space to which the color space of the aerial image is converted from RGB color space is less than a difference between an average luminance of the shadowed region and an average luminance of the unshadowed region represented in RGB color space; correct tones of the aerial image with the tone correction factor; and convert the color space of the aerial image from RGB color space to the predetermined color space to generate a color conversion image.

Abstract: A method of detecting unusual motion is provided, including: determining features occurring during a fixed time period; grouping the features into first and second subsets of the fixed time period; grouping the features in each of the first and second subsets into at least one pattern interval; and determining when an unusual event has occurred using at least one of the pattern intervals.

Abstract: A method for measuring an antenna downtilt angle based on a multi-scale deep semantic segmentation network is disclosed, including: collecting base station antenna data by using an unmanned aerial vehicle, and labeling an acquired antenna image with a labeling tool to make a data set; calling the data set for training and debugging a model; recognizing and detecting a target antenna, performing semantic segmentation on an output image, finally obtaining a target image finally segmented, and calculating a downtilt angle of the target image. The method is highly applicable, cost-effective, and safe.

Abstract: Systems and methods for generating object segmentations across videos are provided. An example system can enable an annotator to identify objects within a first image frame of a video sequence by clicking anywhere within the object. The system processes the first image frame and a second, subsequent, image frame to assign each pixel of the second image frame to one of the objects identified in the first image frame or the background. The system refines the resulting object masks for the second image frame using a recurrent attention module based on contextual features extracted from the second image frame. The system receives additional user input for the second image frame and uses the input, in combination with the object masks for the second image frame, to determine object masks for a third, subsequent, image frame in the video sequence. The process is repeated for each image in the video sequence.

Abstract: A method of tracking an animal in a population of animals is disclosed that includes applying a visual identifier to the animal, associating reference identifier data of the visual identifier to animal data of the animal, capturing a digital image of the visual identifier applied to the animal, recognizing the visual identifier by digitally processing the captured digital image, and identifying the animal upon recognition of the visual identifier.

Abstract: The computer stores the plurality of first positions in the image captured by the image pickup device and the second positions corresponding to the plurality of first positions in the graphic corresponding to the image as a relationship. The computer acquires a third position related to the object in the image, a positional relationship between a part of the plurality of first positions and the third position, and one of the relationships. A method specifies a position in a figure corresponding to a position in an image including generating a fourth position in the figure corresponding to the third position using parts or all. In a method, a part of the plurality of first positions is three first positions closest to the third position among the plurality of first positions. A method includes generating the fourth position using the sides of a triangle composed of the three first positions.

Abstract: Methods and systems disclosed herein relate generally to systems and methods for modifying pixel values of an image to improve the visibility of target pixel patterns. A pixel-simulation application accesses an initial image including an initial set of pixel values. The initial set of pixel values define, in an initial color space, a particular color of pixels that indicate a target pixel pattern. The pixel-simulation application generates, based on the initial set of pixel values, a simulated image including a modified set of pixel values that visually indicate another color of pixels in an intermediate color space. The pixel-simulation application generates a pixel map by identifying a difference between the initial set pixel values of the initial image and the modified set of pixel values of simulated image. The pixel-simulation application generates, for display, an output image based at least in part on the pixel map.

Abstract: Occlusion of facial features may be detected and assessed in an image captured by a camera on a device. Landmark heat maps may be used to estimate the location of landmarks such as the eyes, mouth, and nose of a user's face in the captured image. An occlusion heat map may also be generated for the captured image. The occlusion heat map may include values representing the amount of occlusion in regions of the face. The estimated locations of the eyes, mouth, and nose may be used in combination with the occlusion heat map to assess occlusion scores for the landmarks. The occlusion scores for the landmarks may be used control one or more operations of the device.

Abstract: One or more embodiments of the disclosure include systems and methods that use meta-learning to learn how to optimally find a new neural network architecture for a task using past architectures that were optimized for other tasks, including for example tasks associated with autonomous, semi-autonomous, assisted, or other driving applications. A computer implemented method of the disclosure includes configuring a search space lattice comprising nodes representing operator choices, edges, and a maximum depth. The method includes defining an objective function. The method further includes configuring a graph network over the search space lattice to predict edge weights over the search space lattice.

Abstract: Systems and methods for panoptic image segmentation are disclosed herein. One embodiment performs semantic segmentation and object detection on an input image, wherein the object detection generates a plurality of bounding boxes associated with an object in the input image; selects a query bounding box from among the plurality of bounding boxes; maps at least one of the bounding boxes in the plurality of bounding boxes other than the query bounding box to the query bounding box based on similarity between the at least one of the bounding boxes and the query bounding box to generate a mask assignment for the object, the mask assignment defining a contour of the object; compares the mask assignment with results of the semantic segmentation to produce a refined mask assignment for the object; and outputs a panoptic segmentation of the input image that includes the refined mask assignment for the object.

Abstract: A method for processing an image is presented. The method locates a subject and an object of a subject-object interaction in the image. The method determines relative weights of the subject, the object, and a context region for classification. The method further classifies the subject-object interaction based on a classification of a weighted representation of the subject, a weighted representation of the object, and a weighted representation of the context region.

Abstract: Banding effects in an image or video are assessed. The image or each frame of a video is decomposed into luminance or color channels. Signal activity is computed at each spatial location in each channel. A significance of the signal activity at each spatial location is determined by comparing each element of the signal activity with a significance threshold. Banding pixels are detected as those pixels of the image or frame having significant signal activity at their respective spatial locations and having non-significant signal activity of at least a minimum threshold percentage of neighboring pixels to the respective spatial locations.

Abstract: A processing apparatus combines a plurality of images based on a plurality of object images formed on an imaging plane of an image sensor by a plurality of lens units and to generate a combined image, and includes at least one processor or circuit that serves as an acquisition task configured to acquire information on a center position of each of the plurality of object images on the imaging plane, information on a correspondence relationship between the center position and positions of the plurality of images in the combined image, and conversion information for converting a first coordinate system in the imaging plane into a second coordinate system in the combined image, the conversion information being generated based on a correction function for correcting the plurality of object images, and a processing task configured to generate the combined image using the conversion information.

Abstract: The present disclosure provides an interactive image matting method, a computer readable memory medium, and a computer device. The interactive image matting method includes steps: obtaining an original image; collecting foreground sample points on a hair edge foreground region of the original image and collecting background sample points on a hair edge background region of the original image by a human-computer interaction method to correspondingly obtain a foreground sample space and a background sample space; receiving a marking operation instruction input by a user, and smearing a hair region of the original image according to the marking operation instruction to mark unknown regions; traversing the unknown regions to obtain a pixel of each unknown region, traversing all the sample pairs to select a sample pair with a minimum overall cost function value for the pixel of each unknown region.

Abstract: A method, system and computer program product for segmenting generic foreground objects in images and videos. For segmenting generic foreground objects in videos, an appearance stream of an image in a video frame is processed using a first deep neural network. Furthermore, a motion stream of an optical flow image in the video frame is processed using a second deep neural network. The appearance and motion streams are then joined to combine complementary appearance and motion information to perform segmentation of generic objects in the video frame. Generic foreground objects are segmented in images by training a convolutional deep neural network to estimate a likelihood that a pixel in an image belongs to a foreground object. After receiving the image, the likelihood that the pixel in the image is part of the foreground object as opposed to background is then determined using the trained convolutional deep neural network.

Abstract: A system and method for encoding a digital image may include obtaining image data of the digital image, wherein the image data comprises a plurality of image pixels, selecting a plurality of sample pixels from the plurality of image pixels, clustering the plurality of sample pixels based on an influence function to generate a plurality of hue clusters, classifying the image pixels to the plurality of the hue clusters, and assigning hue values of the hue clusters to the plurality of the image pixels.

Abstract: Embodiments are disclosed for identify a proportional dominant color palette of a vector graphics content and recolor the vector graphics content by editing the proportional dominant color palette. In particular, one method comprises obtaining a vector graphic, determining, by a palette extraction manager, a plurality of colors from the vector graphic, grouping, by the palette extraction manager, the plurality of colors into a plurality of groups based on color characteristics, determining, by the palette extraction manager, a plurality of dominant colors corresponding to the plurality of groups based on a subset of the plurality of colors associated with each group, determining, by the palette extraction manager, a plurality of weights corresponding to the plurality of colors, and generating, by the palette extraction manager, a proportional dominant color palette for the vector graphic based on the plurality of dominant colors and the corresponding plurality of weights.

Abstract: A method and a device for processing feature points of an image are provided. A specific embodiment of the method includes obtaining an image to be processed; determining weights of the feature points of the image to be processed to obtain a weight set; and according to the weights, selecting target numbered feature points as target feature points of the image to be processed. The weights include a texture weight; the texture weight and a color change scope of pixels in a target sized image region in which the feature points locate are directly proportional. The embodiment can reduce the number of feature points of the image, and further release the storage pressure of feature points regarding the image.

Abstract: Integrating an image capturing mechanism into an ecommerce application is described. Images of items offered for sale via ecommerce channels associated with an ecommerce service provider and performance metrics associated with the images and/or content items with which the images are presented via the ecommerce channels can be analyzed. Based partly on the analyzing, a characteristic associated with the images that are associated with a performance metric that satisfies a threshold or that are presented by the content items that are associated with the performance metric that satisfies the threshold can be determined. A new image of an item to be offered for sale by a merchant via an ecommerce channel can be received and, based at least partly on the characteristic, it can be determined whether to integrate the new image into a content item for the merchant, wherein the content item to be presented via the ecommerce channel.

Abstract: Signal detection and recognition employees coordinated illumination and capture of images under to facilitate extraction of a signal of interest. Pulsed illumination of different colors facilitates extraction of signals from color channels, as well as improved signal to noise ratio by combining signals of different color channels. The successive pulsing of different color illumination appears white to the user, yet facilitates signal detection, even for lower cost monochrome sensors, as in barcode scanning and other automatic identification equipment.

Abstract: An approach using 3D algorithms to solve 2D head localization problems is disclosed. A system can extrapolate aspects of one part of an object, e.g., extract characteristics of a person's head, using a 2D input image of another part of the object, e.g., a 2D image of the person's face. The system then selects an appropriate 3D model by the use of facial features detected in an image of a person's face. Using the selected 3D model and the 3D rotation angles provided by a face detector, the system rotates the model and then projects the model to a 2D shape. The system then scales and translates, e.g., transforms, the 2D shape to match the 2D face bounding box. Then, using the transformed 2D shape, the system extracts a bounding box for the extracted portion of an object, e.g., the head of the person depicted in the 2D input image.

Abstract: Systems and methods for frame and scene segmentation are disclosed herein. One method includes associating a first primary element from a first frame with a background tag, associating a second primary element from the first frame with a subject tag, generating a background texture using the first primary element, generating a foreground texture using the second primary element, and combining the background texture and the foreground texture into a synthesized frame. The method also includes training a segmentation network using the background tag, the foreground tag, and the synthesized frame.

Abstract: A computer-implemented method for segmenting an input image, the method comprises: generating a first segmentation of the input image using a first machine learning system, the first segmentation comprising multiple segments; receiving, from a user, at least one indication, wherein each indication corresponds to a particular segment from the multiple segments, and indicates one or more locations of the input image as belonging to that particular segment; constructing, for each segment of the multiple segments having at least one corresponding indication, a respective geodesic distance map, based on the input image and the user indications received for that segment; and generating a second segmentation using a second machine learning system based on the input image and the constructed geodesic distance maps.

Abstract: A system and method for decentralizing data and determining performance of different entities in multiple geographical and categorial markets whereby the system may determine complimentary entities to a user's entities or other existing entities for presentation to the user of overlapping procurements and demographics to gather a deeper understanding into the same results yielded by their competition whereby users will be able to provide more personal experiences for each consumer, as well as achieve pricing discovery, greater brand awareness, and marketing strategy.

Abstract: A device may receive a target document. The device may segment the target document into multiple segments. The device may determine, for each segment of the multiple segments, a set of color parameters for a corresponding set of pixels included in that segment. The device may determine, for each segment of the multiple segments, an average color parameter for that segment based on the set of color parameters for the corresponding set of pixels included in that segment. The device may generate a target color profile for the target document based on determining the average color parameter for each segment. The device may compare the target color profile and a model color profile associated with classifying the target document. The device may classify the target document based on comparing the target color profile and the model color profile.

Abstract: Autonomous robots and methods of operating the same are disclosed. An autonomous robot includes a sensor and memory including machine readable instructions. The autonomous robot further includes at least one processor to execute the instructions to generate a velocity costmap associated with an environment in which the robot is located. The processor generates the velocity costmap based on a source image captured by the sensor. The velocity costmap includes velocity information indicative of movement of an obstacle detected in the environment.

Abstract: A method for automatic extraction of data from a graph, including text area locating and text box classification; locating of coordinate axes, and locating of the positions of hatch marks on the coordinate axes; legend locating and information extraction; extracting corresponding bar or polyline connected components according to legend color, and filtering and classification; determining key points on the X-axis and locating a corresponding X-axis label for each key point; locating key points of the bars and polyline according to the X-axis key points, determining labeled numerical text boxes that correspond to the key points, and identifying the numerical text; calculating a corresponding value for each pixel, and estimating corresponding values of the key points of the bars or polyline; determining a final result according to a difference between the estimated values and the recognized labeled values.

Abstract: A semantic segmentation model training method includes: performing, by a semantic segmentation model, image semantic segmentation on at least one unlabeled image to obtain a preliminary semantic segmentation result as the category of the unlabeled image; obtaining, by a convolutional neural network based on the category of the at least one unlabeled image and the category of at least one labeled image, sub-images respectively corresponding to the at least two images and features corresponding to the sub-images, where the at least two images comprise the at least one unlabeled image and the at least one labeled image, and the at least two sub-images carry the categories of the corresponding images; and training the semantic segmentation model on the basis of the categories of the at least two sub-images and feature distances between the at least two sub-images.

Abstract: A system that enables real-time recognition of a real-space environment in a mixed-reality environment includes a server, a portable display device including a display unit for displaying a virtual object to a user and a photographing unit that photographs a real space, and image acquisition devices that acquire images individually from a plurality of fixed points where it is possible to photograph a region in a predetermined real space, the system making it possible to render the virtual object in a superimposed fashion on a real space or a photographed image of the real space, viewed by the user via the display unit, the server or the display device including a three-dimensional-space-data storage part, a table storage part, a color-information determining part, a color-information updating part, a user-environment determining part, a virtual-illumination-information generating part, and a rendering part.

Abstract: Image analysis includes obtaining, from an image signal processor, image processing information corresponding to a previously processed image, obtaining scene classification information for an input image based on the image processing information, generating a processed image by processing the input image based on the scene classification information, and outputting the processed image. The image processing information includes automatic white balance correction information and obtaining the scene classification information includes obtaining the scene classification information based on the automatic white balance correction information.

Abstract: Systems and methods disclosed herein describe a platform that automatically creates and executes a scoring guide for use in anatomical pathology. The platform can employ a fully-automated workflow for clustering the biological objects of interest and for providing cell-by-cell read-outs of heterogeneous tumor biomarkers based on their stain appearance. The platform can include a module for automatically creating and storing a scoring guide in a training database based on training digital images (240, 250), and an object classification module that executes the scoring guide when presented with new digital images to be scored pursuant to the scoring guide (299).

Abstract: Systems and methods of improving alignment in dense prediction neural networks are disclosed. A method includes identifying, at a computing system, an input data set and a label data set with one or more first parts of the input data set corresponding to a label. The computing system processes the input data set using a neural network to generate a predicted label data set that identifies one or more second parts of the input data set predicted to correspond to the label. The computing system determines an alignment result using the predicted label data set and the label data set and a transformation of the one or more first parts, including a shift, rotation, scaling, and/or deformation, based on the alignment result. The computing system computes a loss score using the transformation, label data and the predicted label data set and updates the neural network based on the loss score.

Abstract: A weighed run-length encoding and decoding method and related devices and encoded bitstream. The encoded bitstream can comprise one or more of the following: a skip command packed into a nybble, the skip command indicating how many transparent pixels which are inserted into the bitstream, wherein there is up to a maximum number of transparent pixels; a solid command packed into a nybble, the solid command indicating how many solid pixels should be inserted into the decoded bitstream, wherein there are up to the maximum number of solid pixels; and a quote command packed into a nybble, the quote command indicating how many quoted pixels should be inserted into the decoded bitstream, wherein there are up to the maximum number of quoted pixels.

Abstract: There is provided a system and method for color representation generation. In an aspect, the method includes: receiving three base colors; receiving a patchwork parameter; and generating a color representation having each of the three base colors at a vertex of a triangular face, the triangular face having a color distribution therein, the color distribution discretized into discrete portions, the amount of discretization based on the patchwork parameter, each discrete portion having an interpolated color determined to be a combination of the base colors at respective coordinates of such discrete portion. In further aspects, one or more color representations are generated based on an input image and can be used to modify colors of a reconstructed image.

Abstract: Provided are an artificial intelligence (AI) system for simulating functions such as recognition, determination, and so forth of the human brain by using a mechanical learning algorithm like deep learning, or the like, and an application thereof.

Abstract: An image processing apparatus includes a detection unit, a determination unit, a decision unit, and a processing unit. The detection unit detects an isolated pixel included in an image. The determination unit determines whether the isolated pixel detected by the detection unit changes brighter or darker by edge enhancement processing. The decision unit decides an edge enhancement amount of the isolated pixel based on a determination result of the determination unit. The processing unit performs the edge enhancement processing on the isolated pixel, based on the edge enhancement amount decided by the decision unit.

Abstract: Non-transitory computer-readable media, spectroradiometer systems, and methods for calibrating a spectroradiometer. In one embodiment, a non-transitory computer-readable medium includes instructions that, when executed by an electronic processor, cause the electronic processor to perform a set of operations. The set of operations includes receiving spectral data regarding an object-of-interest that is captured by a handheld spectroradiometer, detecting a characteristic of the object-of-interest by performing a spectral analysis on the spectral data that is received, and controlling a display to display the characteristic of the object-of-interest.

Abstract: The present disclosure includes systems and methods for color transfer. The method includes receiving a target image, and determining dominant source colors. The method further includes transforming the target image into a color model including a target luminance component and a target color information component. Additionally, the method includes segmenting the target image into a plurality of target segments based on the target color information component or the target luminance component and extracting dominant target colors from the target image by extracting information for at least one of the dominant target colors from each target segment of the plurality of target segments. Further, the method includes generating a color mapping relationship between the dominant target colors and the dominant source colors, and creating a recolored target image using the color mapping relationship.

Abstract: A method of generating at least one digital signature for at least one surface of a material element, referred to as an “examined surface.” The method is performed by a data processor system, and may include, for each examined surface: obtaining an “offset” image having at least a portion showing the examined surface; obtaining at least one zone of interest in the offset image by putting into correspondence the offset image with at least one predetermined “equivalent” zone that is included in a model image having at least a portion representing a “reference” surface; obtaining a “registered” image by registering the offset image with the model image by applying a homography transformation to the offset image, wherein the transformation is obtained from the at least one zone of interest and the at least one equivalent zone; and generating the digital signature, which characterizes the structure of the examined surface, from the registered image.