Abstract: This invention concerns a method of learning parameters of a convolutional neural network (CNN) through data processing means (11a, 11b, 11c) of at least one server (1a, 1b, 1c), for detecting items of interest visible in images, from at least one image learning database in which said items of interest, as well as characteristic geometric structures are already annotated, the CNN comprising an encoding layer for generating a representation vector of the detected items of interest, the method being characterized in that said representation vector comprises, for at least a first item of interest category to be detected, at least one descriptive value of at least a characteristic geometric structure of said first item of interest category. The present invention also relates to a process for detecting items of interest visible in an image and a method for associating items of interest visible in an image.

Abstract: A parameter training method for a convolutional neural network, CNN, for detecting items of interest visible in images by a data processor of at least one server. The method is implemented based on a plurality of training image databases. The items of interest are already annotated, the CNN being a CNN common to the plurality of training image databases and having a common core and a plurality of encoding layers, each one specific to one of the plurality of training image databases. The method is also for detecting items of interest visible in an image.

Abstract: The present invention relates to a method for association of items of interest visible in a video consisting of a sequence of K images, characterized in that it comprises the implementation by data processing means of a terminal of steps of: association of each item of interest from a first category visible in a kth image of said video with an item of interest from a second category, different from the first category, visible in said kth image; calculation of an association cost for a plurality of pairs of an item of interest from the first category visible in at least one image of the video with an item of interest from the second category visible in at least one image of the video, depending on at least the counters of association of pairs of one item of interest from the first category with one item of interest from the second category; use of a combinatorial optimization algorithm depending on the calculated association costs so as to reassociate each item of interest from the first category visible in sa

Abstract: This invention concerns a method of learning parameters of a convolutional neural network (CNN) through data processing means (11a, 11b, 11c) of at least one server (1a, 1b, 1c), for detecting items of interest visible in images, from at least one image learning database in which said items of interest, as well as characteristic geometric structures are already annotated, the CNN comprising an encoding layer for generating a representation vector of the detected items of interest, the method being characterized in that said representation vector comprises, for at least a first item of interest category to be detected, at least one descriptive value of at least a characteristic geometric structure of said first item of interest category. The present invention also relates to a process for detecting items of interest visible in an image and a method for associating items of interest visible in an image.

Abstract: The present invention relates to a method for association of items of interest visible in a video consisting of a sequence of K images, characterized in that it comprises the implementation by data processing means (21) of a terminal (2), of steps of: (a) Association of each item of interest from a first category visible in a kth image of said video with an item of interest from a second category, different from the first category, visible in said kth image; (b) Calculation of an association cost for a plurality of pairs of an item of interest from the first category visible in at least one image of the video with an item of interest from the second category visible in at least one image of the video, depending on at least the counters of association of pairs of one item of interest from the first category with one item of interest from the second category; (c) Use of a combinatorial optimization algorithm depending on the calculated association costs so as to reassociate each item of interest from the firs

Abstract: The present invention relates to a parameter training method for a convolutional neural network, CNN, for detecting items of interest visible in images by data processing means (11a, 11b, 11c) of at least one server (1a, 1b, 1c), the method being characterized in that it is implemented based on a plurality of training image databases, wherein said items of interest are already annotated, the CNN being a CNN common to said plurality of training image databases and having a common core and a plurality of encoding layers, each one specific to one of said plurality of training image databases. The present invention also relates to a method for detecting items of interest visible in an image.

Abstract: Provided is a method for verifying the veracity of a finger by a verification device including a projection device projecting a pattern of parallel lines orthogonal to the longitudinal direction of the finger, a capture device capturing an image of the finger and of the pattern, and a processing unit. The method includes: an illumination step in which the projection device illuminates the finger with the pattern; a contactless capture step in which the capture device captures a pattern image of the projected pattern; a fringe-extraction step in which the processing unit extracts fringes from the pattern image; a feature-calculation step in which the processing unit calculates a feature relating to the physical structure of the fringes; a comparison step during which the processing unit compares the calculated feature with reference values; and a decision-making step in which the processing unit makes a decision regarding the veracity of the finger.

Abstract: Provided is a method for verifying the veracity of a finger by a verification device including a projection device projecting a pattern of parallel lines orthogonal to the longitudinal direction of the finger, a capture device capturing an image of the finger and of the pattern, and a processing unit. The method includes: an illumination step in which the projection device illuminates the finger with the pattern; a contactless capture step in which the capture device captures a pattern image of the projected pattern; a fringe-extraction step in which the processing unit extracts fringes from the pattern image; a feature-calculation step in which the processing unit calculates a feature relating to the physical structure of the fringes; a comparison step during which the processing unit compares the calculated feature with reference values; and a decision-making step in which the processing unit makes a decision regarding the veracity of the finger.