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queried at the retrieval stage. The query and database images are 8-bit grayscale images (256 gray levels). Brand sketches are com-Horacio Legal Ayala
This paper introduces a novel descriptor technique denoted as Contour-Point Signature (CPS) useful to find correspondences of points selected from the outer contours of two arbitrary shapes, and to establish a relationship to map an... more
This paper introduces a novel descriptor technique denoted as Contour-Point Signature (CPS) useful to find correspondences of points selected from the outer contours of two arbitrary shapes, and to establish a relationship to map an ordered sequence of contour points from one shape to another. The proposal has proved to be invariant, to translation, scaling and rotation, it also induces a measure which is proved to be non-negative, unique, symmetric and identity-preserving. Experimental tests were performed in shape detection under noise, with image retrieval from a MPEG-7 database and letter recognition. Numerical results show that the proposal is robust for noise perturbation, as well as, having adequate accuracy and hit rate, even with coarse tuning for its parameters. This makes the method attractive to a wide range of applications.
Research Interests: Computer Vision, Image Processing, Pattern Recognition, Object Recognition (Pattern Recognition), Image Analysis, and 10 moreBrand Image, Image segmentation, Digital Image Processing, Segmentation, Image and Video Processing, Signal and Image Processing, Satellite Remote Sensing & Image Processing, Medical Image Segmentation, Segmentación De Imágenes, and Signal Analysis and Pattern Identification
Research Interests:
A research area in Computer Vision focuses on the identification of articulated objects, such as human actions, which can be used in human-computer interaction. This article analyzes a new descriptor, the Contour-Point Signature-CPS;... more
A research area in Computer Vision focuses on the identification of articulated objects, such as human actions, which can be used in human-computer interaction. This article analyzes a new descriptor, the Contour-Point Signature-CPS; which is a point descriptor that allows to achieve a better matching of points between two figures and obtain a transformation between them. With this descriptor, we can achieve more accurate shape features and implement more efficient retrieval under multi-resolution. In addition, CPS is robust to rigid translation, scaling, rotation and independent of the origin point. A measure of dissimilarity between two figures for classifying various human postures in a video sequence is also presented.
Research Interests:
A research area in Computer Vision focuses on the identification of articulated objects, such as human actions and movements of the hand, which can be used in human-computer interaction, surveillance, and other tracking systems. Two... more
A research area in Computer Vision focuses on the identification of articulated objects, such as human actions and movements of the hand, which can be used in human-computer interaction, surveillance, and other tracking systems. Two problems arise: identify when two articulated objects in different stances are in the same class of objects, and differentiate the distinct positions of the same object. In both cases, it is necessary to know how correspond the different points or regions of such objects standing in different attitudes. This article presents the Contour-Point Signature; a point descriptor that allows to establish a method to achieve the better matching of points between two figures, and to thus obtain a transformation which relates them. A measure of dissimilarity between two figures for classifying various human postures in a video sequence is also defined.
Research Interests: Computer Vision, Machine Vision, Gesture Recognition, OpenCv or Computer Vision, Image Processing with OpenCV, and 15 moreGesture Control, Image Descriptor, OpenCV, Hand Gesture Recognition System, Local Shape descriptors, Human posture recognition, Hand Gesture Recognition, Machine Vision Systems Market, Hand Posture Recognition, Android With Opencv, Descriptor Extraction, Descriptors, Local Descriptors, Point descriptors, and Point descriptor
A research area in Computer Vision focuses on the identification of articulated objects, such as human actions, which can be used in human-computer interaction. This article analyzes a new descriptor, the Contour-Point Signature-CPS;... more
A research area in Computer Vision focuses on the identification of articulated objects, such as human actions, which can be used in human-computer interaction. This article analyzes a new descriptor, the Contour-Point Signature-CPS; which is a point descriptor that allows to achieve a better matching of points between two figures and obtain a transformation between them. With this descriptor, we can achieve more accurate shape features and implement more efficient retrieval under multi-resolution. In addition, CPS is robust to rigid translation, scaling, rotation and independent of the origin point. A measure of dissimilarity between two figures for classifying various human postures in a video sequence is also presented.
Research Interests:
Research Interests:
Research Interests:
Research Interests: Computer Vision, Robot Vision, Object Recognition (Computer Vision), Image Recognition (Computer Vision), Machine Vision, and 34 moreObject Tracking (Computer Vision), Gesture Recognition, OpenCv or Computer Vision, Robotics, Computer Vision, Artificial Intelligence, Image Processing with OpenCV, THESIS ON WEARABLE GESTURE RECOGNITION AND WEBCAM WORKING, Swarm Robotics,Industrial Robotics, Mobile Robotics,Bionics, Assistive Robotics, Automation, Machine vision, Artificial Intelligence, PLC, Control Systems, Gesture Control, Image Descriptor, OpenCV, Hand Gesture Recognition System, Image Processing, Image Scaling, OpenCV, Shape Descriptions, Vision-based hand gesture recognition, Local Shape descriptors, PhD Thesis. Vision-based gesture recognition in a robot learning by imitation framework, Recognition of gestures, facial expressions and emotions, Human posture recognition, Hand Gesture Recognition, Machine Vision Systems Market, Shape Descriptor, Hand Posture Recognition, Deveploment, Algorith, Opencv, Android With Opencv, Descriptor Extraction, Shape Descriptors, Shape Description, Contour-Point Signature, Video Sequence, Descriptors, Local Descriptors, Traitement D'image Sous OpenCV, Point descriptors, and Point descriptor
A research area in Computer Vision focuses on the identification of articulated objects, such as human actions and movements of the hand, which can be used in human-computer interaction, surveillance, and other tracking systems. Two... more
A research area in Computer Vision focuses on the identification of articulated objects, such as human actions and movements of the hand, which can be used in human-computer interaction, surveillance, and other tracking systems. Two problems arise: identify when two articulated objects in different stances are in the same class of objects, and differentiate the distinct positions of the same object. In both cases, it is necessary to know how correspond the different points or regions of such objects standing in different attitudes. This article presents the Contour-Point Signature; a point descriptor that allows to establish a method to achieve the better matching of points between two figures, and to thus obtain a transformation which relates them. With this descriptor, we can achieve more accurate shape features and implement more efficient retrieval under multi-resolution. In addition, CPS is robust to rigid translation, scaling, rotation and independent of the origin point. A measure of dissimilarity between two figures for classifying various human postures in a video sequence is also presented.