Dynamic Programming and Skyline Extraction in Catadioptric Infrared Images

International audienceUnmanned Aerial Vehicles (UAV) are the subject of an increasing interest in many applications and a key requirement for autonomous navigation is the attitude/position stabilization of the vehicle. Some previous works have suggested using catadioptric vision, instead of traditional perspective cameras, in order to gather much more information from the environment and therefore improve the robustness of the UAV attitude/position estimation. This paper belongs to a series of recent publications of our research group concerning catadioptric vision for UAVs. Currently, we focus on the extraction of skyline in catadioptric images since it provides important information about the attitude/position of the UAV. For example, the DEM-based methods can match the extracted skyline with a Digital Elevation Map (DEM) by process of registration, which permits to estimate the attitude and the position of the camera. Like any standard cameras, catadioptric systems cannot work in low luminosity situations because they are based on visible light. To overcome this important limitation, in this paper, we propose using a catadioptric infrared camera and extending one of our methods of skyline detection towards catadioptric infrared images. The task of extracting the best skyline in images is usually converted in an energy minimization problem that can be solved by dynamic programming. The major contribution of this paper is the extension of dynamic programming for catadioptric images using an adapted neighborhood and an appropriate scanning direction. Finally, we present some experimental results to demonstrate the validity of our approach

Stereo Matching with the Distinctive Similarity Measure

International audienceThe point ambiguity owing to the ambiguous local appearances of image points is the one of the main causes making the stereo problem difficult. Under the point ambiguity, local similarity measures are easy to be ambiguous and this results in false matches in ambiguous regions. In this paper, we present the new similarity measure to resolve the point ambiguity problem based on the idea that the distinctiveness, not the interest, is the appropriate criterion for the feature selection under the point ambiguity. The proposed similarity measure named the Distinctive Similarity Measure (DSM) is essentially based on the distinctiveness of image points and the dissimilarity between them, which are both closely related to the local appearances of image points; the distinctiveness of an image point is related to the probability of a mismatch while the dissimilarity is related to the probability of a good match. We verify the efficiency of the proposed DSM by using testbed image sets. Experimental results show that the proposed DSM is very effective and can be easily used for improving the performance of existing stereo methods under the point ambiguity

Distinctive Similarity Measure for Stereo Matching Under Point Ambiguity

International audienceThe point ambiguity owing to the ambiguous local appearances of image points is one of the main causes making the stereo problem difficult. Under the point ambiguity, local similarity measures are easy to be ambiguous and this results in false matches in ambiguous areas. In this paper, we present a new similarity measure to resolve the point ambiguity problem based on the idea that the distinctiveness, not the interest, is an appropriate criterion for feature selection under the point ambiguity. Here, the interest of a point represents how much information a point has for facilitating matching, while the distinctiveness of a point represents how much a point is distinguishable from other points. The proposed similarity measure named the Distinctive Similarity Measure (DSM) is essentially based on the distinctiveness of image points and the dissimilarity between them, which are both closely related to the local appearances of image points; the distinctiveness of an image point is related to the probability of a mismatch while the dissimilarity is related to the probability of a good match. We verify the efficiency of the proposed DSM by using testbed image sets. Experimental results prove that the proposed DSM is very effective for both semi-dense and dense stereo matching and considering the point distinctiveness in both images can improve the performance of stereo methods under the point ambiguity

A novel image-based control-law for the visual servoing system under large pose error

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3-d vision techniques for autonomous vehicles

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Object Recognition Using a Generalized Robust Invariant Feature and Gestalt's Law of Proximity and Similarity

International audienceIn this paper, we propose a new context-based method for object recognition. We first introduce a neuro-physiologically motivated visual part detector. We found that the optimal form of the visual part detector is a combination of a radial symmetry detector and a corner-like structure detector. A general context descriptor, named G-RIF (generalized-robust invariant feature), is then proposed, which encodes edge orientation, edge density and hue information in a unified form. Finally, a context-based voting scheme is proposed. This proposed method is inspired by the function of the human visual system, called figure-ground discrimination. We use the proximity and similarity between features to support each other. The contextual feature descriptor and contextual voting method, which use contextual information, enhance the recognition performance enormously in severely cluttered environments

Metric localization using a single artificial landmark for indoor mobile robots

Abstract – We present an accurate metric localization method using a simple artificial landmark for the navigation of indoor mobile robots. The proposed landmark model is designed to have a three-dimensional, multi-colored structure and the projective distortion of the structure encodes the distance and heading of the robot with respect to the landmark. Catadioptric vision is adopted for the robust and easier acquisition of the bearing measurements for the landmark. We propose a practical EKF based self-localization method that uses a single artificial landmark and runs in real time. Index Terms – metric localization, artificial landmark, catadioptric vision, EKF I