Differential invariants for color images

Montesinos, Philippe; Gouet, Valérie; Deriche, Rachid

doi:10.1109/icpr.1998.711280

Cited by 67 publications

(44 citation statements)

References 4 publications

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“…All of these operators require the computation of second order derivatives on the image, which makes them not only very noise sensitive, but also inaccurate for precise localization. Nevertheless, these functions are usually used to obtain initial corner localization estimates, and to refine these estimates, several heuristics can be used, such as multiresolution, non-maximum suppression, and energy minimization [32,59,60,67,84,129,142]. We will now describe briefly our revised version of the variance descent approach originally proposed by Blaszka and Deriche [32] for the refinement of corner localization on images.…”

Section: A11 Cornersmentioning

confidence: 99%

“…To resolve this issue, Deriche and some of his 2.1 : Low-level processing of 2d intensity images 13 students have proposed algorithms for vertex localization and refinement, that although initially based on the same principle of using second-order derivatives, they make use of other techniques such as multiresolution, non-maximum suppression, and energy minimization to come up with refined corner localization estimates [32,59,60,84,142].…”

Section: Low-level Processing Of 2d Intensity Imagesmentioning

confidence: 99%

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Environment Learning for Indoor Mobile Robots

Andrade‐Cetto¹

2006

Springer Tracts in Advanced Robotics

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This thesis focuses on the various aspects of autonomous environment learning for indoor service robots. Particularly, on landmark extraction from sensor data, autonomous map building, and robot localization.To univocally identify landmarks from sensor data, we study several landmark representations, and the mathematical foundation necessary to extract the features that build them from images and laser range data. The features extracted from just one sensor may not suffice in the invariant characterization of landmarks and objects, pushing for the combination of information from multiple sources. We present a new algorithm that fuses complementary information from two low level vision modules into coherent object models that can be tracked and learned in a mobile robotics context. Illumination conditions and occlusions are the most prominent artifacts that hinder data association in computer vision. By using photogrammetric and geometric constraints we restrict the search for landmark matches in successive images, and by locking our interest in one or a set of landmarks in the scene, we track those landmarks along successive frames, reducing considerably the data association problem. We concentrate on those tools from the geometry of multiple views that are relevant to the computation of initial landmark location estimates for coarse motion recovery; a desirable characteristic when odometry is not available or is highly unreliable.Once landmarks are accurately extracted and identified, the second part of the problem is to use these observations for the localization of the robot, as well as the refinement of the landmark location estimates. We consider robot motion and sensor observations as stochastic processes, and treat the problem from an estimation theoretic point of view, dealing with noise by using probabilistic methods.The main drawback we encounter is that current estimation techniques have been devised for static environments, and that they lack robustness in more realistic situations. To aid in those situations in which landmark observations might not be consistent in time, we propose a new set of temporal landmark quality functions, and show how iii by incorporating these functions in the data association tests, the overall estimationtheoretic approach to map building and localization is improved. The basic idea consists on using the history of data association mismatches for the computation of the likelihood of future data association, together with the spatial compatibility tests already available.Special attention is paid in that the removal of spurious landmarks from the map does not violate the basic convergence properties of the localization and map building algorithms already described in the literature; namely, asymptotic convergence and full correlation.The thesis also gives an in depth analysis of the fully correlated model to localization and map building from a control systems theory point of view. Considering the fact that the Kalman filter is nothing else but an optimal observer,...

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Section: A11 Cornersmentioning

confidence: 99%

Section: Low-level Processing Of 2d Intensity Imagesmentioning

confidence: 99%

Environment Learning for Indoor Mobile Robots

Andrade‐Cetto¹

2006

Springer Tracts in Advanced Robotics

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“…The value of an initial sigma is 1.6 and the sigma increase is k = 2 1/8 , and the number of scales used is 5. To work with color images, some authors [13] propose a simple modification of Harris Detector, computing gradients in the RGB space. The parameters obtained by the Harris detector are the magnitude and the orientation and the two Eigenvalues λ 1 and λ 2 .…”

Section: Harris Corner Detectormentioning

confidence: 99%

AdaBoost Multiple Feature Selection and Combination for Face Recognition

Martínez-Contreras

Orrite-Uruñuela

Rincón

2009

Pattern Recognition and Image Analysis

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“…While this detector only applies to grayscale images, Montesinos et al [9] generalized it to color images. The IPs produced by their detector are defined as the positive local extrema of the intermediate grayscale image…”

Section: Color Harris Detectormentioning

confidence: 99%

“…In our approach, an object is characterized by a set of interest points (IPs) obtained with a color Harris detector [9]. Each IP is characterized by its local appearance (a vector of local characteristics).…”

Section: Introductionmentioning

confidence: 99%