scholarly journals Color Histograms Adapted to Query-Target Images for Object Recognition across Illumination Changes

2005 ◽  
Vol 2005 (14) ◽  
Author(s):  
Damien Muselet ◽  
Ludovic Macaire ◽  
Jack-Gérard Postaire
Keyword(s):  
Object Recognition ◽  
Illumination Changes ◽  
Color Histograms

scholarly journals Person Tracking System by Fusing Multicues Based on Patches

2015 ◽  
Vol 2015 ◽  
pp. 1-12
Author(s):  
Song Min Jia ◽  
Li Jia Wang ◽  
Xiu Zhi Li ◽  
Lin Feng Wen
Keyword(s):  
Tracking System ◽  
Previous Knowledge ◽  
Complex Environment ◽  
Person Tracking ◽  
Discrimination Ability ◽  
Depth Maps ◽  
Texture Information ◽  
Illumination Changes ◽  
Color Histograms ◽  
The Given

A person tracking algorithm by fusing multicues based on patches is proposed to solve the problem of distinguishing person, occlusion, and illumination variations. Kinect is mounted on the robot for providing color images and depth maps. A detector representing a person by using the fusion of multicues based on patches is proposed. The detector divides the person into many patches and then represents each patch by using depth-color histograms and depth-texture histograms. The appearance representation, considering depth, color, and texture information, has powerful discrimination ability to handle the problems of occlusion, illumination changes, and pose variations. Considering the motion of the robot and person, a tracker called motion extended Kalman filter (MEKF) is presented to predict the person’s position. The result of the tracker is treated as a candidate sample of the detector, and then the result of the detector is the previous knowledge of the tracker. The detector and tracker supplement each other and improve the tracking performance. To drive the robot towards the given person precisely, a fuzzy based intelligent gear control strategy (FZ-IGS) is implemented. Experiments demonstrate that the proposed approach can track a person in a complex environment and have an optimum performance.

A Robust Edge-Based Corner Detector (EBCD)

2014 ◽  
Vol 14 (04) ◽  
pp. 1450018 ◽  
Author(s):  
Rabih Al Nachar ◽  
Elie Inaty ◽  
Patrick J. Bonnin ◽  
Yasser Alayli
Keyword(s):  
Object Recognition ◽  
Robot Navigation ◽  
Edge Detector ◽  
Interest Points ◽  
Image Scaling ◽  
Illumination Changes ◽  
Intersection Points ◽  
Edge Based ◽  
Image Edges ◽  
Image Transformations

This paper presents a novel and robust edge-based corner detector (EBCD) which finds corners that are considered stable interest points in the framework of 2D object recognition especially for robot navigation. Using the EBCD, the corners are defined as intersection points of non-collinear straight image edges. The straight edge detector plus the corner detector will localize the corner positions. The detected corners have special features which are their angles and their sides length ratios. These features are invariant parameters which make the corners perfect for 2D object recognition. In addition, these corners are shown to be very robust against various image transformations like image scaling, rotation, translation and viewpoint illumination changes. Some updates are applied on the linking edge step in order to extract edges and their intersections that in turn construct the searched corners. Experiments conducted on synthetic and real images show that the proposed EBCD is able to achieve a very good performance in terms of accuracy, stability and especially computational efficiency in comparison with existing algorithms on interest points.

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