An Integrated Method for Multiple Object Detection and Localization
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
Image Representation in Differential Space
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
A new framework for feature descriptor based on SIFT
Pattern Recognition Letters
A new pyramid-based color image representation for visual localization
Image and Vision Computing
Image retrieval based on multi-texton histogram
Pattern Recognition
Object re-detection using SIFT and MPEG-7 color descriptors
MCAM'07 Proceedings of the 2007 international conference on Multimedia content analysis and mining
Content-based image retrieval using shape and depth from an engineering database
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part II
ICSR'10 Proceedings of the Second international conference on Social robotics
Colour and rotation invariant textural features based on Markov random fields
Pattern Recognition Letters
A target-based color space for sea target detection
Applied Intelligence
Hybrid machine learning approach for object recognition: fusion of features and decisions
Machine Graphics & Vision International Journal
Estimating shadows with the bright channel cue
ECCV'10 Proceedings of the 11th European conference on Trends and Topics in Computer Vision - Volume Part II
An improvement to the SIFT descriptor for image representation and matching
Pattern Recognition Letters
DeepCAPTCHA: an image CAPTCHA based on depth perception
Proceedings of the 5th ACM Multimedia Systems Conference
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In this paper, we propose a new scheme that merges color- and shape-invariant information for object recognition. To obtain robustness against photometric changes, color-invariant derivatives are computed first. Color invariance is an important aspect of any object recognition scheme, as color changes considerably with the variation in illumination, object pose, and camera viewpoint. These color invariant derivatives are then used to obtain similarity invariant shape descriptors. Shape invariance is equally important as, under a change in camera viewpoint and object pose, the shape of a rigid object undergoes a perspective projection on the image plane. Then, the color and shape invariants are combined in a multidimensional color-shape context which is subsequently used as an index. As the indexing scheme makes use of a color-shape invariant context, it provides a high-discriminative information cue robust against varying imaging conditions. The matching function of the color-shape context allows for fast recognition, even in the presence of object occlusion and cluttering. From the experimental results, it is shown that the method recognizes rigid objects with high accuracy in 3-D complex scenes and is robust against changing illumination, camera viewpoint, object pose, and noise.