A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A physical approach to color image understanding
International Journal of Computer Vision
Physics-based segmentation of complex objects using multiple hypotheses of image formation
Computer Vision and Image Understanding - Special issue on physics-based modeling and reasoning in computer vision
Feature Detection with Automatic Scale Selection
International Journal of Computer Vision
Evaluation of Interest Point Detectors
International Journal of Computer Vision - Special issue on a special section on visual surveillance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer and Robot Vision
Color Edge Detection by Photometric Quasi-Invariants
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Classifying color edges in video into shadow-geometry, highlight, or material transitions
IEEE Transactions on Multimedia
Boosting Color Saliency in Image Feature Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image retrieval measures based on illumination invariant textural MRF features
Proceedings of the 6th ACM international conference on Image and video retrieval
Constructing cylindrical coordinate colour spaces
Pattern Recognition Letters
Handling of impreciseness in gray level corner detection using fuzzy set theoretic approach
Applied Soft Computing
Color in image and video processing: most recent trends and future research directions
Journal on Image and Video Processing - Color in Image and Video Processing
Performance evaluation of local colour invariants
Computer Vision and Image Understanding
A novel high performance multi-modal approach for content based image retrieval
Proceedings of the International Conference and Workshop on Emerging Trends in Technology
Color-texture image segmentation by combining region and photometric invariant edge information
MCAM'07 Proceedings of the 2007 international conference on Multimedia content analysis and mining
Shadow edge detection using geometric and photometric features
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
CCIW'11 Proceedings of the Third international conference on Computational color imaging
CCIW'11 Proceedings of the Third international conference on Computational color imaging
Corner detectors for affine invariant salient regions: is color important?
CIVR'06 Proceedings of the 5th international conference on Image and Video Retrieval
Coloring local feature extraction
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Illumination invariant color model for object recognition in robot soccer
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part II
Non-local spatial redundancy reduction for bottom-up saliency estimation
Journal of Visual Communication and Image Representation
Evaluating a color-based active basis model for object recognition
Computer Vision and Image Understanding
A new biologically inspired color image descriptor
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
Per-patch descriptor selection using surface and scene properties
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
An effective method for illumination-invariant representation of color images
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume 2
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
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Feature detection is used in many computer vision applications such as image segmentation, object recognition, and image retrieval. For these applications, robustness with respect to shadows, shading, and specularities is desired. Features based on derivatives of photometric invariants, which we will call full invariants, provide the desired robustness. However, because computation of photometric invariants involves nonlinear transformations, these features are unstable and, therefore, impractical for many applications. We propose a new class of derivatives which we refer to as quasi-invariants. These quasi-invariants are derivatives which share with full photometric invariants the property that they are insensitive for certain photometric edges, such as shadows or specular edges, but without the inherent instabilities of full photometric invariants. Experiments show that the quasi-invariant derivatives are less sensitive to noise and introduce less edge displacement than full invariant derivatives. Moreover, quasi-invariants significantly outperform the full invariant derivatives in terms of discriminative power.