A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Three-dimensional object recognition from single two-dimensional images
Artificial Intelligence
Feature detection from local energy
Pattern Recognition Letters
On the classification of image features
Pattern Recognition Letters
A dynamic approach for clustering data
Signal Processing
The role of integral features for perceiving image discriminability
Pattern Recognition Letters
The Selection of Natural Scales in 2D Images Using Adaptive Gabor Filtering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Information Theoretic Measure for Visual Target Distinctness
IEEE Transactions on Pattern Analysis and Machine Intelligence
Defining a target distinctness measure through a single-channel computational model of vision
Pattern Recognition Letters
Rotation invariant texture classification using even symmetric Gabor filters
Pattern Recognition Letters
Distances between frequency features for 3D visual pattern partitioning
Pattern Recognition Letters
Motion representation using composite energy features
Pattern Recognition
Detection of visual attention regions in images using robust subspace analysis
Journal of Visual Communication and Image Representation
Data-driven synthesis of composite-feature detectors for 3D image analysis
Image and Vision Computing
Dissimilarity measures for visual pattern partitioning
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part II
Image inpainting with nonsubsampled contourlet transform
Pattern Recognition Letters
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This paper describes a system for the automatically learned partitioning of 驴visual patterns驴 in 2D images, based on a sophisticated, band-pass, filtering operation with fixed scale and orientation sensitivity. In this scheme, the 驴visual patterns驴 are defined as the features which have the highest degree of alignment in the statistical structure across different frequency bands. The analysis reorganizes the image according to a constraint of invariance in statistical structure and consists of three stages: 1) pre-attentive stage, 2) integration stage, and 3) learning stage. The first stage takes the input image and performs filtering with a set of log-Gabor filters. Based on their responses, activated filters which are selectively sensitive to patterns in the image are short listed. In the integration stage, common grounds between several activated sensors are explored. The filtered responses are analyzed through a family of statistics. For any given two activated filters, a distance between them is derived via distances between their statistics. The third stage, the learning stage, performs cluster partitioning as a mechanism for learning the subspace of log-Gabor filters needed to partition the image data. The clustering is based on a dissimilarity measure intended to highlight scale and orientation invariance of the filtered responses. The technique is illustrated on real and simulated data sets.Finally, this paper presents a computational visual distinctness measure computed from the image representational model based on visual patterns. It is applied to quantify the visual distinctness of targets in complex natural scenes. Several experiments are performed to investigate the relation between the computational distinctness measure and the visual target distinctness measured by human observers.