Using Polygons to Recognize and Locate Partially Occluded Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognition of occluded objects: a cluster-structure algorithm
Pattern Recognition
Fundamentals of digital image processing
Fundamentals of digital image processing
Natural Representations for Straight Lines and the Hough Transform on Discrete Arrays
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Adaptive Reduction Procedure for the Piecewise Linear Approximation of Digitized Curves
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural network fundamentals with graphs, algorithms, and applications
Neural network fundamentals with graphs, algorithms, and applications
Use of the Hough transformation to detect lines and curves in pictures
Communications of the ACM
An Information-Theoretic Approach to Neural Computing
An Information-Theoretic Approach to Neural Computing
Self-Organizing Maps
Computer Vision and Image Understanding
Deformation tolerant generalized Hough transform for sketch-based image retrieval in complex scenes
Image and Vision Computing
Hi-index | 0.14 |
An automated approach for template-free identification of partially occluded objects is presented. The contour of each relevant object in the analyzed scene is modeled with an approximating polygon whose edges are then projected into the Hough space. A structurally adaptive self-organizing map neural network generates clusters of collinear and/or parallel edges, which are used as the basis for identifying the partially occluded objects within each polygonal approximation. Results on a number of cases under different conditions are provided.