A survey of the Hough transform
Computer Vision, Graphics, and Image Processing
Extracting geometric primitives
CVGIP: Image Understanding
An extension to the randomized Hough transform exploiting connectivity
Pattern Recognition Letters
Building detection and description from a single intensity image
Computer Vision and Image Understanding
Detection and Modeling of Buildings from Multiple Aerial Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geometric Primitive Extraction Using a Genetic Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Robust Method for Unknown Forms Analysis
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
Rectangle Detection based on a Windowed Hough Transform
SIBGRAPI '04 Proceedings of the Computer Graphics and Image Processing, XVII Brazilian Symposium
Detecting boundaries in a vector field
IEEE Transactions on Signal Processing
Evolutionary optimization with Markov random field prior
IEEE Transactions on Evolutionary Computation
Automatic image segmentation by integrating color-edge extraction and seeded region growing
IEEE Transactions on Image Processing
Part-based adaptive detection of workpieces using differential evolution
Signal Processing
Journal of Visual Communication and Image Representation
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Rectangular shape object detection in color images is a critical step of many image recognition systems. However, there are few reports on this matter. In this paper, we proposed a hierarchical approach, which combines a global contour-based line segment detection algorithm and an Markov random field (MRF) model, to extract rectangular shape objects from real color images. Firstly, we use an elaborate edge detection algorithm to obtain image edge map and accurate edge pixel gradient information (magnitude and direction). Then line segments are extracted from the edge map and some neighboring parallel segments are merged into a single line segment. Finally all segments lying on the boundary of unknown rectangular shape objects are labeled via an MRF model built on line segments. Experimental results show that our method is robust in locating multiple rectangular shape objects simultaneously with respect to different size, orientation and color.