A Novel Approach of Rectangular Shape Object Detection in Color Images Based on An MRF Model
Yangxing Liu, Takeshi Ikenaga, Satoshi Goto · 2006
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 a Markov random field (MRF) model, to extract rectangular shape objects from real color images. First, 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