Linked edges as stable region boundaries

Many of the recently popular shape based category recognition methods require stable, connected and labeled edges as input. This paper introduces a novel method to find the most stable region boundaries in grayscale images for this purpose. In contrast to common edge detection algorithms as Canny, which only analyze local discontinuities in image brightness, our method integrates mid-level information by analyzing regions that support the local gradient magnitudes. We use a component tree where every node contains a single connected region obtained from thresholding the gradient magnitude image. Edges in the tree are defined by an inclusion relationship between nested regions in different levels of the tree. Region boundaries which are similar in shape (i. e. have a low chamfer distance) across several levels of the tree are included in the final result. Since the component tree can be calculated in quasi-linear time and chamfer matching between nodes in the component tree is reduced to analysis of the distance transformation, results are obtained in an efficient manner. The proposed detection algorithm labels all identified edges during calculation, thus avoiding the cumbersome post-processing of connecting and labeling edge responses. We evaluate our method on two reference data sets and demonstrate improved performance for shape prototype based localization of objects in images.

[1]  Andrew Blake,et al.  Contour-based learning for object detection , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[2]  Alan L. Yuille,et al.  Statistical Edge Detection: Learning and Evaluating Edge Cues , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Michel Couprie,et al.  Quasilinear algorithm for the component tree , 2004, IS&T/SPIE Electronic Imaging.

[4]  Jiri Matas,et al.  Robust wide-baseline stereo from maximally stable extremal regions , 2004, Image Vis. Comput..

[5]  Martial Hebert,et al.  Beyond Local Appearance: Category Recognition from Pairwise Interactions of Simple Features , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[6]  David Nistér,et al.  Linear Time Maximally Stable Extremal Regions , 2008, ECCV.

[7]  Kurt Keutzer,et al.  Efficient, high-quality image contour detection , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[8]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Luc Van Gool,et al.  Object Detection by Contour Segment Networks , 2006, ECCV.

[10]  Frédéric Jurie,et al.  Groups of Adjacent Contour Segments for Object Detection , 2008, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Zhuowen Tu,et al.  Detecting Object Boundaries Using Low-, Mid-, and High-level Information , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[12]  Pablo Andrés Arbeláez,et al.  Boundary Extraction in Natural Images Using Ultrametric Contour Maps , 2006, 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06).

[13]  Andrew Blake,et al.  Multiscale Categorical Object Recognition Using Contour Fragments , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Laurent Najman,et al.  Geodesic Saliency of Watershed Contours and Hierarchical Segmentation , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Jitendra Malik,et al.  Learning to detect natural image boundaries using local brightness, color, and texture cues , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Zhuowen Tu,et al.  Supervised Learning of Edges and Object Boundaries , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[17]  Shimon Ullman,et al.  Learning to Segment , 2004, ECCV.

[18]  Dima Damen,et al.  Recognizing linked events: Searching the space of feasible explanations , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[19]  Ronald Jones,et al.  Connected Filtering and Segmentation Using Component Trees , 1999, Comput. Vis. Image Underst..

[20]  Dariu Gavrila,et al.  Pedestrian Detection from a Moving Vehicle , 2000, ECCV.

[21]  Andrew Zisserman,et al.  A Boundary-Fragment-Model for Object Detection , 2006, ECCV.