Improving Chamfer Template Matching Using Image Segmentation

This letter proposes an effective method to improve object location in Chamfer template matching (CTM) based object detection using image segmentation. In our method, object bounding boxes are iteratively adjusted to fit with the object images obtained from image segmentation in a probabilistic model. The proposed method was tested with state-of-the-art CTM-based object detectors. Experimental results have shown the proposed method improved the location accuracy of the object detectors and reduce the false alarms rate.

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

[2]  Dariu Gavrila,et al.  A Bayesian, Exemplar-Based Approach to Hierarchical Shape Matching , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Longin Jan Latecki,et al.  Boosting Chamfer Matching by Learning Chamfer Distance Normalization , 2010, ECCV.

[4]  Wanqing Li,et al.  An Improved Template Matching Method for Object Detection , 2009, ACCV.

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

[6]  Daniel P. Huttenlocher,et al.  Efficient Graph-Based Image Segmentation , 2004, International Journal of Computer Vision.

[7]  Rama Chellappa,et al.  Fast directional chamfer matching , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[8]  Rachid Deriche,et al.  A Review of Statistical Approaches to Level Set Segmentation: Integrating Color, Texture, Motion and Shape , 2007, International Journal of Computer Vision.

[9]  Cordelia Schmid,et al.  Bandit Algorithms for Tree Search , 2007, UAI.

[10]  Daniel P. Huttenlocher,et al.  Distance Transforms of Sampled Functions , 2012, Theory Comput..

[11]  Farida Cheriet,et al.  Region-Based Segmentation via Non-Rigid Template Matching , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[12]  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.

[13]  Duc Thanh Nguyen,et al.  A Novel Chamfer Template Matching Method Using Variational Mean Field , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[14]  Jin Chen,et al.  Multi-leaf alignment from fluorescence plant images , 2014, IEEE Winter Conference on Applications of Computer Vision.

[15]  Wanqing Li,et al.  A novel shape-based non-redundant local binary pattern descriptor for object detection , 2013, Pattern Recognit..

[16]  Laurent D. Cohen,et al.  Prior-Based Piecewise-Smooth Segmentation by Template Competitive Deformation Using Partitions of Unity , 2008, ECCV.

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