Part-based adaptive detection of workpieces using differential evolution

In many industrial applications, detection of workpieces is the prerequisite of the subsequent operations such as automatic grasping and assembly tasks. However, the detection of workpieces under challenging conditions such as occlusion and cluttered background is still an open problem, which needs better solutions and further investigations. In this paper, a part-based adaptive detection approach is proposed to deal with abovementioned problems. The whole workpiece template is automatically divided into multiple subtemplates, which are equipped with adjustable weights adjusted according to their discriminative abilities. Then the weight adjustment process and the object localization process are finally embedded in an optimization framework-Differential Evolution (DE), which finally leads to the detection of workpieces. Experimental results demonstrate the effectiveness and robust performance of the proposed algorithm under challenging conditions.

[1]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[2]  Daniel P. Huttenlocher,et al.  Pictorial Structures for Object Recognition , 2004, International Journal of Computer Vision.

[3]  Gunilla Borgefors,et al.  Hierarchical Chamfer Matching: A Parametric Edge Matching Algorithm , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

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

[5]  Jitendra Malik,et al.  Shape matching and object recognition using shape contexts , 2010, 2010 3rd International Conference on Computer Science and Information Technology.

[6]  Tomaso A. Poggio,et al.  Example-Based Object Detection in Images by Components , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Wen Gao,et al.  Learned local Gabor patterns for face representation and recognition , 2009, Signal Process..

[8]  Rainer Lienhart,et al.  An extended set of Haar-like features for rapid object detection , 2002, Proceedings. International Conference on Image Processing.

[9]  Satoshi Goto,et al.  An MRF model-based approach to the detection of rectangular shape objects in color images , 2007, Signal Process..

[10]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..