Hierarchical Chamfer Matching: A Parametric Edge Matching Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Example-Based Object Detection in Images by Components
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Global Optimization
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pictorial Structures for Object Recognition
International Journal of Computer Vision
A Bayesian, Exemplar-Based Approach to Hierarchical Shape Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learned local Gabor patterns for face representation and recognition
Signal Processing
Adaptive object detection by implicit sub-class sharing features
Signal Processing
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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.