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Title: Recognition of Occluded Object Using Wavelets
Authors: DU TIEHUA
Keywords: Partial occlusion, object recognition, corner detection, feature extraction, multi-resolution analysis, hierarchical matching
Issue Date: 23-Apr-2007
Citation: DU TIEHUA (2007-04-23). Recognition of Occluded Object Using Wavelets. ScholarBank@NUS Repository.
Abstract: Object recognition has extensive applications in many areas, such as visual inspection, part assembly, artificial intelligence, etc. It is a major and also a challenging task in computer vision. Although humans perform object recognition effortlessly and instantaneously, implementation of this task on machines is very difficult. The problem is even more complicated when there is partial occlusion situation. Many researchers have dedicated themselves into this area and made great contributions in the past few decades. However, existing algorithms have various shortcomings and limitations, such as their limited applicability to the polygonal shapes, and the necessary prior knowledge of the scale. This research is aimed at developing a novel 2-D object recognition algorithm applicable for both stand-alone and partial occluded objects using wavelet techniques. Wavelet is a more recent mathematical tool in comparison with Fourier transform, and it has several exciting properties which can be well used in this research, e.g. multiresolution analysis, singularity detection and local analysis. A wavelet-based object recognition algorithm is presented in this thesis. The feature to represent the object is the wavelet representation of curve segments of the object boundary. To achieve the consistent boundary partitioning, a wavelet-based corner detection algorithm is proposed and verified. After partitioning, each curve segment is normalized, which makes it invariant to similarity transformation. An adaptive fast wavelets decomposition using bi-orthonormal wavelet is then applied on each segment to extract multiresolution representation, which facilitates hierarchical matching. After thresholding to eliminate the noise and quantization error, the resultant scaling coefficients and wavelet coefficients are the features for recognition. In matching process, firstly, we match the features of segments between object in the scene and the model in an object database to find out segment-pair candidates with similar geometric shape. Hierarchical matching strategy is adopted to accelerate the matching speed. If valid segment-pairs between object in scene and model are found, relative orientation and scale information are then applied for further verification to eliminate false matching. Experiment results show that our proposed recognition algorithm is invariant to similarity transform, robust to partial occlusion, and that it is computationally efficient.
Appears in Collections:Ph.D Theses (Open)

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