Abstract
Problem statement: In this study, a new method has been proposed for the recognition of 3D objects based on the various views of the object. The proposed method is evolved from the two promising methods available for object recognition. Approach: The proposed method uses both the local and global features extracted from the images. For feature extraction, Hus Moment invariant is computed for global feature to represent the image and Hessian-Laplace detector and PCA-SIFT descriptor as local feature for the given image. The multi-classs SVM-KNN classifier is applied to the feature vector to recognize the object. The proposed method uses the COIL-100 and CALTECH image databases for its experimentation. Results and Conclusion: The proposed method is implemented in MATLAB and tested. The results of the proposed method are better when comparing with other methods like KNN, SVM and BPN.
Highlights
This study addresses the problem of recognizing 3D objects in images
A view based 3D object recognition model is proposed as a hybrid of Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) method as classifiers with the local and global features of 2D images as features
Support Vector Machines are used for classification and regression; it belongs to generalized kind of SVM is better and they are selected based on linear classifiers (Chen et al, 2010)
Summary
This study addresses the problem of recognizing 3D objects in images. The 3D object recognition is a prominent research area for last two decades; many researchers were involved in developing real-world object recognition applications. A view based 3D object recognition model is proposed as a hybrid of Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) method as classifiers with the local and global features of 2D images as features. The proposed work in this study is an extension of the previous work in object recognition using local and global features on 2D images (Muralidharan and Chandrasekar, 2012). Pontil and Verri (1998) used Support Vector Machine for training and testing the 3D object recognition with a subset of COIL-100 image dataset (consisting of 32 objects). Huang et al (2010) suggests that the support vector machine performs well in identifying micro parts He et al (2007) applies different classifier for global Generally features are categorized into two types; they feature and local feature. Keypoints are localized in space at the maxima of the Hessian determinant (Lindeberg, 1998) and in scale at the local maxima of the Laplacian-of-Gaussian
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