Proceedings ArticleDOI
Appearance-based object recognition using support vector machines
E.M. dos Santos,Herman Martins Gomes +1 more
- pp 399
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TLDR
This work investigates the ability of SVM to perform appearance-based object recognition and initial experiments indicated that this may be a promising approach to the problem.Abstract:
Support vector machines (SVM) are a class of algorithms derived from the statistical learning theory that are receiving growing interest by the computer vision community as they present some advantages over classical techniques. This work investigates the ability of SVM to perform appearance-based object recognition. Initial experiments indicated that this may be a promising approach to the problem.read more
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Book ChapterDOI
Applications of Support Vector Machines for Pattern Recognition: A Survey
Hyeran Byun,Seong-Whan Lee +1 more
TL;DR: A brief introduction of SVMs is described and its numerous applications are summarized, which show good generalization performance on many real-life data and the approach is properly motivated theoretically.
Journal ArticleDOI
A survey on pattern recognition applications of support vector machines
Hyeran Byun,Seong-Whan Lee +1 more
TL;DR: A brief introduction of SVMs is described and its various pattern recognition applications are summarized, which have been applied to wide range of applications.
Journal ArticleDOI
Particle Pollution Estimation Based on Image Analysis
TL;DR: A method to estimate PM air pollution based on analysis of a large number of outdoor images available for Beijing, Shanghai and Phoenix shows that the image analysis method provides good prediction of PM2.5 indexes, and different features have different significance levels in the prediction.
Journal ArticleDOI
A neural-network appearance-based 3-D object recognition using independent component analysis
H.S. Sahambi,Khashayar Khorasani +1 more
TL;DR: This paper presents results on appearance-based three-dimensional (3-D) object recognition (3DOR) accomplished by utilizing a neural-network architecture developed based on independent component analysis (ICA), suggesting that the use of ICA may not necessarily always give better results than PCA, and that the application of I CA is highly data dependent.
Proceedings ArticleDOI
3D object recognition and pose estimation using kernel PCA
TL;DR: Results of appearance-based object recognition accomplished by employing a neural network architecture on the base of kernel PCA are presented, indicating that the proposed method is well-suited for object recognition and pose estimation.
References
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Book
The Nature of Statistical Learning Theory
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
Journal ArticleDOI
The Nature Of Statistical Learning Theory
TL;DR: As one of the part of book categories, the nature of statistical learning theory always becomes the most wanted book.
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Support vector machines for 3D object recognition
TL;DR: The proposed system does not require feature extraction and performs recognition on images regarded as points of a space of high dimension without estimating pose, indicating that SVMs are well-suited for aspect-based recognition.
Proceedings ArticleDOI
Real-time 100 object recognition system
TL;DR: A real-time vision system is described that can recognize 100 complex three-dimensional objects and its recognition rate was found to be 100% and object pose was estimated with a mean absolute error of 2.02 degrees and standard deviation of 1.67 degrees.
Journal ArticleDOI
Learning to recognize objects
Guy Wallis,Heinrich H. Bülthoff +1 more
TL;DR: Evidence from neurophysiological and psychological studies is coming together to shed light on how the authors represent and recognize objects, supporting two major hypotheses: the first is that objects are represented in a mosaic-like form in which objects are encoded by combinations of complex, reusable features, rather than two-dimensional templates, or three-dimensional models.