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GMM based Hidden Markov Random Field for Color Ima
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In this model , we first study the Gaussian-based hidden Markov random field (HMRF) model and its expectationmaximization (EM) algorithm. Then we generalize it to aussian mixture model-based hidden Markov random field. The algorithm is implemented in MATLAB. We also apply this algorithm to color image segmentation problems and 3D volume segmentation problems.
Markov random fields (MRFs) have been widely used for computer vision problems, such as image segmentation , surface reconstruction and depth inference.
msm732128 2 0
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A new Support Vector Machine(SVM) machine based fa
4.0
This method presents a face recognition algorithm by using Gabor wavelet transform for facial features extraction and Support Vector Machines (SVM) for face recognition, Gabor wavelets coefficients are used to represent local facial features. The implementations of algorithm are as follows:
Firstly, facial feature points are located roughly by using a set of node templates. Secondly, Gabor wavelet coefficients are extracted at every facial feature point, and all the Gabor wavelet coefficients are catenated to represent a face image. Lastly, SVM classifiers are used for face recognition. The experimental results demonstrate the effectiveness of the face recognition algorithm.
msm732128 0 0
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Image processing
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This is a new fast and efficient hidden markov model for face recognition.The recognition rate of my project is 96.5% which is better than most of the face recognition techniques.In my project order statistic filter has been used for image preprocessing and singular value decomposition (SVM) has been used for feature extraction.Because of using SVM as feature extraction technique, recognition speed has been faster than any other technique. I have used AT&T database.
Here 7 states (hair,forehead,eyebrow,eye,nose,mouth,chin) have been used in the hidden markov model for recognizing a person rather than 5 states which have been used before which has also helped to improve performance.
msm732128 2 0
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