Research Interests:
Computer vision, image/signal processing, computational biology, medical image analysis, statistical modeling and machine learning.
Current Projects:
Image Segmentation and Smoothing:
We present continuous mixture models which are spatially varying, adaptive, convolution based approaches for smoothing and segmentation. These new and innovative approaches afford to preserve the complicated local geometries of the boundaries of objects in real scenes without using any prior information. First, we extract the local orientation information using Gabor filters. The orientation information at each lattice point is then represented by a continuous mixture model of appropriate basis functions. We present two such models; one involving a continuous mixture over the covariance matrices of Gaussian basis functions, and another involving a continuous mixture over the mean direction vectors of antipodally symmetric Watson basis functions. These continuous mixture models are then used to construct spatially varying kernels which are convolved with the input function to achieve feature preserving smoothing or segmentation as desired.
Publications:
Subakan, O.; Vemuri, B.C., "Continuous Mixture Models for Feature Preserving Smoothing and Segmentation," Submitted to IEEE Trans. of Pattern Analysis and Machine Intelligence.
Technical Report (PDF)
Subakan, O.N.; Vemuri, B.C., "Image segmentation via convolution of a level-set function with a Rigaut Kernel," Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on, vol., no., pp.1-6, 23-28 June 2008
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=4587460&isnumber=4587335
Subakan, O.; Jian, B.; Vemuri, B.C.; Vallejos, C.E., "Feature Preserving Image Smoothing Using a Continuous Mixture of Tensors," Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on, vol., no., pp.1-6, 14-21 Oct. 2007
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=4408918&isnumber=4408819
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