Sugata Ghosal

A fast scalable algorithm for discontinuous optical flow estimation

Multiple moving objects, partially occluded objects, or even a single object moving against the background gives rise to discontinuities in the optical flow field in corresponding image sequences. While uniform global regularization based moderately fast techniques cannot provide accurate estimates of the discontinuous flow field, statistical optimization based accurate techniques suffer from excessive solution time. A weighted anisotropic smoothness based numerically robust algorithm is proposed that can generate discontinuous optical flow field with high speed and linear computational complexity. Weighted sum of the first-order spatial derivatives of the flow field is used for regularization. Less regularization is performed where strong gradient information is available. The flow field at any point is interpolated more from those at neighboring points along the weaker intensity gradient component. Such intensity gradient weighted regularization leads to Euler-Lagrange equations with strong anisotropies coupled with discontinuities in their coefficients. A robust multilevel iterative technique, that recursively generates coarse-level problems based on intensity gradient weighted smoothing weights, is employed to estimate discontinuous optical flow field. Experimental results are presented to demonstrate the efficacy of the proposed technique.

Detection of composite edges

The paper presents a new parametric model-based approach to high-precision composite edge detection using orthogonal Zernike moment-based operators. It deals with two types of composite edges: (a) generalized step and (b) pulse/staircase edges. A 2-D generalized step edge is modeled in terms of five parameters: two gradients on two sides of the edge, the distance from the center of the candidate pixel, the orientation of the edge and the step size at the location of the edge. A 2-D pulse/staircase edge is modeled in terms of two steps located at two positions within the mask, and the edge orientation. A pulse edge is formed if the steps are of opposite polarities whereas a staircase edge results from two steps having the same polarity. Two complex and two real Zernike moment-based masks are designed to determine parameters of both the 2-D edge models. For a given edge model, estimated parameter values at a point are used to detect the presence or absence of that type of edge. Extensive noise analysis is performed to demonstrate the robustness of the proposed operators. Experimental results with intensity and range images are included to demonstrate the efficacy of the proposed edge detection technique as well as to compare its performance with the geometric moment-based step edge detection technique and Cannys (1986) edge detector.

A moment-based unified approach to image feature detection

In this paper, a novel model-based approach is proposed for generating a set of image feature maps (or primal sketches). For each type of feature, a piecewise smooth parametric model is developed to characterize the local intensity function in an image. Projections of the intensity profile onto a set of orthogonal Zernike-moment-generating polynomials are used to estimate model-parameters and, in turn, generate the desired feature map. A small set of moment-based detectors is identified that can extract various kinds of primal sketches from intensity as well as range images. One main advantage of using parametric model-based techniques is that it is possible to extract complete information (i.e., model parameters) about the underlying image feature, which is desirable in many high-level vision tasks. Experimental results are included to demonstrate the effectiveness of proposed feature detectors.

Robust optical flow estimation using semi-invariant local features

This paper presents a robust algorithm for computation of 2-D optical flow using the principle of conservation of a set of invariant local features that are representatives of local gray-level properties in an image. Specifically, a set of rotation-invariant local orthogonal Zernike moments is used as invariant features. These are inherently integral-based features, and therefore are expected to be robust against possible variations of intensity values that may occur over a sequence of images due to sensor noise, varying illumination etc. The 2-D local optical flow field is obtained using singular value decomposition of an overdetermined set of linear equations of velocity field components, resulting from the principle of conservation of invariant features in a small neighborhood. The proposed moment-based approach is compared with two existing optical flow techniques. Experimental results with synthetic as well as real sequences are presented to demonstrate the overall robustness of the proposed approach.

Zernike moment-based feature detectors

A novel model-based unified approach is proposed for generating a set of image feature maps (or primal sketches). For each type of feature, a parametric model is developed to characterize the local intensity function in an image. Projections of intensity profile onto a set of orthogonal Zernike moment-generating polynomials are used to estimate model-parameters and in turn generate the desired feature map. A small set of moment-based detectors is identified that can extract various kinds of primal sketches from intensity as well as range images. One main advantage of using parametric model-based techniques is that it is possible to extract complete information (i.e., model parameters) about the underlying image feature, which is desirable in many high-level vision tasks. Experimental results are included to demonstrate the effectiveness of the proposed feature detectors.<<ETX>>

Robust optical flow estimation

The paper presents a robust algorithm for computation of optical flow using the principle of conservation of a set of semi-invariant local features that are representatives of local gray-level properties in an image. Specifically, a set of rotation-invariant local orthogonal Zernike moments is used as features. These are inherently integral-based features, and therefore are robust against possible variations of intensity values that may occur over a sequence of images due to sensor noise, varying illumination etc. The 2D local optical flow field is obtained by the singular value decomposition of an overdetermined set of linear equations of velocity field components, resulting from the principle of conservation of features in a small neighborhood. The proposed approach is compared with Horn-Schunck (1981), and Lucas-Kanades optical flow techniques. Experimental results with synthetic as well as real sequences are presented to demonstrate the effectiveness of the proposed approach.<<ETX>>

Automatic substructuring for domain decomposition using neural networks

Application of neural networks for guiding solutions of large numerical problems is an emerging area of research. Automatic generation of subdomains from large 3D finite element meshes is a key preprocessing step in domain decomposition techniques and extremely important for proper load balancing, reducing communication bandwidth and latency, and efficient processor coordination and synchronization in a parallel computing environment. It is desired that the subdomains are approximately of same size, and the total number of interface nodes between adjacent subdomains is minimal. We propose two neural network algorithms employing the philosophy of competitive learning and Hopfield network, that can automatically generate substructures from large 3D meshes with reasonable speed. Both these techniques are implemented in such as a way that they have almost linear complexity w.r.t. the problem size for serial execution. Experimental results show more than 25% improvement over an existing greedy algorithm.<<ETX>>