Kunal N. Chaudhury

Stability and convergence of the level set method in computer vision

Several computer vision problems, like segmentation, tracking and shape modeling, are increasingly being solved using level set methodologies. But the critical issues of stability and convergence have always been neglected in most of the level set implementations. This often leads to either complete breakdown or premature/delayed termination of the curve evolution process, resulting in unsatisfactory results. We present a generic convergence criterion and also a means of determining the optimal time-step involved in the numerical solution of the level set equation. The significant improvement in the performance of level set algorithms, as a result of the proposed changes, is demonstrated using object tracking and shape-contour extraction results.

Global Registration of Multiple Point Clouds Using Semidefinite Programming

Consider

Image denoising using optimally weighted bilateral filters: A sure and fast approach

N

Fast and Provably Accurate Bilateral Filtering

points in

Sequential Optimization for PV Panel Parameter Estimation

\mathbb{R}^d

Fast separable nonlocal means

and

On Fast Bilateral Filtering Using Fourier Kernels

M

Large-scale sensor network localization via rigid subnetwork registration

local coordinate systems that are related through unknown rigid transforms. For each point we are given (possibly noisy) measurements of its local coordinates in some of the coordinate systems. Alternatively, for each coordinate system, we observe the coordinates of a subset of the points. The problem of estimating the global coordinates of the

Generalized Semantic Preserving Hashing for N-Label Cross-Modal Retrieval

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FAST AND ACCURATE BILATERAL FILTERING USING GAUSS-POLYNOMIAL DECOMPOSITION

points (up to a rigid transform) from such measurements comes up in distributed approaches to molecular conformation and sensor network localization, and also in computer vision and graphics. The least-squares formulation of this problem, though non-convex, has a well known closed-form solution when