Srikumar Ramalingam

Randomized trees for human pose detection

This paper addresses human pose recognition from video sequences by formulating it as a classification problem. Unlike much previous work we do not make any assumptions on the availability of clean segmentation. The first step of this work consists in a novel method of aligning the training images using 3D Mocap data. Next we define classes by discretizing a 2D manifold whose two dimensions are camera viewpoint and actions. Our main contribution is a pose detection algorithm based on random forests. A bottom-up approach is followed to build a decision tree by recursively clustering and merging the classes at each level. For each node of the decision tree we build a list of potentially discriminative features using the alignment of training images; in this paper we consider Histograms of Orientated Gradient (HOG). We finally grow an ensemble of trees by randomly sampling one of the selected HOG blocks at each node. Our proposed approach gives promising results with both fixed and moving cameras.

Exact inference in multi-label CRFs with higher order cliques

This paper addresses the problem of exactly inferring the maximum a posteriori solutions of discrete multi-label MRFs or CRFs with higher order cliques. We present a framework to transform special classes of multi-label higher order functions to submodular second order Boolean functions (referred to as Fs 2), which can be minimized exactly using graph cuts and we characterize those classes. The basic idea is to use two or more Boolean variables to encode the states of a single multi-label variable. There are many ways in which this can be done and much interesting research lies in finding ways which are optimal or minimal in some sense. We study the space of possible encodings and find the ones that can transform the most general class of functions to Fs 2. Our main contributions are two-fold. First, we extend the subclass of submodular energy functions that can be minimized exactly using graph cuts. Second, we show how higher order potentials can be used to improve single view 3D reconstruction results. We believe that our work on exact minimization of higher order energy functions will lead to similar improvements in solutions of other labelling problems.

Minimal solutions for generic imaging models

A generic imaging model refers to a non-parametric camera model where every camera is treated as a set of unconstrained projection rays. Calibration would simply be a method to map the projection rays to image pixels; such a mapping can be computed using plane based calibration grids. However, existing algorithms for generic calibration use more point correspondences than the theoretical minimum. It has been well-established that non-minimal solutions for calibration and structure-from-motion algorithms are generally noise-prone compared to minimal solutions. In this work we derive minimal solutions for generic calibration algorithms. Our algorithms for generally central cameras use 4 point correspondences in three calibration grids to compute the motion between the grids. Using simulations we show that our minimal solutions are more robust to noise compared to non-minimal solutions. We also show very accurate distortion correction results on fisheye images.

Efficient minimization of higher order submodular functions using monotonic Boolean functions

Submodular function minimization is a key problem in a wide variety of applications in machine learning, economics, game theory, computer vision and many others. The general solver has a complexity of

Learning priors for calibrating families of stereo cameras

O(n^6+n^5L)

A Minimal Closed-Form Solution for Multi-perspective Pose Estimation using Points and Lines

where

Simultaneous Edge Alignment and Learning

L

A Generic Calibration Concept : Theory and Algorithms

is the time required to evaluate the function and

A generic structure-from-motion algorithm for cross-camera scenarios

n

Theory and Experiments towards Complete Generic Calibration

is the number of variables cite{orlin09}. On the other hand, many useful applications in computer vision and machine learning applications are defined over a special subclasses of submodular functions in which that can be written as the sum of many submodular cost functions defined over cliques containing few variables. In such functions, the pseudo-Boolean (or polynomial) representation cite{BorosH02} of these subclasses are of degree (or order, or clique size)