Unsupervised 3D Pose Estimation for Hierarchical Dance Video

Dance experts often view dance as a hierarchy of information, spanning low-level (raw images, image sequences), mid-levels (human poses and bodypart movements), and high-level (dance genre). We propose a Hierarchical Dance Video Recognition framework (HDVR). HDVR estimates 2D pose sequences, tracks dancers, and then simultaneously estimates corresponding 3D poses and 3D-to-2D imaging parameters, without requiring ground truth for 3D poses. Read More “Unsupervised 3D Pose Estimation for Hierarchical Dance Video”

Stereo Matching Using Epipolar Distance Transform

In this paper, we propose a simple but effective image transform, called the epipolar distance transform, for matching low-texture regions. It converts image intensity values to a relative location inside a planar segment along the epipolar line, such that pixels in the low-texture regions become distinguishable. We theoretically prove that the transform is affine invariant, thus the transformed images can be directly used for stereo matching. Read More “Stereo Matching Using Epipolar Distance Transform”

Surface Reflectance and Normal Estimation from Photometric Stereo

In this paper, we propose a new photometric stereo method for estimating diffuse reflection and surface normal from color images. Using dichromatic reflection model, we introduce surface chromaticity as a matching invariant for photometric stereo, which serves as the foundation of the theory of this paper. An extremely simple and robust reflection components separation method is proposed based on the invariant. Read More “Surface Reflectance and Normal Estimation from Photometric Stereo”

Simultaneous Estimation of Illumination Chromaticity, Correspondence and Specular Reflection

Based on a new correspondence matching invariant called \emph{Illumination Chromaticity Constancy}, we present a new solution for illumination chromaticity estimation, correspondence searching and specularity removal. Using as few as two images, the core of our method is the computation of a vote distribution for a number of illumination chromaticity hypotheses via correspondence matching. Read More “Simultaneous Estimation of Illumination Chromaticity, Correspondence and Specular Reflection”

A Constant-Space Belief Propagation Algorithm for Stereo Matching

In this paper, we consider the problem of stereo matching using loopy belief propagation. Unlike previous methods which focus on the original spatial resolution, we hierarchically reduce the disparity search range. By fixing the number of disparity levels on the original resolution, our method solves the message updating problem in a time linear in the number of pixels contained in the image and requires only constant memory space. Read More “A Constant-Space Belief Propagation Algorithm for Stereo Matching”

Non-Lambertian Surface Reconstruction and Reflectance Modelling

Non-lambertian surfaces causes difficulties for many stereo systems. We describe methods to recover both 3D surface shape and reflectance models of an object from multiple views. We use an iterative method, based on multi-view shape from shading, to estimate shape and reflectance models. The estimated models can be used to generate objects in new views and under new lighting conditions using computer graphics techniques. Read More “Non-Lambertian Surface Reconstruction and Reflectance Modelling”

3D Object Modeling

Given multiple calibrated pictures of a real world object captured from different viewpoints, reconstruct a three-dimensional model of the object.

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Dense Stereo Maping Using Kernel Maximum Likelihood Estimation

A robust stereo matching algorithm using kernel representation of the probability density functions (pdf’s) of the sources that generate the stereoscopic images. Matching is done using either a Maximum Likelihood framework or using correlation in the pdf domain and an MRF prior to model the disparity function.

  • A. Jagmohan, M. Singh and N.
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3D Surfaces and Illumination from Stereo and Shading

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3D Surface Orientation from Texture Gradient

3D Surface Orientation from Texture Gradient computed in a single image of a homogeneously textured surface.

In an image containing texture elements at a range of scales, detect all elements, their relative locations and mutual containment relationships.

OBJECTIVE
Given a slanted view of a planar, homogeneously textured surface, estimate the surface slant from the image texture gradient. Read More “3D Surface Orientation from Texture Gradient”

Surfaces from Binocular Spatial Stereo

Given multiple images of a scene, taken from multiple cameras and different viewpoints, find the 3D depth map and surfaces

  • W. Hoff and N. Ahuja, Surfaces from Stereo, Proc. DARPA Image Understanding Workshop, Miami, December 9-10, 1985, 98-106.
  • W. Hoff and N. Ahuja, Surfaces from Stereo, 8th International Conference on Pattern Recognition, Paris, France, October 28-31, 1986, 516-518.
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