We propose a new bilateral filtering algorithm with computational
complexity invariant to filter kernel size, socalled
O(1) or constant time in the literature. By showing
that a...
We present a new approach for the discriminative training
of continuous-valued Markov Random Field (MRF)
model parameters. In our approach we train the MRF
model by optimizing t...
We propose a novel formulation of stereo matching that considers each pixel as a feature vector. Under this view, matching two or more images can be cast as matching point clouds i...
We present a new approach to iteratively estimate both
high-quality depth map and alpha matte from a single image
or a video sequence. Scene depth, which is invariant
to illumin...
Jiejie Zhu (University of Kentucky), Miao Liao (Un...
Stereo matching commonly requires rectified images that
are computed from calibrated cameras. Since all under-
lying parametric camera models are only approximations,
calibratio...
Radiometric variations between input images can seriously
degrade the performance of stereo matching algorithms.
In this situation, mutual information is a very popular
and powe...
Yong Seok Heo (Seoul National University), Kyoung ...
We propose a scheme for indoor place identication based on the recognition of global scene
views. Scene views are encoded using a holistic representation that provides low-resolu...
We propose a novel framework for constrained spectral
clustering with pairwise constraints which specify whether
two objects belong to the same cluster or not. Unlike previous
m...
Zhenguo Li (The Chinese University of Hong Kong), ...
Feature misalignment in object detection refers to
the phenomenon that features which re up in some
positive detection windows do not re up in other pos-
itive detection windo...
Zhe Lin (University of Maryland at College Park), ...
Many traditional methods for shape classification involve
establishing point correspondences between shapes to
produce matching scores, which are in turn used as similarity
meas...