139,570 research outputs found
Matching Image Sets via Adaptive Multi Convex Hull
Traditional nearest points methods use all the samples in an image set to
construct a single convex or affine hull model for classification. However,
strong artificial features and noisy data may be generated from combinations of
training samples when significant intra-class variations and/or noise occur in
the image set. Existing multi-model approaches extract local models by
clustering each image set individually only once, with fixed clusters used for
matching with various image sets. This may not be optimal for discrimination,
as undesirable environmental conditions (eg. illumination and pose variations)
may result in the two closest clusters representing different characteristics
of an object (eg. frontal face being compared to non-frontal face). To address
the above problem, we propose a novel approach to enhance nearest points based
methods by integrating affine/convex hull classification with an adapted
multi-model approach. We first extract multiple local convex hulls from a query
image set via maximum margin clustering to diminish the artificial variations
and constrain the noise in local convex hulls. We then propose adaptive
reference clustering (ARC) to constrain the clustering of each gallery image
set by forcing the clusters to have resemblance to the clusters in the query
image set. By applying ARC, noisy clusters in the query set can be discarded.
Experiments on Honda, MoBo and ETH-80 datasets show that the proposed method
outperforms single model approaches and other recent techniques, such as Sparse
Approximated Nearest Points, Mutual Subspace Method and Manifold Discriminant
Analysis.Comment: IEEE Winter Conference on Applications of Computer Vision (WACV),
201
Adaptive Multi-Pattern Fast Block-Matching Algorithm Based on Motion Classification Techniques
Motion estimation is the most time-consuming subsystem in a video codec. Thus, more efficient methods of motion estimation should be investigated. Real video sequences usually exhibit a wide-range of motion content as well as different degrees of detail, which become particularly difficult to manage by typical block-matching algorithms. Recent developments in the area of motion estimation have focused on the adaptation to video contents. Adaptive thresholds and multi-pattern search algorithms have shown to achieve good performance when they success to adjust to motion characteristics. This paper proposes an adaptive algorithm, called MCS, that makes use of an especially tailored classifier that detects some motion cues and chooses the search pattern that best fits to them. Specifically, a hierarchical structure of binary linear classifiers is proposed. Our experimental results show that MCS notably reduces the computational cost with respect to an state-of-the-art method while maintaining the qualityPublicad
Phased Array Feed Calibration, Beamforming and Imaging
Phased array feeds (PAFs) for reflector antennas offer the potential for
increased reflector field of view and faster survey speeds. To address some of
the development challenges that remain for scientifically useful PAFs,
including calibration and beamforming algorithms, sensitivity optimization, and
demonstration of wide field of view imaging, we report experimental results
from a 19 element room temperature L-band PAF mounted on the Green Bank
20-Meter Telescope. Formed beams achieved an aperture efficiency of 69% and
system noise temperature of 66 K. Radio camera images of several sky regions
are presented. We investigate the noise performance and sensitivity of the
system as a function of elevation angle with statistically optimal beamforming
and demonstrate cancelation of radio frequency interference sources with
adaptive spatial filtering.Comment: 19 pages, 13 figure
Planogram Compliance Checking Based on Detection of Recurring Patterns
In this paper, a novel method for automatic planogram compliance checking in
retail chains is proposed without requiring product template images for
training. Product layout is extracted from an input image by means of
unsupervised recurring pattern detection and matched via graph matching with
the expected product layout specified by a planogram to measure the level of
compliance. A divide and conquer strategy is employed to improve the speed.
Specifically, the input image is divided into several regions based on the
planogram. Recurring patterns are detected in each region respectively and then
merged together to estimate the product layout. Experimental results on real
data have verified the efficacy of the proposed method. Compared with a
template-based method, higher accuracies are achieved by the proposed method
over a wide range of products.Comment: Accepted by MM (IEEE Multimedia Magazine) 201
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