39,573 research outputs found
Robust 3D Action Recognition through Sampling Local Appearances and Global Distributions
3D action recognition has broad applications in human-computer interaction
and intelligent surveillance. However, recognizing similar actions remains
challenging since previous literature fails to capture motion and shape cues
effectively from noisy depth data. In this paper, we propose a novel two-layer
Bag-of-Visual-Words (BoVW) model, which suppresses the noise disturbances and
jointly encodes both motion and shape cues. First, background clutter is
removed by a background modeling method that is designed for depth data. Then,
motion and shape cues are jointly used to generate robust and distinctive
spatial-temporal interest points (STIPs): motion-based STIPs and shape-based
STIPs. In the first layer of our model, a multi-scale 3D local steering kernel
(M3DLSK) descriptor is proposed to describe local appearances of cuboids around
motion-based STIPs. In the second layer, a spatial-temporal vector (STV)
descriptor is proposed to describe the spatial-temporal distributions of
shape-based STIPs. Using the Bag-of-Visual-Words (BoVW) model, motion and shape
cues are combined to form a fused action representation. Our model performs
favorably compared with common STIP detection and description methods. Thorough
experiments verify that our model is effective in distinguishing similar
actions and robust to background clutter, partial occlusions and pepper noise
Hierarchical image simplification and segmentation based on Mumford-Shah-salient level line selection
Hierarchies, such as the tree of shapes, are popular representations for
image simplification and segmentation thanks to their multiscale structures.
Selecting meaningful level lines (boundaries of shapes) yields to simplify
image while preserving intact salient structures. Many image simplification and
segmentation methods are driven by the optimization of an energy functional,
for instance the celebrated Mumford-Shah functional. In this paper, we propose
an efficient approach to hierarchical image simplification and segmentation
based on the minimization of the piecewise-constant Mumford-Shah functional.
This method conforms to the current trend that consists in producing
hierarchical results rather than a unique partition. Contrary to classical
approaches which compute optimal hierarchical segmentations from an input
hierarchy of segmentations, we rely on the tree of shapes, a unique and
well-defined representation equivalent to the image. Simply put, we compute for
each level line of the image an attribute function that characterizes its
persistence under the energy minimization. Then we stack the level lines from
meaningless ones to salient ones through a saliency map based on extinction
values defined on the tree-based shape space. Qualitative illustrations and
quantitative evaluation on Weizmann segmentation evaluation database
demonstrate the state-of-the-art performance of our method.Comment: Pattern Recognition Letters, Elsevier, 201
Multiresolution vector quantization
Multiresolution source codes are data compression algorithms yielding embedded source descriptions. The decoder of a multiresolution code can build a source reproduction by decoding the embedded bit stream in part or in whole. All decoding procedures start at the beginning of the binary source description and decode some fraction of that string. Decoding a small portion of the binary string gives a low-resolution reproduction; decoding more yields a higher resolution reproduction; and so on. Multiresolution vector quantizers are block multiresolution source codes. This paper introduces algorithms for designing fixed- and variable-rate multiresolution vector quantizers. Experiments on synthetic data demonstrate performance close to the theoretical performance limit. Experiments on natural images demonstrate performance improvements of up to 8 dB over tree-structured vector quantizers. Some of the lessons learned through multiresolution vector quantizer design lend insight into the design of more sophisticated multiresolution codes
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