2,832 research outputs found
Strengthening the Effectiveness of Pedestrian Detection with Spatially Pooled Features
We propose a simple yet effective approach to the problem of pedestrian
detection which outperforms the current state-of-the-art. Our new features are
built on the basis of low-level visual features and spatial pooling.
Incorporating spatial pooling improves the translational invariance and thus
the robustness of the detection process. We then directly optimise the partial
area under the ROC curve (\pAUC) measure, which concentrates detection
performance in the range of most practical importance. The combination of these
factors leads to a pedestrian detector which outperforms all competitors on all
of the standard benchmark datasets. We advance state-of-the-art results by
lowering the average miss rate from to on the INRIA benchmark,
to on the ETH benchmark, to on the TUD-Brussels
benchmark and to on the Caltech-USA benchmark.Comment: 16 pages. Appearing in Proc. European Conf. Computer Vision (ECCV)
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Fast and Efficient Entropy Coding Architectures for Massive Data Compression
The compression of data is fundamental to alleviating the costs of transmitting and storing massive datasets employed in myriad fields of our society. Most compression systems employ an entropy coder in their coding pipeline to remove the redundancy of coded symbols. The entropy-coding stage needs to be efficient, to yield high compression ratios, and fast, to process large amounts of data rapidly. Despite their widespread use, entropy coders are commonly assessed for some particular scenario or coding system. This work provides a general framework to assess and optimize different entropy coders. First, the paper describes three main families of entropy coders, namely those based on variable-to-variable length codes (V2VLC), arithmetic coding (AC), and tabled asymmetric numeral systems (tANS). Then, a low-complexity architecture for the most representative coder(s) of each family is presented-more precisely, a general version of V2VLC, the MQ, M, and a fixed-length version of AC and two different implementations of tANS. These coders are evaluated under different coding conditions in terms of compression efficiency and computational throughput. The results obtained suggest that V2VLC and tANS achieve the highest compression ratios for most coding rates and that the AC coder that uses fixed-length codewords attains the highest throughput. The experimental evaluation discloses the advantages and shortcomings of each entropy-coding scheme, providing insights that may help to select this stage in forthcoming compression systems
Graph Spectral Image Processing
Recent advent of graph signal processing (GSP) has spurred intensive studies
of signals that live naturally on irregular data kernels described by graphs
(e.g., social networks, wireless sensor networks). Though a digital image
contains pixels that reside on a regularly sampled 2D grid, if one can design
an appropriate underlying graph connecting pixels with weights that reflect the
image structure, then one can interpret the image (or image patch) as a signal
on a graph, and apply GSP tools for processing and analysis of the signal in
graph spectral domain. In this article, we overview recent graph spectral
techniques in GSP specifically for image / video processing. The topics covered
include image compression, image restoration, image filtering and image
segmentation
Similarity of Scenic Bilevel Images
This paper has been submitted to IEEE Transaction on Image Processing in May 2015.This paper presents a study of bilevel image similarity, including new objective metrics intended to quantify similarity consistent with human perception, and a subjective experiment to obtain ground truth for judging the performance of the objective similarity metrics. The focus is on scenic bilevel images, which are complex, natural or hand-drawn images, such as landscapes or portraits.
The ground truth was obtained from ratings by 77 subjects of 44 distorted versions of seven scenic images, using a modified version of the SDSCE testing methodology.
Based on hypotheses about human perception of bilevel images, several new metrics are proposed that outperform existing ones in the sense of attaining significantly higher Pearson and Spearman-rank correlation coefficients with respect to the ground truth from the subjective experiment. The new metrics include Adjusted Percentage Error, Bilevel Gradient Histogram and Connected Components Comparison. Combinations of these metrics are also proposed, which exploit their complementarity to attain even better performance.
These metrics and the ground truth are then used to assess the relative severity of various kinds of distortion and the performance of several lossy bilevel compression methods.http://deepblue.lib.umich.edu/bitstream/2027.42/111737/2/Similarity of Scenic Bilevel Images.pdfDescription of Similarity of Scenic Bilevel Images.pdf : Main article ("Similarity of Scenic Bilevel Images"
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