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An affine Invariant Hyperspectral Texture Descriptor Based Upon Heavy-tailed Distributions and Fourier Analysis

An affine Invariant Hyperspectral Texture Descriptor Based U...

引用

ieee computer society conference on computer vision and pattern recognition workshops

作者： Pattaraporn Khuwuthyakorn Antonio Robles-Kelly Jun Zhou Cooperative Research Centre for National Plant Biosecurity Bruce ACT 2617 Australia. Australian National University Canberra ACT 0200 Australia National ICT Australia (NICTA) Australian National University Australia National ICT Australia (NICTA) Australian National University National ICT Australia (NICTA)

ISBN: (纸本)9781424439942

In this paper, we address the problem of recovering a hyperspectral texture descriptor. We do this by viewing the wavelength-indexed bands corresponding to the texture in the image as those arising from a stochastic process whose statistics can be captured making use of the relationships between moment generating functions and Fourier kernels. In this manner, we can interpret the probability distribution of the hyperspectral texture as a heavy-tailed one which can be rendered invariant to affine geometric transformations on the texture plane making use of the spectral power of its Fourier cosine transform. We do this by recovering the affine geometric distortion matrices corresponding to the probability density function for the texture under study. This treatment permits the development of a robust descriptor which has a high information compaction property and can capture the space and wavelength correlation for the spectra in the hyperspectral images. We illustrate the utility of our descriptor for purposes of recognition and provide results on real-world datasets. We also compare our results to those yielded by a number of alternatives.

关键词： Hyperspectral imaging Image texture analysis Stochastic processes Statistical distributions Kernel Probability distribution Rendering (computer graphics) Fourier transforms Probability density function Robustness

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A Novel Benchmark RGBD Dataset for Dormant Apple Trees and Its Application to Automatic Pruning

A Novel Benchmark RGBD Dataset for Dormant Apple Trees and I...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Shayan A. Akbar Somrita Chattopadhyay Noha M. Elfiky Avinash Kak School of Electrical and Computer Engineering Purdue University

Dormant pruning is a necessary procedure in the field of specialty crop production. In order to mitigate the need of huge labor, automation of this pruning process has become a topic of utmost importance in the field of horticulture. 3D modeling and reconstruction is a major step in such robotics precision agriculture. In this paper, we introduce a new public dataset which can be used for reconstructing dormant apple trees. Our dataset comprises of 9 different apple trees in both indoor and outdoor evironment. The images are collected using a portable Kinect2 sensor. To the best of our knowledge, this is the first publicly available dataset for the application like 3D modeling of dormant trees. We hope that the dataset will provide the entire research community working towards mechanizing dormant pruning a baseline benchmark for evaluating different 3D reconstruction and modeling algorithms.

关键词： Three-dimensional displays Vegetation Robot sensing systems Image reconstruction Benchmark testing Solid modeling

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Real-Time Face Identification via CNN and Boosted Hashing Forest

Real-Time Face Identification via CNN and Boosted Hashing Fo...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Yury Vizilter Vladimir Gorbatsevich Andrey Vorotnikov Nikita Kostromov State Research Institute of Aviation Systems (GosNIIAS) Moscow Russia

The family of real-time face representations is obtained via Convolutional Network with Hashing Forest (CNHF). We learn the CNN, then transform CNN to the multiple convolution architecture and finally learn the output hashing transform via new Boosted Hashing Forest (BHF) technique. This BHF generalizes the Boosted SSC approach for hashing learning with joint optimization of face verification and identification. CNHF is trained on CASIA-WebFace dataset and evaluated on LFW dataset. We code the output of single CNN with 97% on LFW. For Hamming embedding we get CBHF-200 bit (25 byte) code with 96.3% and 2000-bit code with 98.14% on LFW. CNHF with 2000×7-bit hashing trees achieves 93% rank-1 on LFW relative to basic CNN 89.9% rank-1. CNHF generates templates at the rate of 40+ fps with CPU Core i7 and 120+ fps with GPU GeForce GTX 650.

关键词： Face Encoding Transforms Convolution Convolutional codes computer architecture Optimization

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Scale-adaptive feature points extraction and dual-scale based high-precision image registration

Scale-adaptive feature points extraction and dual-scale base...

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2012 ieee 12th International conference on computer and Information Technology, CIT 2012

作者： Gao, Zunqi Ming, Delie Xiao, Liping Wang, Hai Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan China National Laboratory of Aerospace Intelligence Control Technology Beijing Aerospace Automatic Control Institute Beijing China Department of Computer Science and Engineering Pohang University of Science and Technology Pohang Korea Republic of

ISBN: (纸本)9780769548586

The SIFT algorithm had a profound impact on the area of image local feature extraction and computer vision, since its inception. Because of its robustness and anti-interference performance, it has been widely applied in engineering. However, in practical usage of SIFT, there are actually no sufficient feature points that can be extracted to perform the image registration. For tiny scale targets, this situation becomes even worse. Based on the analysis of the scale-space, this paper puts forward a scale-adaptive feature points extraction method (SFPE). SFPE can extract more feature points than traditional SIFT algorithm, especially when the target scale is tiny. Furthermore, a dual-scale based high-precision image registration process (DHIR) is proposed in this paper. In DHIR, a small-scale matching (precise matching) process is performed after the large-scale registration. Experimental results show that the DHIR is more effective to eliminate mismatches than traditional SIFT algorithm. © 2012 ieee.

关键词： Feature extraction

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An approach for evaluating the effectiveness of design patterns in software evolution

An approach for evaluating the effectiveness of design patte...

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35th Annual ieee International computer Software and Applications conference workshops, COMPSACW 2011

作者： Hsueh, Nien-Lin Wen, Lin-Chieh Ting, Der-Hong Chu, William Chang, Chih-Hung Koong, Chorng-Shiuh Dept. of Info. Engineering and Computer Science Feng-Chia University Taichung Taiwan Dept. of Computer Science and Information Engineering Tunghai University Taichung Taiwan Department of Information Management Hsuping Institute of Technology Taichung Taiwan Department of Computer and Information Science National Taichung University Taichung Taiwan

ISBN: (纸本)9780769544595

In recent years, the influences of design patterns on software quality have attracted increasing attention in the area of software engineering, as design patterns encapsulate valuable knowledge to resolve design problems, and more importantly to improve the design quality. Numerous design patterns improve design quality by means of providing a more flexible architecture to enhance the maintainability. Therefore, many systems applied design patterns in the early design and expect the patterns will contribute to the system evolution. However, many systems applied wrong patterns- the patterns are never applied in the future evolution. In this case, we call these patterns are "effective-less in the evolution". In this paper, we will propose an evaluation approach to evaluate the effectiveness of a pattern in an evolution. We also develop a tool and to evaluate some well-known open source systems in this research. © 2011 ieee.

关键词： Open source software

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Medical Image Computing and computer-Assisted Intervention – MICCAI 2006 1

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丛书名： Lecture Notes in computer Science

1000年

作者： Rasmus Larsen Mads Nielsen Jon Sporring

ISBN: (数字)9783540447085

ISBN: (纸本)9783540447078

The 9th International conference on Medical Image Computing and computer Assisted Intervention, MICCAI 2006, was held in Copenhagen, Denmark at the Tivoli Concert Hall with satellite workshops and tutorials at the IT University of Copenhagen, October 1-6, 2006. The conference has become the premier international conference with - depth full length papers in the multidisciplinary ?elds of medical image c- puting, computer-assisted intervention, and medical robotics. The conference brings together clinicians, computer scientists, engineers, physicists, and other researchers and o?ers a forum for the exchange of ideas in a multidisciplinary setting. MICCAI papers are of high standard and have a long lifetime. In this v- ume as well as in the latest journal issues of Medical Image Analysis and ieee Transactions on Medical Imaging papers cite previous MICCAIs including the ?rst MICCAI conference in Cambridge, Massachusetts, 1998. It is obvious that the community requires the MICCAI papers as archive material. Therefore the proceedingsofMICCAIarefrom2005andhenceforthbeing indexedbyMedline. Acarefulreviewandselectionprocesswasexecutedinordertosecurethebest possible program for the MICCAI 2006 conference. We received 578 scienti?c papers from which 39 papers were selected for the oral program and 193 papers for the poster program.

关键词： Image Processing and computer vision pattern recognition computer Graphics Artificial Intelligence Imaging / Radiology Health Informatics

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Mobile image search with multimodal context-aware queries

Mobile image search with multimodal context-aware queries

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Xin Yang Sydney Pang K. T. Tim Cheng Department of Electrical and Computer Engineering University of California Santa Barbara CA USA

The proliferation of camera-equipped mobile devices with enhanced mobile computing power and network connectivity results in a rising demand for mobile image search. Although image search has been studied extensively over the last few decades, most existing solutions are based on and optimized for desktop and server platforms, not for mobile devices. In this paper, we address some of the challenging issues unique in mobile search scenarios and suggest a list of potential solutions. As a case study, we design a mobile landmark image search system to evaluate the effectiveness of some proposed solutions. To enhance the mobile search experience, we propose a multimodal search scheme which uses both image content and user location to increase search precision and thus minimize network usage. We also suggest a post-search result pruning method designed to match the most relevant results to a user's search interests. Experiments conducted on our Landmark-450 image dataset demonstrate that the proposed methods can significantly increase the relevance of selected results in response to mobile image search while also reducing the amount of data transferred across the network.

关键词： Mobile computing Random access memory Feature extraction computer networks Broadband communication Power engineering computing Network servers Image analysis Algorithm design and analysis Smart phones

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A Thermal Infrared Video Benchmark for Visual Analysis

A Thermal Infrared Video Benchmark for Visual Analysis

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Zheng Wu Nathan Fuller Diane Theriault Margrit Betke The Mathworks Inc. Natick USA Department of Biology Boston University Boston USA Department of Computer Science Boston University Boston USA

We hereby publish a new thermal infrared video benchmark, called TIV, for various visual analysis tasks, which include single object tracking in clutter, multi-object tracking in single or multiple views, analyzing motion patterns of large groups, and censusing wild animals in flight. Our data describe real world scenarios, such as bats emerging from their caves in large numbers, a crowded street view during a marathon competition, and students walking through an atrium during class break. We also introduce baseline methods and evaluation protocols for these tasks. Our TIV benchmark enriches and diversifies video data sets available to the research community with thermal infrared footage, which poses new and challenging video analysis problems. We hope the TIV benchmark will help the community to better understand these interesting problems, generate new ideas, and value it as a testbed to compare solutions.

关键词： Tracking Cameras Benchmark testing Three-dimensional displays Thermal analysis Visualization Image resolution

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A new robust operator for computer vision: theoretical analysis

A new robust operator for computer vision: theoretical analy...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Stewart Department of Computer Science Rensselaer Polytechnic Institute Troy NY USA

MINPRAN, a new robust operator, finds good fits in data sets where more than 50% of the points are outliers. Unlike other techniques that handle large outlier percentages, MINPRAN does not rely on a known error bound for the good data. Instead it assumes that the bad data are randomly (uniformly) distributed within the dynamic range of the sensor. Based on this, MINPRAN uses random sampling to search for the fit and the number of inliers to the fit that are least likely to have occurred randomly. It runs in time O(N/sup 2/+SNlogN), where S is the number of random samples and N is the number of data points. We demonstrate analytically and experimentally that MINPRAN distinguishes good fits from fits to random data, and that MINPRAN finds accurate fits and nearly the correct number of inliers, regardless of the percentage of true inliers.< >

关键词： Machine vision Statistics

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A new robust operator for computer vision: application to range data

A new robust operator for computer vision: application to ra...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Stewart Department of Computer Science Rensselaer Polytechnic Institute Troy NY

The basic MINPRAN (MINimize the Probability of RANdomness) technique, introduced by C.V. Stewart (1994), is extended to handle range data taken from complex scenes. Such data often includes: (1) a large numbers of outliers, (2) points from multiple surfaces interspersed over large image regions, and (3) extended regions containing only bad data. The initial version of MINPRAN handles cases (1) and (3). For (2), given an image region containing data from more than one surface, the basic technique tends to favor a single fit that "bridges" two surfaces. We analyze the extent of this problem and introduce two modifications to solve it. The new version of the algorithm, called MINPRAN2, produces extremely good results on difficult range data.< >

关键词： Machine vision Probability Minimization methods Surfaces

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