自动化学报 Pub Date : 2014-10-01 DOI: 10.1016/S1874-1029(14)60364-5

Zheng-Cai CAO , Feng-Le MA , Yi-Li FU , Jian ZHANG

{"title":"A Scale Invariant Interest Point Detector in Gabor Based Energy Space","authors":"Zheng-Cai CAO , Feng-Le MA , Yi-Li FU , Jian ZHANG","doi":"10.1016/S1874-1029(14)60364-5","DOIUrl":"10.1016/S1874-1029(14)60364-5","url":null,"abstract":"<div><p><strong>Abstract</strong> Interest point detection is a fundamental issue in many intermediate level vision problems and plays a significant role in vision systems. The previous interest point detectors are designed to detect some special image structures such as corners, junctions, line terminations and so on. These detectors based on some simplified 2D feature models will not work for image features that differ significantly from the models. In this paper, a scale invariant interest point detector, which is appropriate for most types of image features, is proposed based on an iterative method in the Gabor based energy space. It detects interest points by noting that there are some similarities in the phase domain for all types of image features, which are obtained by different detectors respectively. Firstly, this approach obtains the positions of candidate points by detecting the local maxima of a series of energy maps constructed by Gabor filter responses. Secondly, an iterative algorithm is adopted to select the corresponding characteristic scales and accurately locate the interest points simultaneously in the Gabor based energy space. Finally, in order to improve the real-time performance of the approach, a fast implementation of Gabor function is used to accelerate the process of energy space construction. Experiments show that this approach has a broader applicability than the other detectors and has a good performance under rotation and some other image changes.</p></div>","PeriodicalId":35798,"journal":{"name":"自动化学报","volume":"40 10","pages":"Pages 2356-2363"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/S1874-1029(14)60364-5","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"56928298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Properties and Data-driven Design of Perceptual Reasoning Method Based Linguistic Dynamic Systems 基于感知推理方法的语言动态系统的特性与数据驱动设计

自动化学报 Pub Date : 2014-10-01 DOI: 10.1016/S1874-1029(14)60360-8

Cheng-Dong LI , Gui-Qing ZHANG , Hui-Dong WANG , Wei-Na REN

引用次数: 9

Sensor Fault Estimation Filter Design for Discrete-time Linear Time-varying Systems 离散线性时变系统的传感器故障估计滤波器设计

自动化学报 Pub Date : 2014-10-01 DOI: 10.1016/S1874-1029(14)60365-7

Zhen-Hua WANG , Mickael RODRIGUES , Didier THEILLIOL , Yi SHEN

引用次数: 13

Iterative adaptive dynamic programming approach to power optimal control for smart grid with energy storage devices 基于迭代自适应动态规划的储能智能电网功率最优控制

自动化学报 Pub Date : 2014-09-01 DOI: 10.3724/SP.J.1004.2014.01984

Cheng Wang, D. Liu, Qinglai Wei, Dongbin Zhao, Zhenglong Xia

引用次数: 10

Box-constrained Total-variation Image Restoration with Automatic Parameter Estimation 基于自动参数估计的盒约束全变分图像恢复

自动化学报 Pub Date : 2014-08-01 DOI: 10.1016/S1874-1029(14)60019-7

Chuan HE , Chang-Hua HU , Wei ZHANG , Biao SHI

引用次数: 8

Line Matching Across Views Based on Multiple View Stereo 基于多视点立体的跨视点线匹配

自动化学报 Pub Date : 2014-08-01 DOI: 10.1016/S1874-1029(14)60017-3

Kang-Ping FU , Shu-Han SHEN , Zhan-Yi HU

引用次数: 4

Online Learning a Binary Classifier for Improving Google Image Search Results 在线学习二分类器改进谷歌图像搜索结果

自动化学报 Pub Date : 2014-08-01 DOI: 10.1016/S1874-1029(14)60018-5

Yu-Chai WAN , Xia-Bi LIU , Fei-Fei HAN , Kun-Qi TONG , Yu LIU

{"title":"Online Learning a Binary Classifier for Improving Google Image Search Results","authors":"Yu-Chai WAN , Xia-Bi LIU , Fei-Fei HAN , Kun-Qi TONG , Yu LIU","doi":"10.1016/S1874-1029(14)60018-5","DOIUrl":"10.1016/S1874-1029(14)60018-5","url":null,"abstract":"<div><p>It is promising to improve web image search results through exploiting the results' visual contents for learning a binary classifier which is used to refine the results' relevance degrees to the given query. This paper proposes an algorithm framework as a solution to this problem and investigates the key issue of training data selection under the framework. The training data selection process is divided into two stages: initial selection for triggering the classifier learning and dynamic selection in the iterations of classifier learning. We investigate two main ways of initial training data selection, including clustering based and ranking based, and compare automatic training data selection schemes with manual manner. Furthermore, support vector machines and the max-min pseudo-probability (MMP) based Bayesian classifier are employed to support image classification, respectively. By varying these factors in the framework, we implement eight algorithms and tested them on keyword based image search results from Google search engine. The experimental results confirm that how to select the training data from noisy search results is really a key issue in the problem considered in this paper and show that the proposed algorithm is effective to improve Google search results, especially at top ranks, thus is helpful to reduce the user labor in finding the desired images by browsing the ranking in depth. Even so, it is still worth meditative to make automatic training data selection scheme better towards perfect human annotation.</p></div>","PeriodicalId":35798,"journal":{"name":"自动化学报","volume":"40 8","pages":"Pages 1699-1708"},"PeriodicalIF":0.0,"publicationDate":"2014-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/S1874-1029(14)60018-5","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"56927709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Online Minimum-acceleration Trajectory Planning with the Kinematic Constraints 基于运动学约束的在线最小加速度轨迹规划

自动化学报 Pub Date : 2014-07-01 DOI: 10.1016/S1874-1029(14)60014-8

Ying-Shi WANG , Lei SUN , Lu ZHOU , Jing-Tai LIU

引用次数: 11

Statistical χ2 Testing Based Fault Detection for Linear Discrete Time-delay Systems 基于统计χ2检验的线性离散时滞系统故障检测

自动化学报 Pub Date : 2014-07-01 DOI: 10.1016/S1874-1029(14)60013-6

Bo-Ang LIU , Hao YE

引用次数: 3

Explicit G2-constrained Merging of a Pair of Bézier Curves by Control Point Optimization 控制点优化法对bsamizier曲线的显式g2约束归并

自动化学报 Pub Date : 2014-07-01 DOI: 10.1016/S1874-1029(14)60016-1

Li-Zheng LU , Yu-Yang QIU

引用次数: 0

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