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15th International Conference on System Theory, Control and Computing, ICSTCC 2011

作者： Smochina, Cristian Manta, Vasile Kropatsch, Walter Department of Computer Science and Engineering Gheorghe Asachi Technical University of Iasi Romania Pattern Recognition and Image Processing Group Institute of Computer Graphics and Algorithms Vienna University of Technology Austria

ISBN: (纸本)9781457711732

The objective of semantic segmentation in microscopic images is to extract the cellular, nuclear or tissue components. This problem is challenging due to the large variations of these components features (size, shape, orientation or texture). In this paper we improve the technique presented in [17] used to identify the epithelial nuclei (crypt) against interstitial nuclei in microscopic images taken from colon tissues. In the proposed enhanced approach, the crypt inner boundary is detected using the closing morphological pyramid instead of morphological hierarchy. The outer crypt border is determined by the epithelial nuclei, overlapped by the maximal isoline of the inner boundary. The use of sampling in building the pyramid offers computational efficiency, reduces the amount of used memory, increase the robustness and preserve the quality results. An analysis of the two approaches is performed considering the number of pixels processed to create each level. Also the relation between the levels of the hierarchical structures is established. © 2011 Faculty of Auto Control.

关键词： Computational efficiency

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Position identification by actively localizing spacial sound beacons

Position identification by actively localizing spacial sound...

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作者： Li, Huakang Huang, Jie Zhao, Qunfei School of Computer Science Engineering University of Aizu Aizuwakamatsu-shi 965-8580 Japan Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai 200240 China

In this paper, we propose a method for robot self-position identification by active sound localization. This method can be used for autonomous security robots working in room environments. A system using an AIBO robot equipped with two microphones and a wireless network is constructed and used for position identification experiments. Differences in arrival time to the robot's microphones are used as localization cues. To overcome the ambiguity of front-back confusion, a three-head-position measurement method is proposed. The position of robot can be identified by the intersection of circles restricted using the azimuth differences among different sound beacon pairs. By localizing three or four loudspeakers as sound beacons positioned at known locations, the robot can identify its position with an average error of 7 cm in a 2.5 × 3.0m2 working space in the horizontal plane. We propose adjusting the arrival time differences (ATDs) to reduce the errors caused when the sound beacons are high mounted. A robot navigation experiment was conducted to demonstrate the effectiveness of the proposed position-identification system. © 2011 The institute of Electronics, Information and Communication Engineers.

关键词： Robots

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3D multipath planning for UAV based on network graph

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Journal of Systems Engineering and Electronics 2011年第4期22卷 640-646页

作者： Xin Liu Chengping Zhou Mingyue Ding Institute for Pattern Recognition and Artificial Intelligence Key Laboratory of National Defense Science and Technology forMulti-spectral Information Processing Technologies Huazhong University of Science and Technology Wuhan 430074 R R. China School of Life Science and Technology Image Processing and Intelligence Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Wuhan 430072 P. R. China

According to the characteristic and the requirement of multipath planning, a new multipath planning method is proposed based on network. This method includes two steps： the construction of network and multipath searching. The construction of network proceeds in three phases： the skeleton extraction of the configuration space, the judgment of the cross points in the skeleton and how to link the cross points to form a network. Multipath searching makes use of the network and iterative penalty method （IPM） to plan multi-paths, and adjusts the planar paths to satisfy the requirement of maneuverability of unmanned aerial vehicle （UAV）. In addition, a new height planning method is proposed to deal with the height planning of 3D route. The proposed algorithm can find multiple paths automatically according to distribution of terrain and threat areas with high efficiency. The height planning can make 3D route following the terrain. The simulation experiment illustrates the feasibility of the proposed method.

关键词： path planning skeleton graph iterative penaltymethod （IPM） network graph.

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A Scatter-Glare Correction Method Based on Equivalent Lucite Thickness Estimation

A Scatter-Glare Correction Method Based on Equivalent Lucite...

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International Conference on Bioinformatics and Biomedical Engineering (ICBBE)

作者： Ying Yang Xuanqin Mou Institute of Image Processing and Pattern Recognition Xi''an Jiaotong University Xi'an China

In this paper, we propose a scatter-glare correction method based on thickness estimation and digital filtration. The method is an improvement of the scatter-glare correction method proposed by Ersahin, et al The method estimates scatter glare fraction in two steps. The first step is to assign an equivalent Lucite thickness to every pixel in the image, and the second step is to estimate the scatter-glare fraction from the thickness information. Experiments show that this method can well estimate the scatter-glare fraction in a wide range of thicknesses, and a higher accuracy for scatter-glare estimation can be obtained by this method compared with Ersahin's method.

关键词： Estimation Phantoms Electron tubes Gray-scale Pixel Polynomials Fitting

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Saliency based Natural image Understanding

Saliency based Natural Image Understanding

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第一届亚洲模式识别会议

作者： Qingshan Li Yue Zhou Lei Xu Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Institute of Image Processing and Pattern RecognitionShanghai Jiao Tong UniversityShanghai China

ISBN: (纸本)9781457701221

This paper presents a novel method for natural image understanding. We improved the effect of saliency detection for the purpose of image segmentation at first. Then Graph cuts are used to find global optimal segmentation of N-dimensional image. After that, we adopt the scheme of supervised learning to classify the scene type of the image. The main advantages of our method are that: Firstly we revised the existed sparse saliency model to better suit for image segmentation, Secondly we propose a new color modeling method during the process of GrabCut segmentation. Finally we extract object-level top down information and low-level image cues together to distinguish the type of images. Experiments show that our proposed scheme can obtain comparable performance to other approaches.

关键词： Visual Saliency image segmentation GrabCut image Understanding

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Saliency based image segmentation

Saliency based image segmentation

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International Conference on Multimedia Technology

作者： Qingshan Li Yue Zhou Jie Yang Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai China

ISBN: (纸本)9781612847719

This paper presents a novel method for image segmentation. We improved the effect of saliency detection for the purpose of image segmentation. Graph cuts are used to find global optimal segmentation of N-dimensional image. With the guidance of saliency, users do not have to select foreground object and background seeds. The main advantages of our method are that: Firstly we revised the existed sparse saliency model to better suit for image segmentation, Secondly we propose a new color modeling method during the process of GrabCut segmentation. Finally we combine these two processes together to segment images without interference. We demonstrate our proposed scheme for image segmentation on several databases and get satisfactory results.

关键词： image segmentation image color analysis Databases Object segmentation Minimization Visualization Biological system modeling

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Hierarchical interactive image segmentation using irregular pyramids

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2011年 6658 LNCS卷 245-254页

作者： Gerstmayer, Michael Haxhimusa, Yll Kropatsch, Walter G. Pattern Recognition and Image Processing Group Institute of Computer Graphics and Algorithms Vienna University of Technology Austria

ISBN: (纸本)9783642208430

In this paper we describe modifications of irregular image segmentation pyramids based on user-interaction. We first build a hierarchy of segmentations by the minimum spanning tree based method, then regions from different (granularity) levels are combined to a final (better) segmentation with user-specified operations guiding the segmentation process. Based on these operations the users can produce a final image segmentation that best suits their applications. This work can be used for applications where we need accuracy in image segmentation, in annotating images or creating ground truth among others. © 2011 Springer-Verlag Berlin Heidelberg.

关键词： image segmentation

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Content-based image quality assessment of natural scene image distorted by quantization

Content-based image quality assessment of natural scene imag...

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IEEE Visual Communications and image processing (VCIP)

作者： Tao Luo Chao Wang Xuanqin Mou Institute of Image Processing and Pattern Recognition Xi'an Jiaotong University Xi'an China

In this paper, the subjective image quality for different image content is investigated by psychophysical experiments. The experimental images are the parts from natural scenes distorted by integer transform and quantization in H.264 frame. These images are divided into two types based on the scene content, type I and type II. The perceived thresholds and subjective graded scores for different quantization are obtained using forced choice staircase experiments and graded response experiments, respectively. The subjective assessment results showed that the image quality of type I degrades much more than the type II when quantization steps increase, and the preliminary experiment showed that the existed IQA metric, i.e. SSIM, could not predict it well. We also present a content-based image classifier to predict the two image types. The results show good accordance between the classifier and the subjective assessment.

关键词： Quantization image quality Measurement image edge detection Transforms Histograms image coding

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Multi-regularization parameters estimation for Gaussian mixture classifier based on MDL principle

Multi-regularization parameters estimation for Gaussian mixt...

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International Conference on Neural Computation Theory and Applications, NCTA 2011

作者： Zhou, Xiuling Guo, Ping Philip Chen, C.L. Laboratory of Image Processing and Pattern Recognition Beijing Normal University Beijing 100875 China Artificial Intelligence Institute Beijing City University Beijing China Faculty of Science and Technology University of Macau Macau China

ISBN: (纸本)9789898425843

Regularization is a solution to solve the problem of unstable estimation of covariance matrix with a small sample set in Gaussian classifier. And multi-regularization parameters estimation is more difficult than single parameter estimation. In this paper, KLIM-L covariance matrix estimation is derived theoretically based on MDL (minimum description length) principle for the small sample problem with high dimension. KLIM-L is a generalization of KLIM (Kullback-Leibler information measure) which considers the local difference in each dimension. Under the framework of MDL principle, multi-regularization parameters are selected by the criterion of minimization the KL divergence and estimated simply and directly by point estimation which is approximated by two-order Taylor expansion. It costs less computation time to estimate the multi-regularization parameters in KLIM-L than in RDA (regularized discriminant analysis) and in LOOC (leave-one-out covariance matrix estimate) where cross validation technique is adopted. And higher classification accuracy is achieved by the proposed KLIM-L estimator in experiment.

关键词： Parameter estimation

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Accurate moving object detection in thermal imagery

Accurate moving object detection in thermal imagery

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IEEE International Conference on Computer Science and Automation Engineering (CSAE)

作者： Zhang, Yan Zhao, Ting Gu, Jian Yu, Shengyang Testing Center of Security Products for Computer Information System Ministry of Public Security Quality Supervision Ministry of Public Security Shanghai China Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai China

ISBN: (纸本)9781424487257

Accurate detection of moving object provides a fundamental capability that drives numerous high-level computer vision applications. In this paper, a novel algorithm is proposed to detect objects in widely varying thermal imagery. On the basis of properties of thermal imagery, effective foreground and background model are first presented, and then these models are competitively used in an unified MAP-MRF framework to detect objects. Experimental results on challenging dataset show the robustness and effectivity of the proposed algorithm. © 2011 IEEE.

关键词： Object detection

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