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13th IEEE Digital Signal Processing Workshop/5th IEEE Signal Processing Education Workshop

作者： Saad, Michele A. Bovik, Alan C. Univ Texas Austin Austin TX 78712 USA

ISBN: (纸本)9781424436767

In this paper we explore four distinct approaches to extracting regions of interest (ROI) from still images. We show the results obtained for each of the proposed approaches, and we demonstrate where each method outperforms the other. The four approaches are: 1) a block-based discrete wavelet transform (DWT) algorithm, 2) a color saliency approach, 3) a wavelet coefficients variance saliency approach, and 4) an approach based on mean-shift clustering of image pixels. The wavelet-based approaches are shown to perform well on natural scene images that usually contain regions of distinct textures. The color saliency approach performs well on images containing objects of high saturation and brightness, and the mean-shift clustering approach partitions the image into regions according to the density distribution of pixel intensities.

关键词： Regions of interest color saliency discrete wavelet transform mean shift algorithm

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algorithm of Target Tracking Based on mean shift with RBF Neural Network

Algorithm of Target Tracking Based on Mean Shift with RBF Ne...

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27th Chinese Control Conference

作者： Zhou Bin Wang Junzheng Mao Jiali Beijing Inst Technol Sch Informat Sci & Technol Beijing 100081 Peoples R China

ISBN: (纸本)9787900719706

The limitation of mean shift algorithm under crossing occlusion is analyzed. To solve this problem, a new tracking algorithm using mean shift with RBF neural network is proposed. According to the formal information about the object's location, the iteration start position is found with RBF neural network. And the object's real center is calculated by mean shift algorithm. Experimental results show that the proposal algorithm is stable to solve the crossing occlusion problem, and the iteration number is reduced.

关键词： mean shift algorithm Motion object tracking RBF neural network

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Real-Time Face Detection Using AdaBoot algorithm

Real-Time Face Detection Using AdaBoot Algorithm

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International Conference on Control, Automation and Systems

作者： Han, Cheol Hun Sim, Kwee-Bo Chung Ang Univ Dept Elect & Elect Engn Seoul 156756 South Korea

ISBN: (纸本)9788995003893

In this paper, we propose to use the AdaBoost algorithm for face detection. AdaBoost is a kind of large margin classifiers and is efficient for on-line learning. In order to adapt the AdaBoost algorithm to fast face detection, use the original AdaBoost algorithm, the original AdaBoost which uses a given features is compared with the boosting along feature dimensions. The comparable results assure the use of the latter, which is faster for classification. The AdaBoost is typically a classification between two classes. This face detection system operates without the aid of initializing stage and realizes automatic face detection system. The overall structure adopts window scanning and image pyramid structure so that various size of face is allowed to be detected. In addition, real-time performance rate can be achieved through constituting strong classifier with extracting a few but efficient weak classifiers by the AdaBoost learning.

关键词： Face detection AdaBoost Classifiers Haar-like feature mean shift algorithm

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A CI feature-based pulmonary nodule segmentation using three-domain mean shift clustering

A CI feature-based pulmonary nodule segmentation using three...

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5th International Conference on Wavelet Analysis and Pattern Recognition

作者： Nie, Sheng-Dong Li, Li-Hong Chen, Zhao-Xue Shanghai Univ Sci & Technol Sch Med Instrumentat & Foodstuff Inst Med Imaging Engn Shanghai 200093 Peoples R China Shanghai Jiao Tong Univ Inst Image Proc & Pattern Recognit Shanghai 200030 Peoples R China

ISBN: (纸本)9781424410651

A novel and more effective algorithm used for segmenting pulmonary nodules in thoracic spiral CT images was presented The algorithm is based on mean shift clustering method and Cl (Convergence Index) features, which can represent the multiple Gaussian model of pulmonary nodules both for solid and sub-solid, substantially The algorithm has the following steps: (1) calculating the Cl features of all pixels in the region of interest (ROI), (2) combining the CI features with the intensity range and the spatial position of the pixels to form a feature vector set, (3) grouping the feature vector set to clusters with mean shift clustering algorithm. Owing to our algorithm can represent the multiple Gaussian model both for solid and sub-solid nodules, it can be used in any user interested nodule regions, especially suitable for the segmentation of sub-solid nodules. Experiments demonstrated that our algorithm can figure out the Outline of pulmonary nodules of different forms more precisely.

关键词： CT images nodule segmentation solid nodule sub-solid nodule CI feature mean shift algorithm

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Improved mean-shift-based pitch determination

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Journal of Southeast University(English Edition) 2007年第4期23卷 494-499页

作者：吴红卫吴镇扬赵力东南大学信息科学与工程学院

The underlying principle of pitch determination based on the mean shift algorithm is studied, and the cause of pitch error propagation in the original pseudo code is analyzed. The problem of error propagation is solved by choosing an appropriate initial pitch candidate F00. The theoretical choice guideline in a pitch epoch is obtained as ensuring the true pitch F0 satisfying F00/2 〈 F0 〈 3F00/2. The validity of the choice guideline is verified by the F00 experiment. meanwhile, the algorithm is extended to the pitch determination in the noisy case and compared with the method of subharmonic-to-harmonic ratio （SHR）. The experimental results show that the improved algorithm bears comparison with SHR and it runs much faster than SHR.

关键词： pitch pitch determination mean shift algorithm

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A robust road segmentation method

A robust road segmentation method

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5th International Conference on Wavelet Analysis and Pattern Recognition

作者： Zhang, Li Zhou, Wen-Hui Liu, Ji-Lin Zhejiang Univ Sch Informat Sci & Engn Hangzhou 310027 Peoples R China

ISBN: (纸本)9781424410651

A robust method of road segmentation for Autonomous Land Vehicle (ALV) navigation system is presented. The Main contribution of the present road segmentation method Consists of an effective improvement on the mean shift algorithin dedicated to road segmentation and an extension to the Bayesian method due to its suffering from incorrectly predicted edge and the non-generalization from the sampled pixels to the unsampled pixels. The improved mean shift algorithm transforms the road image to get better feature representation which highlights the intrinsic characteristics of road images. Road images are segmented by fusing with the results gotten by the improved mean shift algorithm and the extended Bayesian method, the scene variation information between adjacent frames, and the vehicle motion information. The method needn't assume a simplified road model and overcomes the shortcomings brought out by it. The experimental results show that the method has good performance and increases the segmentation accuracy to an extent.

关键词： road segmentation mean shift algorithm Bayesian method information fusion visual navigation homography

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Multi-information incorporation approach to kernel-based infrared target model construction with application to target tracking

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OPTICAL ENGINEERING 2006年第11期45卷 110502-1-110502页

作者： Ling, Jianguo Liu, Erqi Yang, Lei Yang, Jie Shanghai Jiao Tong Univ Inst Image Proc & Pattern Recognit Shanghai 200240 Peoples R China China Aerosp Sci & Ind Corp Inst Acad 2 Beijing 100854 Peoples R China

We present an approach that incorporates multi-information, including intensity value, spatial relation, and local standard deviation information of the pixels in target region, into kernel density estimation for constructing the kernel-based infrared (IR) target model. The incorporated information can complement each other for a target-tracking task. This constructed target model is evaluated based on the relative entropy of the two classes and is applied in a mean shift tracking system for IR target tracking to verify the effectiveness. (c) 2006 Society of Photo-Optical Instrumentation Engineers.

关键词： infrared target tracking target model kernel density estimation multi-information incorporation mean shift algorithm

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Fast, shape-directed, landmark-based deep gray matter segmentation for quantification of iron deposition

Fast, shape-directed, landmark-based deep gray matter segmen...

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Medical Imaging 2006 Conference

作者： Ekin, Ahmet Jasinschi, Radu van der Grond, Jeroen van Buchem, Mark A. van Muiswinkel, Arianne Philips Res Video Proc Grp HTC 36 NL-5656 AE Eindhoven Netherlands Leiden Univ Med Ctr Dept Radiol Leiden Netherlands Philips Med Syst NL-5680 DA Best Netherlands

ISBN: (纸本)0819464236

This paper introduces image processing methods to automatically detect the 3D volume-of-interest (VOI) and 2D region-of-interest (ROI) for deep gray matter organs (thalamus, globus pallidus, putamen, and caudate nucleus) of patients with suspected iron deposition from MR dual echo images. Prior to the VOI and ROI detection, cerebrospinal fluid (CSF) region is segmented by a clustering algorithm. For the segmentation, we automatically determine the cluster centers with the mean shift algorithm that can quickly identify the modes of a distribution. After the identification of the modes, we employ the K-Harmonic means clustering algorithm to segment the volumetric MR data into CSF and non-CSF. Having the CSF mask and observing that the frontal lobe of the lateral ventricle has more consistent shape accross age and pathological abnormalities, we propose a shape-directed landmark detection algorithm to detect the VOI in a speedy manner. The proposed landmark detection algorithm utilizes a novel shape model of the front lobe of the lateral ventricle for the slices where thalamus, globus pallidus, putamen, and caudate nucleus are expected to appear. After this step, for each slice in the VOI, we use horizontal and vertical projections of the CSF map to detect the approximate locations of the relevant organs to define the ROL We demonstrate the robustness of the proposed VOI and ROI localization algorithms to pathologies, including severe amounts of iron accumulation as well as white matter lesions, and anatomical variations. The proposed algorithms achieved very high detection accuracy, 100 % in the VOI detection, over a large set of a challenging MR dataset.

关键词： shape-based brain organ detection deep gray matter organ segmentation detection of brain iron deposition K-Harmonic means clustering mean shift algorithm landmark detection

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mean shift blob tracking with kernel histogram filtering and hypothesis testing

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PATTERN RECOGNITION LETTERS 2005年第5期26卷 605-614页

作者： Peng, NS Yang, J Liu, Z Shanghai Jiao Tong Univ Inst Image Proc & Pattern Recognit Shanghai 200030 Peoples R China Henan Univ Sci & Technol Inst Elect & Informat Luoyang 471039 Peoples R China

We propose a new adaptive model update mechanism for the real-time mean shift blob tracking. Since the Kalman filter has been used mainly for smoothing the object trajectory in the tracking system, it is novel for us to use adaptive Kalman filters for filtering object kernel histogram so as to obtain the optimal estimate of object model. The acceptance of the object estimate for the next frame tracking is determined by a robust criterion, i.e. the result of hypothesis testing with the samples from the filtering residuals. Therefore, the tracker can not only update object model in time but also handle severe occlusion and dramatic appearance changes to avoid over model update. We have applied the proposed method to track real object under the changes of scale and appearance with encouraging results. (c) 2004 Elsevier B.V. All rights reserved.

关键词： mean shift algorithm object tracking adaptive kalman filter hypothesis testing model update

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Unsupervised texture segmentation with one-step mean shift and boundary Markov random fields

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PATTERN RECOGNITION LETTERS 2001年第10期22卷 1073-1081页

作者： Yang, XY Liu, J Nanyang Technol Univ Sch Elect & Elect Engn Infor Syst Res Lab Nanyang 639798 Singapore

This paper presents an unsupervised texture segmentation method with the one-step mean shift algorithm and the boundary Markov random field. For Gaussian mixture models, the one-step mean shift is capable of determining the boundary points which separate neighboring Gaussian component distribution on histograms. The one-step mean shift algorithm is able to provide a coarse image segmentation result based on the image histogram. In order to improve the segmentation result with the constraints of smoothness, the boundary Markov random field is introduced. In the boundary Markov random field, the multilevel logistic distribution (MLL) is employed for the purpose of smoothing regions with its characteristic of region forming, and the boundary information is added to the energy function of the DLL distribution to preserve the discontinuity at boundaries. (C) 2001 Published by Elsevier Science B.V.

关键词： Markov random field mean shift algorithm simulated annealing texture segmentation

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