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Annual IEEE India Conference

作者： Swapnil V. Tathe Sandipan P. Narote Sinhgad College of Engineering Pune India

ISBN: (纸本)9781479922765

Real-time human detection and tracking is a vast, challenging and important field of research. It has wide range of applications in human recognition, human computer interaction (HCI), video surveillance etc. The research for biometric authentication of a person has reached far but the real-time tracking of human beings has not gained much importance. Tracking of human being can be used as a prior step in biometric face recognition. Keeping continuous track of person will allow to identify person at any time. The system consist of two parts first human detection and secondly tracking. Human detection step is split into face detection and eye detection. Face is a vital part of human being represent most important information about the individual. Eyes are the important biometric feature used in person identification. Face detection is done using skin color based methods. YC_bC_r color model is used to detect skin regions as it represents intensity and color information separately. For eye region detection projection function and pixel count methods are used. Finally the mean shift algorithm and kalman filter algorithm is used for tracking.

关键词： Face Detection Eye Detection Projection Function Kalman Filter mean shift algorithm

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SUPERCUT: AN ACCURATE AND EFFECTIVE INTERACTIVE IMAGE SEGMENTATION algorithm

SUPERCUT: AN ACCURATE AND EFFECTIVE INTERACTIVE IMAGE SEGMEN...

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IEEE International Conference on Image Processing

作者： Qingsong Zhu Ling Shao Zhan Song Yaoqin Xie Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Department of Electronic and Electrical Engineering University of Sheffield Sheffield UK The Chinese University of Hong Kong Hong Kong China

ISBN: (纸本)9781479923427

The task of interactive image segmentation has attracted a significant attention in recent years. The ultimate goal is to extract an object with as few user interactions as possible. In this paper, we present SUPERCUT, a novel interactive algorithm for foreground object extraction and segmentation in images. In the algorithm, the mean shift algorithm with a boundary confidence prior is introduced to efficiently presegment the original image into super-pixels with precise boundary. Secondly, a Bayes decision theory is introduced to model and cluster the super-pixels so as to obtain an initial effective classification of super-pixels. To achieve a more accurate object segmentation result, a boundary refinement using Interactive rectangle box with GMM learning is adopted. Experimental results on a benchmark data set show that the proposed framework is highly effective and can accurately segment a wide variety of natural images with ease.

关键词： Interactive Image segmentation mean shift algorithm Graph cut Interactive box images image segmentation Interactive Object segmentation Decision Theory algorithms original image Boundary boundary layer extraction of object

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3-D mapping of a multi-layered Mediterranean forest using ALS data

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REMOTE SENSING OF ENVIRONMENT 2012年 121卷 210-223页

作者： Ferraz, Antonio Bretar, Frederic Jacquemoud, Stephane Goncalves, Gil Pereira, Luisa Tome, Margarida Soares, Paula Univ Paris Est Lab MATIS IGN F-94160 St Mande France Univ Paris Diderot Inst Phys Globe Paris Sorbonne Paris Cite CNRS UMR 7154 F-75013 Paris France Inst Engn Sistemas & Comp Coimbra P-3000033 Coimbra Portugal CETE Norrnandie Ctr Lab Ponts & Chaussees F-76121 Grand Quevilly France Univ Coimbra Dept Matemat P-3001454 Coimbra Portugal Univ Aveiro Escola Super Tecnol & Gestcio Agueda P-3754909 Agueda Portugal Univ Tecn Lisboa Inst Super Agron Ctr Estudos Florestais P-1349017 Lisbon Portugal

This study presents a robust approach for characterization of multi-layered forests using airborne laser scanning (ALS) data. Fuel mapping or biomass estimation requires knowing the diversity and boundaries of the forest patches, as well as their spatial pattern. This includes the thickness of the main vegetation layers, but also the spatial arrangement and size of the individual plants that compose each stratum. In order to decompose the ALS point cloud into genuine 3-D segments corresponding to individual vegetation features, such as shrubs or tree crowns, we apply a statistical approach based on the mean shift algorithm. The segments are progressively assigned to a forest layer: ground vegetation, understory or overstory. Our method relies on a single biophysically meaningful parameter, the kernel bandwidth, which is related to the local forest structure. It is validated on 44 plots of a Portuguese forest, composed mainly of eucalyptus (Eucalyptus globulus Labill.) and maritime pine (Pinus pinaster Ait.) trees. The number of detected trees varies with the dominance position: from 98.6% for the dominant trees to 12.8% for the suppressed trees. Linear regression models explain up to 70% of the variability associated with ground vegetation and understory height. (c) 2012 Elsevier Inc. All rights reserved.

关键词： Airborne laser scanning LiDAR Multi-layered forest Unsupervised segmentation mean shift algorithm Fuel mapping Vertical stratification Tree crown 3-D mapping Ground vegetation Understory Overstory

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A fast approach for person detection and tracking

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INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY 2012年第3期44卷 210-216页

作者： Ali, Ahmed Terada, Kenji Univ Tokushima Grad Sch Adv Technol & Sci 2-1 Minami Josanjima Tokushima 7708506 Japan

Object detection and tracking is one of the most popular areas of video processing and the essential requirement of any surveillance system. A fast approach for person detection and tracking is presented. This work proposes to do target tracking with Kalman filter. In case of a misdetection, which would lead a wrong update of the filter, a fast mean shift iteration based on integral computation is performed to propose a more accurate detection. Tracking results are demonstrated for complex scenes and evaluation of the proposed tracking approach is presented.

关键词： Kalman filter mean shift algorithm object tracking

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Mode Seeking with an Adaptive Distance Measure 12

Mode Seeking with an Adaptive Distance Measure

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12th European Conference on Computer Vision (ECCV)

作者： Pan, Guodong Shang, Lifeng Schnieders, Dirk Wong, Kwan-Yee K. Univ Hong Kong Dept Comp Sci Pokfulam Rd Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9783642338854;9783642338847

The mean shift algorithm is a widely used non-parametric clustering algorithm. It has been extended to cluster a mixture of linear subspaces for solving problems in computer vision such as multi-body motion segmentation, etc. Existing methods only work with a set of subspaces, which are computed from samples of observations. However, noises from observations can distort these subspace estimates and influence clustering accuracy. We propose to use both subspaces and observations to improve performance. Furthermore, while these mean shift methods use fixed metrics for computing distances, we prefer an adaptive distance measure. The insight is, we can use temporary modes in a mode seeking process to improve this measure and obtain better performance. In this paper, an adaptive mode seeking algorithm is proposed for clustering linear subspaces. By experiments, the proposed algorithm compares favorably to the state-of-the-art algorithm in terms of clustering accuracy.

关键词： mean shift algorithm Metric Learning

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Robust Foveal Wavelet-Based Object Tracking

Robust Foveal Wavelet-Based Object Tracking

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Maalouf, Aldo Larabi, Mohamed-Chaker Univ Poitiers CNRS UMR 6172 XLIM SIC Lab Poitiers France

ISBN: (纸本)9781467300469

In this work, a foveal wavelet-based mean shift Tracking algorithm is presented. The foveal wavelets introduced by Mallat [16] are known by their high capability to precisely characterize the holder regularity of singularities. Therefore, by using the foveal wavelet transform, image features are accurately identified and are well discriminated from noise. These wavelets are used to extract the texture features of the target object. The extracted features are then used to construct a joint color-foveal textures histogram to represent the target object. Once the joint histogram is obtained, it is applied to the mean shift framework in order to track a target object in a video sequence. The experimental results showed that the proposed approach overcomes the traditional mean shift tracking technique as well as other existing tracking algorithms.

关键词： Object Tracking Foveal Wavelet mean shift algorithm

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ROBUST UNSUPERVISED NONPARAMETRIC CHANGE DETECTION OF SAR IMAGES

ROBUST UNSUPERVISED NONPARAMETRIC CHANGE DETECTION OF SAR IM...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Garzelli, Andrea Zoppetti, Claudia Aiazzi, Bruno Baronti, Stefano Alparone, Luciano Univ Siena Dept Informat Engn I-53100 Siena Italy

ISBN: (纸本)9781467311595

This paper presents an unsupervised nonparametric method for change detection in multitemporal synthetic aperture radar (SAR) imagery. The proposed method relies on a novel feature capable of capturing the structural changes between the two images and discarding almost completely the statistical changes due to speckle patterns or co-registration inaccuracies. This feature utilizes the scatterplots of the amplitude levels in the two SAR images and applies a fast version of the mean-shift (MS) algorithm to find the modes of the underlying bivariate distribution. The value of the probability density function (PDF) is translated to a value of conditional information and given to all image pixels originating such modes. Experimental results have been carried out with simulated changes and true SAR images acquired by the COSMO-SkyMed satellite constellation. The proposed feature exhibits significantly better discrimination capability than both the classical log-ratio (LR) and is particularly robust if applied to SAR images having different processing and/or acquisition angles.

关键词： Change detection information-theoretic features mean shift algorithm multi-temporal images nonparametric methods synthetic aperture radar (SAR)

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ROBUST UNSUPERVISED NONPARAMETRIC CHANGE DETECTION OF SAR IMAGES

ROBUST UNSUPERVISED NONPARAMETRIC CHANGE DETECTION OF SAR IM...

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IEEE International Geoscience and Remote Sensing Symposium

作者： Andrea Garzelli Claudia Zoppetti Bruno Aiazzi Stefano Baronti Luciano Alparone Dept. Information Engineering University of Siena IFAC-CNR Research Area of Florence Dept. Electron. & Telecomm University of Florence

ISBN: (纸本)9781467311601

关键词： Change detection Information-theoretic features mean shift algorithm Multi-temporal images Non-parametric methods Synthetic aperture radar (SAR)

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Image smoothing based on the mean shift algorithm

Image smoothing based on the mean shift algorithm

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IEEE International Conference on Control and Automation

作者： Liu, Wei Duan, Yubo Shao, Keyong Zhang, Lijian Inst Daqing Petr Dept Automat Daqing Heilongjiang Pr Peoples R China Univ Harbin Engn Coll Sci Harbin Heilongjiang Peoples R China

ISBN: (纸本)9781424408177

For reducing noise and preferably keeping the edge of image, the mean shift algorithm based on the HIS space is proposed. Firstly the image is changed from RGB space to HIS space. Because the correlation of HIS is very little, H,I and S separately is smoothed based on the different step sizes. Finally the smoothed image is gotten by transforming the HIS space into the RGB space. Because the correlation of the RGB space is more strong, the drawback of the image which could not be separately smoothed by R, G and B is overcomed. The convergence of the moving dot is strictly proved. We prove that the algorithm could get better result than wiener filtering and the mean-shift based on RGB space by experiment.

关键词： image smoothing mean shift algorithm

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Implementation of a Real-Time Image Object Tracking System for PTZ Cameras

Implementation of a Real-Time Image Object Tracking System f...

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5th International Conference on Convergence and Hybrid Information Technology (ICHIT)

作者： Lee, Sang-Gu Batkhishig, R. Hannam Univ Dept Comp Engn Taejon 306791 South Korea

ISBN: (纸本)9783642241055

In this paper, we implement a real-time surveillance monitoring and image object tracking system using PTZ (Pan-Tilt-Zoom) camera. For image object tracking, we use the mean shift tracking algorithm based on the color image distribution of detected object. mean shift algorithm is efficient for real-time tracking because of its fast and stable performance. In this system. Mat Lab language is used for clustering moving object and accessing the PTZ protocol and RS-485 communication for controlling the position of PTZ cam-era in order to arrange the moving objects in the middle part of the monitor screen. This system can be applied to an effective and fast image surveillance system for continuous object tracking in a wider area.

关键词： Image object tracking PTZ camera mean shift algorithm

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