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SUPERCUT: AN ACCURATE AND EFFECTIVE INTERACTIVE IMAGE SEGMENTATION algorithm

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

引用

20th IEEE International Conference on Image Processing (ICIP)

作者： Zhu, Qingsong Shao, Ling Song, Zhan Xie, Yaoqin Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China Chinese Acad Sci Key Lab Hlth Informat Shenzhen Peoples R China Chinese Univ Hong Kong Hong Kong Hong Kong Peoples R China Univ Sheffield Dept Elect & Elect Engn Sheffield S1 3JD S Yorkshire England Stanford Univ Sch Med Stanford CA 94305 USA

ISBN: (纸本)9781479923410

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 pre-segment 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

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Real-Time Human Detection and Tracking

Real-Time Human Detection and Tracking

引用

10th Annual Conference of the IEEE-India-Council (INDICON)

作者： Tathe, Swapnil V. Narote, Sandipan P. Sinhgad Coll Engn Pune Maharashtra India

ISBN: (纸本)9781479922741;9781479922758

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. Y CbCr 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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Remote Sensing Image Segmentation Based on Hadoop Cloud Platform

Remote Sensing Image Segmentation Based on Hadoop Cloud Plat...

引用

International Conference on Optical Instruments and Technology (OIT) at the Topical Meeting on Optoelectronic Imaging/Spectroscopy and Signal Processing Technology

作者： Li, Jie Zhu, Lingling Cao, Fubin Changchun Univ Elect & Informat Engn Changchun 130022 Jilin Peoples R China Changchun Univ Grad Sch Changchun 130022 Jilin Peoples R China

ISBN: (数字)9781510617520

ISBN: (纸本)9781510617520;9781510617513

To solve the problem that the remote sensing image segmentation speed is slow and the real-time performance is poor, this paper studies the method of remote sensing image segmentation based on Hadoop platform. On the basis of analyzing the structural characteristics of Hadoop cloud platform and its component MapReduce programming, this paper proposes a method of image segmentation based on the combination of OpenCV and Hadoop cloud platform. Firstly, the MapReduce image processing model of Hadoop cloud platform is designed, the input and output of image are customized and the segmentation method of the data file is rewritten. Then the mean shift image segmentation algorithm is implemented. Finally, this paper makes a segmentation experiment on remote sensing image, and uses MATLAB to realize the mean shift image segmentation algorithm to compare the same image segmentation experiment. The experimental results show that under the premise of ensuring good effect, the segmentation rate of remote sensing image segmentation based on Hadoop cloud Platform has been greatly improved compared with the single MATLAB image segmentation, and there is a great improvement in the effectiveness of image segmentation.

关键词： Remote sensing image Hadoop platform Image segmentation mean shift algorithm

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Optical Fish Estimation and Detection in Noisy Environment

Optical Fish Estimation and Detection in Noisy Environment

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Oceans Conference

作者： Boudhane, Mohcine Badri-Hoeher, Sabah Nsiri, Benayad Univ Appl Sci Fac Comp Sci & Elect Engn Grenzstr 5 D-24149 Kiel Germany Univ Hassan 2 Fac Sci Casablanca 20000 Morocco

ISBN: (纸本)9781479949182

Detecting fish in submarine environment is a challenge due to the properties of the water such as light absorption and scattering. In this work, we present a method for preprocessing images in submarine environment. In the first step, we model the underwater environment as overlapp of two processes. The first process is considered as a Poisson distribution, while the second one is considered as a Gaussian mixture. The resulting distribution is called Poisson-Gaussian mixture (PGM). To estimate the noise parameters, we propose an iterative algorithm based on the expectation maximization approach. This allows us to jointly estimate the scale of the Poisson parameter as well as the standard deviation and the mean of all Gaussian distributions. In order to facilitate the detection of objects, to correct the illumination problem of the scene and to restore the colors, we integrate a color correction algorithm. Finally, detection and localization of fish complete the pre-processing in the images. To obtain medium or small regions, the mean shift algorithm is used with a reduced threshold. In the segmentation process, the proposed detector scan the image region by region. This detector allows to estimate statistically the type of the region (object or non-object). The method is tested under different underwater conditions. Experimental results show that the proposed approach outperforms conventional methods.

关键词： Gaussian processes Poisson distribution expectation-maximisation algorithm geophysical image processing image restoration image segmentation iterative methods light absorption light scattering object detection oceanographic techniques underwater optics Poisson parameter Poisson-Gaussian mixture color correction algorithm expectation maximization approach fish localization illumination problem image preprocessing iterative algorithm mean shift algorithm noise parameters noisy submarine environment optical fish detection optical fish estimation segmentation process standard deviation underwater conditions underwater environment water properties Colored noise Estimation Image color analysis Image segmentation PSNR Underwater vehicles

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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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Video based crowd stability analysis used in emegency evacuation 3

Video based crowd stability analysis used in emegency evacua...

引用

IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)

作者： Zhao, Rongyong Dong, Daheng Li, Cuiling Liu, Qiong Hu, Qianshan Ma, Yunlong Zhang, Qin Tongji Univ CIMS Res Ctr Shanghai 201804 Peoples R China Tongji Univ Coll Elect & Informat Engn Shanghai 201804 Peoples R China Shanghai Maritime Univ Elect Engn & Automat Dept Shanghai 201306 Peoples R China

ISBN: (纸本)9781538662434

The study of crowd stability in emergency evacuation is great significant to the public safety. In this paper, we put forward a video based crowd stability analysis model. In this model, we conduct a study on crowd flow video in public places to analyze the stability of crowd and provide valuable guidance for public safety management. Firstly, we use Hough circle transform and mean shift algorithm to respectively detect crowd density and pedestrian movement trajectory, and further calculate crowd velocity. Then, according to the stability criterion of crowd evacuation proposed by The Green Guide, we analyze the stability of crowd in the video. According to the results of stability analysis, the safety management department could take timely safety measures. Finally, the effectiveness of the method proposed in this paper is validated through case study.

关键词： video crowd evacuation stability Hough circle transform mean shift algorithm stability criterion

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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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Robust Foveal Wavelet-Based Object Tracking

Robust Foveal Wavelet-Based Object Tracking

引用

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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A robust road segmentation method

A robust road segmentation method

引用

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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An unsupervised method for quality assessment of despeckling: An evaluation on COSMO-SkyMed data

An unsupervised method for quality assessment of despeckling...

引用

Conference on SAR Image Analysis, Modeling, and Techniques XI

作者： Aiazzi, B. Alparone, L. Argenti, F. Baronti, S. Bianchi, T. Lapini, A. Ist Fis Appl Nello Carrara Area Ric Firenze I-50019 Sesto Fiorentino Italy Univ Florence Dipartimento Elettron & Telecomunicaz I-50139 Florence Italy

ISBN: (纸本)9780819488060

Goal of this paper is the development and evaluation of a fully automatic method for quality assessment of despeckled synthetic aperture radar (SAR) images. The rationale of the new approach is that any structural perturbation introduced by despeckling, e. g. a local bias of mean or the blur of a sharp edge or the suppression of a point target, may be regarded either as the introduction of a new structure or as the suppression of an existing one. Conversely, plain removal of random noise does not change structures in the image. Structures are identified as clusters in the scatterplot of original to filtered image. Ideal filtering should produce clusters all aligned along the main diagonal. In practice clusters are moved far from the diagonal. Clusters' centers are detected through the mean shift algorithm. A structural change feature is defined at each pixel from the position and population of off-diagonal cluster, according to Shannon's information theoretic concepts. Results on true SAR images (COSMO-SkyMed) will be presented. Bayesian estimators (LMMSE: liner minimum mean squared error: MAP: maximum alpha-posteriori probability) operating in the undecimated wavelet domain have been coupled with segment-based processing. Quality measurements of despeckled SAR images carried out by means of the proposed method highlight the benefits of segmented MAP filtering.

关键词： Clustering despeckling mean shift algorithm multivariate analysis quality measurements SAR imagery

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