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image Segmentation Method Using Fuzzy C Mean Clustering Based on Multi-Objective Optimization

Image Segmentation Method Using Fuzzy C Mean Clustering Base...

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2nd international conference on Machine vision and Information Technology (CMVIT 2018)

作者： CHEN Jinlin YANG Chunzhi XU Guangkui Li Ning School of Finance Huainan normal University

image segmentation is not only one of the hottest topics in digital image processing, but also an important part of computer vision applications. As one kind of image segmentation algorithms, fuzzy C-means clustering is an effective and concise segmentation algorithm. However, the drawback of FCM is that it is sensitive to image noise. To solve the problem, this paper designs a novel fuzzy C-mean clustering algorithm based on multi-objective optimization. We add a parameter ? to the fuzzy distance measurement formula to improve the multi-objective optimization. The parameter ? can adjust the weights of the pixel local information. In the algorithm, the local correlation of neighboring pixels is added to the improved multi-objective mathematical model to optimize the clustering cent. Two different experimental results show that the novel fuzzy C-means approach has an efficient performance and computational time while segmenting images by different type of noises.

关键词： image FCM image Segmentation Method Using Fuzzy C Mean Clustering Based on Multi-Objective Optimization Fuzzy

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An Improved TLD with Harris Corner and Color Moment 8

An Improved TLD with Harris Corner and Color Moment

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8th international conference on Graphic and image processing (ICGIP)

作者： Li, Ling Liu, Kai Cheng, Fei Bao, Di Xidian Univ Sch Comp Sci & Technol Xian Peoples R China

ISBN: (纸本)9781510609518;9781510609525

Video tracking is a main field of computer vision, and TLD algorithm plays a key role in long-term tracking. However, the original TLD ignores the color features of patch in detection, and tracks the common points from grid, then, the tracking accuracy is limited to both of them. This paper presents a novel TLD algorithm with Harris corner and color moment to overcome this drawback. Instead of tracking common points, we screen more important points utilizing Harris corner to reject a half patches, these points are better able to show the object's textural features. In addition, the color moment classifier replaces patch variance to reduce the errors of detection. The classifier compares mine-dimensional color moment vectors so that it can keep the TLD's stable speed. Experiment has proved that our TLD tracks a more reliable position and higher ability without affecting the speed.

关键词： computer vision long-term tracking TLD color moment Harris corner

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Foreground Extraction for Moving RGBD Cameras 8

Foreground Extraction for Moving RGBD Cameras

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8th international conference on Graphic and image processing (ICGIP)

作者： Junejo, Imran N. Ahmed, Naveed Univ Sharjah UAE Sharjah U Arab Emirates

ISBN: (纸本)9781510609518;9781510609525

In this paper, we propose a simple method to perform foreground extraction for a moving RGBD camera. These cameras have now been available for quite some time. Their popularity is primarily due to their low cost and ease of availability. Although the field of foreground extraction or background subtraction has been explored by the computer vision researchers since a long time, the depth-based subtraction is relatively new and has not been extensively addressed as of yet. Most of the current methods make heavy use of geometric reconstruction, making the solutions quite restrictive. In this paper, we make a novel use RGB and RGBD data: from the RGB frame, we extract corner features (FAST) and then represent these features with the histogram of oriented gradients (HoG) descriptor. We train a non-linear SVM on these descriptors. During the test phase, we make used of the fact that the foreground object has distinct depth ordering with respect to the rest of the scene. That is, we use the positively classified FAST features on the test frame to initiate a region growing to obtain the accurate segmentation of the foreground object from just the RGBD data. We demonstrate the proposed method of a synthetic datasets, and demonstrate encouraging quantitative and qualitative results.

关键词： computer vision Background Subtraction Depth image processing

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The Research of Texture Transmission Algorithm Combined with Texture Synthesis 20

The Research of Texture Transmission Algorithm Combined with...

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20th IEEE international conference on Computational Science and Engineering (CSE) / 15th IEEE/IFIP international conference on Embedded and Ubiquitous Computing (EUC)

作者： Sun, Xiaoting Sun, Yujuan Jian, Muwei Ludong Univ Coll Informat & Elect Engn Yantai Peoples R China Shandong Univ Finance & Econ Sch Comp Sci & Technol Jinan Shandong Peoples R China

ISBN: (纸本)9781538632215

Texture synthesis is the hot research topic in the field of computer vision, computer graphics and image processing. Sample-based texture synthesis method has been proposed and becomes a new texture tiling technique with the development of the texture mapping and procedural texture synthesis. image Quilting stitching algorithm is an ideal algorithm for the texture stitching. In this article, by using the image Quilting algorithm to finish the texture transfer and stitch the objective texture image, whose texture style does not exist in the initial image. In experiments, by transferring the style of the human face image and character image respectively, verified the effectiveness of this proposed algorithm.

关键词： image quilting Texture synthesis Texture transmission

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3D local transform patterns: A new feature descriptor for image retrieval

3D local transform patterns: A new feature descriptor for im...

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international conference on computer vision and image processing, cvip 2016

作者： Gonde, Anil Balaji Murala, Subrahmanyam Vipparthi, Santosh Kumar Maheshwari, Rudraprakash Balasubramanian, R. Department of Electronics and Telecommunication Engineering SGGSIE and T NandedMaharashtra India Department of Electrical Engineering IIT Ropar India Department of Computer Science Engineering MNIT JaipurRajasthan India Department of Electrical Engineering IIT Roorkee India Department of Computer Science and Engineering IIT Roorkee India

ISBN: (纸本)9789811021060

In this paper, authors proposed a novel approach for image retrieval in transform domain using 3D local transform pattern (3D-LTraP). The various existing spatial domain techniques such as local binary pattern (LBP), Local ternary pattern (LTP), Local derivative pattern (LDP) and Local tetra pattern (LTrP) are encoding the spatial relationship between the neighbors with their center pixel in image plane. The first attempt has been made in 3D using spherical symmetric three dimensional local ternary pattern (SS-3D-LTP). But, the performance of SS-3D-LTP is depend on the proper selection of threshold value for ternary pattern calculation. Also, multiscale and color information are missing in SS-3D-LTP method. In the proposed method i.e. 3D-LTraP, the first problem is overcome by using binary approach Similarly, the other lacunas are avoided by using wavelet transform which provide directional as well as multiscale information and color features are embedded in feature generation process itself. Two different databases which included natural and biomedical database (Coral 10 K and OASIS databases) are used for experimental purpose. The experimental results demonstrate a noteworthy improvement in precision and recall as compared to SS-3D-LTP and recent methods. © Springer Science+Business Media Singapore 2017.

关键词： Local binary pattern

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Vison-based Traffic Flow Prediction using Dynamic Texture Model and Gaussian Process 2

Vison-based Traffic Flow Prediction using Dynamic Texture Mo...

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2nd international conference on Multimedia and image processing (ICMIP)

作者： Liu, Bin Ji, Hao Dai, Yi Nanjing Univ Posts & Telecommun Sch Comp Sci & Technol Nanjing 210023 Jiangsu Peoples R China

ISBN: (纸本)9781509059546

In this paper, a work in progress towards a real-time vision-based traffic flow prediction (TFP) system is presented. The proposed method consists of three elemental operators, that are dynamic texture model based motion segmentation, feature extraction and Gaussian process (GP) regression. The objective of motion segmentation is to recognize the target regions covering the moving vehicles in the sequence of visual processes. The feature extraction operator aims to extract useful features from the target regions. The extracted features are then mapped to the number of vehicles through the operator of GP regression. A training stage using historical visual data is required for determining the parameter values of the GP. Based on a low-resolution visual data set, preliminary evaluations on the performance of the proposed method are performed. The results show that the proposed method beats a benchmark solution based on Gaussian mixture model, and has the potential to be developed into qualified and practical solutions to real-time TFP.

关键词： computer vision dynamic texture model gaussian process traffic flow prediction

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A semi-automated method for object segmentation in infant’s egocentric videos to study object perception

A semi-automated method for object segmentation in infant’s...

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international conference on computer vision and image processing, cvip 2016

作者： Mirsharif, Qazaleh Sadani, Sidharth Shah, Shishir Yoshida, Hanako Burling, Joseph Department of Computer Science University of Houston Houston77204 United States Department of Electronics & Communication Indian Institute of Technology Roorkee India Department of Psychology University of Houston 126 Heyne Building HoustonTX77204-5022 United States

ISBN: (纸本)9789811021060

Object segmentation in infant’s egocentric videos is a fundamental step in studying how children perceive objects in early stages of development. From the computer vision perspective, object segmentation in such videos poses quite a few challenges because the child’s view is unfocused, often with large head movements, effecting in sudden changes in the child’s point of view which leads to frequent change in object properties such as size, shape and illumination. In this paper, we develop a semi-automated, domain specific method, to address these concerns and facilitate the object annotation process for cognitive scientists, allowing them to select and monitor the object under segmentation. The method starts with an annotation of the desired object by user and employs graph cut segmentation and optical flow computation to predict the object mask for subsequent video frames automatically. To maintain accurate segmentation of objects, we use domain specific heuristic rules to re-initialize the program with new user input whenever object properties change dramatically. The evaluations demonstrate the high speed and accuracy of the presented method for object segmentation in voluminous egocentric videos. We apply the proposed method to investigate potential patterns in object distribution in child’s view at progressive ages. © Springer Science+Business Media Singapore 2017.

关键词： Optical flows

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On-line gesture based user authentication system robust to shoulder surfing

On-line gesture based user authentication system robust to s...

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international conference on computer vision and image processing, cvip 2016

作者： Bhoi, Suman Dogra, Debi Prosad Roy, Partha Pratim School of Electrical Sciences Indian Institute of Technology Bhubaneswar India Department of Computer Science and Engineering Indian Institute of Technology Roorkee India

ISBN: (纸本)9789811021060

People often prefer to preserve a lot of confidential information in different electronic devices such as laptops, desktops, tablets, etc. Access to these personalized devices are managed through well known and robust user authentication techniques. Therefore, designing authentication methodologies using various input modalities receivedmuch attention of the researchers of this domain. Sincewe access such personalized devices everywhere including crowded places such as offices, public places, meeting halls, etc., the risk of an imposter gaining one’s identification information becomes highly feasible. One of the oldest but effective form of identity theft by observation is known as shoulder surfing. Patterns drawn by the authentic user on tablet surfaces or keys typed through keyboard can easily be recognized through shoulder surfing. Contact-less user interface devices such as Leap Motion controller can be used to mitigate some of the limitations of existing contact-based input methodologies. In this paper, we propose a robust user authentication technique that has been designed to counter the chances of getting one’s identity stolen by shoulder surfing. Our results reveal that, the proposed methodology can be quite effective to design robust user authentication systems, especially for personalized electronic devices. © Springer Science+Business Media Singapore 2017.

关键词： Authentication

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Camera Orientation Estimation Using Motion Based Vanishing Point Detection for Automatic Driving Assistance System

Camera Orientation Estimation Using Motion Based Vanishing P...

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IEEE international conference on Consumer Electronics (ICCE)

作者： Jo, Youngran Jang, Jinbeum Paik, Joonki Chung Ang Univ Grad Sch Adv Imaging Sci Multimedia & Film Image Proc & Intelligent Syst Lab Seoul South Korea

ISBN: (纸本)9781538630259

This paper presents a camera orientation estimation method based on 3-line RANSAC using motion vector in a driving straight ahead vehicle. The proposed method consists of three steps: i) motion vector and z-axis vanishing point estimation using feature extraction and matching, ii) line detection and classification using 3-line RANSAC algorithm, and iii) camera orientation estimation with vanishing points using classified lines. The experimental result shows proposed method effectively estimate camera orientation parameter. Therefore, the proposed method can be applied in vehicle systems for automatic driving assistance system.

关键词： Cameras Estimation Calibration Classification algorithms Parallel processing computer vision Real-time systems

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Robust pose recognition using deep learning

Robust pose recognition using deep learning

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international conference on computer vision and image processing, cvip 2016

作者： Mohanty, Aparna Ahmed, Alfaz Goswami, Trishita Das, Arpita Vaishnavi, Pratik Sahay, Rajiv Ranjan Department of Electrical Engineering Indian Institute of Technology Kharagpur Kharagpur India Department of Computer Science and Technology Indian Institute of Engineering Science and Technology Shibpur Kolkata India Sardar Vallabhai National Institute of Technology Surat Surat India

ISBN: (纸本)9789811021060

Current pose estimation methods make unrealistic assumptions regarding the body postures. Here, we seek to propose a general scheme which does not make assumptions regarding the relative position of body parts. Practitioners of Indian classical dances such as Bharatnatyam often enact several dramatic postures called Karanas. However, several challenges such as long flowing dresses of dancers, occlusions, change of camera viewpoint, poor lighting etc. affect the performance of state of- the-art pose estimation algorithms [1, 2] adversely. Body postures enacted by practitioners performing Yoga also violate the assumptions used in current techniques for estimating pose. In this work, we adopt an image recognition approach to tackle this problem. We propose a dataset consisting of 864 images of 12 Karanas captured under controlled laboratory conditions and 1260 real-world images of 14 Karanas obtained from Youtube videos for Bharatnatyam. We also created a new dataset consisting of 400 real-world images of 8 Yoga postures. We use two deep learning methodologies, namely, convolutional neural network (CNN) and stacked auto encoder (SAE) and demonstrate that both these techniques achieve high recognition rates on the proposed datasets. © Springer Science+Business Media Singapore 2017.

关键词： image recognition

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