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IEEE 5th International Conference for Convergence in Technology (I2CT)

作者： Intwala, Nishi Banerjee, Arkav Meenakshi Gala, Nikhil SVKMs NMIMS Univ Mukesh Patel Sch Technol Management & Engn Mumbai Campus Mumbai 400056 Maharashtra India

ISBN: (纸本)9781538680759

People with hearing and speech impairments have to face a lot of difficulties while communicating with the general public. Being a minority, the sign language used by them is not known to a majority of people. In this paper, an Indian sign language converter was developed using a Convolutional Neural Network algorithm with the aim to classify the 26 letters of the Indian Sign Language into their equivalent alphabet letters by capturing a real time image of that sign and converting it to its text equivalent. First a database was created in various backgrounds and various image pre-processing techniques were used to make the database ready for feature extraction. After feature extraction, the images were fed into the CNN using the python software. Several real time images were tested to find the accuracy and efficiency. The results showed a 96% accuracy for the testing images and an accuracy of 87.69% for real time images.

关键词： Indian sign language Convolutional Neural Networks Transfer learning grabcut algorithm

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Fully automatic figure-ground segmentation algorithm based on deep convolutional neural network and grabcut

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IET IMAGE PROCESSING 2016年第12期10卷 937-942页

作者： Fu, Ruigang Li, Biao Gao, Yinghui Wang, Ping Natl Univ Def Technol ATR Key Lab Changsha Hunan Peoples R China

Figure-ground segmentation is used to extract the foreground from the background, where the foreground is usually defined as the region containing the most meaningful object of the image. In fact, the algorithms that take advantage of humancomputer interaction often attain better performance and they are based on the 'one-to-one' model. In this study, the authors present a novel algorithm for figure-ground segmentation based on the grabcut algorithm, which is a common segmentation algorithm that is user interactive. However, instead of a real user, they attempt to use a pre-trained deep convolutional neural network to interact with grabcut for completing its job successfully. Weizmann's segmentation evaluation database is used as the test dataset and the results show that their algorithm works well for figure-ground segmentation. While the previous automatic segmentation algorithms are required to rank their segments empirically in order to find the position of the foreground after the segmentation, their algorithm is fully automatic.

关键词： image segmentation convolution neural nets feature extraction human computer interaction deep convolutional neural network grabcut algorithm image object human–computer interaction fully automatic figure-ground segmentation algorithm Weizmann segmentation evaluation database

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Segmentation method for medical image based on improved grabcut

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INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY 2017年第4期27卷 383-390页

作者： Lu, Yu-Wei Jiang, Jian-Guo Qi, Mei-Bin Zhan, Shu Yang, Jie Hefei Univ Technol Sch Comp & Informat 193 Tunxi Rd Hefei 230009 Anhui Peoples R China Anhui Univ Tradit Chinese Med Sch Integrated Tradit & Western Med Dept Publ Hlth & Gen Med 103 Meishan Rd Hefei 230038 Anhui Peoples R China

Segmentation of medical images has a lot of interferences because of the low contrast and fuzzy boundaries. It's hard to get perfect effect using present image segmentation methods, so we put forward an improved algorithm based on grabcut and Gaussian mixture model (GMM) in this paper in order to obtain simplify interactive operation and better segmentation precision. We extend the grabcut approach in 2 respects. Firstly, the initial GMMs of foreground and background were obtained by training sets, which could improve the algorithm's convergence rate. Secondly, the segmentation was restricted by the figure of foreground from training. Experimental results showed that compared with the traditional grabcut algorithm, our proposed algorithm can simplify interactive operation (t=14.33, P<.01) and improve the segmentation speed (t=16.77, P<.01). In addition, in respect of segmentation precision, our proposed algorithm was obviously better than the traditional algorithms such as Graph Cut, grabcut and Lazy Snapping. (F=149.546, P<.01). The improved algorithm we proposed in this manuscript is especially suitable for processing large-scale medical images.

关键词： figure of foreground Gaussian Mixture model grabcut algorithm image segmentation training set

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Novel Contour Based Detection and grabcut Segmentation for Sign Language Recognition 2

Novel Contour Based Detection and GrabCut Segmentation for S...

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2nd IEEE International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET)

作者： Kumar, Malladi Sai Phani Lathasree, Veerapalli Karishma, S. N. IIT Kharagpur ATDC Kharagpur 721302 W Bengal India Infosys Ltd Hyderabad 500045 Andhra Prades India IIT Kharagpur Dept E&ECE Kharagpur 721302 W Bengal India

ISBN: (纸本)9781509044429

The main intention of this paper is to build an automatic computer aided hand gesture to voice conversion system for people suffering from Aphonia, a medical term for speech impairment. We have assumed the input gesture images given to the system as simple and complex depending on the background of the image. A novel contour based image segmentation algorithm is proposed in this paper to detect the boundary of the foreground from images with simple dark background. The traditional grabcut algorithm is employed for segmentation of foreground from images with complex background. This algorithm iteratively segments the image to extract the foreground accurately. The American Sign Language (ASL) 26 finger-spelled alphabet images are taken as the dataset for the two above mentioned algorithms. For the dataset that we have generated, it is observed from the results that contour based segmentation algorithm provides absolutely perfect results. The number of iterations required for grabcut algorithm to segment the foreground may vary from image to image depending on the background. Out of all the 26 gesture of alphabets, Q, R and S need 6 number of iterations at maximum. A minimum of 1 iteration is required for alphabets E, J and O. On an average, 3 iterations of grabcut algorithm is required to completely segment the foreground from images with complex background.

关键词： complex background contour based detection grabcut algorithm gesture segmentation American Sign Language

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Segmentation of White Blood Cells Image Using Adaptive Location and Iteration

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2017年第6期21卷 1644-1655页

作者： Liu, Yuehua Cao, Feilong Zhao, Jianwei Chu, Jianjun China Jiliang Univ Coll Sci Hangzhou 310018 Zhejiang Peoples R China Jiashan Jasdaq Med Device Corp Ltd Jiashan 314100 Peoples R China

Segmentation of white blood cells (WBCs) image is meaningful but challenging due to the complex internal characteristics of the cells and external factors, such as illumination and different microscopic views. This paper addresses two problems of the segmentation: WBC location and subimage segmentation. To locate WBCs, a method that uses multiple windows obtained by scoring multiscale cues to extract a rectangular region is proposed. In this manner, the location window not only covers the whole WBC completely, but also achieves adaptive adjustment. In the subimage segmentation, the subimages preprocessed from the location window with a replace procedure are taken as initialization, and the grabcut algorithm based on dilation is iteratively run to obtain more precise results. The proposed algorithm is extensively evaluated using a CellaVision dataset as well as a more challenging Jiashan dataset. Compared with the existing methods, the proposed algorithm is not only concise, but also can produce high-quality segmentations. The results demonstrate that the proposed algorithm consistently outperforms other location and segmentation methods, yielding higher recall and better precision rates.

关键词： Adaptive adjustment grabcut algorithm image segmentation location white blood cell (WBC)

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Classification of Indian Classical Dance Forms 8th

Classification of Indian Classical Dance Forms

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8th International Conference on Intelligent Human Computer Interaction (IHCI)

作者： Shubhangi Tiwary, Uma Shanker Indian Inst Informat Technol Dept Informat Technol Allahabad Uttar Pradesh India

ISBN: (纸本)9783319525037;9783319525020

The algorithm proposed in this paper aims to achieve pose recognition in Indian classical dance domain. Three different dance forms namely Bharatnatyam, Kathak and Odissi, all together with 15 poses have been considered for pose classification problem. An initial database is created consisting of 100 images and split into training and testing dataset. Hu moments have been chosen as the feature extraction technique to describe the shape context of an image since they are scale, translation and rotation invariant. To extract Hu moments from the image, the foreground and the background of the images must be separated. The resultant images are then converted to binary. Since it is a multiclass classification problem, SVM using one vs one approach as well as one vs all approach has been implemented and the results are contrasted with linear and RBF kernels for both the approaches.

关键词： Pose recognition Hu moments Multiclass SVM grabcut algorithm

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A High-Precision Fusion algorithm for Fabric Quality Inspection

A High-Precision Fusion Algorithm for Fabric Quality Inspect...

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2017 2nd International Conference on Computer Engineering,Information Science and Internet Technology(CII 2017)

作者： YUJUN CHEN LIMING WU XIN LI YANAN ZHAO School of mechanical and electrical engineering

In order to improve the accuracy of fabric texture defect detection,a combination of algorithm with grabcut and convolutional neural network is ***,a segmentation algorithm based on grab-cuts is used to locate and segment the defects in fabric images ***,the sample images are expanded to increasing the number of the training *** then,the convolutional neural network is optimized to learn the features of the fabric defects more efficiently,which make it suitable for fabric defect recognition and *** results shows that compared with other traditional algorithms,our model gets better performance with high accuracy of fabric defect detection.

关键词： Fabric defect detection grabcut algorithm convolutional neural network

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A Fast Segmentation algorithm of PET Images Based on Visual Saliency Model 2

A Fast Segmentation Algorithm of PET Images Based on Visual ...

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2nd International Conference on Intelligent Computing, Communication & Convergence (ICCC)

作者： Liu Lu Yu Xiaoting Ding Bo Harbin Univ Sci & Technol HRBUST Sch Comp & Technol Harbin 150080 Peoples R China

Owing to fogged image quality and similar gray scale of PET, the current main segmentation algorithm cannot give much attention to its effects and efficiency. Therefore, this paper presents a segmentation algorithm on the basis of visual saliency model of PET images. Firstly, manual operation substituted by optimized Itti visual saliency model distinguishes PET images in a fast way. Secondly, one should preprocess the salient images acquired, and then initialize the Gaussian mixture model of foreground and background area. Finally, the PET salient images are segmented by optimized grabcut algorithm, thus obtaining results. Compared with the other two algorithms, experimental results show that the proposed algorithm has some advantages in the simple operation, the efficient algorithm and the accurate results. At the same time, it effectively improves the efficiency of PET image segmentation and ensures the segmentation results. (C) 2016 The Authors. Published by Elsevier B.V.

关键词： PET Image Segmentation Itti Model grabcut algorithm

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Interactive Vision-based Intelligent System for Active Macfouling and Microfouling Detection on Hull

Interactive Vision-based Intelligent System for Active Macfo...

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OCEANS Conference

作者： Guo, J. Y. Chin, C. S. Clare, A. S. Ma, Maode Newcastle Univ Sch Marine Sci & Technol Newcastle Upon Tyne NE1 7RU Tyne & Wear England Nanyang Technol Univ Sch Elect & Elect Engn Nanyang Ave Singapore 639798 Singapore

ISBN: (纸本)9781467397247

A system to detect macfouling and microfouling on the ship hull is always challenging. The proposed system uses computer vision to analyze types and amount of fouling on the hull for subsequent analysis. The graphical-user-interface in a mobile microprocessor can be accessed and remotely controlled using platforms such as Windows and Android phone. Experiments have been tested on 40 different fouling images. The results show a mean accuracy of the fouling detection of approximately 79.97%, median of around 84.20% and a standard deviation of about 18.49% followed by the underwater experiments that give a static error of approximately 0.35% of the test area.

关键词： macrofouling microfouling image processing hull grabcut algorithm RaspberryPi graphical user interface

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A Fast Segmentation algorithm of PET Images Based on Visual Saliency Model

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Procedia Computer Science 2016年 92卷 361-370页

作者： Liu Lu Yu Xiaoting Ding Bo School of Computer and Technology Harbin University of Science & Technology (HRBUST) 150080 China

关键词： PET Image Segmentation Itti Model grabcut algorithm

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