Proceedings of the 2nd International Conference on Biomedical Signal and Image Processing最新文献

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Classification of Handwritten Tamil Characters in Palm Leaf Manuscripts Using SVM Based Smart Zoning Strategies 基于支持向量机智能分区策略的棕榈叶手写体泰米尔文字分类
R. S. Sabeenian, M. Paramasivam, P. M. Dinesh, R. Adarsh, Gokul Ravi Kumar
{"title":"Classification of Handwritten Tamil Characters in Palm Leaf Manuscripts Using SVM Based Smart Zoning Strategies","authors":"R. S. Sabeenian, M. Paramasivam, P. M. Dinesh, R. Adarsh, Gokul Ravi Kumar","doi":"10.1145/3133793.3133804","DOIUrl":"https://doi.org/10.1145/3133793.3133804","url":null,"abstract":"Palm leaf manuscripts has been one of the ancient methods for writingbut with timethe quality of palm leaf manuscripts degrades and the content needs to be scribed in new set of leaves. In this paper, we have presented an alternate solution to save the Tamil literature contents in palm leaf manuscripts by identifying the handwritten Tamil characters in the manuscripts and storing them digitally. It includes extraction of features like perimeter, Euler number (4,8), Directional features from uniform zones and Zernike moments by dividing the image into different smart zones. The efficiency for character recognition is calculated by using confusion matrix from classification learner tool.","PeriodicalId":217183,"journal":{"name":"Proceedings of the 2nd International Conference on Biomedical Signal and Image Processing","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130825731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 7
Pattern Recognition in Thought-Form Images Using Radon Transform and Histograms 基于Radon变换和直方图的思想形态图像模式识别
R. S. Prasad, Shishir Prasad, V. Prasad
{"title":"Pattern Recognition in Thought-Form Images Using Radon Transform and Histograms","authors":"R. S. Prasad, Shishir Prasad, V. Prasad","doi":"10.1145/3133793.3133806","DOIUrl":"https://doi.org/10.1145/3133793.3133806","url":null,"abstract":"Nature and behavior of human beings are reflected in their Thought-forms which have been shown as inseparable part of human biofield. In scientific experiments so far, only detection and measurement of biophotons has been possible. Experiments have shown the emission of ultraweak biophotons in a spectrum of several colors. The challenges faced in capturing the image of ultraweak photons are today the topic of research for the development of a biophotonic camera. A literature survey on thought form images found a large number of true color images in Theosophical texts published a hundred years ago. Each image was attributed comments of 'Very Good, Good, Bad, or Very Bad, or mix of all' based on the three aspects of Color, Form (Shape), and Outline of the images but without any scientific proof. It was found that there is great deal of similarity of views of Theosophists and Biophysicists on the structural form of human beings. This provided the motivation to analyze the thought form images on the aspect of color using HSV (Hue Saturation Value) space in two recently reported papers. In this paper, the second aspect of shape, has been investigated using Radon Transform and Histograms on a sample of thirty one thought-form images out of several from Theosophical literature. Results show that all images, except one, were classified correctly in the four patterns when compared with the comments in the text.","PeriodicalId":217183,"journal":{"name":"Proceedings of the 2nd International Conference on Biomedical Signal and Image Processing","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125825702","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 5
Comparison of Rotavirus A and Rotavirus D Segment 1 Using Apriori Algorithm, Decision Tree, and Support Vector Machine (SVM) 基于Apriori算法、决策树和支持向量机的轮状病毒A和轮状病毒D片段1的比较
Yeojin Jung, Yejin Jeong, Taeseon Yoon, Naeun Lee
{"title":"Comparison of Rotavirus A and Rotavirus D Segment 1 Using Apriori Algorithm, Decision Tree, and Support Vector Machine (SVM)","authors":"Yeojin Jung, Yejin Jeong, Taeseon Yoon, Naeun Lee","doi":"10.1145/3133793.3133797","DOIUrl":"https://doi.org/10.1145/3133793.3133797","url":null,"abstract":"Rotaviruses are the viruses that commonly cause gastroenteritis especially among infants and young children worldwide. Symptoms of rotavirus infection include diarrhea, fever, vomiting and dehydration. There are eight species of this virus: A, B, C, D, E, F, G and H. Among them, Rotavirus A is the most common species that cause more than 90% of rotavirus infections in humans whereas Rotavirus D is exclusively found in birds. We harbored suspicion on the factor that causes the difference in infection organisms of two viruses and attempted to compare and contrast segment 1 of Rotavirus A and D for deeper understanding of the specific difference in infection. In this study, we sought for any difference in genome and amino acid sequences between two viruses by applying three kinds of algorithms: Apriori algorithm, Decision Tree, and Support Vector Machine(SVM). Based on the results derived from these algorithms, we concluded that the functional difference in infection originates from the significant distribution of amino acids. Discovery of this relationship between frequencies of amino acids and differences in two viruses and especially characteristics of avian viral infections will contribute to the enhancement of the understanding rotavirus itself, and moreover, development of relevant vaccination for relevant forms of mutation.","PeriodicalId":217183,"journal":{"name":"Proceedings of the 2nd International Conference on Biomedical Signal and Image Processing","volume":"121 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134486322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Automatic Identification of Circulating Tumor Cells in Fluorescence Microscopy Images Based on ANN 基于神经网络的荧光显微图像循环肿瘤细胞自动识别
Kouki Tsuji, Huimin Lu, J. Tan, Hyoungseop Kim, K. Yoneda, F. Tanaka
{"title":"Automatic Identification of Circulating Tumor Cells in Fluorescence Microscopy Images Based on ANN","authors":"Kouki Tsuji, Huimin Lu, J. Tan, Hyoungseop Kim, K. Yoneda, F. Tanaka","doi":"10.1145/3133793.3133798","DOIUrl":"https://doi.org/10.1145/3133793.3133798","url":null,"abstract":"Circulating tumor cells (CTCs) are a useful biomarker since they may have some information about cancer metastasis. The blood from cancer patient is analyzed by a fluorescence microscope. It takes a large number of photos for each case, and many cells are contained in the microscopy images. Thus, analyzing them is hard work for pathologists. This work tends to depend on the individual skill of pathologist so misdiagnosis may be happen. In this paper, we develop an automatic CTCs identification method in fluorescence microscopy images based on artificial neural network. We applied our proposed method to 5040 microscopy images (6 cases), and evaluated the effectiveness of our method by using leave-one-out cross validation. We achieve a true positive rate of 98.65 [%] and a false positive rate of 18.24 [%].","PeriodicalId":217183,"journal":{"name":"Proceedings of the 2nd International Conference on Biomedical Signal and Image Processing","volume":"209 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123384926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
SAM Filter Based Convolution Neural Network Alogrithm for Leukocyte Classification 基于SAM滤波的卷积神经网络白细胞分类算法
Qinming Zhang, Xiyue Hou, Mei Zhou, Song Qiu, Li Sun, Hongying Liu, Qingli Li, Yiting Wang
{"title":"SAM Filter Based Convolution Neural Network Alogrithm for Leukocyte Classification","authors":"Qinming Zhang, Xiyue Hou, Mei Zhou, Song Qiu, Li Sun, Hongying Liu, Qingli Li, Yiting Wang","doi":"10.1145/3133793.3133800","DOIUrl":"https://doi.org/10.1145/3133793.3133800","url":null,"abstract":"In biomedical field, the analysis of red blood cells (RBC) and white blood cells (WBC) were of vital importance for diagnosing diseases. As for WBC, it can be classified into basophils (B), lymphocytes (L), neutrophils (N), monocytes (M), and eosinophils (E) five components. Based on varieties methods of hyperspectral imaging, a novel white blood cell classification method, which was a new implementation algorithm in the field of medical research, was designed by three main blocks: the realization of spectral angle match algorithm, morphological processing method and basic structure of the convolution neural network system. In the case of basophils, eosinophils, lymphocyte and neutrophils, the classifications accuracies were 95.3%, 93.2%, 90.8%, 92.7% respectively, improved by nearly 10% with respect to the SAM-only cases.","PeriodicalId":217183,"journal":{"name":"Proceedings of the 2nd International Conference on Biomedical Signal and Image Processing","volume":"475 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133557689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Design and Evaluation of the Lower-limb Robotic Orthosis for Gait Rehabilitation Actuated by Pneumatic Artificial Muscle 气动人工肌肉驱动下肢机器人矫形器的设计与评价
Q. Dao, Moriko Hagiwara, Shin-ichiroh Yamamoto
{"title":"Design and Evaluation of the Lower-limb Robotic Orthosis for Gait Rehabilitation Actuated by Pneumatic Artificial Muscle","authors":"Q. Dao, Moriko Hagiwara, Shin-ichiroh Yamamoto","doi":"10.1145/3133793.3133810","DOIUrl":"https://doi.org/10.1145/3133793.3133810","url":null,"abstract":"In this study, a robotic orthosis for lower-limb rehabilitation training is developed. The robot includes two hip and knee joints. Each joint is actuated by a pneumatic artificial muscle (PAM) in an antagonistic configuration. The bi-articular muscles are used to increase the stiffness of robotic orthosis. The robotic orthosis is evaluated not only by comparing to the normal human walking but also in trajectory tracking control mode. The experiment results show that the angle trajectory of the robotic orthosis is closed to the trajectory of normal human walking and it can also guide the subject to it designated trajectory.","PeriodicalId":217183,"journal":{"name":"Proceedings of the 2nd International Conference on Biomedical Signal and Image Processing","volume":"293 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117350062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
Player Trajectory Reconstruction from Broadcast Basketball Video 基于广播篮球视频的球员轨迹重建
Liang-Hua Chen, Hsin-Wen Chang, Hsiang-An Hsiao
{"title":"Player Trajectory Reconstruction from Broadcast Basketball Video","authors":"Liang-Hua Chen, Hsin-Wen Chang, Hsiang-An Hsiao","doi":"10.1145/3133793.3133801","DOIUrl":"https://doi.org/10.1145/3133793.3133801","url":null,"abstract":"To increase the performance of sport team, the tactics analysis of team from game video is essential. Trajectories of the players are the most useful cues in a sport video for tactics analysis. In this paper, we propose a technique to reconstruct the trajectories of players from broadcast basketball videos. We first propose a mosaic based approach to detect the boundary lines of court. Then, the locations of players are determined by the integration of shape and color visual information. A layered graph is constructed for the detected players, which includes all possible trajectories. A dynamic programming based algorithm is applied to find the trajectory of each player. Finally, the trajectories of players are displayed on a standard basketball court model by a homography transformation. In contrast to related works, our approach exploits more spatio-temporal information in video. Experimental results show that the proposed approach works well and outperforms some existing technique.","PeriodicalId":217183,"journal":{"name":"Proceedings of the 2nd International Conference on Biomedical Signal and Image Processing","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124981353","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
Proceedings of the 2nd International Conference on Biomedical Signal and Image Processing 第二届生物医学信号与图像处理国际会议论文集
{"title":"Proceedings of the 2nd International Conference on Biomedical Signal and Image Processing","authors":"","doi":"10.1145/3133793","DOIUrl":"https://doi.org/10.1145/3133793","url":null,"abstract":"","PeriodicalId":217183,"journal":{"name":"Proceedings of the 2nd International Conference on Biomedical Signal and Image Processing","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115771809","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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