Despite the progress in tongue body segmentation, it remains a challenge to correctly segment the tongue body from any tongue images. As one possible solution, conventional interactive segmentation methods usually are not tailored for tongue body segmentation, and suffer from several limitations like poor efficiency and less specificity. In this paper, we extend the GaborFM method, and propose an interactive tongue body segmentation (ITBS) method by considering three principles: convergence, efficiency, and ease of use. Using the segmentation result of GaborFM for initialization, the proposed method allows the user to modify the segmentation by simply drawing a line segment at the true tongue body contour. Then ITBS exploits the information provided by the user to update the masks and edges, and update contour using fast marching. Experimental results show that ITBS can obtain correct results after less than 0.5 times of interactions in average, and is much efficient than conventional interactive segmentation methods.
{"title":"Interactive Tongue Body Segmentation","authors":"Zhenchao Cui, Hongzhi Zhang, W. Zuo","doi":"10.1109/ICMB.2014.12","DOIUrl":"https://doi.org/10.1109/ICMB.2014.12","url":null,"abstract":"Despite the progress in tongue body segmentation, it remains a challenge to correctly segment the tongue body from any tongue images. As one possible solution, conventional interactive segmentation methods usually are not tailored for tongue body segmentation, and suffer from several limitations like poor efficiency and less specificity. In this paper, we extend the GaborFM method, and propose an interactive tongue body segmentation (ITBS) method by considering three principles: convergence, efficiency, and ease of use. Using the segmentation result of GaborFM for initialization, the proposed method allows the user to modify the segmentation by simply drawing a line segment at the true tongue body contour. Then ITBS exploits the information provided by the user to update the masks and edges, and update contour using fast marching. Experimental results show that ITBS can obtain correct results after less than 0.5 times of interactions in average, and is much efficient than conventional interactive segmentation methods.","PeriodicalId":273636,"journal":{"name":"2014 International Conference on Medical Biometrics","volume":"17 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114128530","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}
Lin Ma, Ying He, Haifeng Li, Naimin Li, David Zhang
This paper proposes a novel framework for iris image processing based on conformal geometric algebra (CGA) and Markov random field (MRF). Texture complexity and individual differences are two unique features of iris image, which bring many difficulties to automatic analysis and diagnosis. We propose a circle detection algorithm based on CGA for iris image segmentation. The algorithm is simple and has a wide scope of application. What's more, it can detect the inside and outside boundaries of iris simultaneously without any denoising. Then we propose a novel scheme for texture representation of iris image based on MRF. By learning the statistical texture differences of different pathological features, such as holes, cracks, a MRF based texture representation method shows different pathological regions in iris. Experimental results demonstrated that the proposed framework is very practical, provides a great help for subsequent diagnosis as well.
{"title":"A CGA-MRF Hybrid Method for Iris Texture Analysis and Modeling","authors":"Lin Ma, Ying He, Haifeng Li, Naimin Li, David Zhang","doi":"10.1109/ICMB.2014.8","DOIUrl":"https://doi.org/10.1109/ICMB.2014.8","url":null,"abstract":"This paper proposes a novel framework for iris image processing based on conformal geometric algebra (CGA) and Markov random field (MRF). Texture complexity and individual differences are two unique features of iris image, which bring many difficulties to automatic analysis and diagnosis. We propose a circle detection algorithm based on CGA for iris image segmentation. The algorithm is simple and has a wide scope of application. What's more, it can detect the inside and outside boundaries of iris simultaneously without any denoising. Then we propose a novel scheme for texture representation of iris image based on MRF. By learning the statistical texture differences of different pathological features, such as holes, cracks, a MRF based texture representation method shows different pathological regions in iris. Experimental results demonstrated that the proposed framework is very practical, provides a great help for subsequent diagnosis as well.","PeriodicalId":273636,"journal":{"name":"2014 International Conference on Medical Biometrics","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114737963","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}
The quintessence of the diagnosis in traditional Chinese medicine is syndrome differentiation and treatment. Syndrome differentiation consists of four methods: observing, hearing as well as smelling, asking, and touching. The examination of the observing is the most important procedure in the method of "tongue." In recent years, numerous medical studies have identified the close relations between sublingual veins and human organs. Therefore, sublingual pathological symptoms, as well as demographical information of patients, imply pathological changes in the organs, and early diagnosis is beneficial for early treatment. However, the diagnosis of sublingual pathological symptoms is usually influenced by the doctor's subjective interpretation, experience, and environmental factors. The results can easily be limited by subjective factors such as knowledge, experience, mentality, diagnostic techniques, color perception and interpretation. Different doctors may make different judgments on the same tongue, presenting less than desirable repeatability. Therefore, assisting doctors' diagnoses with scientific methods and standardizing the differentiating process to obtain reliable diagnoses and enhance the clinical applicability of Chinese medicine is an important issue. In its wake, this study aims to construct an Automatic Sublingual Vein Feature Extraction System based on image processing technologies to allow objective and quantified computer readings. The extraction of sublingual vein features mainly captures the back of the tongue and extract the sublingual vein area for feature expression analysis. Firstly, the patient's back of the tongue is photographed and color-graded to compensate for color distortion, and then the tongue-back area is extracted. This study extracts tongue-back imagery by analyzing the RGB color expression of the back of the tongue, lips, teeth and skin, translating it into the HSI color space easily perceived by the human eye, along with skin area removal, rectangle detection, teeth area removal, black area removal and control point detection. The captured tongue-back image goes through histogram equalization and hue shift to enhance color contrast. Sublingual veins are extracted through analyzing RGB color component shift, hues, saturation and brightness. Then the sublingual vein color information and positioning are used to differentiate hues, lengths and branches. Thinning analysis is used to determine the presence of varicose veins. At the same time, the surrounding features of sublingual veins, such as columnar vein, bubbly vein, petechiae and bloodshot, are extracted. The information regarding features and lingual vein conditions are integrated and analyzed for doctors' clinical reference. This study utilizes 199 lingual images for statistic testing and three lingual diagnostic experts in Chinese medicine for lingual reading. The accuracy for the extractions are: tongue back 86%, sublingual vein 80%, varicose veins 90%, b
{"title":"Automatic Sublingual Vein Feature Extraction System","authors":"Hung-Jen Lin, Yi-Jing Chen, Natsagdorj Damdinsuren, Tan-Hsu Tan, Tsung-Yu Liu, J. Chiang","doi":"10.1109/ICMB.2014.17","DOIUrl":"https://doi.org/10.1109/ICMB.2014.17","url":null,"abstract":"The quintessence of the diagnosis in traditional Chinese medicine is syndrome differentiation and treatment. Syndrome differentiation consists of four methods: observing, hearing as well as smelling, asking, and touching. The examination of the observing is the most important procedure in the method of \"tongue.\" In recent years, numerous medical studies have identified the close relations between sublingual veins and human organs. Therefore, sublingual pathological symptoms, as well as demographical information of patients, imply pathological changes in the organs, and early diagnosis is beneficial for early treatment. However, the diagnosis of sublingual pathological symptoms is usually influenced by the doctor's subjective interpretation, experience, and environmental factors. The results can easily be limited by subjective factors such as knowledge, experience, mentality, diagnostic techniques, color perception and interpretation. Different doctors may make different judgments on the same tongue, presenting less than desirable repeatability. Therefore, assisting doctors' diagnoses with scientific methods and standardizing the differentiating process to obtain reliable diagnoses and enhance the clinical applicability of Chinese medicine is an important issue. In its wake, this study aims to construct an Automatic Sublingual Vein Feature Extraction System based on image processing technologies to allow objective and quantified computer readings. The extraction of sublingual vein features mainly captures the back of the tongue and extract the sublingual vein area for feature expression analysis. Firstly, the patient's back of the tongue is photographed and color-graded to compensate for color distortion, and then the tongue-back area is extracted. This study extracts tongue-back imagery by analyzing the RGB color expression of the back of the tongue, lips, teeth and skin, translating it into the HSI color space easily perceived by the human eye, along with skin area removal, rectangle detection, teeth area removal, black area removal and control point detection. The captured tongue-back image goes through histogram equalization and hue shift to enhance color contrast. Sublingual veins are extracted through analyzing RGB color component shift, hues, saturation and brightness. Then the sublingual vein color information and positioning are used to differentiate hues, lengths and branches. Thinning analysis is used to determine the presence of varicose veins. At the same time, the surrounding features of sublingual veins, such as columnar vein, bubbly vein, petechiae and bloodshot, are extracted. The information regarding features and lingual vein conditions are integrated and analyzed for doctors' clinical reference. This study utilizes 199 lingual images for statistic testing and three lingual diagnostic experts in Chinese medicine for lingual reading. The accuracy for the extractions are: tongue back 86%, sublingual vein 80%, varicose veins 90%, b","PeriodicalId":273636,"journal":{"name":"2014 International Conference on Medical Biometrics","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123336599","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}
The color images produced by digital cameras are usually not in conformity with their inherent colors. This will seriously impact computer-aided facial image analysis because it is on the basis of accurate rendering of color information. To solve that, we propose a novel color correction framework. Firstly, we utilize 122 undistorted facial images to demarcate complexion gamut. Secondly, several training sets based on complexion gamut are compared experimentally for the selection of optimal training samples. Thirdly, we select an adaptive target device-independent color space for our facial images color correction task. Finally, we evaluate the performance of three most popular color correction algorithms in color science area, and select the most suitable one to build our final regression model. Compared with the previous work, our color correction framework is characterized by mission dependence and statistical reliability. Besides, its trained model has low complexity and high accuracy. All of these features make it effective for facial images color correction.
{"title":"A Novel Color Correction Framework for Facial Images","authors":"Jinling Niu, Changbo Zhao, Guozheng Li","doi":"10.1109/ICMB.2014.16","DOIUrl":"https://doi.org/10.1109/ICMB.2014.16","url":null,"abstract":"The color images produced by digital cameras are usually not in conformity with their inherent colors. This will seriously impact computer-aided facial image analysis because it is on the basis of accurate rendering of color information. To solve that, we propose a novel color correction framework. Firstly, we utilize 122 undistorted facial images to demarcate complexion gamut. Secondly, several training sets based on complexion gamut are compared experimentally for the selection of optimal training samples. Thirdly, we select an adaptive target device-independent color space for our facial images color correction task. Finally, we evaluate the performance of three most popular color correction algorithms in color science area, and select the most suitable one to build our final regression model. Compared with the previous work, our color correction framework is characterized by mission dependence and statistical reliability. Besides, its trained model has low complexity and high accuracy. All of these features make it effective for facial images color correction.","PeriodicalId":273636,"journal":{"name":"2014 International Conference on Medical Biometrics","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126240509","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}
This paper studies the principle of ECG signals applied to identification, particularly considers the case of users' ECG abnormal conditions. This paper presents an improved multi-template matching algorithm for identification, which can achieve good discrimination effects under ECG abnormality. Normal and abnormal ECG templates are constructed by QRS complex, the discrimination is based on the correlation coefficient of the testing data and template. We used 44 ECG data files from the MIT-BIH Arrhythmia Database (MITDB) to measure the performance of the algorithm, extracted normal templates in 18 data files as well as normal and abnormal templates in the remaining 26 data files. The experiment obtained an 88.06% accuracy of template matching, when considering the discrimination results of all the testing data belong to one user, the individual recognition accuracy reaches 100%. Experiments showed that the improved multi-template matching algorithm characterized by QRS complex can be used to identify individuals in the state of arrhythmia.
{"title":"Research on ECG Biometric in Cardiac Irregularity Conditions","authors":"Zhao Wang, Yue Zhang","doi":"10.1109/ICMB.2014.35","DOIUrl":"https://doi.org/10.1109/ICMB.2014.35","url":null,"abstract":"This paper studies the principle of ECG signals applied to identification, particularly considers the case of users' ECG abnormal conditions. This paper presents an improved multi-template matching algorithm for identification, which can achieve good discrimination effects under ECG abnormality. Normal and abnormal ECG templates are constructed by QRS complex, the discrimination is based on the correlation coefficient of the testing data and template. We used 44 ECG data files from the MIT-BIH Arrhythmia Database (MITDB) to measure the performance of the algorithm, extracted normal templates in 18 data files as well as normal and abnormal templates in the remaining 26 data files. The experiment obtained an 88.06% accuracy of template matching, when considering the discrimination results of all the testing data belong to one user, the individual recognition accuracy reaches 100%. Experiments showed that the improved multi-template matching algorithm characterized by QRS complex can be used to identify individuals in the state of arrhythmia.","PeriodicalId":273636,"journal":{"name":"2014 International Conference on Medical Biometrics","volume":"109 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130174785","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}
L. Lo, T. Cheng, Yi-Jing Chen, S. Natsagdorj, J. Chiang
This paper investigates discriminating tongue features to distinguish between early stage BC patients and normal persons via non-invaded methods, expecting to detect BC in the early stage and give treatment in time to increase the recovery rate and lower relapse rate. The tongue features for 67 breast cancer patients of 0 and 1 stages, and 70 normal persons are extracted by the Automatic Tongue Diagnosis System (ATDS) [4-6, 28-31]. A total of nine tongue features, namely, tongue color, tongue quality, tongue fissure, tongue fur, red dot, ecchymosis, tooth mark, saliva, and tongue shape are identified for each tongue. Features extracted are further sub-divided according to the areas located, i.e., spleen-stomach, liver-gall-left, liver-gall right, kidney, and heart-lung area. The purpose focuses on inducing significant tongue features (p<;0.05) to discriminate early-stage breast cancer patients from normal persons. The Mann-Whitney test shows that the amount of tongue fur (p = 0.024), maximum covering area of tongue fur (p = 0.009), thin tongue fur (p = 0.009), the average area of red dot (p = 0.049), the maximum area of red dot (p = 0.009), red dot in the spleen stomach area (p = 0.000), and red dot in the heart-lung area (p = 0.000) demonstrate significant differences. Next, the data collected are further classified into two groups. The training group consists of 57 early-stage breast cancer patients and 60 normal persons, while the testing group is composed of 10 early stage breast cancer patients and 10 normal persons. The logistic regression by utilizing these 7 tongue features with significant differences in Mann-Whitney test as factors is performed. In order to reduce the number of tongue features employed in prediction, we remove one of the 7 tongue features with lesser significant difference (p>0.05) and perform logistic regression twice. In the first time, we remove the maximum area of red dot (p = 0.266), and perform logistic regression. Among them, the amount of tongue fur (p = 0.000), the maximum covering area of tongue fur (p = 0.000), thin tongue fur (p = 0.008), the average area of red dot (p = 0.056), red dot in the spleen-stomach area (p = 0.005), red dot in the heart-lung area (p = 0.011) reveal independently significant meaning. In the second round, the average area of red dot (p = 0.056) is removed. The logistic regression shows that the amount of tongue fur (p = 0.001), the maximum covering area of tongue fur (p = 0.000), thin tongue fur (p = 0.007), red dot in the spleen-stomach area (p = 0.006), red dot in the heart-lung area (p = 0.003) reveal independently significant meaning. The tongue features of the testing group are employed in the aforementioned three models to test the power of significant tongue features identified in predicting early-stage breast cancer. An accuracy of 80%, 80% and 90% is reached on normal peoples by applying the 7, 6 and 5 significant tongue features obtained through Mann-Whitney test, respectiv
{"title":"Traditional Chinese Medicine Tongue Diagnosis Index of Early-Stage Breast Cancer","authors":"L. Lo, T. Cheng, Yi-Jing Chen, S. Natsagdorj, J. Chiang","doi":"10.1109/ICMB.2014.9","DOIUrl":"https://doi.org/10.1109/ICMB.2014.9","url":null,"abstract":"This paper investigates discriminating tongue features to distinguish between early stage BC patients and normal persons via non-invaded methods, expecting to detect BC in the early stage and give treatment in time to increase the recovery rate and lower relapse rate. The tongue features for 67 breast cancer patients of 0 and 1 stages, and 70 normal persons are extracted by the Automatic Tongue Diagnosis System (ATDS) [4-6, 28-31]. A total of nine tongue features, namely, tongue color, tongue quality, tongue fissure, tongue fur, red dot, ecchymosis, tooth mark, saliva, and tongue shape are identified for each tongue. Features extracted are further sub-divided according to the areas located, i.e., spleen-stomach, liver-gall-left, liver-gall right, kidney, and heart-lung area. The purpose focuses on inducing significant tongue features (p<;0.05) to discriminate early-stage breast cancer patients from normal persons. The Mann-Whitney test shows that the amount of tongue fur (p = 0.024), maximum covering area of tongue fur (p = 0.009), thin tongue fur (p = 0.009), the average area of red dot (p = 0.049), the maximum area of red dot (p = 0.009), red dot in the spleen stomach area (p = 0.000), and red dot in the heart-lung area (p = 0.000) demonstrate significant differences. Next, the data collected are further classified into two groups. The training group consists of 57 early-stage breast cancer patients and 60 normal persons, while the testing group is composed of 10 early stage breast cancer patients and 10 normal persons. The logistic regression by utilizing these 7 tongue features with significant differences in Mann-Whitney test as factors is performed. In order to reduce the number of tongue features employed in prediction, we remove one of the 7 tongue features with lesser significant difference (p>0.05) and perform logistic regression twice. In the first time, we remove the maximum area of red dot (p = 0.266), and perform logistic regression. Among them, the amount of tongue fur (p = 0.000), the maximum covering area of tongue fur (p = 0.000), thin tongue fur (p = 0.008), the average area of red dot (p = 0.056), red dot in the spleen-stomach area (p = 0.005), red dot in the heart-lung area (p = 0.011) reveal independently significant meaning. In the second round, the average area of red dot (p = 0.056) is removed. The logistic regression shows that the amount of tongue fur (p = 0.001), the maximum covering area of tongue fur (p = 0.000), thin tongue fur (p = 0.007), red dot in the spleen-stomach area (p = 0.006), red dot in the heart-lung area (p = 0.003) reveal independently significant meaning. The tongue features of the testing group are employed in the aforementioned three models to test the power of significant tongue features identified in predicting early-stage breast cancer. An accuracy of 80%, 80% and 90% is reached on normal peoples by applying the 7, 6 and 5 significant tongue features obtained through Mann-Whitney test, respectiv","PeriodicalId":273636,"journal":{"name":"2014 International Conference on Medical Biometrics","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129700407","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}
In this paper we propose a method to distinguish Healthy and Disease individuals through tongue image analysis, specifically via tongue geometry features with Sparse Representation Classifier (SRC). After a tongue is captured using our non-invasive device, it is first segmented to remove its background pixels. Thirteen geometry features based on areas, measurements, distances, and their ratios are then extracted from the tongue foreground pixels. These features then form two sub-dictionaries in the SRC process, a Healthy geometry feature sub-dictionary, and Disease geometry feature sub-dictionary. Experimental results are conducted on a dataset consisting of 130 Healthy and 130 Disease samples. Using all thirteen geometry features SRC achieved a sensitivity of 86.15%, a specificity of 72.31%, and an average accuracy of 79.23% at Healthy vs. Disease classification.
{"title":"Disease Detection Using Tongue Geometry Features with Sparse Representation Classifier","authors":"Han Zhang, Bob Zhang","doi":"10.1109/ICMB.2014.25","DOIUrl":"https://doi.org/10.1109/ICMB.2014.25","url":null,"abstract":"In this paper we propose a method to distinguish Healthy and Disease individuals through tongue image analysis, specifically via tongue geometry features with Sparse Representation Classifier (SRC). After a tongue is captured using our non-invasive device, it is first segmented to remove its background pixels. Thirteen geometry features based on areas, measurements, distances, and their ratios are then extracted from the tongue foreground pixels. These features then form two sub-dictionaries in the SRC process, a Healthy geometry feature sub-dictionary, and Disease geometry feature sub-dictionary. Experimental results are conducted on a dataset consisting of 130 Healthy and 130 Disease samples. Using all thirteen geometry features SRC achieved a sensitivity of 86.15%, a specificity of 72.31%, and an average accuracy of 79.23% at Healthy vs. Disease classification.","PeriodicalId":273636,"journal":{"name":"2014 International Conference on Medical Biometrics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128878286","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}
Image visual saliency detection without prior knowledge of image details is fundamental for many computer vision tasks including object recognition, image retrieval, and image segmentation. In order to achieve more accurate and quick detection, this paper proposed a novel global contrast method to generate full resolution saliency maps using Gaussian distribution model. Compared with existing methods, this developed algorithm could be implemented in real-time with a higher accuracy. After a reasonable estimation of the parameters in our method, comparison experiments were conducted with five typical algorithms, experimental results demonstrate our approach is faster than the current real time approaches and accurate in maintaining high quality.
{"title":"Real-Time Visual Saliency Detection Using Gaussian Distribution","authors":"Haoqian Wang, Chunlong Zhang, Xingzheng Wang","doi":"10.1109/ICMB.2014.41","DOIUrl":"https://doi.org/10.1109/ICMB.2014.41","url":null,"abstract":"Image visual saliency detection without prior knowledge of image details is fundamental for many computer vision tasks including object recognition, image retrieval, and image segmentation. In order to achieve more accurate and quick detection, this paper proposed a novel global contrast method to generate full resolution saliency maps using Gaussian distribution model. Compared with existing methods, this developed algorithm could be implemented in real-time with a higher accuracy. After a reasonable estimation of the parameters in our method, comparison experiments were conducted with five typical algorithms, experimental results demonstrate our approach is faster than the current real time approaches and accurate in maintaining high quality.","PeriodicalId":273636,"journal":{"name":"2014 International Conference on Medical Biometrics","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127103753","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}
Charles Z. Liew, Raymond Shaw, Lanlan Li, Yongqiang Yang
This paper discusses the information security issue on biometric data. A brief survey is given at first with the discussion on the significance of biometric application. A novel encrypting strategy combined with Bernoulli-Logistic chaotic cipher system is proposed to improve the performance on cryptographic text with consideration both of volatility and correlation. Experimental results show that the proposed approach for encryption provides an efficient and more secure performance.
{"title":"Survey on Biometric Data Security and Chaotic Encryption Strategy with Bernoulli Mapping","authors":"Charles Z. Liew, Raymond Shaw, Lanlan Li, Yongqiang Yang","doi":"10.1109/ICMB.2014.37","DOIUrl":"https://doi.org/10.1109/ICMB.2014.37","url":null,"abstract":"This paper discusses the information security issue on biometric data. A brief survey is given at first with the discussion on the significance of biometric application. A novel encrypting strategy combined with Bernoulli-Logistic chaotic cipher system is proposed to improve the performance on cryptographic text with consideration both of volatility and correlation. Experimental results show that the proposed approach for encryption provides an efficient and more secure performance.","PeriodicalId":273636,"journal":{"name":"2014 International Conference on Medical Biometrics","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122991673","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}
Over the last years, there has been an increasing research interest in the application of medical biometrics data for many kinds of automated recognition algorithms. The need for more security and health monitoring is increasing with new functionalities and features made available. To improve different device/application security and health monitoring we propose an stable biometric electrocardiography (ECG) recognition approach with a stable cycle detection mechanism and comparison algorithm. Unlike previous work on wearable ECG recognition, which was based from cabled non-wireless systems, this paper reports new wireless technology and techniques for which can improve the performance, by using simple and low cost approaches. Pre-processing, cycle detection and recognition analysis were applied to the ECG signal. The performance of the system was evaluated having 30 volunteers (5 sessions per volunteer) and resulted in an equal error rate (EER) less than 1%.
{"title":"Real-Time Wireless ECG Biometrics with Mobile Devices","authors":"M. Derawi, Iu. M. Voitenko, Pal Erik Endrerud","doi":"10.1109/ICMB.2014.34","DOIUrl":"https://doi.org/10.1109/ICMB.2014.34","url":null,"abstract":"Over the last years, there has been an increasing research interest in the application of medical biometrics data for many kinds of automated recognition algorithms. The need for more security and health monitoring is increasing with new functionalities and features made available. To improve different device/application security and health monitoring we propose an stable biometric electrocardiography (ECG) recognition approach with a stable cycle detection mechanism and comparison algorithm. Unlike previous work on wearable ECG recognition, which was based from cabled non-wireless systems, this paper reports new wireless technology and techniques for which can improve the performance, by using simple and low cost approaches. Pre-processing, cycle detection and recognition analysis were applied to the ECG signal. The performance of the system was evaluated having 30 volunteers (5 sessions per volunteer) and resulted in an equal error rate (EER) less than 1%.","PeriodicalId":273636,"journal":{"name":"2014 International Conference on Medical Biometrics","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121967381","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}
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