Pub Date : 2018-12-01DOI: 10.1109/DICTA.2018.8615850
Sui Paul Ang, S. L. Phung, M. Schira, A. Bouzerdoum, S. T. Duong
Accurate segmentation of different brain tissue types is an important step in the study of neuronal activities using functional magnetic resonance imaging (fMRI). Traditionally, due to the low spatial resolution of fMRI data and the absence of an automated segmentation approach, human experts often resort to superimposing fMRI data on high resolution structural MRI images for analysis. The recent advent of fMRI with higher spatial resolutions offers a new possibility of differentiating brain tissues by their spatio-temporal characteristics, without relying on the structural MRI images. In this paper, we propose a patch-wise deep learning method for segmenting human brain tissues into five types, which are gray matter, white matter, blood vessel, non-brain and cerebrospinal fluid. The proposed method achieves a classification rate of 84.04% and a Dice similarity coefficient of 76.99%, which exceed those by several other methods.
{"title":"Human Brain Tissue Segmentation in fMRI using Deep Long-Term Recurrent Convolutional Network","authors":"Sui Paul Ang, S. L. Phung, M. Schira, A. Bouzerdoum, S. T. Duong","doi":"10.1109/DICTA.2018.8615850","DOIUrl":"https://doi.org/10.1109/DICTA.2018.8615850","url":null,"abstract":"Accurate segmentation of different brain tissue types is an important step in the study of neuronal activities using functional magnetic resonance imaging (fMRI). Traditionally, due to the low spatial resolution of fMRI data and the absence of an automated segmentation approach, human experts often resort to superimposing fMRI data on high resolution structural MRI images for analysis. The recent advent of fMRI with higher spatial resolutions offers a new possibility of differentiating brain tissues by their spatio-temporal characteristics, without relying on the structural MRI images. In this paper, we propose a patch-wise deep learning method for segmenting human brain tissues into five types, which are gray matter, white matter, blood vessel, non-brain and cerebrospinal fluid. The proposed method achieves a classification rate of 84.04% and a Dice similarity coefficient of 76.99%, which exceed those by several other methods.","PeriodicalId":130057,"journal":{"name":"2018 Digital Image Computing: Techniques and Applications (DICTA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120974070","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}
Pub Date : 2018-12-01DOI: 10.1109/DICTA.2018.8615773
G. Belous, Andrew Busch, D. Rowlands, Yongsheng Gao
Accurate delineation of the left ventricle (LV) endocardial border in echocardiography is of vital importance for the diagnosis and treatment of heart disease. Effective segmentation of the LV is challenging due to low contrast, signal dropout and acoustic noise. In the situation where low level and region-based image cues are unable to define the LV boundary, shape prior models are critical to infer shape. These models perform well when there is low variability in the underlying shape subspace and the shape instance produced by appearance cues does not contain gross errors, however in the absence of these conditions results are often much poorer. In this paper, we first propose a shape model to overcome the problem of modelling complex shape subspaces. Our method connects the implicit relationship between image features and shape by extending graph regularized sparse nonnegative matrix factorization (NMF) to jointly learn the structure and connection between two low dimensional manifolds comprising image features and shapes, respectively. We extend conventional NMF learning to an online learning-based approach where the input image is used to leverage the learning and connection of each manifold to the most relevant subspace regions. This ensures robust shape inference and a shape model constructed from contextually relevant shapes. A fully automatic segmentation approach using a probabilistic framework is then proposed to detect the LV endocardial border. Our method is applied to a diverse dataset that contains multiple views of the LV. Results show the effectiveness of our approach compared to state-of-the-art methods.
{"title":"Online Relational Manifold Learning for Multiview Segmentation in Echocardiography","authors":"G. Belous, Andrew Busch, D. Rowlands, Yongsheng Gao","doi":"10.1109/DICTA.2018.8615773","DOIUrl":"https://doi.org/10.1109/DICTA.2018.8615773","url":null,"abstract":"Accurate delineation of the left ventricle (LV) endocardial border in echocardiography is of vital importance for the diagnosis and treatment of heart disease. Effective segmentation of the LV is challenging due to low contrast, signal dropout and acoustic noise. In the situation where low level and region-based image cues are unable to define the LV boundary, shape prior models are critical to infer shape. These models perform well when there is low variability in the underlying shape subspace and the shape instance produced by appearance cues does not contain gross errors, however in the absence of these conditions results are often much poorer. In this paper, we first propose a shape model to overcome the problem of modelling complex shape subspaces. Our method connects the implicit relationship between image features and shape by extending graph regularized sparse nonnegative matrix factorization (NMF) to jointly learn the structure and connection between two low dimensional manifolds comprising image features and shapes, respectively. We extend conventional NMF learning to an online learning-based approach where the input image is used to leverage the learning and connection of each manifold to the most relevant subspace regions. This ensures robust shape inference and a shape model constructed from contextually relevant shapes. A fully automatic segmentation approach using a probabilistic framework is then proposed to detect the LV endocardial border. Our method is applied to a diverse dataset that contains multiple views of the LV. Results show the effectiveness of our approach compared to state-of-the-art methods.","PeriodicalId":130057,"journal":{"name":"2018 Digital Image Computing: Techniques and Applications (DICTA)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128932684","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}
Pub Date : 2018-12-01DOI: 10.1109/DICTA.2018.8615804
Di Wu, N. Sharma, M. Blumenstein
Recognizing human actions from the video streams has become one of the very popular research areas in computer vision and deep learning in the recent years. Action recognition is wildly used in different scenarios in real life, such as surveillance, robotics, healthcare, video indexing and human-computer interaction. The challenges and complexity involved in developing a video-based human action recognition system are manifold. In particular, recognizing actions with similar gestures and describing complex actions is a very challenging problem. To address these issues, we study the problem of classifying human actions using Convolutional Neural Networks (CNN) and develop a hierarchical 3DCNN architecture for similar gesture recognition. The proposed model firstly combines similar gesture pairs into one class, and classify them along with all other class, as a stage-1 classification. In stage-2, similar gesture pairs are classified individually, which reduces the problem to binary classification. We apply and evaluate the developed models to recognize the similar human actions on the HMDB51 dataset. The result shows that the proposed model can achieve high performance in comparison to the state-of-the-art methods.
{"title":"Similar Gesture Recognition using Hierarchical Classification Approach in RGB Videos","authors":"Di Wu, N. Sharma, M. Blumenstein","doi":"10.1109/DICTA.2018.8615804","DOIUrl":"https://doi.org/10.1109/DICTA.2018.8615804","url":null,"abstract":"Recognizing human actions from the video streams has become one of the very popular research areas in computer vision and deep learning in the recent years. Action recognition is wildly used in different scenarios in real life, such as surveillance, robotics, healthcare, video indexing and human-computer interaction. The challenges and complexity involved in developing a video-based human action recognition system are manifold. In particular, recognizing actions with similar gestures and describing complex actions is a very challenging problem. To address these issues, we study the problem of classifying human actions using Convolutional Neural Networks (CNN) and develop a hierarchical 3DCNN architecture for similar gesture recognition. The proposed model firstly combines similar gesture pairs into one class, and classify them along with all other class, as a stage-1 classification. In stage-2, similar gesture pairs are classified individually, which reduces the problem to binary classification. We apply and evaluate the developed models to recognize the similar human actions on the HMDB51 dataset. The result shows that the proposed model can achieve high performance in comparison to the state-of-the-art methods.","PeriodicalId":130057,"journal":{"name":"2018 Digital Image Computing: Techniques and Applications (DICTA)","volume":"204 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131556502","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}
Pub Date : 2018-12-01DOI: 10.1109/DICTA.2018.8615847
Duyen Lam, Atul Sajjanhar
Heuristic evaluation, a systematic inspection, aims to find the usability problems in websites. Numerous sets of usability heuristics have been adopted for specific fields through the examination and the judgment of evaluators. Cultural heritage has drawn significant interest and needs thorough investigations in order to improve the interfaces of websites and help to promote cultural values of a country. An in-deep review of literature on user interface evaluations about cultural heritage is presented. We examine several aspects including cultural dimensions in interface design, cultural-based adaptive web design, and technologies for cultural heritage websites' interfaces. The findings are expected to be a foundation in designing archiving websites in the domain of cultural heritage.
{"title":"Heuristic Evaluations of Cultural Heritage Websites","authors":"Duyen Lam, Atul Sajjanhar","doi":"10.1109/DICTA.2018.8615847","DOIUrl":"https://doi.org/10.1109/DICTA.2018.8615847","url":null,"abstract":"Heuristic evaluation, a systematic inspection, aims to find the usability problems in websites. Numerous sets of usability heuristics have been adopted for specific fields through the examination and the judgment of evaluators. Cultural heritage has drawn significant interest and needs thorough investigations in order to improve the interfaces of websites and help to promote cultural values of a country. An in-deep review of literature on user interface evaluations about cultural heritage is presented. We examine several aspects including cultural dimensions in interface design, cultural-based adaptive web design, and technologies for cultural heritage websites' interfaces. The findings are expected to be a foundation in designing archiving websites in the domain of cultural heritage.","PeriodicalId":130057,"journal":{"name":"2018 Digital Image Computing: Techniques and Applications (DICTA)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132340546","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}
Pub Date : 2018-12-01DOI: 10.1109/DICTA.2018.8615787
S. Kabbour, Pierre-Yves Richard
Dealing with repetitive patterns in images proves to be difficult in Multiview structure from motion. Previous work in the field suggests that this problem can be solved by clearing inconsistent rotations in the visual graph that represents pairwise relations between images. So we present a simple and rather effective algorithm, to clear the graph based on cycles. While trying to generate all cycles within the graph is computationally impossible in most cases, we choose to verify only the cycles that we need, and without relying on the spanning tree method because it puts a big emphasis on certain edges.
{"title":"Clearing Multiview Structure Graph from Inconsistencies","authors":"S. Kabbour, Pierre-Yves Richard","doi":"10.1109/DICTA.2018.8615787","DOIUrl":"https://doi.org/10.1109/DICTA.2018.8615787","url":null,"abstract":"Dealing with repetitive patterns in images proves to be difficult in Multiview structure from motion. Previous work in the field suggests that this problem can be solved by clearing inconsistent rotations in the visual graph that represents pairwise relations between images. So we present a simple and rather effective algorithm, to clear the graph based on cycles. While trying to generate all cycles within the graph is computationally impossible in most cases, we choose to verify only the cycles that we need, and without relying on the spanning tree method because it puts a big emphasis on certain edges.","PeriodicalId":130057,"journal":{"name":"2018 Digital Image Computing: Techniques and Applications (DICTA)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124546928","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}
Pub Date : 2018-12-01DOI: 10.1109/DICTA.2018.8615816
Elham Etemad, Q. Gao
There are many applications such as augmented or mixed reality with limited training data and computing power which results in inapplicability of convolutional neural networks in those domains. In this method, we have extracted the perceptual edge map of the image and grouped its perceptual structure-based edge elements according to gestalt psychology. The connecting points of these groups, called curve partitioning points (CPPs), are descriptive areas of the image and are utilized for image representation. In this method, the global perceptual image features, and local image representation methods are combined to encode the image according to the generated bag of CPPs using the spatial pyramid matching. The experiments on multi-label and single-label datasets show the superiority of the proposed method.
{"title":"Image Representation using Bag of Perceptual Curve Features","authors":"Elham Etemad, Q. Gao","doi":"10.1109/DICTA.2018.8615816","DOIUrl":"https://doi.org/10.1109/DICTA.2018.8615816","url":null,"abstract":"There are many applications such as augmented or mixed reality with limited training data and computing power which results in inapplicability of convolutional neural networks in those domains. In this method, we have extracted the perceptual edge map of the image and grouped its perceptual structure-based edge elements according to gestalt psychology. The connecting points of these groups, called curve partitioning points (CPPs), are descriptive areas of the image and are utilized for image representation. In this method, the global perceptual image features, and local image representation methods are combined to encode the image according to the generated bag of CPPs using the spatial pyramid matching. The experiments on multi-label and single-label datasets show the superiority of the proposed method.","PeriodicalId":130057,"journal":{"name":"2018 Digital Image Computing: Techniques and Applications (DICTA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128797825","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}
Pub Date : 2018-12-01DOI: 10.1109/DICTA.2018.8615842
Son Anh Vo, J. Scanlan, L. Mirowski, P. Turner
This paper describes how conventional image processing techniques can be applied to the grading of Southern Rock Lobsters (SRL) to produce a high quality data layer which could be an input into product traceability. The research is part of a broader investigation into designing a low-cost biometric identification solution for use along the entire lobster supply chain. In approaching the image processing for lobster grading a key consideration is to develop a system capable of using low cost consumer grade cameras readily available in mobile phones. The results confirm that by combining a number of common techniques in computer vision it is possible to capture and process a set of valuable attributes from sampled lobster image including color, length, weight, legs and sex. By combining this image profile with other pre-existing data on catch location and landing port each lobster can be verifiably tracked along the supply chain journey to markets in China. The image processing research results achieved in the laboratory show high accuracy in measuring lobster carapace length that is vital for weight conversion calculations. The results also demonstrate the capability to obtain reliable values for average color, tail shape and number of legs on a lobster used in grading classifications. The findings are a major first step in the development of individual lobster biometric identification and will directly contribute to automating lobster grading in this valuable Australian fishery.
{"title":"Image Processing for Traceability: A System Prototype for the Southern Rock Lobster (SRL) Supply Chain","authors":"Son Anh Vo, J. Scanlan, L. Mirowski, P. Turner","doi":"10.1109/DICTA.2018.8615842","DOIUrl":"https://doi.org/10.1109/DICTA.2018.8615842","url":null,"abstract":"This paper describes how conventional image processing techniques can be applied to the grading of Southern Rock Lobsters (SRL) to produce a high quality data layer which could be an input into product traceability. The research is part of a broader investigation into designing a low-cost biometric identification solution for use along the entire lobster supply chain. In approaching the image processing for lobster grading a key consideration is to develop a system capable of using low cost consumer grade cameras readily available in mobile phones. The results confirm that by combining a number of common techniques in computer vision it is possible to capture and process a set of valuable attributes from sampled lobster image including color, length, weight, legs and sex. By combining this image profile with other pre-existing data on catch location and landing port each lobster can be verifiably tracked along the supply chain journey to markets in China. The image processing research results achieved in the laboratory show high accuracy in measuring lobster carapace length that is vital for weight conversion calculations. The results also demonstrate the capability to obtain reliable values for average color, tail shape and number of legs on a lobster used in grading classifications. The findings are a major first step in the development of individual lobster biometric identification and will directly contribute to automating lobster grading in this valuable Australian fishery.","PeriodicalId":130057,"journal":{"name":"2018 Digital Image Computing: Techniques and Applications (DICTA)","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117154461","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}
Pub Date : 2018-12-01DOI: 10.1109/DICTA.2018.8615814
Allan Pennings, I. Svalbe
The presence of salt and pepper noise in imaging is a common issue that needs to be overcome in image analysis. Many potential solutions to remove this noise have been discussed over the years, but these algorithms often make the common assumption that salt noise and pepper noise appear in equal densities. This is not necessarily the case. In this paper several filters are proposed and tested across a range of different salt to pepper ratios, which result in higher PSNR and SSIM when compared to other existing filters.
{"title":"A New Method for Removing Asymmetric High Density Salt and Pepper Noise","authors":"Allan Pennings, I. Svalbe","doi":"10.1109/DICTA.2018.8615814","DOIUrl":"https://doi.org/10.1109/DICTA.2018.8615814","url":null,"abstract":"The presence of salt and pepper noise in imaging is a common issue that needs to be overcome in image analysis. Many potential solutions to remove this noise have been discussed over the years, but these algorithms often make the common assumption that salt noise and pepper noise appear in equal densities. This is not necessarily the case. In this paper several filters are proposed and tested across a range of different salt to pepper ratios, which result in higher PSNR and SSIM when compared to other existing filters.","PeriodicalId":130057,"journal":{"name":"2018 Digital Image Computing: Techniques and Applications (DICTA)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125767391","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}
Pub Date : 2018-12-01DOI: 10.1109/DICTA.2018.8615763
Afsaneh Koohestani, P. Kebria, A. Khosravi, S. Nahavandi
Monitoring the drivers behaviour and detecting their awareness are of vital importance for road safety. Drivers distraction and low awareness are already known to be the main reason for accidents in the world. Distraction-related crashes have greatly increased in recent years due to the proliferation of communication, entertainment, and malfunctioning of driver assistance systems. Accordingly, there is a need for advanced systems to monitor the drivers behaviour and generate a warning if a degradation in a drivers performance is detected. The purpose of this study is to analyse the vehicle and drivers data to detect the onset of distraction. Physiological measurements, such as palm electrodermal activity, heart rate, breathing rate, and perinasal perspiration are analysed and applied for the development of the monitoring system. The dataset used in this research has these measurements for 68 healthy participants (35 male, 33 female/17 elderly, 51 young). These participants completed two driving sessions in a driving simulator, including the normal and loaded drive. In the loaded scenario, drivers were texting back words. The lane deviation of vehicle was recorded as the response variable. Different classification algorithms such as generalised linear, support vector model, K-nearest neighbour and random forest machines are implemented to classify the driver's performance based on input features. Prediction results indicate that random forest performs the best by achieving an area under the curve (AUC) of over 91%. It is also found that biographic features are not informative enough to analyse drivers performance while perinasal perspiration carries the most information.
{"title":"Drivers Performance Evaluation using Physiological Measurement in a Driving Simulator","authors":"Afsaneh Koohestani, P. Kebria, A. Khosravi, S. Nahavandi","doi":"10.1109/DICTA.2018.8615763","DOIUrl":"https://doi.org/10.1109/DICTA.2018.8615763","url":null,"abstract":"Monitoring the drivers behaviour and detecting their awareness are of vital importance for road safety. Drivers distraction and low awareness are already known to be the main reason for accidents in the world. Distraction-related crashes have greatly increased in recent years due to the proliferation of communication, entertainment, and malfunctioning of driver assistance systems. Accordingly, there is a need for advanced systems to monitor the drivers behaviour and generate a warning if a degradation in a drivers performance is detected. The purpose of this study is to analyse the vehicle and drivers data to detect the onset of distraction. Physiological measurements, such as palm electrodermal activity, heart rate, breathing rate, and perinasal perspiration are analysed and applied for the development of the monitoring system. The dataset used in this research has these measurements for 68 healthy participants (35 male, 33 female/17 elderly, 51 young). These participants completed two driving sessions in a driving simulator, including the normal and loaded drive. In the loaded scenario, drivers were texting back words. The lane deviation of vehicle was recorded as the response variable. Different classification algorithms such as generalised linear, support vector model, K-nearest neighbour and random forest machines are implemented to classify the driver's performance based on input features. Prediction results indicate that random forest performs the best by achieving an area under the curve (AUC) of over 91%. It is also found that biographic features are not informative enough to analyse drivers performance while perinasal perspiration carries the most information.","PeriodicalId":130057,"journal":{"name":"2018 Digital Image Computing: Techniques and Applications (DICTA)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123474926","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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