This paper presents a study that performs a statistical analysis of signal intensities of the cartilage using magnetic resonance images. The aim of the study is to investigate whether it is possible to differentiate cartilage that is normal and cartilage that has damage/lesions in a quantitative manner. Because damaged cartilage tends to have abnormally high signal intensities than that of normal cartilage in fast spin echo proton density weighted (PD) images, we hypothesize that there is a relationship between the signal intensities of the cartilage and the size of the damaged cartilage presence. Twelve MR data sets with different degrees of cartilage damage and five data sets of normal cartilage were used in this study. Femoral articular cartilage was manually segmented using PD images and the MR signal intensities of the cartilage were analyzed. Results show that there is a linear relationship between the difference in mean and median of the cartilage signals (mean-median) and the percentage of damaged cartilage presence (R2 = 0.799, p
本文提出了一项研究，利用磁共振图像对软骨的信号强度进行统计分析。本研究的目的是探讨是否可以定量区分正常软骨和有损伤/病变的软骨。由于在快速自旋回声质子密度加权(PD)图像中，受损软骨往往比正常软骨具有异常高的信号强度，因此我们假设软骨的信号强度与受损软骨的大小之间存在关系。本研究使用了12组不同程度软骨损伤的MR数据集和5组正常软骨的MR数据集。利用PD图像对股骨关节软骨进行人工分割，并对软骨的MR信号强度进行分析。结果表明，软骨信号的均值和中位数之差(均值-中位数)与软骨损伤的存在率呈线性关系(R2 = 0.799, p
{"title":"Differentiating Healthy Cartilage and Damaged Cartilage Using Magnetic Resonance Images in a Quantitative Manner","authors":"C. Poh, T. K. Chuah, K. Sheah","doi":"10.1109/DICTA.2010.98","DOIUrl":"https://doi.org/10.1109/DICTA.2010.98","url":null,"abstract":"This paper presents a study that performs a statistical analysis of signal intensities of the cartilage using magnetic resonance images. The aim of the study is to investigate whether it is possible to differentiate cartilage that is normal and cartilage that has damage/lesions in a quantitative manner. Because damaged cartilage tends to have abnormally high signal intensities than that of normal cartilage in fast spin echo proton density weighted (PD) images, we hypothesize that there is a relationship between the signal intensities of the cartilage and the size of the damaged cartilage presence. Twelve MR data sets with different degrees of cartilage damage and five data sets of normal cartilage were used in this study. Femoral articular cartilage was manually segmented using PD images and the MR signal intensities of the cartilage were analyzed. Results show that there is a linear relationship between the difference in mean and median of the cartilage signals (mean-median) and the percentage of damaged cartilage presence (R2 = 0.799, p","PeriodicalId":246460,"journal":{"name":"2010 International Conference on Digital Image Computing: Techniques and Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125882265","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 proposes a probabilistic model using conditional random field (CRF) for region labelling that encodes and exploits the spatial context of a region. Potential functions for a region depend on a combination of the labels of neighbouring regions as well as their relative location, and a set of typical neighbourhood configurations or prototypes. These are obtained by clustering neighbourhood configurations obtained from a set of annotated images. Inference is achieved by minimising the cost function defined over the CRF model using standard Markov Chain Monte Carlo (MCMC) technique. We validate our approach on a dataset of hand segmented and labelled images of buildings and show that the model outperforms similar such models that utilise either only contextual information or only non-contextual measures.
{"title":"CRF Based Region Classification Using Spatial Prototypes","authors":"M. Jahangiri, D. Heesch, M. Petrou","doi":"10.1109/DICTA.2010.92","DOIUrl":"https://doi.org/10.1109/DICTA.2010.92","url":null,"abstract":"This paper proposes a probabilistic model using conditional random field (CRF) for region labelling that encodes and exploits the spatial context of a region. Potential functions for a region depend on a combination of the labels of neighbouring regions as well as their relative location, and a set of typical neighbourhood configurations or prototypes. These are obtained by clustering neighbourhood configurations obtained from a set of annotated images. Inference is achieved by minimising the cost function defined over the CRF model using standard Markov Chain Monte Carlo (MCMC) technique. We validate our approach on a dataset of hand segmented and labelled images of buildings and show that the model outperforms similar such models that utilise either only contextual information or only non-contextual measures.","PeriodicalId":246460,"journal":{"name":"2010 International Conference on Digital Image Computing: Techniques and Applications","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125487665","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 first-order differential invariants of optic flow, namely divergence, curl, and deformation, provide useful shape indicators of objects passing through view. However, as differential quantities these are often difficult to extract reliably. In this paper we present a filter-based method for computing these invariants with sufficient accuracy to permit the construction of a partial scene model. The noise robustness of our method is analysed using both synthetic and real world images. We also demonstrate that the deformation of a dense optic flow field encodes sufficient information to reliably estimate surface orientations if viewer ego-motion is purely translational.
{"title":"Robust Extraction of Optic Flow Differentials for Surface Reconstruction","authors":"S. Fu, P. Kovesi","doi":"10.1109/DICTA.2010.85","DOIUrl":"https://doi.org/10.1109/DICTA.2010.85","url":null,"abstract":"The first-order differential invariants of optic flow, namely divergence, curl, and deformation, provide useful shape indicators of objects passing through view. However, as differential quantities these are often difficult to extract reliably. In this paper we present a filter-based method for computing these invariants with sufficient accuracy to permit the construction of a partial scene model. The noise robustness of our method is analysed using both synthetic and real world images. We also demonstrate that the deformation of a dense optic flow field encodes sufficient information to reliably estimate surface orientations if viewer ego-motion is purely translational.","PeriodicalId":246460,"journal":{"name":"2010 International Conference on Digital Image Computing: Techniques and Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130721751","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}
Facial expression is one way humans convey their emotional states. Accurate recognition of facial expressions via image analysis plays a vital role in perceptual human-computer interaction, robotics and online games. This paper focuses on recognising the smiling from the neutral facial expression. We propose a face alignment method to address the localisation error in existing face detection methods. In this paper, smiling and neutral facial expression are differentiated using a novel neural architecture that combines fixed and adaptive non-linear 2-D filters. The fixed filters are used to extract primitive features, whereas the adaptive filters are trained to extract more complex features for facial expression classification. The proposed approach is evaluated on the JAFFE database and it correctly aligns and crops all images, which is better than several existing methods evaluated on the same database. Our system achieves a classification rate of 99.0% for smiling versus neutral expressions.
{"title":"Automatic Recognition of Smiling and Neutral Facial Expressions","authors":"Peiyao Li, S. L. Phung, A. Bouzerdoum, F. Tivive","doi":"10.1109/DICTA.2010.103","DOIUrl":"https://doi.org/10.1109/DICTA.2010.103","url":null,"abstract":"Facial expression is one way humans convey their emotional states. Accurate recognition of facial expressions via image analysis plays a vital role in perceptual human-computer interaction, robotics and online games. This paper focuses on recognising the smiling from the neutral facial expression. We propose a face alignment method to address the localisation error in existing face detection methods. In this paper, smiling and neutral facial expression are differentiated using a novel neural architecture that combines fixed and adaptive non-linear 2-D filters. The fixed filters are used to extract primitive features, whereas the adaptive filters are trained to extract more complex features for facial expression classification. The proposed approach is evaluated on the JAFFE database and it correctly aligns and crops all images, which is better than several existing methods evaluated on the same database. Our system achieves a classification rate of 99.0% for smiling versus neutral expressions.","PeriodicalId":246460,"journal":{"name":"2010 International Conference on Digital Image Computing: Techniques and Applications","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125134973","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 proposes a graph-based method for segmentation of a text image using a selected colour-channel image. The text colour information usually presents a two polarity trend. According to the observation that the histogram distributions of the respective colour channel images are usually different from each other, we select the colour channel image with the histogram having the biggest distance between the two main peaks, which represents the main foreground colour strength and background colour strength respectively. The peak distance is estimated by the mean-shift procedure performed on each individual channel image. Then, a graph model is constructed on a selected channel image to segment the text image into foreground and background. The proposed method is tested on a public database, and its effectiveness is demonstrated by the experimental results.
{"title":"Graph-Based Text Segmentation Using a Selected Channel Image","authors":"Chao Zeng, W. Jia, Xiangjian He, Jie Yang","doi":"10.1109/DICTA.2010.95","DOIUrl":"https://doi.org/10.1109/DICTA.2010.95","url":null,"abstract":"This paper proposes a graph-based method for segmentation of a text image using a selected colour-channel image. The text colour information usually presents a two polarity trend. According to the observation that the histogram distributions of the respective colour channel images are usually different from each other, we select the colour channel image with the histogram having the biggest distance between the two main peaks, which represents the main foreground colour strength and background colour strength respectively. The peak distance is estimated by the mean-shift procedure performed on each individual channel image. Then, a graph model is constructed on a selected channel image to segment the text image into foreground and background. The proposed method is tested on a public database, and its effectiveness is demonstrated by the experimental results.","PeriodicalId":246460,"journal":{"name":"2010 International Conference on Digital Image Computing: Techniques and Applications","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133762248","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}
An automatic method for detection of mammographic masses is presented which utilizes statistical region merging for segmentation (SRM) and linear discriminant analysis (LDA) for classification. The performance of the scheme was evaluated on 36 images selected from the local database of mammograms and on 48 images taken from the Digital Database for Screening Mammography (DDSM). The Az value (area under the ROC curve) for classifying each region was 0.90 for the local dataset and 0.96 for the images from DDSM. Results indicate that SRM segmentation can form part of an robust and efficient basis for analysis of mammograms.
{"title":"Mammographic Mass Detection with Statistical Region Merging","authors":"M. Bajger, Fei Ma, Simon Williams, M. Bottema","doi":"10.1109/DICTA.2010.14","DOIUrl":"https://doi.org/10.1109/DICTA.2010.14","url":null,"abstract":"An automatic method for detection of mammographic masses is presented which utilizes statistical region merging for segmentation (SRM) and linear discriminant analysis (LDA) for classification. The performance of the scheme was evaluated on 36 images selected from the local database of mammograms and on 48 images taken from the Digital Database for Screening Mammography (DDSM). The Az value (area under the ROC curve) for classifying each region was 0.90 for the local dataset and 0.96 for the images from DDSM. Results indicate that SRM segmentation can form part of an robust and efficient basis for analysis of mammograms.","PeriodicalId":246460,"journal":{"name":"2010 International Conference on Digital Image Computing: Techniques and Applications","volume":"199 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134129941","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 new approach for reconstructing 3D curves from a sequence of 2D images taken by uncalibrated cameras. A curve in 3D space is represented by a sequence of 3D points sampled along the curve, and the 3D points are reconstructed by minimizing the distances from their projections to the measured 2D curves on different images (i.e., 2D curve reprojection error). The minimization problem is solved by an iterative algorithm which is guaranteed to converge to a (local) minimum of the 2D reprojection error. Without requiring calibrated cameras or additional point features, our method can reconstruct multiple 3D curves simultaneously from multiple images and it readily handles images with missing and/or partially occluded curves.
{"title":"3D Curves Reconstruction from Multiple Images","authors":"F. Mai, Y. Hung","doi":"10.1109/DICTA.2010.84","DOIUrl":"https://doi.org/10.1109/DICTA.2010.84","url":null,"abstract":"In this paper, we propose a new approach for reconstructing 3D curves from a sequence of 2D images taken by uncalibrated cameras. A curve in 3D space is represented by a sequence of 3D points sampled along the curve, and the 3D points are reconstructed by minimizing the distances from their projections to the measured 2D curves on different images (i.e., 2D curve reprojection error). The minimization problem is solved by an iterative algorithm which is guaranteed to converge to a (local) minimum of the 2D reprojection error. Without requiring calibrated cameras or additional point features, our method can reconstruct multiple 3D curves simultaneously from multiple images and it readily handles images with missing and/or partially occluded curves.","PeriodicalId":246460,"journal":{"name":"2010 International Conference on Digital Image Computing: Techniques and Applications","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129292498","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}
As digital imaging techniques continue to advance, new image compression standards are needed to keep the transmission time and storage space low for increasing image sizes. The Joint Photographic Expert Group (JPEG) fulfilled this need with the ratification of the JPEG2000 standard in December of 2000. JPEG2000 adds many features to image compression technology but also increases the computational complexity of traditional encoders. To mitigate the added computational complexity, the JPEG2000 algorithm allows processing parts in parallel, increasing the benefits of implementing the algorithm in application specific integrated circuits (ASICs) or field programmable gate arrays (FPGAs). A ¿exible FPGA implementation of the JPEG2000 binary arithmetic decoder, the core component of the JPEG2000 decoding algorithm, is presented in this paper. The proposed JPEG2000 binary arithmetic decoder reduces the amount of resources used on the FPGA allowing 17% more entropy block decoders to fit on chip and consequently increasing the throughput by 35% beyond previous designs.
{"title":"An Increased Throughput FPGA Design of the JPEG2000 Binary Arithmetic Decoder","authors":"D. Lucking, E. Balster","doi":"10.1109/DICTA.2010.74","DOIUrl":"https://doi.org/10.1109/DICTA.2010.74","url":null,"abstract":"As digital imaging techniques continue to advance, new image compression standards are needed to keep the transmission time and storage space low for increasing image sizes. The Joint Photographic Expert Group (JPEG) fulfilled this need with the ratification of the JPEG2000 standard in December of 2000. JPEG2000 adds many features to image compression technology but also increases the computational complexity of traditional encoders. To mitigate the added computational complexity, the JPEG2000 algorithm allows processing parts in parallel, increasing the benefits of implementing the algorithm in application specific integrated circuits (ASICs) or field programmable gate arrays (FPGAs). A ¿exible FPGA implementation of the JPEG2000 binary arithmetic decoder, the core component of the JPEG2000 decoding algorithm, is presented in this paper. The proposed JPEG2000 binary arithmetic decoder reduces the amount of resources used on the FPGA allowing 17% more entropy block decoders to fit on chip and consequently increasing the throughput by 35% beyond previous designs.","PeriodicalId":246460,"journal":{"name":"2010 International Conference on Digital Image Computing: Techniques and Applications","volume":"188 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133858398","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}
Active appearance model (AAM) representations have been used to great effect recently in the accurate detection of expression events (e.g., action units, pain, broad expressions, etc.). The motivation for their use, and rationale for their success, lies in their ability to: (i) provide dense (i.e. 60- 70 points on the face) registration accuracy on par with a human labeler, and (ii) the ability to decompose the registered face image to separate appearance and shape representations. Unfortunately, this human-like registration performance is isolated to registration algorithms that are specifically tuned to the illumination, camera and subject being tracked (i.e. "subject dependent'' algorithms). As a result, it is rare, to see AAM representations being employed in the far more useful "subject independent'' situations (i.e., where illumination, camera and subject is unknown) due to the inherent increased geometric noise present in the estimated registration. In this paper we argue that "AAM like'' expression detection results can be obtained in the presence of noisy dense registration through the employment of registration invariant representations (e.g., Gabor magnitudes and HOG features). We demonstrate that good expression detection performance can still be enjoyed over the types of geometric noise often encountered with the more geometrically noisy state of the art generic algorithms (e.g., Bayesian Tangent Shape Models (BTSM), Constrained Local Models (CLM), etc). We show these results on the extended Cohn-Kanade (CK+) database over all facial action units.
{"title":"Registration Invariant Representations for Expression Detection","authors":"P. Lucey, S. Lucey, J. Cohn","doi":"10.1109/DICTA.2010.53","DOIUrl":"https://doi.org/10.1109/DICTA.2010.53","url":null,"abstract":"Active appearance model (AAM) representations have been used to great effect recently in the accurate detection of expression events (e.g., action units, pain, broad expressions, etc.). The motivation for their use, and rationale for their success, lies in their ability to: (i) provide dense (i.e. 60- 70 points on the face) registration accuracy on par with a human labeler, and (ii) the ability to decompose the registered face image to separate appearance and shape representations. Unfortunately, this human-like registration performance is isolated to registration algorithms that are specifically tuned to the illumination, camera and subject being tracked (i.e. \"subject dependent'' algorithms). As a result, it is rare, to see AAM representations being employed in the far more useful \"subject independent'' situations (i.e., where illumination, camera and subject is unknown) due to the inherent increased geometric noise present in the estimated registration. In this paper we argue that \"AAM like'' expression detection results can be obtained in the presence of noisy dense registration through the employment of registration invariant representations (e.g., Gabor magnitudes and HOG features). We demonstrate that good expression detection performance can still be enjoyed over the types of geometric noise often encountered with the more geometrically noisy state of the art generic algorithms (e.g., Bayesian Tangent Shape Models (BTSM), Constrained Local Models (CLM), etc). We show these results on the extended Cohn-Kanade (CK+) database over all facial action units.","PeriodicalId":246460,"journal":{"name":"2010 International Conference on Digital Image Computing: Techniques and Applications","volume":"53-54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131004538","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 introduces the topic of appearance-based re-identification of people in video. This work is based on colour information of people’s clothing. Most of the work described in the literature uses full body histogram. This paper evaluates the histogram method and describes ways of including spatial colour information. The paper proposes a colour-based appearance descriptor called Colour Context People Descriptor. All the methods are evaluated extensively. The results are reported in the experiments. It is concluded at the end that adding spatial colour information greatly improves the re-identification results.
{"title":"Appearance-Based Re-identification of People in Video","authors":"Arif Khan, Jian Zhang, Yang Wang","doi":"10.1109/DICTA.2010.67","DOIUrl":"https://doi.org/10.1109/DICTA.2010.67","url":null,"abstract":"This paper introduces the topic of appearance-based re-identification of people in video. This work is based on colour information of people’s clothing. Most of the work described in the literature uses full body histogram. This paper evaluates the histogram method and describes ways of including spatial colour information. The paper proposes a colour-based appearance descriptor called Colour Context People Descriptor. All the methods are evaluated extensively. The results are reported in the experiments. It is concluded at the end that adding spatial colour information greatly improves the re-identification results.","PeriodicalId":246460,"journal":{"name":"2010 International Conference on Digital Image Computing: Techniques and Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130507403","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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