Fluid vector flow (FVF) is a recently developed edge-based parametric active contour model for segmentation. By keeping its merits of large capture range and ability to handle acute concave shapes, we improved the model from two aspects: edge leakage and control point selection. Experimental results of cavity segmentation in chest radiographs show that the proposed method provides at least 8% improvement over the original FVF method.
{"title":"An Improved Fluid Vector Flow for Cavity Segmentation in Chest Radiographs","authors":"Tao Xu, I. Cheng, M. Mandal","doi":"10.1109/ICPR.2010.824","DOIUrl":"https://doi.org/10.1109/ICPR.2010.824","url":null,"abstract":"Fluid vector flow (FVF) is a recently developed edge-based parametric active contour model for segmentation. By keeping its merits of large capture range and ability to handle acute concave shapes, we improved the model from two aspects: edge leakage and control point selection. Experimental results of cavity segmentation in chest radiographs show that the proposed method provides at least 8% improvement over the original FVF method.","PeriodicalId":309591,"journal":{"name":"2010 20th International Conference on Pattern Recognition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115677059","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}
Indexing into large database systems is essential for a number of applications. This paper presents a new indexing structure, which overcomes an important restriction of a previous indexing technique using a recently developed theorem from the domain of matrix analysis. Specifically, given a set of distance values computed by distance function, which do not necessarily satisfy the triangle inequality, this paper shows that computing its nearest distance values that obey the properties of a metric enables us to overcome the limitations of the previous indexing algorithm. We demonstrate the proposed framework in the context of a recognition task.
{"title":"Efficient Shape Retrieval Under Partial Matching","authors":"M. Demirci","doi":"10.1109/ICPR.2010.749","DOIUrl":"https://doi.org/10.1109/ICPR.2010.749","url":null,"abstract":"Indexing into large database systems is essential for a number of applications. This paper presents a new indexing structure, which overcomes an important restriction of a previous indexing technique using a recently developed theorem from the domain of matrix analysis. Specifically, given a set of distance values computed by distance function, which do not necessarily satisfy the triangle inequality, this paper shows that computing its nearest distance values that obey the properties of a metric enables us to overcome the limitations of the previous indexing algorithm. We demonstrate the proposed framework in the context of a recognition task.","PeriodicalId":309591,"journal":{"name":"2010 20th International Conference on Pattern Recognition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115570017","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}
Researches of image-based 3D reconstruction have recently produced a number of good results, but they assume that the accurate foreground to be reconstructed is already extracted from each input image. This paper proposes a novel approach to extract more accurate foregrounds by iteratively performing foreground extraction and 3D reconstruction in a manner similar to an EM algorithm on regions segmented in an initial stage, called segments. After definitively extracting the foregrounds in multi-views based on simply selecting segments corresponding to the real foreground in only one image, further improved foregrounds are extracted by back-projecting 3D objects reconstructed based on the foreground extracted in the previous step into segments of each image in multi-views. These two steps are iteratively performed until the energy function is optimized. In the experiments, more accurate boundaries were obtained, although the proposed method used a simple 3D reconstruction method.
{"title":"Segment-Based Foreground Extraction Dedicated to 3D Reconstruction","authors":"Jungwhan Kim, Anjin Park, K. Jung","doi":"10.1109/ICPR.2010.875","DOIUrl":"https://doi.org/10.1109/ICPR.2010.875","url":null,"abstract":"Researches of image-based 3D reconstruction have recently produced a number of good results, but they assume that the accurate foreground to be reconstructed is already extracted from each input image. This paper proposes a novel approach to extract more accurate foregrounds by iteratively performing foreground extraction and 3D reconstruction in a manner similar to an EM algorithm on regions segmented in an initial stage, called segments. After definitively extracting the foregrounds in multi-views based on simply selecting segments corresponding to the real foreground in only one image, further improved foregrounds are extracted by back-projecting 3D objects reconstructed based on the foreground extracted in the previous step into segments of each image in multi-views. These two steps are iteratively performed until the energy function is optimized. In the experiments, more accurate boundaries were obtained, although the proposed method used a simple 3D reconstruction method.","PeriodicalId":309591,"journal":{"name":"2010 20th International Conference on Pattern Recognition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122583234","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}
O. V. R. Murthy, Karthik Muthuswamy, D. Rajan, L. Chia
A simple algorithm for image retargeting in the compressed domain is proposed. Most existing retargeting algorithms work directly in the spatial domain of the raw image. Here, we work on the DCT coefficients of a JPEG-compressed image to generate a gradient map that serves as an importance map to help identify those parts in the image that need to be retained during the retargeting process. Each 8×8 block of DCT coefficients is scaled based on the least importance value. Retargeting can be done both in the horizontal and vertical directions with the same framework. We also illustrate image enlargement using the same method. Experimental results show that the proposed algorithm produces less distortion in the retargeted image compared to some other algorithms reported recently.
{"title":"Image Retargeting in Compressed Domain","authors":"O. V. R. Murthy, Karthik Muthuswamy, D. Rajan, L. Chia","doi":"10.1109/ICPR.2010.1075","DOIUrl":"https://doi.org/10.1109/ICPR.2010.1075","url":null,"abstract":"A simple algorithm for image retargeting in the compressed domain is proposed. Most existing retargeting algorithms work directly in the spatial domain of the raw image. Here, we work on the DCT coefficients of a JPEG-compressed image to generate a gradient map that serves as an importance map to help identify those parts in the image that need to be retained during the retargeting process. Each 8×8 block of DCT coefficients is scaled based on the least importance value. Retargeting can be done both in the horizontal and vertical directions with the same framework. We also illustrate image enlargement using the same method. Experimental results show that the proposed algorithm produces less distortion in the retargeted image compared to some other algorithms reported recently.","PeriodicalId":309591,"journal":{"name":"2010 20th International Conference on Pattern Recognition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127553572","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}
his paper presents a new method to detect slip and fall events by analyzing the integrated spatiotemporal energy (ISTE) map. ISTE map includes motion and time of motion occurrence as our motion feature. The extracted human shape is represented by an ellipse that provides crucial information of human motion activities. We use this features to detect the events in the video with non-fixed frame rate. This work assumes that the person lies on the ground with very little motion after the fall accident. Experimental results show that our method is effective for fall and slip detection.
{"title":"Slip and Fall Events Detection by Analyzing the Integrated Spatiotemporal Energy Map","authors":"Tim Liao, Chung-Lin Huang","doi":"10.1109/ICPR.2010.425","DOIUrl":"https://doi.org/10.1109/ICPR.2010.425","url":null,"abstract":"his paper presents a new method to detect slip and fall events by analyzing the integrated spatiotemporal energy (ISTE) map. ISTE map includes motion and time of motion occurrence as our motion feature. The extracted human shape is represented by an ellipse that provides crucial information of human motion activities. We use this features to detect the events in the video with non-fixed frame rate. This work assumes that the person lies on the ground with very little motion after the fall accident. Experimental results show that our method is effective for fall and slip detection.","PeriodicalId":309591,"journal":{"name":"2010 20th International Conference on Pattern Recognition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126304540","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}
To address the challenges in critical care monitoring, we present a multi-modality bio-signal modeling and analysis modeling framework for real-time human state classification and predication. The novel bioinformatic framework is developed to solve the human state classification and predication issues from two aspects: a) achieve 1:1 mapping between the bio-signal and the human state via discriminant feature analysis and selection by using probabilistic principle component analysis (PPCA); b) avoid time-consuming data analysis and extensive integration resources by using Dynamic Bayesian Network (DBN). In addition, intelligent and automatic selection of the most suitable sensors from the bio-sensor array is also integrated in the proposed DBN.
{"title":"Human State Classification and Predication for Critical Care Monitoring by Real-Time Bio-signal Analysis","authors":"Xiaokun Li, F. Porikli","doi":"10.1109/ICPR.2010.602","DOIUrl":"https://doi.org/10.1109/ICPR.2010.602","url":null,"abstract":"To address the challenges in critical care monitoring, we present a multi-modality bio-signal modeling and analysis modeling framework for real-time human state classification and predication. The novel bioinformatic framework is developed to solve the human state classification and predication issues from two aspects: a) achieve 1:1 mapping between the bio-signal and the human state via discriminant feature analysis and selection by using probabilistic principle component analysis (PPCA); b) avoid time-consuming data analysis and extensive integration resources by using Dynamic Bayesian Network (DBN). In addition, intelligent and automatic selection of the most suitable sensors from the bio-sensor array is also integrated in the proposed DBN.","PeriodicalId":309591,"journal":{"name":"2010 20th International Conference on Pattern Recognition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126407930","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 process of compressing the high luminance values into the displayable range inevitably incurs the loss of image contrasts. Although the local adaptation process, such as the two-scale contrast reduction scheme, is capable of preserving details during the HDR compression process, it cannot be used to enhance the local contrasts of image contents. Moreover, the effect of noise artifacts cannot be eliminated when the detail manipulation is subsequently performed. We propose a new tone reproduction scheme, which incorporates the local contrast enhancement and the noise suppression processes, for the display of HDR images. Our experimental results show that the proposed scheme is indeed effective in enhancing local contrasts of image contents and suppressing noise artifacts during the increase of the visibility of HDR scenes.
{"title":"Noise-Insensitive Contrast Enhancement for Rendering High-Dynamic-Range Images","authors":"Hsueh-Yi Sean Lin","doi":"10.1109/ICPR.2010.656","DOIUrl":"https://doi.org/10.1109/ICPR.2010.656","url":null,"abstract":"The process of compressing the high luminance values into the displayable range inevitably incurs the loss of image contrasts. Although the local adaptation process, such as the two-scale contrast reduction scheme, is capable of preserving details during the HDR compression process, it cannot be used to enhance the local contrasts of image contents. Moreover, the effect of noise artifacts cannot be eliminated when the detail manipulation is subsequently performed. We propose a new tone reproduction scheme, which incorporates the local contrast enhancement and the noise suppression processes, for the display of HDR images. Our experimental results show that the proposed scheme is indeed effective in enhancing local contrasts of image contents and suppressing noise artifacts during the increase of the visibility of HDR scenes.","PeriodicalId":309591,"journal":{"name":"2010 20th International Conference on Pattern Recognition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134109742","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}
We present a method for 3D shape reconstruction of inextensible deformable surfaces from monocular image sequences. The key of our approach is to represent the surface as 3D triangulated mesh and formulate the reconstruction problem as a sequence of Linear Programming (LP) problems which can be effectively solved. The LP problem consists of data constraints which are 3D-to-2D keypoint correspondences and shape constraints which prevent large changes of the edge orientation between consecutive frames. Furthermore, we use a refined bisection algorithm to accelerate the computing speed. The robustness and efficiency of our approach are validated on both synthetic and real data.
{"title":"Monocular 3D Tracking of Deformable Surfaces Using Linear Programming","authors":"Chenhao Wang, Xiong Li, Yuncai Liu","doi":"10.1109/ICPR.2010.423","DOIUrl":"https://doi.org/10.1109/ICPR.2010.423","url":null,"abstract":"We present a method for 3D shape reconstruction of inextensible deformable surfaces from monocular image sequences. The key of our approach is to represent the surface as 3D triangulated mesh and formulate the reconstruction problem as a sequence of Linear Programming (LP) problems which can be effectively solved. The LP problem consists of data constraints which are 3D-to-2D keypoint correspondences and shape constraints which prevent large changes of the edge orientation between consecutive frames. Furthermore, we use a refined bisection algorithm to accelerate the computing speed. The robustness and efficiency of our approach are validated on both synthetic and real data.","PeriodicalId":309591,"journal":{"name":"2010 20th International Conference on Pattern Recognition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131645084","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 a semi-supervised distance metric learning algorithm which uses pair-wise equivalence (similarity and dissimilarity) constraints to improve the original distance metric in lower-dimensional input spaces. We restrict ourselves to pseudo-metrics that are in quadratic forms parameterized by positive semi-definite matrices. The proposed method works in both the input space and kernel in-duced feature space, and learning distance metric is formulated as a quadratic optimization problem which returns a global optimal solution. Experimental results on several databases show that the learned distance metric improves the performances of the subsequent classification and clustering algorithms.
{"title":"Semi-supervised Distance Metric Learning by Quadratic Programming","authors":"Hakan Cevikalp","doi":"10.1109/ICPR.2010.818","DOIUrl":"https://doi.org/10.1109/ICPR.2010.818","url":null,"abstract":"This paper introduces a semi-supervised distance metric learning algorithm which uses pair-wise equivalence (similarity and dissimilarity) constraints to improve the original distance metric in lower-dimensional input spaces. We restrict ourselves to pseudo-metrics that are in quadratic forms parameterized by positive semi-definite matrices. The proposed method works in both the input space and kernel in-duced feature space, and learning distance metric is formulated as a quadratic optimization problem which returns a global optimal solution. Experimental results on several databases show that the learned distance metric improves the performances of the subsequent classification and clustering algorithms.","PeriodicalId":309591,"journal":{"name":"2010 20th International Conference on Pattern Recognition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127831611","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}
Decomposition of cells in clumps is a difficult segmentation task requiring region splitting techniques. Techniques that do not employ prior shape constraints usually fail to achieve accurate segmentation. Those using shape constraints are unable to cope with large clumps and occlusions. In this work, we propose a model-constrained region splitting algorithm for cell clump decomposition. We build the cell model using joint probability distribution of invariant shape features. The shape model, the contour smoothness and the gradient information along the cut are used to optimize the splitting in a recursive manner. The short cut rule is also adopted as a strategy to speed up the process. The algorithm performs well in validation experiments using 60 images with 4516 cells and 520 clumps.
{"title":"A Recursive and Model-Constrained Region Splitting Algorithm for Cell Clump Decomposition","authors":"W. Xiong, S. Ong, Joo-Hwee Lim","doi":"10.1109/ICPR.2010.1073","DOIUrl":"https://doi.org/10.1109/ICPR.2010.1073","url":null,"abstract":"Decomposition of cells in clumps is a difficult segmentation task requiring region splitting techniques. Techniques that do not employ prior shape constraints usually fail to achieve accurate segmentation. Those using shape constraints are unable to cope with large clumps and occlusions. In this work, we propose a model-constrained region splitting algorithm for cell clump decomposition. We build the cell model using joint probability distribution of invariant shape features. The shape model, the contour smoothness and the gradient information along the cut are used to optimize the splitting in a recursive manner. The short cut rule is also adopted as a strategy to speed up the process. The algorithm performs well in validation experiments using 60 images with 4516 cells and 520 clumps.","PeriodicalId":309591,"journal":{"name":"2010 20th International Conference on Pattern Recognition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127059184","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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