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Proceedings. International Conference on Image Processing最新文献

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ACCELERATED DYNAMIC MRI USING STRUCTURED LOW RANK MATRIX COMPLETION. 利用结构化低秩矩阵补全加速动态成像。
Proceedings. International Conference on Image Processing Pub Date : 2016-09-01 Epub Date: 2016-08-19 DOI: 10.1109/icip.2016.7532680
Arvind Balachandrasekaran, Greg Ongie, Mathews Jacob
{"title":"ACCELERATED DYNAMIC MRI USING STRUCTURED LOW RANK MATRIX COMPLETION.","authors":"Arvind Balachandrasekaran, Greg Ongie, Mathews Jacob","doi":"10.1109/icip.2016.7532680","DOIUrl":"10.1109/icip.2016.7532680","url":null,"abstract":"<p><p>We introduce a fast structured low-rank matrix completion algorithm with low memory & computational demand to recover the dynamic MRI data from undersampled measurements. The 3-D dataset is modeled as a piecewise smooth signal, whose discontinuities are localized to the zero sets of a bandlimited function. We show that a structured matrix corresponding to convolution with the Fourier coefficients of the signal derivatives is highly low-rank. This property enables us to recover the signal from undersampled measurements. The application of this scheme in dynamic MRI shows significant improvement over state of the art methods.</p>","PeriodicalId":74572,"journal":{"name":"Proceedings. International Conference on Image Processing","volume":"2016 ","pages":"1858-1862"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7885618/pdf/nihms-1667948.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25377815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
CONVEX CLUSTERING AND RECOVERY OF PARTIALLY OBSERVED DATA. 部分观测数据的凸聚类和恢复。
Proceedings. International Conference on Image Processing Pub Date : 2016-09-01 Epub Date: 2016-08-19 DOI: 10.1109/icip.2016.7533010
Sunrita Poddar, Mathews Jacob
{"title":"CONVEX CLUSTERING AND RECOVERY OF PARTIALLY OBSERVED DATA.","authors":"Sunrita Poddar,&nbsp;Mathews Jacob","doi":"10.1109/icip.2016.7533010","DOIUrl":"https://doi.org/10.1109/icip.2016.7533010","url":null,"abstract":"<p><p>We propose a convex clustering and reconstruction algorithm for data with missing entries. The algorithm uses a similarity measure between every pair of points to cluster and recover the data. The cluster centres can be recovered reliably when the ground-truth similarity matrix is available. Moreover, the similarity matrix can also be reliably estimated from the partially observed data, when the clusters are well-separated and the coherence of the difference between points from different clusters is low. The algorithm performs well using the estimated similarity matrix on a simulated dataset. The method is also successful in reconstructing images from under-sampled Fourier data.</p>","PeriodicalId":74572,"journal":{"name":"Proceedings. International Conference on Image Processing","volume":"2016 ","pages":"3498-3502"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/icip.2016.7533010","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25395794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Multiple Degree Total Variation (MDTV) Regularization for Image Restoration. 多度全变分(MDTV)正则化用于图像恢复。
Proceedings. International Conference on Image Processing Pub Date : 2016-09-01 Epub Date: 2016-08-19 DOI: 10.1109/icip.2016.7532700
Yue Hu, Mathews Jacob
{"title":"Multiple Degree Total Variation (MDTV) Regularization for Image Restoration.","authors":"Yue Hu,&nbsp;Mathews Jacob","doi":"10.1109/icip.2016.7532700","DOIUrl":"https://doi.org/10.1109/icip.2016.7532700","url":null,"abstract":"<p><p>We introduce a novel image regularization termed as multiple degree total variation (MDTV). This type of regularization combines the first and second degree directional derivatives, thus providing a good balance between preservation of edges and region smoothness. In order to solve the resulting optimization problem, we proposed a fast majorize minimize algorithm. We demonstrate the utility of the MDTV regularization in the context of image denoising and compressed sensing. We compare the proposed method with standard TV, and the state of the art higher degree methods, including higher degree total variation (HDTV) and total generalized variation (TGV) based schemes. Numerical results indicate that MDTV penalty provides improved image recovery performance.</p>","PeriodicalId":74572,"journal":{"name":"Proceedings. International Conference on Image Processing","volume":"2016 ","pages":"1958-1962"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/icip.2016.7532700","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25408395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
AN AUTOMATIC 3D CT/PET SEGMENTATION FRAMEWORK FOR BONE MARROW PROLIFERATION ASSESSMENT. 用于骨髓增殖评估的自动三维ct / pet分割框架。
Proceedings. International Conference on Image Processing Pub Date : 2016-09-01 Epub Date: 2016-08-19 DOI: 10.1109/ICIP.2016.7533136
Chuong Nguyen, Joseph Havlicek, Quyen Duong, Sara Vesely, Ronald Gress, Liza Lindenberg, Peter Choyke, Jennifer Holter Chakrabarty, Kirsten Williams
{"title":"AN AUTOMATIC 3D CT/PET SEGMENTATION FRAMEWORK FOR BONE MARROW PROLIFERATION ASSESSMENT.","authors":"Chuong Nguyen,&nbsp;Joseph Havlicek,&nbsp;Quyen Duong,&nbsp;Sara Vesely,&nbsp;Ronald Gress,&nbsp;Liza Lindenberg,&nbsp;Peter Choyke,&nbsp;Jennifer Holter Chakrabarty,&nbsp;Kirsten Williams","doi":"10.1109/ICIP.2016.7533136","DOIUrl":"https://doi.org/10.1109/ICIP.2016.7533136","url":null,"abstract":"<p><p>Clinical assessment of bone marrow is limited by an inability to evaluate the marrow space comprehensively and dynamically and there is no current method for automatically assessing hematopoietic activity within the medullary space. Evaluating the hematopoietic space in its entirety could be applicable in blood disorders, malignancies, infections, and medication toxicity. In this paper, we introduce a CT/PET 3D automatic framework for measurement of the hematopoietic compartment proliferation within osseous sites. We first perform a full-body bone structure segmentation using 3D graph-cut on the CT volume. The vertebrae are segmented by detecting the discs between adjacent vertebrae. Finally, we register the bone marrow CT volume with its corresponding PET volume and capture the spinal bone marrow volume. The proposed framework was tested on 17 patients, achieving an average accuracy of 86.37% and a worst case accuracy of 82.3% in automatically extracting the aggregate volume of the spinal marrow cavities.</p>","PeriodicalId":74572,"journal":{"name":"Proceedings. International Conference on Image Processing","volume":"2016 ","pages":"4126-4130"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/ICIP.2016.7533136","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35112981","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 4
CELL TRACKING USING PARTICLE FILTERS WITH IMPLICIT CONVEX SHAPE MODEL IN 4D CONFOCAL MICROSCOPY IMAGES. 在四维共聚焦显微镜图像中使用隐式凸形状模型的粒子滤波器进行细胞跟踪。
Proceedings. International Conference on Image Processing Pub Date : 2014-10-01 DOI: 10.1109/ICIP.2014.7025089
Nisha Ramesh, Tolga Tasdizen
{"title":"CELL TRACKING USING PARTICLE FILTERS WITH IMPLICIT CONVEX SHAPE MODEL IN 4D CONFOCAL MICROSCOPY IMAGES.","authors":"Nisha Ramesh,&nbsp;Tolga Tasdizen","doi":"10.1109/ICIP.2014.7025089","DOIUrl":"https://doi.org/10.1109/ICIP.2014.7025089","url":null,"abstract":"<p><p>Bayesian frameworks are commonly used in tracking algorithms. An important example is the particle filter, where a stochastic motion model describes the evolution of the state, and the observation model relates the noisy measurements to the state. Particle filters have been used to track the lineage of cells. Propagating the shape model of the cell through the particle filter is beneficial for tracking. We approximate arbitrary shapes of cells with a novel implicit convex function. The importance sampling step of the particle filter is defined using the cost associated with fitting our implicit convex shape model to the observations. Our technique is capable of tracking the lineage of cells for nonmitotic stages. We validate our algorithm by tracking the lineage of retinal and lens cells in zebrafish embryos.</p>","PeriodicalId":74572,"journal":{"name":"Proceedings. International Conference on Image Processing","volume":"2014 ","pages":"446-450"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/ICIP.2014.7025089","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34723610","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
ANALYSIS OF FOOD IMAGES: FEATURES AND CLASSIFICATION. 食品图像分析:特征与分类。
Proceedings. International Conference on Image Processing Pub Date : 2014-10-01 Epub Date: 2015-01-29 DOI: 10.1109/ICIP.2014.7025555
Ye He, Chang Xu, Nitin Khanna, Carol J Boushey, Edward J Delp
{"title":"ANALYSIS OF FOOD IMAGES: FEATURES AND CLASSIFICATION.","authors":"Ye He,&nbsp;Chang Xu,&nbsp;Nitin Khanna,&nbsp;Carol J Boushey,&nbsp;Edward J Delp","doi":"10.1109/ICIP.2014.7025555","DOIUrl":"https://doi.org/10.1109/ICIP.2014.7025555","url":null,"abstract":"<p><p>In this paper we investigate features and their combinations for food image analysis and a classification approach based on k-nearest neighbors and vocabulary trees. The system is evaluated on a food image dataset consisting of 1453 images of eating occasions in 42 food categories which were acquired by 45 participants in natural eating conditions. The same image dataset is used to test the classification system proposed in the previously reported work [1]. Experimental results indicate that using our combination of features and vocabulary trees for classification improves the food classification performance about 22% for the Top 1 classification accuracy and 10% for the Top 4 classification accuracy.</p>","PeriodicalId":74572,"journal":{"name":"Proceedings. International Conference on Image Processing","volume":"2014 ","pages":"2744-2748"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/ICIP.2014.7025555","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35053139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 75
CONTEXT BASED FOOD IMAGE ANALYSIS. 基于上下文的食物图像分析。
Proceedings. International Conference on Image Processing Pub Date : 2013-09-01 Epub Date: 2014-02-13 DOI: 10.1109/ICIP.2013.6738566
Ye He, Chang Xu, Nitin Khanna, Carol J Boushey, Edward J Delp
{"title":"CONTEXT BASED FOOD IMAGE ANALYSIS.","authors":"Ye He,&nbsp;Chang Xu,&nbsp;Nitin Khanna,&nbsp;Carol J Boushey,&nbsp;Edward J Delp","doi":"10.1109/ICIP.2013.6738566","DOIUrl":"https://doi.org/10.1109/ICIP.2013.6738566","url":null,"abstract":"<p><p>We are developing a dietary assessment system that records daily food intake through the use of food images. Recognizing food in an image is difficult due to large visual variance with respect to eating or preparation conditions. This task becomes even more challenging when different foods have similar visual appearance. In this paper we propose to incorporate two types of contextual dietary information, food co-occurrence patterns and personalized learning models, in food image analysis to reduce ambiguity in food visual appearance and improve food recognition accuracy. We evaluate our model on 1453 food images acquired by 45 participants in natural eating conditions. The result shows that incorporating contextual dietary information improves the food categorization accuracy by about 10%.</p>","PeriodicalId":74572,"journal":{"name":"Proceedings. International Conference on Image Processing","volume":"2013 ","pages":"2748-2752"},"PeriodicalIF":0.0,"publicationDate":"2013-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/ICIP.2013.6738566","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35053138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 15
MODEL-BASED FOOD VOLUME ESTIMATION USING 3D POSE. 利用三维姿态进行基于模型的食物体积估算。
Proceedings. International Conference on Image Processing Pub Date : 2013-09-01 Epub Date: 2014-02-13 DOI: 10.1109/ICIP.2013.6738522
Chang Xu, Ye He, Nitin Khanna, Carol J Boushey, Edward J Delp
{"title":"MODEL-BASED FOOD VOLUME ESTIMATION USING 3D POSE.","authors":"Chang Xu, Ye He, Nitin Khanna, Carol J Boushey, Edward J Delp","doi":"10.1109/ICIP.2013.6738522","DOIUrl":"10.1109/ICIP.2013.6738522","url":null,"abstract":"<p><p>We are developing a dietary assessment system to automatically identify and quantify foods and beverages consumed by analyzing meal images captured with a mobile device. After food items are segmented and identified, accurately estimating the volume of the food in the image is important for determining the nutrient content of the food. In this paper, we proposed a novel food portion size estimation method for rigid food items using a single image. First, we create a 3D graphical model during the training step using 3D reconstruction from multiple views. Then, for each food image, we determine the translation and elevation parameters of each of the food items, which are relative to the camera coordinate through camera calibration. Using these geometric parameters we project the pre-built 3D model of each food item back to the image plane. Subsequently, the remaining degrees-of-freedom (DOF) for the final pose is estimated by image similarity measure. The experimental results of our volume estimation method for four food categories validate the accuracy and reliability of our model-based approach.</p>","PeriodicalId":74572,"journal":{"name":"Proceedings. International Conference on Image Processing","volume":"2013 ","pages":"2534-2538"},"PeriodicalIF":0.0,"publicationDate":"2013-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5448795/pdf/nihms823614.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35053198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
DTI BASED STRUCTURAL DAMAGE CHARACTERIZATION FOR DISORDERS OF CONSCIOUSNESS. 基于 DTI 的意识障碍结构损伤鉴定。
Proceedings. International Conference on Image Processing Pub Date : 2012-09-01 Epub Date: 2013-02-21 DOI: 10.1109/ICIP.2012.6467095
F Gómez, A Soddu, Q Noirhomme, A Vanhaudenhuyse, L Tshibanda, N Leporé, S Laureys
{"title":"DTI BASED STRUCTURAL DAMAGE CHARACTERIZATION FOR DISORDERS OF CONSCIOUSNESS.","authors":"F Gómez, A Soddu, Q Noirhomme, A Vanhaudenhuyse, L Tshibanda, N Leporé, S Laureys","doi":"10.1109/ICIP.2012.6467095","DOIUrl":"10.1109/ICIP.2012.6467095","url":null,"abstract":"<p><p>MRI Diffusion Tensor Imaging (DTI) has been recently proposed as a highly discriminative measurement to detect structural damages in Disorders of Consciousness patients (Vegetative State/Unresponsive Wakefulness Syndrome-(VS/UWS) and Minimally Consciousness State-MCS). In the DTI analysis, certain tensor features are often used as simplified scalar indices to represent these alterations. Those characteristics are mathematically and statistically more tractable than the full tensors. Nevertheless, most of these quantities are based on a tensor diffusivity estimation, the arithmetic average among the different strengths of the tensor orthogonal directions, which is supported on a symmetric linear relationship among the three directions, an unrealistic assumption for severely damaged brains. In this paper, we propose a new family of scalar quantities based on Generalized Ordered Weighted Aggregations (GOWA) to characterize morphological damages. The main idea is to compute a tensor diffusitivity estimation that captures the deviations in the water diffusivity associated to damaged tissue. This estimation is performed by weighting and combining differently each tensor orthogonal strength. Using these new scalar quantities we construct an affine invariant DTI tensor feature using regional tissue histograms. An evaluation of these new scalar quantities on 48 patients (23 VS/UWS and 25 MCS) was conducted. Our experiments demonstrate that this new representation outperforms state-of-the-art tensor based scalar representations for characterization and classification problems.</p>","PeriodicalId":74572,"journal":{"name":"Proceedings. International Conference on Image Processing","volume":"2012 ","pages":"1257-1260"},"PeriodicalIF":0.0,"publicationDate":"2012-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6014740/pdf/nihms608382.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36253564","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
SEMI-SUPERVISED OBJECT RECOGNITION USING STRUCTURE KERNEL. 使用结构核进行半监督对象识别。
Proceedings. International Conference on Image Processing Pub Date : 2012-01-01 DOI: 10.1109/icip.2012.6467320
Botao Wang, Hongkai Xiong, Xiaoqian Jiang, Fan Ling
{"title":"SEMI-SUPERVISED OBJECT RECOGNITION USING STRUCTURE KERNEL.","authors":"Botao Wang, Hongkai Xiong, Xiaoqian Jiang, Fan Ling","doi":"10.1109/icip.2012.6467320","DOIUrl":"10.1109/icip.2012.6467320","url":null,"abstract":"<p><p>Object recognition is a fundamental problem in computer vision. Part-based models offer a sparse, flexible representation of objects, but suffer from difficulties in training and often use standard kernels. In this paper, we propose a positive definite kernel called \"structure kernel\", which measures the similarity of two part-based represented objects. The structure kernel has three terms: 1) the global term that measures the global visual similarity of two objects; 2) the part term that measures the visual similarity of corresponding parts; 3) the spatial term that measures the spatial similarity of geometric configuration of parts. The contribution of this paper is to generalize the discriminant capability of local kernels to complex part-based object models. Experimental results show that the proposed kernel exhibit higher accuracy than state-of-art approaches using standard kernels.</p>","PeriodicalId":74572,"journal":{"name":"Proceedings. International Conference on Image Processing","volume":" ","pages":"2157-2160"},"PeriodicalIF":0.0,"publicationDate":"2012-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3648669/pdf/nihms456791.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"31425387","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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