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Margarita Torre; Beatriz Remeseiro; Petia Radeva; Fernando Martinez |
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DeepNEM: Deep Network Energy-Minimization for Agricultural Field Segmentation |
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2020 |
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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JSTAEOR |
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13 |
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726-737 |
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One of the main characteristics of agricultural fields is that the appearance of different crops and their growth status, in an aerial image, is varied, and has a wide range of radiometric values and high level of variability. The extraction of these fields and their monitoring are activities that require a high level of human intervention. In this article, we propose a novel automatic algorithm, named deep network energy-minimization (DeepNEM), to extract agricultural fields in aerial images. The model-guided process selects the most relevant image clues extracted by a deep network, completes them and finally generates regions that represent the agricultural fields under a minimization scheme. DeepNEM has been tested over a broad range of fields in terms of size, shape, and content. Different measures were used to compare the DeepNEM with other methods, and to prove that it represents an improved approach to achieve a high-quality segmentation of agricultural fields. Furthermore, this article also presents a new public dataset composed of 1200 images with their parcels boundaries annotations. |
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MILAB |
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Admin @ si @ TRR2020 |
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3410 |
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Francesco Ciompi; Oriol Pujol; Carlo Gatta; O. Rodriguez-Leor; J. Mauri; Petia Radeva |
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Fusing in-vitro and in-vivo intravascular ultrasound data for plaque characterization |
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2010 |
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International Journal of Cardiovascular Imaging |
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IJCI |
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26 |
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7 |
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763–779 |
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Accurate detection of in-vivo vulnerable plaque in coronary arteries is still an open problem. Recent studies show that it is highly related to tissue structure and composition. Intravascular Ultrasound (IVUS) is a powerful imaging technique that gives a detailed cross-sectional image of the vessel, allowing to explore arteries morphology. IVUS data validation is usually performed by comparing post-mortem (in-vitro) IVUS data and corresponding histological analysis of the tissue. The main drawback of this method is the few number of available case studies and validated data due to the complex procedure of histological analysis of the tissue. On the other hand, IVUS data from in-vivo cases is easy to obtain but it can not be histologically validated. In this work, we propose to enhance the in-vitro training data set by selectively including examples from in-vivo plaques. For this purpose, a Sequential Floating Forward Selection method is reformulated in the context of plaque characterization. The enhanced classifier performance is validated on in-vitro data set, yielding an overall accuracy of 91.59% in discriminating among fibrotic, lipidic and calcified plaques, while reducing the gap between in-vivo and in-vitro data analysis. Experimental results suggest that the obtained classifier could be properly applied on in-vivo plaque characterization and also demonstrate that the common hypothesis of assuming the difference between in-vivo and in-vitro as negligible is incorrect. |
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1569-5794 |
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MILAB;HUPBA |
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BCNPCL @ bcnpcl @ CPG2010 |
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1305 |
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Debora Gil; Aura Hernandez-Sabate; Oriol Rodriguez; Josepa Mauri; Petia Radeva |
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Statistical Strategy for Anisotropic Adventitia Modelling in IVUS |
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2006 |
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IEEE Transactions on Medical Imaging |
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25 |
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6 |
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768-778 |
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Corners; T-junctions; Wavelets |
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Vessel plaque assessment by analysis of intravascular ultrasound sequences is a useful tool for cardiac disease diagnosis and intervention. Manual detection of luminal (inner) and mediaadventitia (external) vessel borders is the main activity of physicians in the process of lumen narrowing (plaque) quantification. Difficult definition of vessel border descriptors, as well as, shades, artifacts, and blurred signal response due to ultrasound physical properties trouble automated adventitia segmentation. In order to efficiently approach such a complex problem, we propose blending advanced anisotropic filtering operators and statistical classification techniques into a vessel border modelling strategy. Our systematic statistical analysis shows that the reported adventitia detection achieves an accuracy in the range of interobserver variability regardless of plaque nature, vessel geometry, and incomplete vessel borders. Index Terms–-Anisotropic processing, intravascular ultrasound (IVUS), vessel border segmentation, vessel structure classification. |
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IAM;MILAB |
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IAM @ iam @ GHR2006 |
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1525 |
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Author |
Albert Clapes; Miguel Reyes; Sergio Escalera |
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Title |
Multi-modal User Identification and Object Recognition Surveillance System |
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2013 |
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Pattern Recognition Letters |
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PRL |
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34 |
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7 |
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799-808 |
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Multi-modal RGB-Depth data analysis; User identification; Object recognition; Intelligent surveillance; Visual features; Statistical learning |
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We propose an automatic surveillance system for user identification and object recognition based on multi-modal RGB-Depth data analysis. We model a RGBD environment learning a pixel-based background Gaussian distribution. Then, user and object candidate regions are detected and recognized using robust statistical approaches. The system robustly recognizes users and updates the system in an online way, identifying and detecting new actors in the scene. Moreover, segmented objects are described, matched, recognized, and updated online using view-point 3D descriptions, being robust to partial occlusions and local 3D viewpoint rotations. Finally, the system saves the historic of user–object assignments, being specially useful for surveillance scenarios. The system has been evaluated on a novel data set containing different indoor/outdoor scenarios, objects, and users, showing accurate recognition and better performance than standard state-of-the-art approaches. |
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Elsevier |
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HUPBA; 600.046; 605.203;MILAB |
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Admin @ si @ CRE2013 |
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2248 |
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Author |
Frederic Sampedro; Anna Domenech; Sergio Escalera |
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Title |
Static and dynamic computational cancer spread quantification in whole body FDG-PET/CT scans |
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Journal Article |
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Year |
2014 |
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Journal of Medical Imaging and Health Informatics |
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JMIHI |
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4 |
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6 |
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825-831 |
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CANCER SPREAD; COMPUTER AIDED DIAGNOSIS; MEDICAL IMAGING; TUMOR QUANTIFICATION |
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In this work we address the computational cancer spread quantification scenario in whole body FDG-PET/CT scans. At the static level, this setting can be modeled as a clustering problem on the set of 3D connected components of the whole body PET tumoral segmentation mask carried out by nuclear medicine physicians. At the dynamic level, and ad-hoc algorithm is proposed in order to quantify the cancer spread time evolution which, when combined with other existing indicators, gives rise to the metabolic tumor volume-aggressiveness-spread time evolution chart, a novel tool that we claim that would prove useful in nuclear medicine and oncological clinical or research scenarios. Good performance results of the proposed methodologies both at the clinical and technological level are shown using a dataset of 48 segmented whole body FDG-PET/CT scans. |
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HuPBA;MILAB |
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Admin @ si @ SDE2014b |
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2548 |
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