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Dissertations / Theses on the topic 'Lines detection and segmentation'

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1

Li, Yaqian. "Image segmentation and stereo vision matching based on declivity line : application for vehicle detection." Thesis, Rouen, INSA, 2010. http://www.theses.fr/2010ISAM0010.

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Dans le cadre de systèmes d’aide à la conduite, nous avons contribué aux approches de stéréovision pour l’extraction de contour, la mise en correspondance des images stéréoscopiques et la détection de véhicules. L’extraction de contour réalisée est basée sur le concept declivity line que nous avons proposé. La declivity line est construite en liant des déclivités selon leur position relative et similarité d’intensité. L’extraction de contour est obtenue en filtrant les declivity lines construites basées sur leurs caractéristiques. Les résultats expérimentaux montrent que la declivity lines mét
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Bonakdar, Sakhi Omid. "Segmentation of heterogeneous document images : an approach based on machine learning, connected components analysis, and texture analysis." Phd thesis, Université Paris-Est, 2012. http://tel.archives-ouvertes.fr/tel-00912566.

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Document page segmentation is one of the most crucial steps in document image analysis. It ideally aims to explain the full structure of any document page, distinguishing text zones, graphics, photographs, halftones, figures, tables, etc. Although to date, there have been made several attempts of achieving correct page segmentation results, there are still many difficulties. The leader of the project in the framework of which this PhD work has been funded (*) uses a complete processing chain in which page segmentation mistakes are manually corrected by human operators. Aside of the costs it re
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Khairallah, Mahmoud. "Flow-Based Visual-Inertial Odometry for Neuromorphic Vision Sensors." Electronic Thesis or Diss., université Paris-Saclay, 2022. http://www.theses.fr/2022UPAST117.

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Plutôt que de générer des images de manière constante et synchrone, les capteurs neuromorphiques de vision -également connus sous le nom de caméras événementielles, permettent à chaque pixel de fournir des informations de manière indépendante et asynchrone chaque fois qu'un changement de luminosité est détecté. Par conséquent, les capteurs de vision neuromorphiques n'ont pas les problèmes des caméras conventionnelles telles que les artefacts d'image et le Flou cinétique. De plus, ils peuvent fournir une compression sans perte de donné avec une résolution temporelle et une plage dynamique plus
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Wigington, Curtis Michael. "End-to-End Full-Page Handwriting Recognition." BYU ScholarsArchive, 2018. https://scholarsarchive.byu.edu/etd/7099.

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Despite decades of research, offline handwriting recognition (HWR) of historical documents remains a challenging problem, which if solved could greatly improve the searchability of online cultural heritage archives. Historical documents are plagued with noise, degradation, ink bleed-through, overlapping strokes, variation in slope and slant of the writing, and inconsistent layouts. Often the documents in a collection have been written by thousands of authors, all of whom have significantly different writing styles. In order to better capture the variations in writing styles we introduce a nove
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Torr, Philip Hilaire Sean. "Motion segmentation and outlier detection." Thesis, University of Oxford, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.308173.

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Deng, Jingjing (Eddy). "Adaptive learning for segmentation and detection." Thesis, Swansea University, 2017. https://cronfa.swan.ac.uk/Record/cronfa36297.

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Segmentation and detection are two fundamental problems in computer vision and medical image analysis, they are intrinsically interlinked by the nature of machine learning based classification, especially supervised learning methods. Many automatic segmentation methods have been proposed which heavily rely on hand-crafted discriminative features for specific geometry and powerful classifier for delinearating the foreground object and background region. The aimof this thesis is to investigate the adaptive schemes that can be used to derive efficient interactive segmentation methods for medical
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Hastings, Joseph R. 1980. "Incremental Bayesian segmentation for intrusion detection." Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/28399.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, February 2004.<br>Includes bibliographical references (leaves 131-133).<br>This thesis describes an attempt to monitor patterns of system calls generated by a Unix host in order to detect potential intrusion attacks. Sequences of system calls generated by privileged processes are analyzed using incremental Bayesian segmentation in order to detect anomalous activity. Theoretical analysis of various aspects of the algorithm and empirical analysis of performance on synthetic data sets ar
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Nedilko, Bohdan. "Seismic detection of rockfalls on railway lines." Thesis, University of British Columbia, 2016. http://hdl.handle.net/2429/58097.

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Railway operators mitigate the risk of derailments caused by hazardous rocks falling onto the track by installing slide detector fences (SDF). These consist of electrical sensing wires strung on poles located uphill of the track; falling rocks snap these wires and trigger an alarm. Rocks of non-threatening size and migrating animals frequently break the wires causing prolonged false alarms and delaying rail traffic until the SDF is manually repaired, often in a hazardous environment. This thesis is concerned with the development of a prototype of the autonomous Seismic Rockfall Detection Syste
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Torrent, Palomeras Albert. "Simultaneous detection and segmentation for generic objects." Doctoral thesis, Universitat de Girona, 2013. http://hdl.handle.net/10803/117736.

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This thesis deals with the simultaneous detection and segmentation for generic objects in images. The proposed approach is based on building a dictionary of patches, which defines the object and allows the extraction of the detection and segmentation features used to train the classifier. Moreover, we include in the boosting training the ability of crossing information between detection and segmentation with the aim that good detections may help to better segment and vice versa. We adapt also the detection proposal to deal with specific problems of object recognition in medical and astronomica
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HEGSTAM, BJÖRN. "Defect detection and segmentation inmultivariate image streams." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-142069.

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OptoNova is a world leading producer of inspection systems for quality control of surfaces and edges at high rates. They develop their own sensor systems and software and have taken an interest in investigating the possibility of using methods from machine learning to make better use of the available sensor data. The purpose of this project was to develop a method for finding surface defects based on multivariate images. A previous Master’s project done at OptoNova had shown promising results when applying machine learning methods to inspect the sides of kitchen cabinet doors. The model develo
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Beare, Richard. "Image segmentation based on local motion detection /." Title page, contents and abstract only, 1997. http://web4.library.adelaide.edu.au/theses/09PH/09phb3684.pdf.

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Hegstam, Björn. "Defect detection and segmentation inmultivariate image streams." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-150456.

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OptoNova is a world leading producer of inspection systems for qualitycontrol of surfaces and edges at high rates. They develop their ownsensor systems and software and have taken an interest in investigatingthe possibility of using methods from machine learning to make betteruse of the available sensor data.The purpose of this project was to develop a method for finding sur-face defects based on multivariate images. A previous Master’s projectdone at OptoNova had shown promising results when applying machinelearning methods to inspect the sides of kitchen cabinet doors. Themodel developed for
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Eltayef, Khalid Ahmad A. "Segmentation and lesion detection in dermoscopic images." Thesis, Brunel University, 2017. http://bura.brunel.ac.uk/handle/2438/16211.

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Malignant melanoma is one of the most fatal forms of skin cancer. It has also become increasingly common, especially among white-skinned people exposed to the sun. Early detection of melanoma is essential to raise survival rates, since its detection at an early stage can be helpful and curable. Working out the dermoscopic clinical features (pigment network and lesion borders) of melanoma is a vital step for dermatologists, who require an accurate method of reaching the correct clinical diagnosis, and ensure the right area receives the correct treatment. These structures are considered one of t
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Ström, Bartunek Josef. "FINGERPRINT IMAGE ENHANCEMENT, SEGMENTATION AND MINUTIAE DETECTION." Doctoral thesis, Blekinge Tekniska Högskola, Institutionen för tillämpad signalbehandling, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-11149.

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Prior to 1960's, the fingerprint analysis was carried out manually by human experts and for forensic purposes only. Automated fingerprint identification systems (AFIS) have been developed during the last 50 years. The success of AFIS resulted in that its use expanded beyond forensic applications and became common also in civilian applications. Mobile phones and computers equipped with fingerprint sensing devices for fingerprint-based user identification are common today. Despite the intense development efforts, a major problem in automatic fingerprint identification is to acquire reliable matc
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Zhang, Jingdan McMillan Leonard. "Object detection and segmentation using discriminative learning." Chapel Hill, N.C. : University of North Carolina at Chapel Hill, 2009. http://dc.lib.unc.edu/u?/etd,2181.

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Thesis (Ph. D.)--University of North Carolina at Chapel Hill, 2009.<br>Title from electronic title page (viewed Jun. 26, 2009). "... in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Computer Science." Discipline: Computer Science; Department/School: Computer Science.
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Wang, Qiong. "Salient object detection and segmentation in videos." Thesis, Rennes, INSA, 2019. http://www.theses.fr/2019ISAR0003/document.

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Cette thèse est centrée sur le problème de la détection d'objets saillants et de leur segmentation dans une vidéo en vue de détecter les objets les plus attractifs ou d'affecter des identités cohérentes d'objets à chaque pixel d'une séquence vidéo. Concernant la détection d'objets saillants dans vidéo, outre une revue des techniques existantes, une nouvelle approche et l'extension d'un modèle sont proposées; de plus une approche est proposée pour la segmentation d'instances d'objets vidéo. Pour la détection d'objets saillants dans une vidéo, nous proposons : (1) une approche traditionnelle pou
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Mallu, Mallu. "Fashion Object Detection and Pixel-Wise Semantic Segmentation : Crowdsourcing framework for image bounding box detection & Pixel-Wise Segmentation." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-234691.

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Technology has revamped every aspect of our life, one of those various facets is fashion industry. Plenty of deep learning architectures are taking shape to augment fashion experiences for everyone. There are numerous possibilities of enhancing the fashion technology with deep learning. One of the key ideas is to generate fashion style and recommendation using artificial intelligence. Likewise, another significant feature is to gather reliable information of fashion trends, which includes analysis of existing fashion related images and data. When specifically dealing with images, localisation
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Gandhi, Tarak L. "Image sequence analysis for object detection and segmentation." Adobe Acrobat reader required to view the full dissertation, 2000. http://www.etda.libraries.psu.edu/theses/approved/PSUonlyIndex/ETD-18/index.html.

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Donnelley, Martin, and martin donnelley@gmail com. "Computer Aided Long-Bone Segmentation and Fracture Detection." Flinders University. Engineering, 2008. http://catalogue.flinders.edu.au./local/adt/public/adt-SFU20080115.222927.

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Medical imaging has advanced at a tremendous rate since x-rays were discovered in 1895. Today, x-ray machines produce extremely high-quality images for radiologists to interpret. However, the methods of interpretation have only recently begun to be augmented by advances in computer technology. Computer aided diagnosis (CAD) systems that guide healthcare professionals to making the correct diagnosis are slowly becoming more prevalent throughout the medical field. Bone fractures are a relatively common occurrence. In most developed countries the number of fractures associated with age-related b
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Fu, Guoyi. "Data driven low-level object detection and segmentation." Thesis, University of Kent, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.498827.

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Bruce, Jacob Robert. "Mathematical Expression Detection and Segmentation in Document Images." Thesis, Virginia Tech, 2014. http://hdl.handle.net/10919/46724.

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Various document layout analysis techniques are employed in order to enhance the accuracy of optical character recognition (OCR) in document images. Type-specific document layout analysis involves localizing and segmenting specific zones in an image so that they may be recognized by specialized OCR modules. Zones of interest include titles, headers/footers, paragraphs, images, mathematical expressions, chemical equations, musical notations, tables, circuit diagrams, among others. False positive/negative detections, oversegmentations, and undersegmentations made during the detection and segment
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Munnangi, Anirudh. "Innovative Segmentation Strategies for Melanoma Skin Cancer Detection." University of Cincinnati / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1510916097483278.

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Silva, Daniel Torres Couto Coimbra e. "LIDAR target detection and segmentation in road environment." Master's thesis, Universidade de Aveiro, 2013. http://hdl.handle.net/10773/11774.

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Mestrado em Engenharia Mecânica<br>In this project a comparative and exhaustive evaluation of several 2D laser data segmentation algorithms in road scenarios is performed. In a first stage, the segmentation algorithms are implemented using the ROS programming environment; the algorithms are applied to the raw laser scan data in order to extract groups of measurement points which share similar spatial properties and that probably will belong to one single object. Each algorithm has at least one threshold condition parameter that is configurable, and one of the goals is to try to determine the b
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Stainton, John Joseph. "Detection of signatures of selection in commercial chicken lines." Thesis, University of Edinburgh, 2015. http://hdl.handle.net/1842/21057.

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Within the last 100 years, commercial chickens have been split into two main groups. Broiler chickens are produced for meat production while layers are produced for egg production. This has caused large phenotypic changes and the genomic signatures of selection may be detectable using statistical techniques. Genomic regions identified by these techniques may include genes associated with production traits, and is therefore of interest to animal breeders. This thesis investigates signatures of selection in a number of commercial chicken lines using several statistical techniques based on popula
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Pont, Tuset Jordi. "Image segmentation evaluation and its application to object detection." Doctoral thesis, Universitat Politècnica de Catalunya, 2014. http://hdl.handle.net/10803/134354.

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The first parts of this Thesis are focused on the study of the supervised evaluation of image segmentation algorithms. Supervised in the sense that the segmentation results are compared to a human-made annotation, known as ground truth, by means of different measures of similarity. The evaluation depends, therefore, on three main points. First, the image segmentation techniques we evaluate. We review the state of the art in image segmentation, making an explicit difference between those techniques that provide a flat output, that is, a single clustering of the set of pixels into regions; and
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Pons, Rodríguez Gerard. "Computer-aided lesion detection and segmentation on breast ultrasound." Doctoral thesis, Universitat de Girona, 2014. http://hdl.handle.net/10803/129453.

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This thesis deals with the detection, segmentation and classification of lesions on sonography. The contribution of the thesis is the development of a new Computer-Aided Diagnosis (CAD) framework capable of detecting, segmenting, and classifying breast abnormalities on sonography automatically. Firstly, an adaption of a generic object detection method, Deformable Part Models (DPM), to detect lesions in sonography is proposed. The method uses a machine learning technique to learn a model based on Histogram of Oriented Gradients (HOG). This method is also used to detect cancer lesions directly,
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Dursun, Mustafa. "Road Detection By Mean Shift Segmentation And Structural Analysis." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614443/index.pdf.

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Road extraction from satellite or aerial images is a popular issue in remote sensing. Extracted road maps or networks can be used in various applications. Normally, maps for roads are available in geographic information systems (GIS), however these informations are not being produced automatically. Generally they are formed with the aid of human. Road extraction algorithms are trying to detect the roads from satellite or aerial images with the minimum in-teraction of human. Aim of this thesis is to analyze a previously defined algorithm about road extraction and to present alternatives and pos
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Zhai, Yun. "VIDEO CONTENT EXTRACTION: SCENE SEGMENTATION, LINKING AND ATTENTION DETECTION." Master's thesis, University of Central Florida, 2006. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/4007.

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In this fast paced digital age, a vast amount of videos are produced every day, such as movies, TV programs, personal home videos, surveillance video, etc. This places a high demand for effective video data analysis and management techniques. In this dissertation, we have developed new techniques for segmentation, linking and understanding of video scenes. Firstly, we have developed a video scene segmentation framework that segments the video content into story units. Then, a linking method is designed to find the semantic correlation between video scenes/stories. Finally, to better understand
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Schwarz, Christopher. "Detection, Segmentation, and Pose Recognition of Hands in Images." Honors in the Major Thesis, University of Central Florida, 2006. http://digital.library.ucf.edu/cdm/ref/collection/ETH/id/1004.

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This item is only available in print in the UCF Libraries. If this is your Honors Thesis, you can help us make it available online for use by researchers around the world by following the instructions on the distribution consent form at http://library.ucf.edu/Systems/DigitalInitiatives/DigitalCollections/InternetDistributionConsentAgreementForm.pdf You may also contact the project coordinator, Kerri Bottorff, at kerri.bottorff@ucf.edu for more information.<br>Bachelors<br>Engineering and Computer Science<br>Computer Science
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Riste-Smith, Robert. "Edge detection and knowledge based segmentation of medical radiographs." Thesis, University of Portsmouth, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.303486.

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Schofield, Andrew John. "Neural network models for texture segmentation and target detection." Thesis, Keele University, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.358048.

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CAMPOS, VANESSA DE OLIVEIRA. "MULTICRITERION SEGMENTATION FOR LUNG NODULE DETECTION IN COMPUTED TOMOGRAPHY." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2009. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=16423@1.

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COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR<br>Este trabalho propõe um novo algoritmo de segmentação baseado em crescimento de regiões para detecção de nódulos pulmonares em imagens de tomografia computadorizada. Para decidir, em cada iteração, se dois objetos adjacentes são fundidos em um único objeto, o algoritmo de segmentação calcula um índice de heterogeneidade baseada em múltiplos critérios. Entretanto, o algoritmo de segmentação depende de alguns parâmetros os quais foram encontrados utilizando algoritmo genético. Resultados experimentais mostraram que o método é robust
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Espis, Andrea. "Object detection and semantic segmentation for assisted data labeling." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022.

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The automation of data labeling tasks is a solution to the errors and time costs related to human labeling. In this thesis work CenterNet, DeepLabV3, and K-Means applied to the RGB color space, are deployed to build a pipeline for Assisted data labeling: a semi-automatic process to iteratively improve the quality of the annotations. The proposed pipeline pointed out a total of 1547 wrong and missing annotations when applied to a dataset originally containing 8,300 annotations. Moreover, the quality of each annotation has been drastically improved, and at the same time, more than 600 hours of w
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Ghadiri, Farnoosh. "Human shape modelling for carried object detection and segmentation." Doctoral thesis, Université Laval, 2018. http://hdl.handle.net/20.500.11794/30948.

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La détection des objets transportés est un des prérequis pour développer des systèmes qui cherchent à comprendre les activités impliquant des personnes et des objets. Cette thèse présente de nouvelles méthodes pour détecter et segmenter les objets transportés dans des vidéos de surveillance. Les contributions sont divisées en trois principaux chapitres. Dans le premier chapitre, nous introduisons notre détecteur d’objets transportés, qui nous permet de détecter un type générique d’objets. Nous formulons la détection d’objets transportés comme un problème de classification de contours. Nous cla
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Marte, Otto-Carl. "Model driven segmentation and the detection of bone fractures." Master's thesis, University of Cape Town, 2004. http://hdl.handle.net/11427/6414.

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Bibliography: leaves 83-90.<br>The introduction of lower dosage image acquisition devices and the increase in computational power means that there is an increased focus on producing diagnostic aids for the medical trauma environment. The focus of this research is to explore whether geometric criteria can be used to detect bone fractures from Computed Tomography data. Conventional image processing of CT data is aimed at the production of simple iso-surfaces for surgical planning or diagnosis - such methods are not suitable for the automated detection of fractures. Our hypothesis is that through
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Lo, Wing Sze. "Statistics-based Chinese word segmentation and new word detection /." View Abstract or Full-Text, 2002. http://library.ust.hk/cgi/db/thesis.pl?ELEC%202002%20LOW.

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Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2002.<br>Includes bibliographical references (leaves 83-86). Also available in electronic version. Access restricted to campus users.
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Ullah, Habib. "Crowd Motion Analysis: Segmentation, Anomaly Detection, and Behavior Classification." Doctoral thesis, Università degli studi di Trento, 2015. https://hdl.handle.net/11572/369001.

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The objective of this doctoral study is to develop efficient techniques for flow segmentation, anomaly detection, and behavior classification in crowd scenes. Considering the complexities of occlusion, we focused our study on gathering the motion information at a higher scale, thus not associating it to single objects, but considering the crowd as a single entity. Firstly,we propose methods for flow segmentation based on correlation features, graph cut, Conditional Random Fields (CRF), enthalpy model, and particle mutual influence model. Secondly, methods based on deviant orientation informati
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Ullah, Habib. "Crowd Motion Analysis: Segmentation, Anomaly Detection, and Behavior Classification." Doctoral thesis, University of Trento, 2015. http://eprints-phd.biblio.unitn.it/1406/1/PhD_Thesis_Habib.pdf.

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The objective of this doctoral study is to develop efficient techniques for flow segmentation, anomaly detection, and behavior classification in crowd scenes. Considering the complexities of occlusion, we focused our study on gathering the motion information at a higher scale, thus not associating it to single objects, but considering the crowd as a single entity. Firstly,we propose methods for flow segmentation based on correlation features, graph cut, Conditional Random Fields (CRF), enthalpy model, and particle mutual influence model. Secondly, methods based on deviant orientation informati
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Lung-Yut-Fong, Alexandre. "Evaluation of Kernel Methods for Change Detection and Segmentation : Application to Audio Onset Detection." Thesis, Uppsala University, Department of Information Technology, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-98274.

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<p>Finding changes in a signal is a pervasive topic in signal processing. Through the example of audio onset detection to which we apply an online framework, we evaluate the ability of a class of machine learning techniques to solve this task.</p><p>The goal of this thesis is to review and evaluate some kernel methods for thetwo-sample problem (one-class Support Vector Machine, Maximum MeanDiscrepancy and Kernel Fisher Discriminant Analysis) on the change detection task, by benchmarking our proposed framework on a set of annotated audio files to which we can compare our results to the ground-t
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Sarkaar, Ajit Bhikamsingh. "Addressing Occlusion in Panoptic Segmentation." Thesis, Virginia Tech, 2021. http://hdl.handle.net/10919/101988.

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Visual recognition tasks have witnessed vast improvements in performance since the advent of deep learning. Despite the gains in performance, image understanding algorithms are still not completely robust to partial occlusion. In this work, we propose a novel object classification method based on compositional modeling and explore its effect in the context of the newly introduced panoptic segmentation task. The panoptic segmentation task combines both semantic and instance segmentation to perform labelling of the entire image. The novel classification method replaces the object detection pipel
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Feng, Sitao. "Evaluation of Red Colour Segmentation Algorithms in Traffic Signs Detection." Thesis, Högskolan Dalarna, Datateknik, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:du-5806.

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Colour segmentation is the most commonly used method in road signs detection. Road sign contains several basic colours such as red, yellow, blue and white which depends on countries.The objective of this thesis is to do an evaluation of the four colour segmentation algorithms. Dynamic Threshold Algorithm, A Modification of de la Escalera’s Algorithm, the Fuzzy Colour Segmentation Algorithm and Shadow and Highlight Invariant Algorithm. The processing time and segmentation success rate as criteria are used to compare the performance of the four algorithms. And red colour is selected as the targe
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Losch, Max. "Detection and Segmentation of Brain Metastases with Deep Convolutional Networks." Thesis, KTH, Datorseende och robotik, CVAP, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-173519.

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As deep convolutional networks (ConvNets) reach spectacular results on a multitude of computer vision tasks and perform almost as well as a human rater on the task of segmenting gliomas in the brain, I investigated the applicability for detecting and segmenting brain metastases. I trained networks with increasing depth to improve the detection rate and introduced a border-pair-scheme to reduce oversegmentation. A constraint on the time for segmenting a complete brain scan required the utilization of fully convolutional networks which reduced the time from 90 minutes to 40 seconds. Despite some
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Ott, Patrick. "Segmentation features, visibility modeling and shared parts for object detection." Thesis, University of Leeds, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.581947.

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This thesis investigates the problem of object localization in still images and is separated into three individual parts. The first part proposes a new set of feature descriptors, motivated by the problem of pedestrian detection. Sliding window classifiers, notably using the Histogram-of-Gradient (HOG) features proposed by Dalal & Triggs are the state-of-the-art for this task, and we base our method on this approach. We propose a novel feature extraction scheme which computes implicit 'soft segmentations' of image regions into foreground/background. The method yields stronger object/background
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Liang, Kung-Hao. "From uncertainty to adaptivity : multiscale edge detection and image segmentation." Thesis, University of Warwick, 1997. http://wrap.warwick.ac.uk/57578/.

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This thesis presents the research on two different tasks in computer vision: edge detection and image segmentation (including texture segmentation and motion field segmentation). The central issue of this thesis is the uncertainty of the joint space-frequency image analysis, which motivates the design of the adaptive multiscale/multiresolution schemes for edge detection and image segmentation. Edge detectors capture most of the local features in an image, including the object boundaries and the details of surface textures. Apart from these edge features, the region properties of surface textur
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Christogiannopoulos, Georgios. "Detection, segmentation and tracking of moving individuals in cluttered scenes." Thesis, University of Sussex, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.444374.

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Wang, Chen. "2D object detection and semantic segmentation in the Carla simulator." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-291337.

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The subject of self-driving car technology has drawn growing interest in recent years. Many companies, such as Baidu and Tesla, have already introduced automatic driving techniques in their newest cars when driving in a specific area. However, there are still many challenges ahead toward fully autonomous driving cars. Tesla has caused several severe accidents when using autonomous driving functions, which makes the public doubt self-driving car technology. Therefore, it is necessary to use the simulator environment to help verify and perfect algorithms for the perception, planning, and decisio
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Kim, Soowon. "Computational architecture for the detection and segmentation of coherent motion /." The Ohio State University, 1997. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487946776021216.

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Lidayová, Kristína. "Fast Methods for Vascular Segmentation Based on Approximate Skeleton Detection." Doctoral thesis, Uppsala universitet, Avdelningen för visuell information och interaktion, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-318796.

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Modern medical imaging techniques have revolutionized health care over the last decades, providing clinicians with high-resolution 3D images of the inside of the patient's body without the need for invasive procedures. Detailed images of the vascular anatomy can be captured by angiography, providing a valuable source of information when deciding whether a vascular intervention is needed, for planning treatment, and for analyzing the success of therapy. However, increasing level of detail in the images, together with a wide availability of imaging devices, lead to an urgent need for automated t
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Ma, Tianyang. "Graph-based Inference with Constraints for Object Detection and Segmentation." Diss., Temple University Libraries, 2013. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/231622.

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Computer and Information Science<br>Ph.D.<br>For many fundamental problems of computer vision, adopting a graph-based framework can be straight-forward and very effective. In this thesis, I propose several graph-based inference methods tailored for different computer vision applications. It starts from studying contour-based object detection methods. In particular, We propose a novel framework for contour based object detection, by replacing the hough-voting framework with finding dense subgraph inference. Compared to previous work, we propose a novel shape matching scheme suitable for partial
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Wu, Xinheng. "A Deep Unsupervised Anomaly Detection Model for Automated Tumor Segmentation." Thesis, The University of Sydney, 2020. https://hdl.handle.net/2123/22502.

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Many researches have been investigated to provide the computer aided diagnosis (CAD) automated tumor segmentation in various medical images, e.g., magnetic resonance (MR), computed tomography (CT) and positron-emission tomography (PET). The recent advances in automated tumor segmentation have been achieved by supervised deep learning (DL) methods trained on large labelled data to cover tumor variations. However, there is a scarcity in such training data due to the cost of labeling process. Thus, with insufficient training data, supervised DL methods have difficulty in generating effective feat
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