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Relevant bibliographies by topics / Object Detection and Recognition / Dissertations / Theses

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Dissertations / Theses on the topic 'Object Detection and Recognition'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

Anwer, Rao Muhammad. "Color for Object Detection and Action Recognition." Doctoral thesis, Universitat Autònoma de Barcelona, 2013. http://hdl.handle.net/10803/120224.

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Detectar objetos en imágenes es un problema central en el campo de la visión por computador. El marco de detección basado en modelos de partes deformable es actualmente el más eficaz. Generalmente, HOG es el descriptor de imágenes a partir del cual se construyen esos modelos. El reconocimiento de acciones humanas es otro de los tópicos de más interés actualmente en el campo de la visión por computador. En este caso, los modelos usados siguen la idea de conjuntos de palabras (visuales), en inglés bag-of-words, en este caso siendo SIFT uno de los descriptor de imágenes más usados para dar soport

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Mahmood, Hamid. "Visual Attention-based Object Detection and Recognition." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-94024.

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This thesis is all about the visual attention, starting from understanding the human visual system up till applying this mechanism to a real-world computer vision application. This has been achieved by taking the advantage of latest findings about the human visual attention and the increased performance of the computers. These two facts played a vital role in simulating the many different aspects of this visual behavior. In addition, the concept of bio-inspired visual attention systems have become applicable due to the emergence of different interdisciplinary approaches to vision which leads t

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3

Aleixo, Patrícia Nunes. "Object detection and recognition for robotic applications." Master's thesis, Universidade de Aveiro, 2014. http://hdl.handle.net/10773/13811.

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Mestrado em Engenharia Eletrónica e Telecomunicações<br>The computer vision assumes an important relevance in the development of robotic applications. In several applications, robots need to use vision to detect objects, a challenging and sometimes difficult task. This thesis is focused on the study and development of algorithms to be used in detection and identification of objects on digital images to be applied on robots that will be used in practice cases. Three problems are addressed: Detection and identification of decorative stones for textile industry; Detection of the ball in ro

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Dittmar, George William. "Object Detection and Recognition in Natural Settings." PDXScholar, 2013. https://pdxscholar.library.pdx.edu/open_access_etds/926.

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Much research as of late has focused on biologically inspired vision models that are based on our understanding of how the visual cortex processes information. One prominent example of such a system is HMAX [17]. HMAX attempts to simulate the biological process for object recognition in cortex based on the model proposed by Hubel & Wiesel [10]. This thesis investigates the ability of an HMAX-like system (GLIMPSE [20]) to perform object-detection in cluttered natural scenes. I evaluate these results using the StreetScenes database from MIT [1, 8]. This thesis addresses three questions: (1) Can

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5

Ridge, Douglas John. "Imaging for small object detection." Thesis, Queen's University Belfast, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.295423.

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Higgs, David Robert. "Parts-based object detection using multiple views /." Link to online version, 2005. https://ritdml.rit.edu/dspace/handle/1850/1000.

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7

IACONO, MASSIMILIANO. "Object detection and recognition with event driven cameras." Doctoral thesis, Università degli studi di Genova, 2020. http://hdl.handle.net/11567/1005981.

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This thesis presents study, analysis and implementation of algorithms to perform object detection and recognition using an event-based cam era. This sensor represents a novel paradigm which opens a wide range of possibilities for future developments of computer vision. In partic ular it allows to produce a fast, compressed, illumination invariant output, which can be exploited for robotic tasks, where fast dynamics and signiﬁcant illumination changes are frequent. The experiments are carried out on the neuromorphic version of the iCub humanoid platform. The robot is equipped with a nov

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Simonelli, Andrea. "3D Object Detection from Images." Doctoral thesis, Università degli studi di Trento, 2022. http://hdl.handle.net/11572/353602.

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Remarkable advancements in the field of Computer Vision, Artificial Intelligence and Machine Learning have led to unprecedented breakthroughs in what machines are able to achieve. In many tasks such as in Image Classification in fact, they are now capable of even surpassing human performance. While this is truly outstanding, there are still many tasks in which machines lag far behind. Walking in a room, driving on an highway, grabbing some food for example. These are all actions that feel natural to us but can be quite unfeasible for them. Such actions require to identify and localize objec

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9

Yoon, Taehun. "Object Recognition Based on Multi-agent Spatial Reasoning." The Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=osu1206075792.

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10

Bodén, Rikard, and Jonathan Pernow. "SORTED : Serial manipulator with Object Recognition Trough Edge Detection." Thesis, KTH, Skolan för industriell teknik och management (ITM), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-264513.

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Today, there is an increasing demand for smart robots that can make decisions on their own and cooperate with humans in changing environments. The application areas for robotic arms with camera vision are likely to increase in the future of artificial intelligence as algorithms become more adaptable and intelligent than ever. The purpose of this bachelor’s thesis is to develop a robotic arm that recognises arbitrarily placed objects with camera vision and has the ability to pick and place the objects when they appear in unpredictable positions. The robotic arm has three degrees of freedom and

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Irhebhude, Martins. "Object detection, recognition and re-identification in video footage." Thesis, Loughborough University, 2015. https://dspace.lboro.ac.uk/2134/19600.

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There has been a significant number of security concerns in recent times; as a result, security cameras have been installed to monitor activities and to prevent crimes in most public places. These analysis are done either through video analytic or forensic analysis operations on human observations. To this end, within the research context of this thesis, a proactive machine vision based military recognition system has been developed to help monitor activities in the military environment. The proposed object detection, recognition and re-identification systems have been presented in this thesis

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Tonge, Ashwini Kishor. "Object Recognition Using Scale-Invariant Chordiogram." Thesis, University of North Texas, 2017. https://digital.library.unt.edu/ark:/67531/metadc984116/.

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This thesis describes an approach for object recognition using the chordiogram shape-based descriptor. Global shape representations are highly susceptible to clutter generated due to the background or other irrelevant objects in real-world images. To overcome the problem, we aim to extract precise object shape using superpixel segmentation, perceptual grouping, and connected components. The employed shape descriptor chordiogram is based on geometric relationships of chords generated from the pairs of boundary points of an object. The chordiogram descriptor applies holistic properties of the sh

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Nguyen, Chuong Hoang. "Features identification and tracking for an autonomous ground vehicle." Thesis, Virginia Tech, 2013. http://hdl.handle.net/10919/33127.

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This thesis attempts to develop features identification and tracking system for an autonomous ground vehicle by focusing on four fundamental tasks: Motion detection, object tracking, scene recognition, and object detection and recognition. For motion detection, we combined the background subtraction method using the mixture of Gaussian models and the optical flow to highlight any moving objects or new entering objects which stayed still. To increase robustness for object tracking result, we used the Kalman filter to combine the tracking method based on the color histogram and the method based

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Clark, Daniel S. "Object detection and tracking using a parts-based approach /." Link to online version, 2005. https://ritdml.rit.edu/dspace/handle/1850/1167.

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Gadsby, David. "Object recognition for threat detection from 2D X-ray images." Thesis, Manchester Metropolitan University, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.493851.

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This thesis examines methods to identify threat objects inside airport handheld passenger baggage. The work presents techniques for the enhancement and classification of objects from 2-dimensional x-ray images. It has been conducted with the collaboration of Manchester Aviation Services and uses test images from real x-ray baggage machines. The research attempts to overcome the key problem of object occlusion that impedes the performance of x-ray baggage operators identifying threat objects such as guns and knifes in x-ray images. Object occlusions can hide key information on the appearance of

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Garcia, Rui Pedro Figueiredo. "Object recognition for a service robot." Master's thesis, Universidade de Aveiro, 2015. http://hdl.handle.net/10773/17393.

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Mestrado em Engenharia de Computadores e Telemática<br>A contínua evolução da tecnologia e o crescimento no desenvolvimento de aplicações robóticas tornou possível a criação de robôs autónomos que consigam assistir ou até mesmo substituir os humanos em tarefas diárias e trabalhos monótomos. Atualmente, com o envelhecimento da população humana, é esperado que os robôs de serviço venham a ser cada vez mais utilizados para assistência de pessoas idosas ou com deficiência. Para isso, um robô de serviços tem que ser capaz de evitar obstáculos enquanto se movimenta em ambientes conhecidos ou

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17

Case, Isaac. "Automatic object detection and tracking in video /." Online version of thesis, 2010. http://hdl.handle.net/1850/12332.

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Xin, Zhu. "Improvement of Automated Guided Vehicle's image recognition : Object detection and identification." Thesis, Högskolan Väst, Avdelningen för produktionssystem (PS), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:hv:diva-12027.

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Automated Guided Vehicle(AGV) as a kind of material conveying equipment has been widely used in modern manufacturing systems. [1] It carries the goods between the workshop along the designated paths. The ability of localization and recognizing the environment around themselves is the essential technology. AGV navigation is developed from several technologies such as fuzzy theory, neural network and other intelligent technology. Among them, visual navigation is one of the newer navigations, because of its path laying is easy to maintain, can identify variety of road signs. Compared with traditi

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Yang, Xingwei. "Shape Based Object Detection and Recognition in Silhouettes and Real Images." Diss., Temple University Libraries, 2011. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/111091.

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Computer and Information Science<br>Ph.D.<br>Shape is very essential for detecting and recognizing objects. It is robust to illumination, color changes. Human can recognize objects just based on shapes, thus shape based object detection and recognition methods have been popular in many years. Due to problem of segmentation, some researchers have worked on silhouettes instead of real images. The main problem in this area is object recognition and the difficulty is to handle shapes articulation and distortion. Previous methods mainly focus on one to one shape similarity measurement, which ignore

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Amplianitis, Konstantinos. "3D real time object recognition." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät, 2017. http://dx.doi.org/10.18452/17717.

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Die Objekterkennung ist ein natürlicher Prozess im Menschlichen Gehirn. Sie ndet im visuellen Kortex statt und nutzt die binokulare Eigenschaft der Augen, die eine drei- dimensionale Interpretation von Objekten in einer Szene erlaubt. Kameras ahmen das menschliche Auge nach. Bilder von zwei Kameras, in einem Stereokamerasystem, werden von Algorithmen für eine automatische, dreidimensionale Interpretation von Objekten in einer Szene benutzt. Die Entwicklung von Hard- und Software verbessern den maschinellen Prozess der Objek- terkennung und erreicht qualitativ immer mehr die Fähigkeiten des m

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Ke, Yan. "Deep Networks Based Energy Models for Object Recognition from Multimodality Images." Thesis, The University of Sydney, 2016. http://hdl.handle.net/2123/15641.

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Object recognition has been extensively investigated in computer vision area, since it is a fundamental and essential technique in many important applications, such as robotics, auto-driving, automated manufacturing, and security surveillance. According to the selection criteria, object recognition mechanisms can be broadly categorized into object proposal and classification, eye fixation prediction and saliency object detection. Object proposal tends to capture all potential objects from natural images, and then classify them into predefined groups for image description and interpretation. F

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Ross, Benjamin Charles. "An analysis of SIFT object recognition with an emphasis on landmark detection." Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/33341.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.<br>Includes bibliographical references (p. 109-110).<br>In this thesis, I explore the realm of feature-based object. recognition applied to landmark detection. I have built a system using SIFT object recognition and Locality-Sensitive Hashing to quickly and accurately detect landmarks with accuracies ranging from 85-95%. I have also compared PCA-SIFT, a newly developed feature descriptor, to SIFT, and have found that SIFT outperforms it only particular data set. In addition, I h

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23

Ta, Anh Phuong. "Inexact graph matching techniques : application to object detection and human action recognition." Lyon, INSA, 2010. http://theses.insa-lyon.fr/publication/2010ISAL0099/these.pdf.

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Object detection and human action recognition are two active fields of research in computer vision, which have applications ranging from robotics and video surveillance, medical image analysis, human-computer interactions to content-based video annotation and retrieval. At this time, building such robust recognition systems still remain very challenging tasks, because of the variations in action/object classes, different possible viewpoints, as well as illumination changes, moving cameras, complex dynamic backgrounds and occlusions. In this thesis, we deal with object and activity recognition

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Sinivaara, Kristian. "Simultaneous Localisation and Mapping using Autonomous Target Detection and Recognition." Thesis, Linköpings universitet, Reglerteknik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-110410.

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Simultaneous localisation and mapping (SLAM) is an often used positioning approach in GPS denied indoor environments. This thesis presents a novel method of combining SLAM with autonomous/aided target detection and recognition (ATD/R), which is beneficial for both methods. The method uses physical objects that are recognisable by ATR as unambiguous features in SLAM, while SLAM provides the ATR with better position estimates. The intended application is to improve the positioning of a first responder moving through an indoor environment, where the map offers localisation and simultaneously help

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Pan, Xiang. "Approaches for edge detection, pose determination and object representation in computer vision." Thesis, Heriot-Watt University, 1994. http://hdl.handle.net/10399/1378.

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Prokaj, Jan. "DETECTING CURVED OBJECTS AGAINST CLUTTERED BACKGROUNDS." Master's thesis, University of Central Florida, 2008. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2847.

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Detecting curved objects against cluttered backgrounds is a hard problem in computer vision. We present new low-level and mid-level features to function in these environments. The low-level features are fast to compute, because they employ an integral image approach, which makes them especially useful in real-time applications. The mid-level features are built from low-level features, and are optimized for curved object detection. The usefulness of these features is tested by designing an object detection algorithm using these features. Object detection is accomplished by transforming the mid-

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Zhao, Yiheng. "Towards the Design of Neural Network Framework for Object Recognition and Target Region Refining for Smart Transportation Systems." Thesis, Université d'Ottawa / University of Ottawa, 2018. http://hdl.handle.net/10393/37978.

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Object recognition systems have significant influences on modern life. Face, iris and finger point recognition applications are commonly applied for the security purposes; ASR (Automatic Speech Recognition) is commonly implemented on speech subtitle generation for various videos and audios, such as YouTube; HWR (Handwriting Recognition) systems are essential on the post office for cheque and postcode detection; ADAS (Advanced Driver Assistance System) are well applied to improve drivers’, passages’ and pedestrians’ safety. Object recognition techniques are crucial and valuable for academia, co

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Olafsson, Björgvin. "Partially Observable Markov Decision Processes for Faster Object Recognition." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-198632.

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Object recognition in the real world is a big challenge in the field of computer vision. Given the potentially enormous size of the search space it is essential to be able to make intelligent decisions about where in the visual field to obtain information from to reduce the computational resources needed. In this report a POMDP (Partially Observable Markov Decision Process) learning framework, using a policy gradient method and information rewards as a training signal, has been implemented and used to train fixation policies that aim to maximize the information gathered in each fixation. The p

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Peng, Liang. "Object Recognition in Videos Utilizing Hierarchical and Temporal Objectness with Deep Neural Networks." DigitalCommons@USU, 2017. https://digitalcommons.usu.edu/etd/6531.

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This dissertation develops a novel system for object recognition in videos. The input of the system is a set of unconstrained videos containing a known set of objects. The output is the locations and categories for each object in each frame across all videos. Initially, a shot boundary detection algorithm is applied to the videos to divide them into multiple sequences separated by the identified shot boundaries. Since each of these sequences still contains moderate content variations, we further use a cost optimization-based key frame extraction method to select key frames in each sequence and

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Kessi, Louisa. "Unsupervised detection based on spatial relationships : Application for object detection and recognition of colored business document structures." Thesis, Lyon, 2018. http://www.theses.fr/2018LYSEI068.

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Cette thèse a pour objectif de développer un système de reconnaissance de structures logique des documents d'entreprises sans modèle. Il s'agit de reconnaître la fonction logique de blocs de textes qui sont importants à localiser et à identifier. Ce problème est identique à celui de la détection d'objets dans une scène naturelle puisqu'il faut à la fois reconnaître les objets et les localiser dans une image. A la différence de la reconnaissance d'objets, les documents d'entreprises doivent être interprétés sans aucune information a priori sur leurs modèles de structures. La seule solution cons

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Ilestrand, Maja. "Automatic Eartag Recognition on Dairy Cows in Real Barn Environment." Thesis, Linköpings universitet, Datorseende, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-139245.

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All dairy cows in Europe wear unique identification tags in their ears. These eartags are standardized and contains the cows identification numbers, today only used for visual identification by the farmer. The cow also needs to be identified by an automatic identification system connected to milk machines and other robotics used at the farm. Currently this is solved with a non-standardized radio transmitter which can be placed on different places on the cow and different receivers needs to be used on different farms. Other drawbacks with the currently used identification system are that it is

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Coelho, Gavin. "Ota-quadrotor: An Object-tracking Autonomous Quadrotor for Real-time Detection and Recognition." Thesis, University of North Texas, 2012. https://digital.library.unt.edu/ark:/67531/metadc115056/.

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The field of robotics and mechatronics is advancing at an ever-increasing rate and we are starting to see robots making the transition from the factories to the workplace and homes as cost is reduced and they become more useful. In recent years quadrotors have become a popular unmanned air vehicle (UAV) platform. These UAVs or micro air vehicles (MAV) are being used for many new and exciting applications such as aerial monitoring of wildlife, disaster sites, riots and protests. They are also being used in the film industry, as they are significantly cheaper means of getting aerial footage. Whi

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Lillywhite, Kirt D. "Feature Construction Using Evolution-COnstructed Features for General Object Recognition." BYU ScholarsArchive, 2012. https://scholarsarchive.byu.edu/etd/2974.

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Object recognition is a well studied but extremely challenging field. Human detection is an especially important part of object recognition as it has played a role in machine and human interaction, biometrics, unmanned vehicles, as well as tracking and surveillance. We first present a hardware implementation of the successful Histograms of Oriented Gradients (HOG) method for human detection. The implementation significantly speeds up the method achieving 38 frames a second on VGA video while testing 11,160 sliding windows per frame. The accuracy remains comparable to the CPU implementation. An

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Nyberg, Selma. "Video Recommendation Based on Object Detection." Thesis, Uppsala universitet, Avdelningen för systemteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-351122.

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In this thesis, various machine learning domains have been combined in order to build a video recommender system that is based on object detection. The work combines two extensively studied research fields, recommender systems and computer vision, that also are rapidly growing and popular techniques on commercial markets. To investigate the performance of the approach, three different content-based recommender systems have been implemented at Spotify, which are based on the following video features: object detections, titles and descriptions, and user preferences. These systems have then been

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Solmon, Joanna Browne. "Using GIST Features to Constrain Search in Object Detection." PDXScholar, 2014. https://pdxscholar.library.pdx.edu/open_access_etds/1957.

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This thesis investigates the application of GIST features [13] to the problem of object detection in images. Object detection refers to locating instances of a given object category in an image. It is contrasted with object recognition, which simply decides whether an image contains an object, regardless of the object's location in the image. In much of computer vision literature, object detection uses a "sliding window" approach to finding objects in an image. This requires moving various sizes of windows across an image and running a trained classifier on the visual features of each window.

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Papageorgiou, Constantine P. "A Trainable System for Object Detection in Images and Video Sequences." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/5566.

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This thesis presents a general, trainable system for object detection in static images and video sequences. The core system finds a certain class of objects in static images of completely unconstrained, cluttered scenes without using motion, tracking, or handcrafted models and without making any assumptions on the scene structure or the number of objects in the scene. The system uses a set of training data of positive and negative example images as input, transforms the pixel images to a Haar wavelet representation, and uses a support vector machine classifier to learn the differe

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Zhou, Qiang. "Generalized Landmark Recognition in Robot Navigation." Ohio University / OhioLINK, 2004. http://www.ohiolink.edu/etd/view.cgi?ohiou1103304261.

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Orten, Burkay Birant. "Moving Object Identification And Event Recognition In Video Surveillamce Systems." Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/12606294/index.pdf.

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This thesis is devoted to the problems of defining and developing the basic building blocks of an automated surveillance system. As its initial step, a background-modeling algorithm is described for segmenting moving objects from the background, which is capable of adapting to dynamic scene conditions, as well as determining shadows of the moving objects. After obtaining binary silhouettes for targets, object association between consecutive frames is achieved by a hypothesis-based tracking method. Both of these tasks provide basic information for higher-level processing, such as activity analy

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Friberg, Oscar. "Recognizing Semantics in Human Actions with Object Detection." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-212579.

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Two-stream convolutional neural networks are currently one of the most successful approaches for human action recognition. The two-stream convolutional networks separates spatial and temporal information into a spatial stream and a temporal stream. The spatial stream accepts a single RGB frame, while the temporal stream accepts a sequence of optical flow. There have been attempts to further extend the work of the two-stream convolutional network framework. For instance there have been attempts to extend with a third network for auxiliary information, which this thesis mainly focuses on. We see

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Taurone, Francesco. "3D Object Recognition from a Single Image via Patch Detection by a Deep CNN." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/18669/.

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This thesis describes the development of a new technique for recognizing the 3D pose of an object via a single image. The whole project is based on a CNN for recognizing patches on the object, that we use for estimating the pose given an a priori model. The positions of the patches, together with the knowledge of their coordinates in the model, make the estimation of the pose possible through a solution of a PnP problem. The CNN chosen for this project is Yolo. In order to build the training dataset for the network, a new approach is used. Instead of labeling each individual training image

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Jiang, Lixing [Verfasser]. "Object Recognition and Saliency Detection for Indoor Robots using RGB-D Sensors / Lixing Jiang." München : Verlag Dr. Hut, 2016. http://d-nb.info/1106593723/34.

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Letzler, Daniel Charles 1975. "Surface detection and object recognition in a real-time three-dimensional ultrasonic imaging system." Thesis, Massachusetts Institute of Technology, 1999. http://hdl.handle.net/1721.1/80233.

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Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999.<br>Includes bibliographical references (leaf 138).<br>by Daniel Charles Letzler.<br>M.Eng.

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Rijhwani, Vishal. "A biologically inspired optical flow system for motion detection and object identification." Diss., Columbia, Mo. : University of Missouri-Columbia, 2007. http://hdl.handle.net/10355/5064.

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Thesis (M.S.)--University of Missouri-Columbia, 2007.<br>The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on April 7, 2008) Includes bibliographical references.

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Vilaplana, Besler Verónica. "Region-based face detection, segmentation and tracking. framework definition and application to other objects." Doctoral thesis, Universitat Politècnica de Catalunya, 2010. http://hdl.handle.net/10803/33330.

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One of the central problems in computer vision is the automatic recognition of object classes. In particular, the detection of the class of human faces is a problem that generates special interest due to the large number of applications that require face detection as a first step. In this thesis we approach the problem of face detection as a joint detection and segmentation problem, in order to precisely localize faces with pixel accurate masks. Even though this is our primary goal, in finding a solution we have tried to create a general framework as independent as possible of the type of

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Grönwall, Christna. "Ground Object Recognition using Laser Radar Data : Geometric Fitting, Performance Analysis, and Applications." Doctoral thesis, Linköpings universitet, Institutionen för systemteknik, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-7685.

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This thesis concerns detection and recognition of ground object using data from laser radar systems. Typical ground objects are vehicles and land mines. For these objects, the orientation and articulation are unknown. The objects are placed in natural or urban areas where the background is unstructured and complex. The performance of laser radar systems is analyzed, to achieve models of the uncertainties in laser radar data. A ground object recognition method is presented. It handles general, noisy 3D point cloud data. The approach is based on the fact that man-made objects on a large scale ca

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MAZZINI, DAVIDE. "Local Detectors and Descriptors for Object and Scene Recognition." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2018. http://hdl.handle.net/10281/199003.

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Lo scopo di questa tesi è di studiare due principali categorie di algoritmi per la detection di oggetti e il loro uso in particolari applicazioni. La prima categoria esaminata riguarda approcci basati su Keypoint. Diversi esperimenti comparativi vengono eseguiti all'interno della pipeline standard del modello di test MPEG CDVS e viene proposta una pipeline estesa che fa uso di informazione colore. La seconda categoria di object detectors oggetto di indagine si basa su Reti neurali convoluzionali. In particolare, vengono affrontate due applicazioni di reti neurali convoluzionali per il riconosc

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Vellala, Abhinay. "Genre-based Video Clustering using Deep Learning : By Extraction feature using Object Detection and Action Recognition." Thesis, Linköpings universitet, Statistik och maskininlärning, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176942.

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Social media has become an integral part of the Internet. There have been users across the world sharing content like images, texts, videos, and so on. There is a huge amount of data being generated and it has become a challenge to the social media platforms to group the content for further usage like recommending a video. Especially, grouping videos based on similarity requires extracting features. This thesis investigates potential approaches to extract features that can help in determining the similarity between videos. Features of given videos are extracted using Object Detection and Actio

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Mathew, Alex. "Rotation Invariant Histogram Features for Object Detection and Tracking in Aerial Imagery." University of Dayton / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1397662849.

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Ponciano, Jean-Jacques. "Object detection in unstructured 3D data sets using explicit semantics." Thesis, Lyon, 2019. http://www.theses.fr/2019LYSES059.

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Avec l’évolution des technologies et de la robotique, les possibilités offertes par les systèmes d’acquisition 3D ont augmenté. Aujourd’hui, ces systèmes sont utilisés dans différents domaines comme par exemple pour les véhicules autonomes,les robots de sauvetage, le patrimoine culturel. Ces champs d’application nécessitent souvent la reconnaissance d’objets à partir de données acquises. C’est pourquoi diverses méthodologies ont été étudiées pour traiter automatiquementles données 3D des nuages de points afin de détecter les objets contenus. Les meilleures méthodologies dépendent du contexte,

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Akman, Oytun. "Multi-camera Video Surveillance: Detection, Occlusion Handling, Tracking And Event Recognition." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/12608620/index.pdf.

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In this thesis, novel methods for background modeling, tracking, occlusion handling and event recognition via multi-camera configurations are presented. As the initial step, building blocks of typical single camera surveillance systems that are moving object detection, tracking and event recognition, are discussed and various widely accepted methods for these building blocks are tested to asses on their performance. Next, for the multi-camera surveillance systems, background modeling, occlusion handling, tracking and event recognition for two-camera configurations are examined. Various foregro

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