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Дисертації з теми "Detection feature"

Автор: Grafiati

Опубліковано: 3 червня 2025

Оновлено: 31 липня 2025

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1

Sharpe, Lauren. "Feature sets for screenshot detection." Monterey, California: Naval Postgraduate School, 2013. http://hdl.handle.net/10945/34741.

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Approved for public release; distribution is unlimited<br>As digital media capacity continues to increase and the cost continues to decrease, digital forensic examiners need progressively more efficient, effective, and tailored tools in order to perform useful media triage. This thesis documents the development of feature sets for classifying images as either screenshots or non-screenshots. Using linear- and intensity-based image information we developed the first (to our knowledge) screenshot detection algorithm. Four feature sets were developed and combinations of these feature sets were tes

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2

Wang, Aijing. "SELECTIVE AUTOMATIC IMAGE FEATURE DETECTION." Wright State University / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=wright1316218638.

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3

Nilsson, Niklas. "Feature detection for geospatial referencing." Thesis, Umeå universitet, Institutionen för fysik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-159809.

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With the drone industry's recent explosive advancement, aerial photography is becoming increasingly important for an array of applications ranging from construction to agriculture. A drone flyover can give a better overview of regions that are difficult to navigate, and is often significantly faster, cheaper and more accurate than man-made sketches and other alternatives. With this increased use comes a growing need for image processing methods to help in analyzing captured photographs. This thesis presents a method for automatic location detection in aerial photographs using databases of aeri

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4

Hajro, Neira 1978. "Automated nasal feature detection for the lexical access from features project." Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/28401.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.<br>Includes bibliographical references (leaves 150-151).<br>The focus of this thesis was the design, implementation, and evaluation of a set of automated algorithms to detect nasal consonants from the speech waveform in a distinctive feature-based speech recognition system. The study used a VCV database of over 450 utterances recorded from three speakers, two male and one female. The first stage of processing for each speech waveform included automated 'pivot' estimation using t

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5

Sefidcon, Azimeh. "Feature interactions detection in intelligent networks." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape4/PQDD_0020/MQ47832.pdf.

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6

Heiberg, Einar Brandt. "Automated feature detection in multidimensional images /." Linköping : Univ, 2004. http://www.bibl.liu.se/liupubl/disp/disp2005/tek917s.pdf.

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7

Gollapudi, Venkata Lakshmi Sirisha. "Services for biological network feature detection." Thesis, University of Nottingham, 2010. http://eprints.nottingham.ac.uk/13022/.

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The complex environment of a living cell contains many molecules interacting in a variety of ways. Examples include the physical interaction between two proteins, or the biochemical interaction between an enzyme and its substrate. A challenge of systems biology is to understand the network of interactions between biological molecules, derived experimentally or computationally. Sophisticated dynamic modelling approaches provide detailed knowledge about single processes or individual pathways. However such methods are far less tractable for holistic cellular models, which are instead represented

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8

Song, Jingping. "Feature selection for intrusion detection system." Thesis, Aberystwyth University, 2016. http://hdl.handle.net/2160/3143de58-208f-405e-ab18-abcecfc8f33b.

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Intrusion detection is an important task for network operators in today?s Internet. Traditional network intrusion detection systems rely on either specialized signatures of previously seen attacks, or on labeled traffic datasets that are expensive and difficult to reproduce for user-profiling to hunt out network attacks. Machine learning methods could be used in this area since they could get knowledge from signatures or as normal-operation profiles. However, there is usually a large volume of data in intrusion detection systems, for both features and instances. Feature selection can be used t

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9

Treash, Katherine (Katherine Diane) 1975. "Feature detection in grayscale aerial images." Thesis, Massachusetts Institute of Technology, 1999. http://hdl.handle.net/1721.1/79989.

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10

Linguraru, Marius George. "Feature detection in mammographic image analysis." Thesis, University of Oxford, 2004. http://ora.ox.ac.uk/objects/uuid:b92185f0-c7bf-40e1-bc17-bf71065f001f.

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In modern society, cancer has become one of the most terrifying diseases because of its high and increasing death rate. The disease's deep impact demands extensive research to detect and eradicate it in all its forms. Breast cancer is one of the most common forms of cancer, and approximately one in nine women in the Western world will develop it over the course of their lives. Screening programmes have been shown to reduce the mortality rate, but they introduce an enormous amount of information that must be processed by radiologists on a daily basis. Computer Aided Diagnosis (CAD) systems aim

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11

Malady, Amy Colleen. "Cyclostationarity Feature-Based Detection and Classification." Thesis, Virginia Tech, 2011. http://hdl.handle.net/10919/32280.

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Cyclostationarity feature-based (C-FB) detection and classification is a large field of research that has promising applications to intelligent receiver design. Cyclostationarity FB classification and detection algorithms have been applied to a breadth of wireless communication signals â analog and digital alike. This thesis reports on an investigation of existing methods of extracting cyclostationarity features and then presents a novel robust solution that reduces SNR requirements, removes the pre-processing task of estimating occupied signal bandwidth, and can achieve classification rates

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12

Gurbuz, Ali Cafer. "Feature detection algorithms in computed images." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/24718.

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Thesis (Ph.D.)--Electrical and Computer Engineering, Georgia Institute of Technology, 2009.<br>Committee Chair: McClellan, James H.; Committee Member: Romberg, Justin K.; Committee Member: Scott, Waymond R. Jr.; Committee Member: Vela, Patricio A.; Committee Member: Vidakovic, Brani

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13

TOOSI, AMIRHOSEIN. "Feature Fusion for Fingerprint Liveness Detection." Doctoral thesis, Politecnico di Torino, 2018. http://hdl.handle.net/11583/2711594.

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For decades, fingerprints have been the most widely used biometric trait in identity recognition systems, thanks to their natural uniqueness, even in rare cases such as identical twins. Recently, we witnessed a growth in the use of fingerprint-based recognition systems in a large variety of devices and applications. This, as a consequence, increased the benefits for offenders capable of attacking these systems. One of the main issues with the current fingerprint authentication systems is that, even though they are quite accurate in terms of identity verification, they can be easily spoo

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14

Al-Khafaji, Suhad. "Spectral-Spatial Feature Extraction for Hyperspectral Image Matching and Boundary Detection." Thesis, Griffith University, 2020. http://hdl.handle.net/10072/401445.

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A hyperspectral image contains a huge amount of information compared to grayscale and RGB images thanks to its high spectral resolution and wide sensing spectrum. This facilitates analysis and interpretation of properties and features of specific materials in the image. Exploiting both spectral and spatial information can provide more comprehensive and discriminative characteristics of objects of interest than traditional methods. Recently, hyperspectral imaging has been used in many applications such as medicine, agriculture, environment and astronomy. Furthermore, due to the availability and

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15

Fu, Jennifer Qifang. "Feature interaction detection in a telephony network integrated with switch-based features and IN features." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape3/PQDD_0015/MQ48152.pdf.

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16

Fowers, Spencer G. "Limited Resource Feature Detection, Description, and Matching." BYU ScholarsArchive, 2012. https://scholarsarchive.byu.edu/etd/3207.

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The aims of this research work are to develop a feature detection, description, and matching system for low-resource applications. This work was motivated by the need for a vision sensor to assist the flight of a quad-rotor UAV. This application presented a real-world challenge of autonomous drift stabilization using vision sensors. The initial solution implemented a basic feature detector and matching system on an FPGA. The research then pursued ways to improve the vision system. Research began with color feature detection, and the Color Difference of Gaussians feature detector was developed.

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17

Abdul-Razak, Ariffin. "Detection of feature interactions in an object-oriented feature-based design system." Thesis, Heriot-Watt University, 1997. http://hdl.handle.net/10399/651.

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18

Butko, Taras. "Feature selection for multimodal: acoustic event detection." Doctoral thesis, Universitat Politècnica de Catalunya, 2011. http://hdl.handle.net/10803/32176.

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The detection of the Acoustic Events (AEs) naturally produced in a meeting room may help to describe the human and social activity. The automatic description of interactions between humans and environment can be useful for providing: implicit assistance to the people inside the room, context-aware and content-aware information requiring a minimum of human attention or interruptions, support for high-level analysis of the underlying acoustic scene, etc. On the other hand, the recent fast growth of available audio or audiovisual content strongly demands tools for analyzing, indexing, searching a

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19

SUNDHOLM, JOEL. "Feature Extraction for Anomaly Detection inMaritime Trajectories." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-155898.

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The operators of a maritime surveillance system are hardpressed to make complete use of the near real-time informationflow available today. To assist them in this matterthere has been an increasing amount of interest in automated systems for the detection of anomalous trajectories.Specifically, it has been proposed that the framework of conformal anomaly detection can be used, as it provides the key property of a well-tuned alarm rate. However, inorder to get an acceptable precision there is a need to carefully tailor the nonconformity measure used to determine if a trajectory is anomalous. Th

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20

Rafoul, Elias. "Detection of feature interaction using relational algebra." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ38761.pdf.

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21

Myerscough, Peter J. "Time persistent feature detection via phase congruency." Thesis, University of Southampton, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.427413.

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22

Akula, Ravi Kiran. "Botnet Detection Using Graph Based Feature Clustering." Thesis, Mississippi State University, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10751733.

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<p> Detecting botnets in a network is crucial because bot-activities impact numerous areas such as security, finance, health care, and law enforcement. Most existing rule and flow-based detection methods may not be capable of detecting bot-activities in an efficient manner. Hence, designing a robust botnet-detection method is of high significance. In this study, we propose a botnet-detection methodology based on graph-based features. Self-Organizing Map is applied to establish the clusters of nodes in the network based on these features. Our method is capable of isolating bots in small cluster

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23

Raffoul, Joseph Naim. "Blob Feature Extraction for Event Detection Cameras." University of Dayton / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1590165017029087.

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24

Wallis, S. A. "Low level feature detection in human vision." Thesis, Aston University, 2009. http://publications.aston.ac.uk/15404/.

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Influential models of edge detection have generally supposed that an edge is detected at peaks in the 1st derivative of the luminance profile, or at zero-crossings in the 2nd derivative. However, when presented with blurred triangle-wave images, observers consistently marked edges not at these locations, but at peaks in the 3rd derivative. This new phenomenon, termed ‘Mach edges’ persisted when a luminance ramp was added to the blurred triangle-wave. Modelling of these Mach edge detection data required the addition of a physiologically plausible filter, prior to the 3rd derivative computation.

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25

Cohen, Gregory Kevin. "Event-Based Feature Detection, Recognition and Classification." Thesis, Paris 6, 2016. http://www.theses.fr/2016PA066204/document.

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La detection, le suivi de cible et la reconnaissance de primitives visuelles constituent des problèmes fondamentaux de la vision robotique. Ces problématiques sont réputés difficiles et sources de défis. Malgré les progrès en puissance de calcul des machines, le gain en résolution et en fréquence des capteurs, l’état-de-l’art de la vision robotique peine à atteindre des performances en coût d’énergie et en robustesse qu’offre la vision biologique. L’apparition des nouveaux capteurs, appelés "rétines de silicium” tel que le DVS (Dynamic Vision Sensor) et l’ATIS (Asynchronous Time-based Imaging

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26

Eklind, Anna, and Love Stark. "An exploratory research of ARCore's feature detection." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254357.

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Augmented reality has been on the rise for some time now and begun making its way onto the mobile market for both IOS and Android. In 2017 Apple released ARKit for IOS which is a software development kit for developing augmented reality applications. To counter this, Google released their own variant called ARCore on the 1st of march 2018. ARCore is also a software development kit for developing augmented reality applications but made for the Android, Unity and Unreal platforms instead. Since ARCore is released recently it is still unknown what particular limitations may exist for it. The purp

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27

Liu, Chenguang. "Low level feature detection in SAR images." Electronic Thesis or Diss., Institut polytechnique de Paris, 2020. http://www.theses.fr/2020IPPAT015.

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Dans cette thèse, nous développons des détecteurs de caractéristiques de bas niveau pour les images radar à synthèse d'ouverture (SAR) afin de faciliter l'utilisation conjointe des données SAR et optiques. Les segments de droite et les bords sont des caractéristiques de bas niveau très importantes dans les images qui peuvent être utilisées pour de nombreuses applications comme l'analyse ou le stockage d'images, ainsi que la détection d'objets. Alors qu'il existe de nombreux détecteurs efficaces pour les structures bas-niveau dans les images optiques, il existe très peu de détecteurs de ce type

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28

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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Анотація:

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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29

Kapoor, Prince. "Shoulder Keypoint-Detection from Object Detection." Thesis, Université d'Ottawa / University of Ottawa, 2018. http://hdl.handle.net/10393/38015.

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This thesis presents detailed observation of different Convolutional Neural Network (CNN) architecture which had assisted Computer Vision researchers to achieve state-of-the-art performance on classification, detection, segmentation and much more to name image analysis challenges. Due to the advent of deep learning, CNN had been used in almost all the computer vision applications and that is why there is utter need to understand the miniature details of these feature extractors and find out their pros and cons of each feature extractor meticulously. In order to perform our experimentatio

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30

Li, Zhaoqiang. "Specification and detection of feature interactions using MSCs." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape4/PQDD_0015/MQ47827.pdf.

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31

Barbieri, Gillian Sylvia Anna-Stasia. "The role of spatial derivatives in feature detection." Thesis, University of Birmingham, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.368742.

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32

Hillman, Chris. "Real-time feature detection in mass spectrometer data." Thesis, University of Dundee, 2018. https://discovery.dundee.ac.uk/en/studentTheses/92873cb6-6e7a-4c24-a76c-60509590f2d4.

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Анотація:

Proteomics has become an essential component of systems biology in the quest for personalised medicine. Each of us has a unique biology and can respond in different ways to medical treatments. By analysing the complete set of proteins present in humans the field of life sciences is moving closer to the goal of being able to recommend specific drugs to specific individuals thus greatly enhancing the probability of a cure. Extensive pre-processing of the complex files created by mass spectrometers during proteomics experiments is required before it is possible to gain any insight from them. A ty

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33

Liu, Sharlene Anne. "Landmark detection for distinctive feature-based speech recognition." Thesis, Massachusetts Institute of Technology, 1995. http://hdl.handle.net/1721.1/11406.

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Анотація:

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1995.<br>Includes bibliographical references (leaves 187-190).<br>by Sharlene Anne Liu.<br>Ph.D.

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34

Liu, Xian. "Feature Detection from Mobile LiDAR Using Deep Learning." Miami University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=miami1552002747337465.

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35

Al-Sit, Waleed. "Automatic feature detection and interpretation in borehole data." Thesis, University of Liverpool, 2015. http://livrepository.liverpool.ac.uk/2014181/.

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Анотація:

Detailed characterisation of the structure of subsurface fractures is greatly facilitated by digital borehole logging instruments, however, the interpretation of which is typically time-consuming and labour-intensive. Despite recent advances towards autonomy and automation, the final interpretation remains heavily dependent on the skill, experience, alertness and consistency of a human operator. Existing computational tools fail to detect layers between rocks that do not exhibit distinct fracture boundaries, and often struggle characterising cross-cutting layers and partial fractures. This res

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36

Zeng, Guang. "Real-time automatic linear feature detection in images." Connect to this title online, 2008. http://etd.lib.clemson.edu/documents/1239894316/.

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37

Chen, Meihong. "Real-Time Video Object Detection with Temporal Feature Aggregation." Thesis, Université d'Ottawa / University of Ottawa, 2021. http://hdl.handle.net/10393/42790.

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Анотація:

In recent years, various high-performance networks have been proposed for single-image object detection. An obvious choice is to design a video detection network based on state-of-the-art single-image detectors. However, video object detection is still challenging due to the lower quality of individual frames in a video, and hence the need to include temporal information for high-quality detection results. In this thesis, we design a novel interleaved architecture combining a 2D convolutional network and a 3D temporal network. We utilize Yolov3 as the base detector. To explore inter-frame inf

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38

Kreshchenko, Ivan. "Self-Organized Deviation Detection." Thesis, Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-1566.

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<p>A technique to detect deviations in sets of systems in a self-organized way is described in this work. System features are extracted to allow compact representation of the system. Distances between systems are calculated by computing distances between the features. The distances are then stored in an affinity matrix. Deviating systems are detected by assuming a statistical model for the affinities. The key idea is to extract features and and identify deviating systems in a self-organized way, using nonlinear techniques for the feature extraction. The results are compared with those achieved

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39

Danielsson, Max, and Thomas Sievert. "Viability of Feature Detection on Sony Xperia Z3 using OpenCL." Thesis, Blekinge Tekniska Högskola, Institutionen för kreativa teknologier, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-10388.

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Анотація:

Context. Embedded platforms GPUs are reaching a level of perfor-mance comparable to desktop hardware. Therefore it becomes inter-esting to apply Computer Vision techniques to modern smartphones.The platform holds different challenges, as energy use and heat gen-eration can be an issue depending on load distribution on the device. Objectives. We evaluate the viability of a feature detector and de-scriptor on the Xperia Z3. Specifically we evaluate the the pair basedon real-time execution, heat generation and performance. Methods. We implement the feature detection and feature descrip-tor pair H

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40

Avan, Selcuk Kazim. "Feature Set Evaluation For A Generic Missile Detection System." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/2/12608130/index.pdf.

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Missile Detection System (MDS) is one of the main components of a self-protection system developed against the threat of guided missiles for airborne platforms. The requirements such as time critical operation and high accuracy in classification performance make the &lsquo<br>Pattern Recognition&rsquo<br>problem of an MDS a hard task. Problem can be defined in two main parts such as &lsquo<br>Feature Set Evaluation&rsquo<br>(FSE) and &lsquo<br>Classifier&rsquo<br>designs. The main goal of feature set evaluation is to employ a dimensionality reduction process for the input data set, while not d

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41

Keelan, Oliver, and Henrik Mårtensson. "Feature Engineering and Machine Learning for Driver Sleepiness Detection." Thesis, Linköpings universitet, Institutionen för medicinsk teknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-142001.

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Falling asleep while operating a moving vehicle is a contributing factor to the statistics of road related accidents. It has been estimated that 20% of all accidents where a vehicle has been involved are due to sleepiness behind the wheel. To prevent accidents and to save lives are of uttermost importance. In this thesis, given the world’s largest dataset of driver participants, two methods of evaluating driver sleepiness have been evaluated. The first method was based on the creation of epochs from lane departures and KSS, whilst the second method was based solely on the creation of epochs ba

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42

Gerling, Jonas. "Implementing Object and Feature Detection Without Compromising the Performance." Thesis, Linköpings universitet, Programvara och system, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-129276.

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This thesis will cover how some computationally heavy algorithms used in digital image processing and computer vision are implemented with WebGL and computed on the graphics processing unit by utilizing GLSL-shaders. This thesis is based on an already implemented motion detection plug-in used in web based games. This plug-in is enhanced with new features and some already implemented algorithms are improved. The motion detection is based on image subtraction and uses the delta image from previous frames to determine motion. The plug-in is used in web based games so the performance is of utmost

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43

Källström, Elisabeth. "On-board Feature Extraction for Clutch Slippage Deviation Detection." Licentiate thesis, Luleå tekniska universitet, Produkt- och produktionsutveckling, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-26665.

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Анотація:

Construction equipment companies continuously upgrade their products to meetcustomer demands, staying competitive with market challenges as well as improving sales and profits. With increased complexities in heavy duty machines today, up-time is considered an important aspect of the construction equipment business because it reduces warranty and service cost, while increasing sales and overall customer satisfaction. Therefore, a substantial amount of research is directed towards the development of intelligent machines which are capable of automatically monitoring the health of different compon

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Juarez, Dominguez Alma L. "Detection of Feature Interactions in Automotive Active Safety Features." Thesis, 2012. http://hdl.handle.net/10012/6701.

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With the introduction of software into cars, many functions are now realized with reduced cost, weight and energy. The development of these software systems is done in a distributed manner independently by suppliers, following the traditional approach of the automotive industry, while the car maker takes care of the integration. However, the integration can lead to unexpected and unintended interactions among software systems, a phenomena regarded as feature interaction. This dissertation addresses the problem of the automatic detection of feature interactions for automotive active

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45

Chen, Fang. "Facial Feature Point Detection." Thesis, 2011. http://hdl.handle.net/1807/30546.

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Facial feature point detection is a key issue in facial image processing. One main challenge of facial feature point detection is the variation of facial structures due to expressions. This thesis aims to explore more accurate and robust facial feature point detection algorithms, which can facilitate the research on facial image processing, in particular the facial expression analysis. This thesis introduces a facial feature point detection system, where the Multilinear Principal Component Analysis is applied to extract the highly descriptive features of facial feature points. In addition, to

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Chaney, Ronald D. "Feature Extraction Without Edge Detection." 1993. http://hdl.handle.net/1721.1/6794.

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Information representation is a critical issue in machine vision. The representation strategy in the primitive stages of a vision system has enormous implications for the performance in subsequent stages. Existing feature extraction paradigms, like edge detection, provide sparse and unreliable representations of the image information. In this thesis, we propose a novel feature extraction paradigm. The features consist of salient, simple parts of regions bounded by zero-crossings. The features are dense, stable, and robust. The primary advantage of the features is that they have ab

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Serre, Thomas, Bernd Heisele, Sayan Mukherjee, and Tomaso Poggio. "Feature Selection for Face Detection." 2000. http://hdl.handle.net/1721.1/7232.

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We present a new method to select features for a face detection system using Support Vector Machines (SVMs). In the first step we reduce the dimensionality of the input space by projecting the data into a subset of eigenvectors. The dimension of the subset is determined by a classification criterion based on minimizing a bound on the expected error probability of an SVM. In the second step we select features from the SVM feature space by removing those that have low contributions to the decision function of the SVM.

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MUNGRE, YATINDRA. "AGE DETECTION USING FACIAL FEATURE." Thesis, 2016. http://dspace.dtu.ac.in:8080/jspui/handle/repository/16338.

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Анотація:

The project presented here is an attempt to use Facial feature Extraction for human image. The applications instigated from such an attempt will result in to a set of extracted facial features. Using these facial features various recognitions can be derived. Age recognition is the primary recognition, which is under research for various requirements. The project is an attempt to use a basic feature detection algorithm for images which contains human image. The applications instigated from such an attempt will result in facial feature detection of image which has human in it. It tries to d

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MUNGRE, YATINDRA. "AGE DETECTION USING FACIAL FEATURE." Thesis, 2016. http://dspace.dtu.ac.in:8080/jspui/handle/repository/16170.

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Анотація:

The project presented here is an attempt to use Facial feature Extraction for human image. The applications instigated from such an attempt will result in to a set of extracted facial features. Using these facial features various recognitions can be derived. Age recognition is the primary recognition, which is under research for various requirements. The project is an attempt to use a basic feature detection algorithm for images which contains human image. The applications instigated from such an attempt will result in facial feature detection of image which has human in it. It tries to d

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50

Bansal, Manish. "Feature Detection using S-Transform." Thesis, 2013. http://ethesis.nitrkl.ac.in/4967/1/109CS0132.pdf.

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Анотація:

Images are characterized by features. Machines identify and recognize a scene or an image by its features. Edges, objects, and textures are some of the features that distinguish one image from another. There could be many common features in similar images. But, in those commonalities there lies a distinction in terms of features known as subtle features. Numerous algorithms have been reported to extract features from images. Few of them are reliable. Some of them do well under a constrained environment. Many of them fail miserably under low intensity, noise etc. The prominent features are very

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