Relevant bibliographies by topics / Image motion / Dissertations / Theses
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Dissertations / Theses on the topic 'Image motion'

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Published: 4 June 2021
Last updated: 31 January 2023

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1

Hillman, Peter. "Segmentation of motion picture images and image sequences." Thesis, University of Edinburgh, 2002. http://hdl.handle.net/1842/15026.

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For Motion Picture Special Effects, it is often necessary to take a source image of an actor, segment the actor from the unwanted background, and then composite over a new background. The resultant image appears as if the actor was filmed in front of the new background. The standard approach requires the unwanted background to be a blue or green screen. While this technique is capable of handling areas where the foreground (the actor) blends into the background, the physical requirements present many practical problems. This thesis investigates the possibility of segmenting images where the unwanted background is more varied. Standard segmentation techniques tend not to be effective, since motion picture images have extremely high resolution and high accuracy is required to make the result appear convincing. A set of novel algorithms which require minimal human interaction to initialise the processing is presented. These algorithms classify each pixel by comparing its colour to that of known background and foreground areas. They are shown to be effective where there is a sufficient distinction between the colours of the foreground and background. A technique for assessing the quality of an image segmentation in order to compare these algorithms to alternative solutions is presented. Results are included which suggest that in most cases the novel algorithms have the best performance, and that they produce results more quickly than the alternative approaches. Techniques for segmentation of moving images sequences are then presented. Results are included which show that only a few frames of the sequence need to be initialised by hand, as it is often possible to generate automatically the input required to initialise processing for the remaining frames. A novel algorithm which can produce acceptable results on image sequences where more conventional approaches fail or are too slow to be of use is presented.
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2

Cheng, Xin. "Feature-based motion estimation and motion segmentation." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape7/PQDD_0016/MQ55493.pdf.

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3

Tweed, David S. "Motion segmentation across image sequences." Thesis, University of Bristol, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.364960.

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4

Chua, Collin. "Re-sounding images: sound and image in an audiovisual age." Thesis, Chua, Collin (2007) Re-sounding images: sound and image in an audiovisual age. PhD thesis, Murdoch University, 2007. https://researchrepository.murdoch.edu.au/id/eprint/657/.

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This dissertation examines the evolving articulation of sound and image in contemporary culture, with particular reference to film. It argues that sound and image have undergone a historical machined separation, followed by a machined fusion or recombination. The machined fusion of sound and image has enabled the creation of soundful images, which are more than simply the sum of their parts. Through the infusion of sound, images are now routinely reinforced with a performed sense of presence, where they are made to sound more real, more powerful, more authentic. Through association with the image, sounds are reinforced to the extent of becoming 'realer than real'. By tracing the history of sound and image from their initial machined separation to their subsequent machined fusion, it will be argued that a new relationship has been created that has shaped an influential new mode of communication and perception.
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Chua, Collin. "Re-sounding images : sound and image in an audiovisual age /." Chua, Collin (2007) Re-sounding images: sound and image in an audiovisual age. PhD thesis, Murdoch University, 2007. http://researchrepository.murdoch.edu.au/657/.

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This dissertation examines the evolving articulation of sound and image in contemporary culture, with particular reference to film. It argues that sound and image have undergone a historical machined separation, followed by a machined fusion or recombination. The machined fusion of sound and image has enabled the creation of soundful images, which are more than simply the sum of their parts. Through the infusion of sound, images are now routinely reinforced with a performed sense of presence, where they are made to sound more real, more powerful, more authentic. Through association with the image, sounds are reinforced to the extent of becoming 'realer than real'. By tracing the history of sound and image from their initial machined separation to their subsequent machined fusion, it will be argued that a new relationship has been created that has shaped an influential new mode of communication and perception.
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6

Reza-Alikhani, Hamid-Reza. "Motion compensation for image compression : pel-recursive motion estimation algorithm." Thesis, Loughborough University, 2002. https://dspace.lboro.ac.uk/2134/33721.

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In motion pictures there is a certain amount of redundancy between consecutive frames. These redundancies can be exploited by using interframe prediction techniques. To further enhance the efficiency of interframe prediction, motion estimation and compensation, various motion compensation techniques can be used. There are two distinct techniques for motion estimation block matching and pel-recursive block matching has been widely used as it produces a better signal-to-noise ratio or a lower bit rate for transmission than the pel-recursive method. In this thesis, various pel-recursive motion estimation techniques such as steepest descent gradient algorithm have been considered and simulated.
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7

Farnebäck, Gunnar. "Motion-based segmentation of image sequences." Thesis, Linköping University, Linköping University, Computer Vision, 1996. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-54351.

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This Master's Thesis addresses the problem of segmenting an image sequence with respect to the motion in the sequence. As a basis for the motion estimation, 3D orientation tensors are used. The goal of the segmentation is to partition the images into regions, characterized by having a coherent motion. The motion model is affine with respect to the image coordinates. A method to estimate the parameters of the motion model from the orientation tensors in a region is presented. This method can also be generalized to a large class of motion models.

Two segmentation algorithms are presented together with a postprocessing algorithm. All these algorithms are based on the competitive algorithm, a general method for distributing points between a number of regions, without relying on arbitrary threshold values. The first segmentation algorithm segments each image independently, while the second algorithm recursively takes advantage of the previous segmentation. The postprocessing algorithm stabilizes the segmentations of a whole sequence by imposing continuity constraints.

The algorithms have been implemented and the results of applying them to a test sequence are presented. Interesting properties of the algorithms are that they are robust to the aperture problem and that they do not require a dense velocity ¯eld.

It is finally discussed how the algorithms can be developed and improved. It is straightforward to extend the algorithms to base the segmentations on alternative or additional features, under not too restrictive conditions on the features.

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8

Wang, Qi. "Motion compensation for image sequence coding." Thesis, Heriot-Watt University, 1991. http://hdl.handle.net/10399/821.

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9

Giaccone, Paul. "Motion analysis of cinematographic image sequences." Thesis, Kingston University, 2000. http://eprints.kingston.ac.uk/20647/.

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Many digital special effects require knowledge of the motion present in an image sequence. In order for these effects to be realistic, blending seamlessly with unmodified live action or animation, motion must be represented accurately. Most existing methods of motion estimation are unsuitable for use in postproduction for one or more reasons; namely poor accuracy; corruption, by aliasing and the aperture problem, of estimation of large-magnitude motion; failure to handle multiple motions and motion boundaries; representation of curvilinear motion as concatenated translations instead of as smooth curves; slowness of execution and inefficiency in the presence of small variations between successive images. Novel methods of motion estimation are proposed here that are specifically designed for use in postproduction and address all of the above problems. The techniques are based on parametric estimation of optical-flow fields, reformulated in terms of displacements rather than velocities. The paradigm of displacement estimation leads to techniques for iterative updating of motion estimation for accuracy; faster motion estimation by exploiting redundancies between successive images; representation of motion over a sequence of images with a single set of parameters; and curvilinear representation of motion. Robust statistics provides a means for distinguishing separate types of motion and overcoming the problems of motion boundaries. Accurate recovery of the motion of the background in a sequence, combined with other image characteristics, leads to a segmentation procedure that greatly accelerates the rotoscoping and compositing tasks commonly carried out in postproduction. Comparative evaluation of the proposed methods with other techniques for motion estimation and image segmentation indicates that, in most cases, the new work provides considerable improvements in quality.
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10

Saunders, Thomas. "Image motion analysis using inertial sensors." Thesis, University of Bath, 2015. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.687346.

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Understanding the motion of a camera from only the image(s) it captures is a di cult problem. At best we might hope to estimate the relative motion between camera and scene if we assume a static subject, but once we start considering scenes with dynamic content it becomes di cult to di↵erentiate between motion due to the observer or motion due to scene movement. In this thesis we show how the invaluable cues provided by inertial sensor data can be used to simplify motion analysis and relax requirements for several computer vision problems. This work was funded by the University of Bath.
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11

Barnett, Barry Stanley. "Full motion multiparty videoconferencing using motion compensated visual pattern image sequence coding /." Digital version accessible at:, 2000. http://wwwlib.umi.com/cr/utexas/main.

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12

Ould, Meiloud Ahmed. "The image of Arabs in Hollywood films." Laramie, Wyo. : University of Wyoming, 2007. http://proquest.umi.com/pqdweb?did=1445035141&sid=1&Fmt=2&clientId=18949&RQT=309&VName=PQD.

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13

Agarwal, Ankur. "Machine Learning for Image Based Motion Capture." Phd thesis, Grenoble INPG, 2006. http://tel.archives-ouvertes.fr/tel-00390301.

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Image based motion capture is a problem that has recently gained a lot of attention in the domain of understanding human motion in computer vision. The problem involves estimating the 3D configurations of a human body from a set of images and has applications that include human computer interaction, smart surveillance, video analysis and animation. This thesis takes a machine learning based approach to reconstructing 3D pose and motion from monocular images or video. It makes use of a collection of images and motion capture data to derive mathematical models that allow the recovery of full body configurations directly from image features. The approach is completely data-driven and avoids the use of a human body model. This makes the inference extremely fast. We formulate a class of regression based methods to distill a large training database of motion capture and image data into a compact model that generalizes to predicting pose from new images. The methods rely on using appropriately developed robust image descriptors, learning dynamical models of human motion, and kernelizing the input within a sparse regression framework. Firstly, it is shown how pose can effectively and efficiently be recovered from image silhouettes that are extracted using background subtraction. We exploit sparseness properties of the relevance vector machine for improved generalization and efficiency, and make use of a mixture of regressors for probabilistically handling ambiguities that are present in monocular silhouette based 3D reconstruction. The methods developed enable pose reconstruction from single images as well as tracking motion in video sequences. Secondly, the framework is extended to recover 3D pose from cluttered images by introducing a suitable image encoding that is resistant to changes in background. We show that non-negative matrix factorization can be used to suppress background features and allow the regression to selectively cue on features from the foreground human body. Finally, we study image encoding methods in a broader context and present a novel multi-level image encoding framework called ‘hyperfeatures' that proves to be effective for object recognition and image classification tasks.
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14

Andersson, Kenneth. "Motion estimation for perceptual image sequence coding /." Linköping : Univ, 2003. http://www.bibl.liu.se/liupubl/disp/disp2003/tek794s.pdf.

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15

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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16

Elias, D. P. "The motion-based segmentation of image sequences." Thesis, University of Cambridge, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.598806.

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Visual motion is one of the most important cues for the interpretation of image sequences. A contiguous region whose motion can be characterised by a single set of parameters is very likely to correspond to a distinct physical object. Knowledge of the motion and spatial extent of such regions can greatly enhance the efficiency of applications such as image sequence coding and video restoration. The first part of this dissertation analyses existing motion estimation techniques in terms of the extent to which they are able to recover true motion, to identify uncovered background (those areas of an image that were not visible in the preceding image), and to model abrupt motion discontinuities. A novel motion estimation technique is presented that performs well according to these criteria. It is argued that motion estimation alone is insufficient for future applications because estimation is performed at the wrong level of abstraction: the object view is missing. The focus in the latter part of this dissertation is on techniques that treat motion estimation and segmentation as equal and integral parts of the estimation process. A model, known as the layer model, is introduced, which explicitly represents images as the superposition of a number of underlying objects or layers. Research interest has naturally focused on the development of techniques that enable arbitrary image sequences to be efficiently decomposed into their constituent layers. A promising approach, proposed in the literature, is to model the distribution of motion within an image as a probabilistic mixture, the parameters of which may be recovered by the Expectation-Maximisation (EM) algorithm. Extensions to this approach are proposed that encourage spatial coherence and allow more precise modelling of uncovering and occlusion. A number of issues are proposed for further research, notably extending the technique to more accurately model non-rigid motion and to incorporate more sophisticated layer model order selection.
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17

Ildiz, Faith. "Estimation of motion parameters from image sequences." Thesis, Monterey, California. Naval Postgraduate School, 1991. http://hdl.handle.net/10945/28176.

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The image motion analysis algorithms that generate the two dimensional velocity of objects in a sequence of images are developed. The algorithms considered consist of: the parallel extended kalman filter method; the spatiotemporal gradient methods; the spatiotemporal frequency methods; and the one-dimensional FFT methods. These algorithms are designed to perform on low signal to noise ratio images. Each of these algorithms is applied to a sequence of computer generated images with varying signal to noise ratios. Simulations are used to evaluate the performance of each algorithm. (Author)
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18

Aksu, tIbrahim. "Performance analysis of image motion analysis algorithms." Thesis, Monterey, California. Naval Postgraduate School, 1991. http://hdl.handle.net/10945/28443.

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19

Lee, Chong U. "Contour motion compensation for image sequence coding." Thesis, Massachusetts Institute of Technology, 1989. http://hdl.handle.net/1721.1/14515.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1989.
Includes bibliographical references (leaves 124-130).
by Chong Uk Lee.
Ph.D.
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20

Wu, Siu Fan. "General motion estimation and segmentation." Thesis, University of Surrey, 1990. http://epubs.surrey.ac.uk/843155/.

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In this thesis, estimation of motion from an image sequence is investigated. The emphasis is on the novel use of motion model for describing two dimensional motion. Special attention is directed towards general motion models which are not restricted to translational motion. In contrast to translational motion, the 2-D motion is described by the model using motion parameters. There are two major areas which can benefit from the study of general motion model. The first one is image sequence processing and compression. In this context, the use of motion model provides a more compact description of the motion information because the model can be applied to a larger area. The second area is computer vision. The general motion parameters provide clues to the understanding of the environment. This offers a simpler alternative to techniques such as optical flow analysis. A direct approach is adopted here to estimate the motion parameters directly from an image sequence. This has the advantage of avoiding the error caused by the estimation of optical flow. A differential method has been developed for the purpose. This is applied in conjunction with a multi-resolution scheme. An initial estimate is obtained by applying the algorithm to a low resolution image. The initial estimate is then refined by applying the algorithm to image of higher resolutions. In this way, even severe motion can be estimated with high resolution. However, the algorithm is unable to cope with the situation of multiple moving objects, mainly because of the least square estimator used. A second algorithm, inspired by the Hough transform, is therefore developed to estimate the motion parameters of multiple objects. By formulating the problem as an optimization problem, the Hough transform is computed only implicitly. This drastically reduces the computational requirement as compared with the Hough transform. The criterion used in optimization is a measure of the degree of match between two images. It has been shown that the measure is a well behaving function in the vicinity of the motion parameter vectors describing the motion of the objects, depending on the smoothness of the images. Therefore, smoothing an image has the effect of allowing longer range motion to be estimated. Segmentation of the image according to motion is achieved at the same time. The ability to estimate general motion in the situation of multiple moving objects represents a major step forward in 2-D motion estimation. Finally, the application of motion compensation to the problem of frame rate conversion is considered. The handling of the covered and uncovered background has been investigated. A new algorithm to obtain a pixel value for the pixels in those areas is introduced. Unlike published algorithms, the background is not assumed stationary. This presents a major obstacle which requires the study of occlusion in the image. During the research, the art of motion estimation hcis been advanced from simple motion vector estimation to a more descriptive level: The ability to point out that a certain area in an image is undergoing a zooming operation is one example. Only low level information such as image gradient and intensity function is used. In many different situations, problems are caused by the lack of higher level information. This seems to suggest that general motion estimation is much more than using a general motion model and developing an algorithm to estimate the parameters. To advance further the state of the art of general motion estimation, it is believed that future research effort should focus on higher level aspects of motion understanding.
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21

Aitken, Victor C. (Victor Charles) Carleton University Dissertation Engineering Electrical. "Motion and structure estimation in noisy monocular image sequences: specialization to planar motion." Ottawa, 1991.

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22

Kwok, Crystal Lee, and 郭錦恩. "Ghosts and goddesses: women, cinema, & the image." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1995. http://hub.hku.hk/bib/B31950978.

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23

Huang, Jianzhong. "Motion estimation and compensation for video image sequences." Diss., Georgia Institute of Technology, 1995. http://hdl.handle.net/1853/14950.

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Robinson, M. Dirk. "Estimation theoretic analysis of motion in image sequences /." Diss., Digital Dissertations Database. Restricted to UC campuses, 2004. http://uclibs.org/PID/11984.

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McKinnon, David N. R. "Structure from motion from uncalibrated digital image sequences /." [St. Lucia, Qld.], 2002. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe17117.pdf.

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26

Laptev, Ivan. "Local spatio-temporal image features for motion interpretation." Doctoral thesis, KTH, Numerical Analysis and Computer Science, NADA, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3797.

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Visual motion carries information about the dynamics of ascene. Automatic interpretation of this information isimportant when designing computer systems forvisualnavigation, surveillance, human-computer interaction, browsingof video databases and other growing applications.

In this thesis, we address the issue of motionrepresentation for the purpose of detecting and recognizingmotion patterns in video sequences. We localize the motion inspace and time and propose to use local spatio-temporal imagefeatures as primitives when representing and recognizingmotions. To detect such features, we propose to maximize ameasure of local variation of the image function over space andtime and show that such a method detects meaningful events inimage sequences. Due to its local nature, the proposed methodavoids the in.uence of global variations in the scene andovercomes the need for spatial segmentation and tracking priorto motion recognition. These properties are shown to be highlyuseful when recognizing human actions in complexscen es.

Variations in scale and in relative motions of the cameramay strongly in.uence the structure of image sequences andtherefore the performance of recognition schemes. To addressthis problem, we develop a theory of local spatio-temporaladaptation and show that this approach provides invariance whenanalyzing image sequences under scaling and velocitytransformations. To obtain discriminative representations ofmotion patterns, we also develop several types of motiondescriptors and use them for classifying and matching localfeatures in image sequences. An extensive evaluation of thisapproach is performed and results in the context of the problemof human action recognition are presented. I

n summary, this thesis provides the following contributions:(i) it introduces the notion of local features in space-timeand demonstrates the successful application of such featuresfor motion interpretation; (ii) it presents a theory and anevaluation of methods for local adaptation with respect toscale and velocity transformations in image sequences and (iii)it presents and evaluates a set of local motion descriptors,which in combination with methods for feature detection andfeature adaptation allow for robust recognition of humanactions in complexs cenes with cluttered and non-stationarybackgrounds as well as camera motion.

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Santoro, Michael. "Valid motion estimation for super-resolution image reconstruction." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/44765.

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In this thesis, a block-based motion estimation algorithm suitable for Super-Resolution (SR) image reconstruction is introduced. The motion estimation problem is formulated as an energy minimization problem that consists of both a data and regularization term. To handle cases when motion estimation fails, a block-based validity method is introduced, and is shown to outperform all other validity methods in the literature in terms of hybrid de-interlacing. By combining the validity metric into the energy minimization framework, it is shown that 1) the motion vector error is made less sensitive to block size, 2) a more uniform distribution of motion-compensated blocks results, and 3) the overall motion vector error is reduced. The final motion estimation algorithm is shown to outperform several state-of-the-art motion estimation algorithms in terms of both endpoint error and interpolation error, and is one of the fastest algorithms in the Middlebury benchmark. With the new motion estimation algorithm and validity metric, it is shown that artifacts are virtually eliminated from the POCS-based reconstruction of the high-resolution image.
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Cain, James H. "Image motion compensation for an electronic imaging system /." Online version of thesis, 1986. http://hdl.handle.net/1850/8731.

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Konrad, Janusz. "Bayesian estimation of motion fields from image sequences." Thesis, McGill University, 1989. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=74225.

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This thesis addresses the ill-posed problem of estimating two-dimensional motion in time-varying images. The approach proposed here uses the theory of stochastic processes at the formulation and solution stages. Independent Gaussian random variables are used to model the relationship between motion fields and images, and vector and binary Markov random fields are used to model motion and motion discontinuity fields, respectively. These models, combined using Bayes rule, result in Gibbsian a posteriori probability distribution from which the Maximum A Posteriori Probability (MAP) and the Minimum Expected Cost (MEC) estimation criteria are derived. Optimization of these criteria is performed using stochastic relaxation. The MAP estimation is extended to handle large displacements via hierarchical approach. Deterministic approximations to some of the stochastic algorithms proposed are derived and compared with their stochastic counterparts. The colour information is incorporated into the estimation process. Numerous experimental results are included. Two of the methods proposed are applied to motion-compensated interpolation and shown to reduce certain type of errors.
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Amanatidis, Dimitrios E. "Motion estimation and segmentation of colour image sequences." Thesis, Kingston University, 2008. http://eprints.kingston.ac.uk/20300/.

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The principal objective of this thesis is to develop improved motion estimation and segmentation techniques that meet the image-processing requirements of the post¬production industry. Starting with a rigorous taxonomy of existing image segmentation techniques, we proceed by focusing on motion estimation by means of optical flow calculation. A parametric motion model based method to estimate optical flow fields on three consecutive frames is developed and tested on a number of colour real sequences. Initial estimates are robustly refined in an iterative scheme and are enhanced by colour probability distribution information to enable foreground/background segmentation in a maximum a posteriori pixel classification scheme. Experiments, . show the significant contribution of the colour part towards a well-segmented image.Additionally, a very accurate variational optical flow computation method based on brightness constancy, gradient constancy and spatiotemporal smoothness constraints is modified and implemented so that it can robustly estimate global motion over three consecutive frames. Motion is enhanced by colour evidence in a similar manner and the method adopts the same probabilistic labelling procedure. After a comparison of the two methods on the same colour sequences, a third neural network based method is implemented, which initially estimates motion by employing two twin-layer optical flow calculating Gellular Neural Networks and proceeds in a similar manner, (incorporating colour information and probabilistic ally classifying pixels), leading to similar or improved quality results with the added advantage of significantly accelerated performance. Moreover, another CNN is employed with the task of offering spatial and temporal pixel compatibility constraint support, further improving the quality of the segmented images. Weights are used to control the respective contributing terms enabling optimization of the segmentation results for each sequence individually. Finally, as a case study of CNN implementation in hardware (FPGA), the use of Handel-G, a C-like, high-level, parallel, hardware description language, is exploited to allow for rapid translation of our algorithms to efficient hardware.
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Ogunbona, Philip Olurotimi. "Estimation of motion for movement compensated image processing." Thesis, Imperial College London, 1987. http://hdl.handle.net/10044/1/47481.

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32

Bober, Miroslaw Zbigniew. "General motion estimation and segmentation from image sequences." Thesis, University of Surrey, 1994. http://epubs.surrey.ac.uk/843929/.

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This thesis is concerned with the problem of motion estimation and segmentation, mainly related to planar motion in the image plane. The emphasis is placed on several important issues, namely: the study of different motion models and their performance, the benefits resulting from the use of contextual information, the application of multiresolution strategies, and the use of Robust methods and confidence measures. The thesis investigates the application of global motion models, in particular the affine model, in different estimation and segmentation approaches. It is shown that the use of such models, which globally constrain the estimate, results in improved accuracy and robustness. Robust techniques, which can cope with outliers often present when larger data sets are used, are adopted and tested here. The performance is further improved by the use of confidence measures, and of contextual information such as intensity edges or moving feature information. Two broad classes of approach are developed and investigated. The first one is based on the theory of Markov Random Fields. Novel elements in this approach include the introduction of a complex motion model - capable of describing translation, rotation and change of scale - and confidence factors describing the reliability of the data. The application of the Supercoupling approach for multiresolution optimisation speeds up convergence and further improves the quality of the estimate. The second class of algorithms is based on the Hough Transform. An in-depth investigation of the behaviour of the standard Hough Transform is conducted. This leads to the adoption of a robust statistics method providing a better estimate accuracy, better motion segmentation and guaranteed convergence. The use of multiresolution representation in the image plane, in addition to multiresolution in the parameter space, brings the advantage of robust and fast convergence even for large displacements. An important contribution of the research is the evaluation of different kernel functions from the point of view of robustness to noise and change in illumination conditions. Two algorithms from this group have been developed. The first one processes an entire image and provides parallel motion segmentation and estimation. The other is used as a local and robust method for the estimation of optic flow, with the ability to detect multimodal motions. A comparative study with other state-of-the-art methods is conducted, and the results are strongly in favour of the new algorithms. In summary, all stages of motion estimation and segmentation have been investigated. At the low-level, a robust algorithm for optic flow estimation has been developed. It can cope with multiple moving objects, and detects motion boundaries and occluded/uncovered regions. The spatial coherence of motion is enforced here very strongly, resulting in an accurate estimate and reliable confidence measures. This low-level estimate may be globally interpreted, together with other clues and a priori knowledge of the world using a multi-scale Markov Random Field approach. Alternatively, motion estimation and segmentation may be performed in parallel globally using the Robust Hough Transform approach. At this stage meaningful objects can be segmented, thus providing a high-level description of the scene.
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33

Zhou, Jining. "ORGAN MOTION AND IMAGE GUIDANCE IN RADIATION THERAPY." VCU Scholars Compass, 2009. http://scholarscompass.vcu.edu/etd/1681.

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Organ motion and inaccurate patient positioning may compromise radiation therapy outcome. With the aid of image guidance, it is possible to allow for a more accurate organ motion and motion control study, which could lead to the reduction of irradiated healthy tissues and possible dose escalation to the target volume to achieve better treatment results. The studies on the organ motion and image guidance were divided into the following four sections. The first, the interfractional setup uncertainties from day-to-day treatment and intrafractional internal organ motion within the daily treatment from five different anatomic sites were studied with Helical TomoTherapy unit. The pre-treatment mega voltage computed tomography (MVCT) provided the real-time tumor and organ shift coordinates, and can be used to improve the accuracy of patient positioning. The interfractional system errors and random errors were analyzed and the suggested margins for HN, brain, prostate, abdomen and lung were derived. The second, lung stereotactic body radiation therapy using the MIDCO BodyLoc whole body stereotactic localizer combined with TomoTherapy MVCT image guidance were investigated for the possible target and organ motion reduction. The comparison of 3D displacement with and without BodyLoc immobilization showed that, suppression of internal organ motion was improved by using BodyLoc in this study. The third, respiration related tumor motion was accurately studied with the four dimensional computed tomography (4DCT). Deformable registration between different breathing phases was performed to estimate the motion trajectory for lung tumor. Optimization is performed by minimizing the mean squared difference in intensity, and is implemented with a multi-resolution, gradient descent procedure. The fourth, lung tumor mobility and dosimetric benefits were compared with different PTV obtained from 3DCT and 4DCT. The results illustrated that the PTV3D not only included excess normal tissues but also might result in missed target tissue. The normal tissue complication probability (NTCP) from 4D plan was statistically significant smaller than 3D plan for both ipsilateral lung and heart.
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Egbert, Cameron. "Real-time motion transition by example /." Diss., CLICK HERE for online access, 2005. http://contentdm.lib.byu.edu/ETD/image/etd1070.pdf.

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Mackay, Allen Boyd. "Large-displacement linear-motion compliant mechanisms /." Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd1845.pdf.

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Glaze, Tiffini Lynn. "Reasoning About Motion: A Case Study." Diss., CLICK HERE for online access, 2004. http://contentdm.lib.byu.edu/ETD/image/etd488.pdf.

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Monaco, Joseph W. "Generalized motion models for video applications." Diss., Georgia Institute of Technology, 1997. http://hdl.handle.net/1853/14926.

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Ye, Ming. "Robust visual motion analysis : piecewise-smooth optical flow and motion-based detection and tracking /." Thesis, Connect to this title online; UW restricted, 2002. http://hdl.handle.net/1773/6077.

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Dobie, Mark Ralph. "Motion analysis in multimedia systems." Thesis, University of Southampton, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.359240.

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Sklyar, Andrey V. "Testing SPECT Motion Correction Algorithms." Digital WPI, 2010. https://digitalcommons.wpi.edu/etd-theses/419.

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Frequently, testing of Single Photon Emission Computed Tomography (SPECT) motion correction algorithms is done either by using simplistic deformations that do not accurately simulate true patient motion or by applying the algorithms directly to data acquired from a real patient, where the true internal motion is unknown. In this work, we describe a way to combine these two approaches by using imaging data acquired from real volunteers to simulate the data that the motion correction algorithms would normally observe. The goal is to provide an assessment framework which can both: simulate realistic SPECT acquisitions that incorporate realistic body deformations and provide a ground truth volume to compare against. Every part of the motion correction algorithm needs to be exercised: from parameter estimation of the motion model, to the final reconstruction results. In order to build the ground truth anthropomorphic numerical phantoms, we acquire high resolution MRI scans and motion observation data of a volunteer in multiple different configurations. We then extract the organ boundaries using thresholding, active contours, and morphology. Phantoms of radioactivity uptake and density inside the body can be generated from these boundaries to be used to simulate SPECT acquisitions. We present results on extraction of the ribs, lungs, heart, spine, and the rest of the soft tissue in the thorax using our segmentation approach. In general, extracting the lungs, heart, and ribs in images that do not contain the spine works well, but the spine could be better extracted using other methods that we discuss. We also go in depth into the software development component of this work, describing the C++ coding framework we used and the High Level Interactive GUI Language (HLING). HLING solved a lot of problems but introduced a fair bit of its own. We include a set of requirements to provide a foundation for the next attempt at developing a declarative and minimally restrictive methodology for writing interactive image processing applications in C++ based on lessons learned during the development of HLING.
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Magarey, Julian. "Motion estimation using complex wavelets." Thesis, University of Cambridge, 1997. https://www.repository.cam.ac.uk/handle/1810/273016.

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Kwok, Crystal Lee. "Ghosts and goddesses : women, cinema, & the image /." Hong Kong : University of Hong Kong, 1995. http://sunzi.lib.hku.hk/hkuto/record.jsp?B14040220.

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Scheffler, Carl. "Articulated structure from motion." Thesis, University of the Western Cape, 2004. http://etd.uwc.ac.za/index.php?module=etd&action=viewtitle&id=init_2988_1177923873.

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The structure from motion (SfM) problem is that of determining 3-dimensional (3D) information of a scene from sequences of 2-dimensional (2D) images [59]. This information consists of object shape and motion and relative camera motion. In general, objects may undergo complex non-rigid motion and may be occluded by other objects or themselves. These aspects make the general SfM problem under-constrained and the solution subject to missing or incomplete data.
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Duncan, David P. "Motion Compensation of Interferometric Synthetic Aperture Radar." Diss., CLICK HERE for online access, 2004. http://contentdm.lib.byu.edu/ETD/image/etd477.pdf.

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O'Shaughnessy, Richard. "Motion-compensated predictive coding of image sequences : analysis and evaluation." Thesis, McGill University, 1985. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=72065.

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In this study, motion-compensated predictive coding of monochrome image sequences was investigated. The goals were to evaluate the performance limits of this coding method, to determine its relative advantages in comparison to other predictive coding techniques not compensated for motion, and to identify the factors which limit further improvements in performance. A motion-compensated prediction algorithm which estimates displacements only from previously-transmitted data was presented, and was shown to be nearly optimal. At low distortion levels, an adaptive intra-inter-frame predictor not compensated for motion was found to be almost as effective as motion-compensated prediction. At higher distortion levels, however, motion-compensated predictive coding becomes more attractive. The main factors which limit the performance of motion-compensated predictors were identified as: input noise, spatial interpolation errors, and the local nature of the displacement estimation methods. Methods to control these limiting factors were suggested.
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Li, Yanhua. "Efficient recursive factorization methods for determining structure from motion." Title page, abstract and contents only, 2000. http://web4.library.adelaide.edu.au/theses/09PH/09phl6929.pdf.

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Li, Yan. "3D reconstruction and camera calibration from circular-motion image sequences." Click to view the E-thesis via HKUTO, 2005. http://sunzi.lib.hku.hk/hkuto/record/B36365919.

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Moccagatta, Iole. "Image coding and motion field segmentation using vector quantization /." Lausanne : EPFL, 1995. http://library.epfl.ch/theses/?nr=1410.

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Zhong, Huang. "3D metric reconstruction from uncalibrated circular motion image sequences." Click to view the E-thesis via HKUTO, 2006. http://sunzi.lib.hku.hk/hkuto/record/B37043791.

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Lee, Choon. "Interframe image coding with three-dimensional gradient motion estimation." Diss., This resource online, 1990. http://scholar.lib.vt.edu/theses/available/etd-08252008-162144/.

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