Relevant bibliographies by topics / Image motion

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Image motion'

Author: Grafiati
Published: 4 June 2021
Last updated: 31 January 2023

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Journal articles on the topic "Image motion"

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Gaffare, Mayra Garduño, Bertrand Vachon, and Armando Segovia de los Ríos. "Range image generator including robot motion." Robotica 24, no. 1 (October 31, 2005): 113–23. http://dx.doi.org/10.1017/s0263574704001547.

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The system here described has the capability of generating range images that include robot motion. The system has two main modules, the motion and the image generator. Motion is modeled using a Bezier's curve method. To compute a range value corresponding to a pixel image, the robot position in the coordinated system is obtained from trajec-tory generation. In this way, distortion is produced in the image, or sequence of images, as a consequence of motion. The obtained range images represent scenes perceived by the robot from a specific location or during a specified dis-placement in a very “real” view.
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Jo, Hang-Chan, Hyeonwoo Jeong, Junhyuk Lee, Kyung-Sun Na, and Dae-Yu Kim. "Quantification of Blood Flow Velocity in the Human Conjunctival Microvessels Using Deep Learning-Based Stabilization Algorithm." Sensors 21, no. 9 (May 6, 2021): 3224. http://dx.doi.org/10.3390/s21093224.

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The quantification of blood flow velocity in the human conjunctiva is clinically essential for assessing microvascular hemodynamics. Since the conjunctival microvessel is imaged in several seconds, eye motion during image acquisition causes motion artifacts limiting the accuracy of image segmentation performance and measurement of the blood flow velocity. In this paper, we introduce a novel customized optical imaging system for human conjunctiva with deep learning-based segmentation and motion correction. The image segmentation process is performed by the Attention-UNet structure to achieve high-performance segmentation results in conjunctiva images with motion blur. Motion correction processes with two steps—registration and template matching—are used to correct for large displacements and fine movements. The image displacement values decrease to 4–7 μm during registration (first step) and less than 1 μm during template matching (second step). With the corrected images, the blood flow velocity is calculated for selected vessels considering temporal signal variances and vessel lengths. These methods for resolving motion artifacts contribute insights into studies quantifying the hemodynamics of the conjunctiva, as well as other tissues.
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Chen, Chien-Chung, Hiroshi Ashida, Xirui Yang, and Pei-Yin Chen. "Seeing Global Motion in a Random Dot Image Sequence." i-Perception 11, no. 5 (September 2020): 204166952096110. http://dx.doi.org/10.1177/2041669520961104.

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In a stimulus with multiple moving elements, an observer may perceive that the whole stimulus moves in unison if (a) one can associate an element in one frame with one in the next (correspondence) and (b) a sufficient proportion of correspondences signal a similar motion direction (coherence). We tested the necessity of these two conditions by asking the participants to rate the perceived intensity of linear, concentric, and radial motions for three types of stimuli: (a) random walk motion, in which the direction of each dot was randomly determined for each frame, (b) random image sequence, which was a set of uncorrelated random dot images presented in sequence, and (c) global motion, in which 35% of dots moved coherently. The participants perceived global motion not only in the global motion conditions but also in the random image sequences, though not in random walk motion. The type of perceived motion in the random image sequences depends on the spatial context of the stimuli. Thus, although there is neither a fixed correspondence across different frames nor a coherent motion direction, observers can still perceive global motion in the random image sequence. This result cannot be explained by motion energy or local aperture border effects.
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Long, Yawu, Ichiro Sakuma, and Naoki Tomii. "Reconstruction of Motion Images from Single Two-Dimensional Motion-Blurred Computed Tomographic Image of Aortic Valves Using In Silico Deep Learning: Proof of Concept." Applied Sciences 12, no. 18 (September 8, 2022): 9044. http://dx.doi.org/10.3390/app12189044.

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The visualization of motion is important in the diagnosis and treatment of aortic valve disease. It is difficult to perform using computed tomography (CT) because of motion blur. Existing research focuses on suppressing or removing motion blur. The purpose of this study is to prove the feasibility of inferring motion images using motion information from a motion-blurred CT image. An in silico learning method is proposed, to infer 60 motion images from a two-dimensional (2D) motion-blurred CT image, to verify the concept. A dataset of motion-blurred CT images and motion images was generated using motion and CT simulators to train a deep neural network. The trained model was evaluated using two image similarity evaluation metrics, a structural similarity index measure (0.97 ± 0.01), and a peak signal-to-noise ratio (36.0 ± 1.3 dB), as well as three motion feature evaluation metrics, maximum opening distance error between endpoints (0.7 ± 0.6 mm), maximum-swept area velocity error between adjacent images (393.3 ± 423.3 mm2/s), and opening time error (5.5 ± 5.5 ms). According to the results, the trained model can successfully infer 60 motion images from a motion-blurred CT image. This study demonstrates the feasibility of inferring motion images from a motion-blurred CT image under simulated conditions.
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Zhou-xiang Jin, Zhou-xiang Jin, and Hao Qin Zhou-xiang Jin. "Generative Adversarial Network Based on Multi-feature Fusion Strategy for Motion Image Deblurring." 電腦學刊 33, no. 1 (February 2022): 031–41. http://dx.doi.org/10.53106/199115992022023301004.

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<p>Deblurring of motion images is a part of the field of image restoration. The deblurring of motion images is not only difficult to estimate the motion parameters, but also contains complex factors such as noise, which makes the deblurring algorithm more difficult. Image deblurring can be divided into two categories: one is the non-blind image deblurring with known fuzzy kernel, and the other is the blind image deblurring with unknown fuzzy kernel. The traditional motion image deblurring networks ignore the non-uniformity of motion blurred images and cannot effectively recover the high frequency details and remove artifacts. In this paper, we propose a new generative adversarial network based on multi-feature fusion strategy for motion image deblurring. An adaptive residual module composed of deformation convolution module and channel attention module is constructed in the generative network. Where, the deformation convolution module learns the shape variables of motion blurred image features, and can dynamically adjust the shape and size of the convolution kernel according to the deformation information of the image, thus improving the ability of the network to adapt to image deformation. The channel attention module adjusts the extracted deformation features to obtain more high-frequency features and enhance the texture details of the restored image. Experimental results on public available GOPRO dataset show that the proposed algorithm improves the peak signal-to-noise ratio (PSNR) and is able to reconstruct high quality images with rich texture details compared to other motion image deblurring methods.</p> <p>&nbsp;</p>
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Bai, Chongxin, Kewen Liu, Shi Chen, Zhao Li, Weida Xie, Qingjia Bao, and Chaoyang Liu. "Dual-domain unsupervised network for removing motion artifact related to Gadoxetic acid-enhanced MRI." Journal of Physics: Conference Series 2258, no. 1 (April 1, 2022): 012037. http://dx.doi.org/10.1088/1742-6596/2258/1/012037.

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Abstract The transient severe motion may cause severe image degradation during gadoxetic acid-enhanced arterial phase imaging. This work proposes a new dual-domain unsupervised motion artifacts disentanglement network for motion correction related to gadoxetic acid-enhanced MRI. We assume that motion-free images and motion-corrupted images belong to the different domains, then the motion correction is converted to the image-to-image translation problem. The image-to-image translation within the same domain is designed to constrain autoencoders to learn the feature representation. And the cross-domain translation explores the cycle consistency in the absence of paired images. Experimental results demonstrate that our method can effectively reduce artifacts in the gadoxetic acid-enhanced images.
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Chen chen and Daohui Bi. "A Motion Image Pose Contour Extraction Method Based on B-Spline Wavelet." International Journal of Antennas and Propagation 2021 (October 26, 2021): 1–8. http://dx.doi.org/10.1155/2021/4553143.

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In order to improve the accuracy of traditional motion image pose contour extraction and shorten the extraction time, a motion image pose contour extraction method based on B-spline wavelet is proposed. Moving images are acquired through the visual system, the information fusion process is used to perform statistical analysis on the images containing motion information, the location of the motion area is determined, convolutional neural network technology is used to preprocess the initial motion image pose contour, and B-spline wavelet theory is used. The preprocessed motion image pose contour is detected, combined with the heuristic search method to obtain the pose contour points, and the motion image pose contour extraction is completed. The simulation results show that the proposed method has higher accuracy and shorter extraction time in extracting motion image pose contours.
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Fan, Yu, and Xue Feng Wu. "Study on Motion Blur Image Restoration Algorithms." Advanced Materials Research 753-755 (August 2013): 2976–79. http://dx.doi.org/10.4028/www.scientific.net/amr.753-755.2976.

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The restore algorithm of the image blurred by motion is proposed, and a mathematical model based on motion blur system is eomtrueted£®The Point spread function of the motion blur is given£®According to the characteristics of blurred images£¬the parameters of point spread function are estimated ,and three methods are introduced for image restoration. The three methods are inverse filtering of image restoration,Lucy-Richardson image restoration and Wiener image restoration.The principles of the three image restoration methods are analyzed. The motion blurred image restoration experiment is made. The results show that the visibility of the image is improved ,and the image restoration is more stable.
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Kumar, Ankur N., Kurt W. Short, and David W. Piston. "A Motion Correction Framework for Time Series Sequences in Microscopy Images." Microscopy and Microanalysis 19, no. 2 (February 15, 2013): 433–50. http://dx.doi.org/10.1017/s1431927612014250.

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AbstractWith the advent of in vivo laser scanning fluorescence microscopy techniques, time-series and three-dimensional volumes of living tissue and vessels at micron scales can be acquired to firmly analyze vessel architecture and blood flow. Analysis of a large number of image stacks to extract architecture and track blood flow manually is cumbersome and prone to observer bias. Thus, an automated framework to accomplish these analytical tasks is imperative. The first initiative toward such a framework is to compensate for motion artifacts manifest in these microscopy images. Motion artifacts in in vivo microscopy images are caused by respiratory motion, heart beats, and other motions from the specimen. Consequently, the amount of motion present in these images can be large and hinders further analysis of these images. In this article, an algorithmic framework for the correction of time-series images is presented. The automated algorithm is comprised of a rigid and a nonrigid registration step based on shape contexts. The framework performs considerably well on time-series image sequences of the islets of Langerhans and provides for the pivotal step of motion correction in the further automatic analysis of microscopy images.
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Li, Qi Shen, and Jian Gong Chen. "PSF Estimation and Image Restoration for Motion Blurred Images." Advanced Materials Research 562-564 (August 2012): 2124–27. http://dx.doi.org/10.4028/www.scientific.net/amr.562-564.2124.

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Point spread function (PSF) estimation and image restoration algorithm are the hotspots In the research of motion blurred image restoration. In order to improve the efficacy of image restoration, an improved algorithm named quadric transforms (QT) method is proposed in this paper by analyzing the restoration process of motion blurred images. Firstly, Fourier transform and homomorphism transform are applied to the original motion blurred image, and then the Fourier transform and homomorphism transform are used again to the obtained spectrum image. Secondly, the motion blur direction is estimated by Radon transform. Thirdly, the motion blur length is found by differential autocorrelation operations. Finally, utilizing the estimated blur direction and blur length, the motion blurred image is restored by Wiener filtering. Experimental results show that the proposed QT method can get more accurate estimated motion blur angles than the primary transform (PT, that is, Fourier transform and homomorphism transform are used one time) method and can get better restored images under the meaning of peak signal to noise ratio (PSNR).
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Dissertations / Theses on the topic "Image motion"

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

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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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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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Books on the topic "Image motion"

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Goh, Wooi Boon. Image motion estimation. [s.l.]: typescript, 1992.

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Z, Bober Miroslaw. Robust motion analysis. Baldock, Hertfordshire, England: Research Studies Press, 1999.

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Theory of reconstruction from image motion. Berlin: Springer-Verlag, 1993.

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Deleuze, Gilles. The time-image. London: Bloomsbury Academic, 2013.

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Deleuze, Gilles. The movement-image. London: Bloomsbury Academic, 2013.

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Colloquium on Motion Compensated Image Processing (1987 London). Colloquium on 'Motion Compensated Image Processing'. London: Institution of Electrical Engineers Electronics Division, 1987.

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Weng, Juyang. Motion and structure from image sequences. Berlin: Springer-Verlag, 1993.

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C, Labit, ed. Motion analysis for image sequence coding. Amsterdam: Elsevier, 1994.

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Maybank, Stephen. Theory of reconstruction from image motion. Berlin: Springer-Verlag, 1993.

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Maybank, Stephen. Theory of Reconstruction from Image Motion. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-642-77557-4.

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Book chapters on the topic "Image motion"

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Jähne, Bernd. "Motion." In Digital Image Processing, 253–74. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-662-11565-7_14.

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Jähne, Bernd. "Motion." In Digital Image Processing, 253–74. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-662-21817-4_14.

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Jähne, Bernd. "Motion." In Digital Image Processing, 375–412. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-662-04781-1_14.

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Jähne, Bernd. "Motion." In Digital Image Processing, 395–450. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-662-03477-4_13.

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Jähne, Bernd. "Motion." In Digital Image Processing, 253–74. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-662-03174-2_14.

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Min, Junghye, Jin Hyeong Park, and Rangachar Kasturi. "Extraction of Multiple Motion Trajectories in Human Motion." In Image Analysis, 1050–57. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-45103-x_138.

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Burger, Wilhelm, and Bir Bhanu. "Decomposing Image Motion." In Qualitative Motion Understanding, 37–79. Boston, MA: Springer US, 1992. http://dx.doi.org/10.1007/978-1-4615-3566-9_4.

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Ahad, Md A. R. "Motion History Image." In Motion History Images for Action Recognition and Understanding, 31–76. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-4730-5_3.

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Berry, Pat. "Image in motion." In Jung & Film, 70–79. London: Routledge, 2021. http://dx.doi.org/10.4324/9781315783284-5.

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Zhang, Yu-Jin. "Motion Analysis." In Handbook of Image Engineering, 1127–64. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-5873-3_32.

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Conference papers on the topic "Image motion"

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Zhao, Lin. "Image enhancement of restored motion blurred images." In International Conference on Optical Instruments and Technology (OIT2011). SPIE, 2011. http://dx.doi.org/10.1117/12.904786.

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Paramanand, C., and A. N. Rajagopalan. "Motion blur for motion segmentation." In 2013 20th IEEE International Conference on Image Processing (ICIP). IEEE, 2013. http://dx.doi.org/10.1109/icip.2013.6738874.

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Zhao, Yaxiang, Xiaoping Fan, and Shaoqiang Liu. "Global motion estimation combining with motion segmentation." In Fifth International Conference on Digital Image Processing, edited by Yulin Wang and Xie Yi. SPIE, 2013. http://dx.doi.org/10.1117/12.2030893.

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Ollstein, Marty. "Crystal Image: Bridging the Gap between Cinematography and Digital Image Processing." In SMPTE Advanced Motion Imaging Conference. IEEE, 1997. http://dx.doi.org/10.5594/m00213.

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Tang, D. S. "Neurocomputation of image motion." In 1990 IJCNN International Joint Conference on Neural Networks. IEEE, 1990. http://dx.doi.org/10.1109/ijcnn.1990.137600.

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Dunham, Edward, Peter Collins, Andreas Reinacher, and Ulrich Lampater. "SOFIA image motion compensation." In SPIE Astronomical Telescopes + Instrumentation, edited by Ian S. McLean, Suzanne K. Ramsay, and Hideki Takami. SPIE, 2010. http://dx.doi.org/10.1117/12.857731.

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Jia, Shuai, and Jie Wen. "Motion blurred image restoration." In 2013 6th International Congress on Image and Signal Processing (CISP). IEEE, 2013. http://dx.doi.org/10.1109/cisp.2013.6744024.

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Nakamura, Eiji, Takehito Nakamura, and Katsutoshi Sawada. "Fast local motion estimation algorithm using elementary motion detectors." In Visual Communications and Image Processing 2003, edited by Touradj Ebrahimi and Thomas Sikora. SPIE, 2003. http://dx.doi.org/10.1117/12.502261.

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Csillag, Peter, and Lilla Boroczky. "Estimation of accelerated motion for motion-compensated frame interpolation." In Visual Communications and Image Processing '96, edited by Rashid Ansari and Mark J. T. Smith. SPIE, 1996. http://dx.doi.org/10.1117/12.233275.

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Brostow, Gabriel J., and Irfan Essa. "Image-based motion blur for stop motion animation." In the 28th annual conference. New York, New York, USA: ACM Press, 2001. http://dx.doi.org/10.1145/383259.383325.

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Reports on the topic "Image motion"

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Richey, Charles. Advanced Portable Differential Image Motion Monitor. Fort Belvoir, VA: Defense Technical Information Center, April 2000. http://dx.doi.org/10.21236/ada379276.

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O'Carroll, David. Motion Adaptation, its Role in Motion Detection Under Natural Image Conditions and Target Detection. Fort Belvoir, VA: Defense Technical Information Center, June 2005. http://dx.doi.org/10.21236/ada451630.

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Doerry, Armin, and Douglas Bickel. Radar Motion Measurements and Synthetic Aperture Radar Image Geolocation Accuracy. Office of Scientific and Technical Information (OSTI), October 2020. http://dx.doi.org/10.2172/1675035.

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Schulze, Martin E. Effect of Beam Motion on Observed Image at Imaging Station C. Office of Scientific and Technical Information (OSTI), January 2014. http://dx.doi.org/10.2172/1115549.

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Sclaroff, Stan. Shape and Motion Categorization for Content-Based Image and Video Database Search. Fort Belvoir, VA: Defense Technical Information Center, August 1999. http://dx.doi.org/10.21236/ada366945.

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Morita, Toshihiko, and Takeo Kanade. A Sequential Factorization Method for Recovering Shape and Motion from Image Streams. Fort Belvoir, VA: Defense Technical Information Center, May 1994. http://dx.doi.org/10.21236/ada281253.

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Sclaroff, Stan. Shape and Motion Categorization for Content-Based Image and Video Database Search. Fort Belvoir, VA: Defense Technical Information Center, August 1996. http://dx.doi.org/10.21236/ada324626.

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Sclaroff, Stan. Shape and Motion Categorization for Content-Based Image and Video Database Search. Fort Belvoir, VA: Defense Technical Information Center, August 1997. http://dx.doi.org/10.21236/ada328585.

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Ling, Hao. Radar Image Enhancement, Feature Extraction and Motion Compensation Using Joint Time-Frequency Techniques. Fort Belvoir, VA: Defense Technical Information Center, October 2001. http://dx.doi.org/10.21236/ada390630.

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Hao, Ling. Annual Report on Radar Image Enhancement, Feature Extraction and Motion Compensation Using Joint Time-Frequency Techniques. Fort Belvoir, VA: Defense Technical Information Center, May 2000. http://dx.doi.org/10.21236/ada377783.

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