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The integration of motion signals across spaceLoffler, Gunter January 1999 (has links)
No description available.
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Computer vision based detection and identification of potato blemishesBarnes, Michael January 2014 (has links)
This thesis addresses the problem of automatic detection and identification of blemishes in digital images of potatoes. Potatoes are an important food crop, with clear unblemished skin being the main factor affecting consumer preference. Potatoes with defects, diseases and blemishes caused by otherwise benign (to human) skin infections, are strongly avoided by consumers. Most potatoes are sorted into dfferent grades by hand, with inevitable mistakes and losses. The currently deployed computer vision systems for sorting potatoes require manual training and have limited accuracy and high unit costs. A further limitation of typical machine vision systems is that the set of image features for pattern recognition has to be designed by the system engineer to work with a specific configuration of produce, imaging system and operating conditions. Such systems typically do not generalise well to other configurations, where the required image features may well differ from those used to design the original system. The objective of the research presented in this thesis is to introduce an automatic method for detecting and identifying blemishes in digital images of potatoes, where the presented solution involves classifying individual pixels. A human expert is required to mark up areas of blemishes and non-blemishes in a set of training images. For blemish detection, each pixel is classified as either blemish or non-blemish. For blemish identification, each pixel is classified according to a number of pre-determined blemish categories. After training, the system should be able to classify individual pixels in new images of previously unseen potatoes with high accuracy. After segmenting the potato from the image background, a very large set of candidate features, based on statistical information relating to the colour and texture of the region surrounding a given pixel, is first extracted. The features include statistical summaries of the whole potato and local regions centred on each pixel as well as the data of the pixel itself. Then an adaptive boosting algorithm (AdaBoost) is used to automatically select the best features for discriminating between blemishes and non-blemishes. The AdaBoost algorithm (Freund and Schapire, 1999) is used to build a classifier, which combines results from so-called "weak" classifiers, each constructed using one of the candidate features, into one "strong" classifier that performs better than any of the weak classifiers alone. With this approach, different features can be selected for different potato varieties, while also handling the natural variation in fresh produce due to different seasons, lighting conditions, etc. For blemish detection, the classifier was trained using a subset of pixels which had been marked as blemish or non-blemish. Tests were done with the full set of features, "lesion experiments" were carried out to explore the impact of removing specific feature types, and experiments were also carried out on methods of speeding up classification both by restricting the number of weak classifiers and restricting the numbers of unique candidate features which can be used to produce weak classifiers. The results were highly accurate with visible examples of disagreement between classifier output and markup being caused by human inaccuracies in the markup rather than classifier inaccuracy. For blemish identification, a set of classifiers were trained on subsets of pixels marked as each blemish class against a subset of pixels drawn from all other classes. For classification, each pixel was tested with all classifiers and assigned to the classifier which returned the highest confidence of a positive result. Experiments were again performed with methods of speeding up classification as well as lesion experiments. Finally, to demonstrate how the system would work in an industrial context, the classification results were summarised for each potato, providing a high overall accuracy in detecting the presence or absence of significant blemish coverage for each blemish type.
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Two studies in the neuropsychology of visionHumphrey, Nicholas January 1968 (has links)
No description available.
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Shape from texture : a computational analysisStone, J. V. January 1991 (has links)
No description available.
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Understanding Perceived Sense of Movement in Static Visuals Using Deep LearningKale, Shravan 11 January 2019 (has links)
This thesis introduces the problem of learning the representation and the classification of the perceived sense of movement, defined as dynamism in static visuals. To solve the said problem, we study the definition, degree, and real-world implications of dynamism within the field of consumer psychology. We employ Deep Convolutional Neural Networks (DCNN) as a method to learn and predict dynamism in images. The novelty of the task, lead us to collect a dataset which we synthetically augmented for spatial invariance, using image processing techniques. We study the methods of transfer learning to transfer knowledge from another domain, as the size of our dataset was deemed to be inadequate. Our dataset is trained across different network architectures, and transfer learning techniques to find an optimal method for the task at hand. To show a real-world application of our work, we observe the correlation between the two visual stimuli, dynamism and emotions.
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Framing Innovation: Does an Instructional Vision Help Superintendents Gain Acceptance for a Large-Scale Technology Initiative?Flanagan, Gina Eva, Arnold, Erik Paul, Cohen, Peter D., Nolin, Anna Patricia, Turner, Henry J. January 2014 (has links)
Thesis advisor: Diana Pullin / Thesis advisor: Vincent Cho / There is limited research that outlines how a superintendent's instructional vision can help to gain acceptance of a large-scale technology initiative. This study explored how superintendents gain acceptance for a large-scale technology initiative (specifically a 1:1 device program) through various leadership actions. The role of the instructional vision in helping superintendents gain acceptance for a technology initiative was the focus of this research. Five school districts where a large-scale, 1:1 technology initiative was being implemented were the location for this study. These superintendents as well as district administrators with key roles in the technology initiative were interviewed to explore their knowledge and perceptions regarding the district's instructional vision and how it was being utilized to gain acceptance for the technology initiative. The study found that the superintendents utilized various strategic processes to create resonance with stakeholders between the instructional vision and the technology initiative. The superintendents utilized instructional visions that contained many elements of constructivist and 21st century learning skills. However, the definition and communication of the superintendent's specific instructional vision was not always clear and consistent throughout the district. The mission statements, technology plans and district administrators often communicated an instructional vision for the district that was unrelated to the instructional vision communicated by the superintendent. Additionally, while the implementation of the instructional vision was described as a collaborative effort in all of the districts, the development of the instructional vision was primarily limited to the superintendent and his leadership team (principals and central office academic administrators). Study results showed that while there was an understanding amongst district administrators of how technology can support teaching and learning, there was inconsistency in the understanding of the superintendent's instructional vision for the district and how technology should be utilized to help accomplish these goals. Often, it would appear that the technology initiative was driving the instructional vision for the districts and not the other way around. Since there is limited research that outlines how a superintendent's instructional vision can help to gain acceptance of a large-scale technology initiative, this study hopes to highlight the use of the instructional vision in gaining acceptance of a large-scale technology initiative and the practical methods of achieving this. / Thesis (EdD) — Boston College, 2014. / Submitted to: Boston College. Lynch School of Education. / Discipline: Educational Leadership and Higher Education.
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Calibration of multiple camera systems. / CUHK electronic theses & dissertations collectionJanuary 2008 (has links)
In both RMCS calibration and ACS calibration, the corresponding efficiency and robustness are tested by simulation and real experiments. In the real experiment of ACS calibration, the intrinsic and extrinsic parameters of the ACS are obtained simultaneously by our calibration procedure using the same image sequences, no extra data capturing step is required. The corresponding trajectory is recovered and illustrated using the calibration results of the ACS. Since the estimated translations of different cameras in an MCS may scaled by different scale factors, scale factor estimation algorithms are proposed for non-overlapping view RMCS calibration and ACS calibration respectively. To our knowledge, we are the first to study the calibration of ACS. / In this thesis, we focus on developing robust methods for the MCS calibration problems. In particular, we make two contributions. Firstly, we developed a novel extrinsic calibration method for the non-overlapping view Rigid Multiple Camera System (RMCS) using the kinematic information of the RMCS. The input are only the images captured when the non-overlapping RMCS is moved in an environment with enough static feature points. This assumption is true in many vision tasks such as SFM (Structure from Motion), SLAM (Simultaneous Localization and Map). The output is the extrinsic parameters of the cameras of the RMCS. / Multiple Camera Systems (MCS) have been widely applied in many vision applications and attracted much attention recently. Both intrinsic and extrinsic parameters of an MCS are needed to be calibrated before it is used. / Secondly, we proposed to solve the calibration of a particular model of non-rigid Multiple Camera System, namely, Articulated Camera System (ACS). In an ACS, the cameras are fixed on articulated arms with joints, the relative pose between them may change. Two ACS calibration methods are proposed. In the first approach, we assume the cameras have overlapping views. It uses the feature correspondences between the cameras in the ACS. In the second approach, we assume the cameras have no overlapping view. It requires only the ego-motion information of the cameras and can be used for the calibration of the non-overlapping view ACS. In both methods, the ACS is assumed to have performed general transformations in a static environment. / Chen, Junzhou. / Adviser: Kin Hong Wong. / Source: Dissertation Abstracts International, Volume: 70-06, Section: B, page: 3594. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2008. / Includes bibliographical references (leaves 102-110). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts in English and Chinese. / School code: 1307.
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A hypercolumn based stereo vision model.January 1993 (has links)
by Lam Shu Sun. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1993. / Includes bibliographical references (leaves [91]-94). / Chapter Chapter1 --- Introduction: Binocular Depth Visual Perception of Human --- p.1 / Chapter 1.1 --- Introduction --- p.1 / Chapter 1.2 --- The visual pathway --- p.2 / Chapter 1.3 --- The retina --- p.3 / Chapter 1.4 --- The ganglion cells --- p.5 / Chapter 1.5 --- The lateral geniculate nucleus --- p.7 / Chapter 1.6 --- The visual cortex --- p.8 / Chapter 1.6.1 --- The cortical cells --- p.8 / Chapter 1.6.2 --- The organization of the visual cortex --- p.9 / Chapter 1.7 --- Stereopsis --- p.11 / Chapter 1.7.1 --- Corresponding retinal points --- p.12 / Chapter 1.7.2 --- Binocular fusion --- p.14 / Chapter 1.7.3 --- The binocular depth cells --- p.14 / Chapter 1.8 --- Conclusion of chapter 1 --- p.15 / Chapter Chapter2 --- Computational Stereo Vision --- p.15 / Chapter 2.1 --- Stereo image geometry --- p.16 / Chapter 2.1.1 --- The crossed-looking geometry --- p.17 / Chapter 2.1.2 --- The parallel optical axes geometry --- p.19 / Chapter 2.2 --- The false targets problem --- p.20 / Chapter 2.3 --- Feature selection --- p.21 / Chapter 2.3.1 --- Zero-crossing method --- p.21 / Chapter 2.3.2 --- A network model for ganglion cell --- p.24 / Chapter 2.4 --- The constraints of matching --- p.28 / Chapter 2.5 --- Correspondence techniques --- p.29 / Chapter 2.6 --- Conclusion of chapter 2 --- p.29 / Chapter Chapter3 --- A Hypercolumn Based Stereo Vision Model --- p.30 / Chapter 3.1 --- A visual model for stereo vision --- p.30 / Chapter 3.2 --- The model of PSVM (A Computerized Visual Model) --- p.32 / Chapter 3.3 --- Local orientated line extraction (Stage 1 of PSVM) --- p.34 / Chapter 3.3.1 --- Orientated line detection network --- p.35 / Chapter 3.3.2 --- On-type orientated lines and off-type orientated lines --- p.37 / Chapter 3.4 --- Local line matching (Stage 2 of PSVM) --- p.38 / Chapter 3.4.1 --- Structure of hypercolumn in PSVM --- p.39 / Chapter 3.4.2 --- Line length discrimination model (Part of stage 2 of PSVM) --- p.41 / Chapter 3.4.3 --- Orientation-length detector --- p.42 / Chapter 3.4.4 --- Line length selection --- p.45 / Chapter 3.4.5 --- The matching model --- p.46 / Chapter 3.4.6 --- Fusional area in PSVM --- p.48 / Chapter 3.4.7 --- Matching mechanism --- p.49 / Chapter 3.4.8 --- Disparity detection --- p.50 / Chapter 3.5 --- Disparity integrations (Stage 3 of PSVM) --- p.53 / Chapter 3.5.1 --- The voter network --- p.54 / Chapter 3.5.2 --- The redistributor network --- p.55 / Chapter 3.6 --- Conculsion of chpater 3 --- p.57 / Chapter Chapter4 --- Implementation and Analysis --- p.58 / Chapter 4.1 --- The imaging geometry of PSVM --- p.58 / Chapter 4.2 --- Input --- p.59 / Chapter 4.3 --- The hypercolumn construction --- p.59 / Chapter 4.4 --- Analysis of matching mechanism in PSVM --- p.59 / Chapter 4.4.1 --- Fusional condition --- p.61 / Chapter 4.4.2 --- Disparity detection --- p.61 / Chapter 4.5. --- Matching rules in PSVM --- p.63 / Chapter 4.5.1 --- The ordering constraint --- p.63 / Chapter 4.5.2 --- The uniqueness constraint --- p.64 / Chapter 4.5.3 --- The figural continuity constraint --- p.64 / Chapter 4.5.4 --- The smoothness assumption --- p.65 / Chapter 4.6. --- Use multi-lengths of oriented line to solve the occlusion problem --- p.66 / Chapter 4.7 --- Performance of PSVM --- p.67 / Chapter 4.7.1 --- Artificial scene --- p.67 / Chapter 4.7.2 --- Natural images --- p.71 / Chapter 4.8 --- Discussion --- p.83 / Chapter 4.9 --- Overall conclusion --- p.83 / Appendix: Illustration example --- p.85 / References --- p.91
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Stereo matching on objects with fractional boundary.January 2007 (has links)
Xiong, Wei. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2007. / Includes bibliographical references (leaves 56-61). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Background Study --- p.6 / Chapter 2.1 --- Stereo matching --- p.6 / Chapter 2.2 --- Digital image matting --- p.8 / Chapter 2.3 --- Expectation Maximization --- p.9 / Chapter 3 --- Model Definition --- p.12 / Chapter 4 --- Initialization --- p.20 / Chapter 4.1 --- Initializing disparity --- p.20 / Chapter 4.2 --- Initializing alpha matte --- p.24 / Chapter 5 --- Optimization --- p.26 / Chapter 5.1 --- Expectation Step --- p.27 / Chapter 5.1.1 --- "Computing E((Pp(df = d1̐ưجθ(n),U))" --- p.28 / Chapter 5.1.2 --- "Computing E((Pp(db = d2̐ưجθ(n),U))" --- p.29 / Chapter 5.2 --- Maximization Step --- p.31 / Chapter 5.2.1 --- "Optimize α, given {F, B} fixed" --- p.34 / Chapter 5.2.2 --- "Optimize {F, B}, given α fixed" --- p.37 / Chapter 5.3 --- Computing Final Disparities --- p.40 / Chapter 6 --- Experiment Results --- p.42 / Chapter 7 --- Conclusion --- p.54 / Bibliography --- p.56
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Shape from shading with non-parallel light source.January 1999 (has links)
by Siu-Yuk Yeung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1999. / Includes bibliographical references (leaves 96-102). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.5 / Chapter 1.1 --- Shape recovery techniques --- p.5 / Chapter 1.2 --- Shape from Shading algorithms --- p.8 / Chapter 1.2.1 --- Some developments on surface reflection --- p.9 / Chapter 1.2.2 --- Some developments on computing methods --- p.11 / Chapter 1.2.3 --- Some developments on light source model --- p.12 / Chapter 1.3 --- Proposed algorithms in this thesis --- p.13 / Chapter 1.4 --- Thesis outline --- p.14 / Chapter 2 --- Camera and surface reflectance models for SFS --- p.15 / Chapter 2.1 --- Camera models for SFS --- p.16 / Chapter 2.1.1 --- Pinhole camera model and perspective projection --- p.17 / Chapter 2.1.2 --- Approximations of perspective projection --- p.20 / Chapter 2.2 --- Surface reflectance models for SFS --- p.22 / Chapter 2.2.1 --- Lambertian surface model --- p.23 / Chapter 2.2.2 --- Bidirectional Reflectance Distribuction Function --- p.23 / Chapter 2.3 --- Summary --- p.25 / Chapter 3 --- Review of some related SFS algorithms --- p.26 / Chapter 3.1 --- The SFS algorithm proposed by Bichsel and Pentland --- p.27 / Chapter 3.1.1 --- Determine surface height with a minimum downhill principle --- p.28 / Chapter 3.1.2 --- Implementation on a discrete grid --- p.30 / Chapter 3.2 --- The SFS algorithm proposed by Kimmel and Bruckstein --- p.31 / Chapter 3.2.1 --- Level set propagation --- p.32 / Chapter 3.2.2 --- Problem formulation --- p.33 / Chapter 3.2.3 --- Equal height contour propagation using level set method --- p.35 / Chapter 3.3 --- Summary --- p.36 / Chapter 4 --- Multiple extended light source models for SFS --- p.38 / Chapter 4.1 --- Three extended light source models for SFS --- p.40 / Chapter 4.1.1 --- Rectangular light source model --- p.40 / Chapter 4.1.2 --- Spherical light source model --- p.43 / Chapter 4.1.3 --- Cylindrical light source model --- p.48 / Chapter 4.2 --- SFS for an extended light source --- p.53 / Chapter 4.3 --- Multiple extended light source model --- p.53 / Chapter 4.4 --- Simulation and experiment result --- p.54 / Chapter 4.5 --- Error Analysis --- p.55 / Chapter 4.5.1 --- Descriptions of the error --- p.55 / Chapter 4.5.2 --- Errors for different light models --- p.55 / Chapter 4.6 --- Summary --- p.57 / Chapter 5 --- Global SFS for an endoscope image --- p.70 / Chapter 5.1 --- Introduction --- p.71 / Chapter 5.2 --- Local SFS algorithm for endoscope image --- p.73 / Chapter 5.2.1 --- Imaging system and brightness formulation --- p.74 / Chapter 5.2.2 --- Equal distance contour propagation and shape reconstruc- tion --- p.75 / Chapter 5.3 --- Global SFS algorithm for endoscope image --- p.76 / Chapter 5.3.1 --- A global shape from shading algorithm for a parallel light --- p.77 / Chapter 5.3.2 --- The relationship between depth map and distance map --- p.78 / Chapter 5.3.3 --- A global shape from shading algorithm for endoscope image --- p.78 / Chapter 5.4 --- Simulations and experiments results --- p.83 / Chapter 5.5 --- Summary --- p.86 / Chapter 6 --- Summary and conclusion --- p.87 / Chapter 6.1 --- Problems tackled in this thesis --- p.87 / Chapter 6.2 --- Discussion on future developments --- p.88
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