Studies exploring facial emotion recognition (FER) abilities in autism spectrum disorder (ASD) samples have yielded inconsistent results despite the widely-accepted finding that an impairment in emotion recognition is a core component of ASD. The current study aimed to determine if an FER task featuring both unfamiliar and familiar faces would highlight additional group differences between ASD children and typically developing (TD) children. We tested the two groups of 4- to 8-year-olds on this revised task, and also compared their resting-state brain activity using electroencephalogram (EEG) measurements. As hypothesized, the TD group had significantly higher overall emotion recognition percent scores. In addition, there was a significant interaction effect of group by familiarity, with the ASD group recognizing emotional expressions significantly better in familiar faces than in unfamiliar ones. This finding may be related to the preference of children with autism for people and situations which they are accustomed to. TD children did not demonstrate this pattern, as their recognition scores were approximately the same for familiar faces and unfamiliar ones. No significant group differences existed for EEG alpha power or EEG alpha asymmetry in frontal, central, temporal, parietal, or occipital brain regions. Also, neither of these EEG measurements were strongly correlated with the group FER performances. Further evidence is needed to assess the association between neurophysiological measurements and behavioral symptoms of ASD. The behavioral results of this study provide preliminary evidence that an FER task featuring both familiar and unfamiliar expressions produces a more optimal assessment of emotion recognition ability. / Includes bibliography. / Thesis (M.A.)--Florida Atlantic University, 2017. / FAU Electronic Theses and Dissertations Collection

This dissertation introduces our work on face recognition using a novel approach based on creating 3D face model from 2D face images. Together with the pose angle estimation and illumination compensation, this method can be used successfully to recognize 2D faces with 3D recognition algorithms. The results reported here were obtained partially with our own face image database, which had 2D and 3D face images of 50 subjects, with 9 different pose angles. It is shown that by applying even the simple PCA algorithm, this new approach can yield successful recognition rates using 2D probing images and 3D gallery images. The insight gained from the 2D/3D face recognition study was also extended to the case of involving 2D probing and 2D gallery images, which offers a more flexible approach since it is much easier and practical to acquire 2D photos for recognition. To test the effectiveness of the proposed approach, the public AT&T face database, which had 2D only face photos of 40 subjects, with 10 different images each, was utilized in the experimental study. The results from this investigation show that with our approach, the 3D recognition algorithm can be successfully applied to 2D only images. The performance of the proposed approach was further compared with some of the existing face recognition techniques. Studies on imperfect conditions such as domain and pose/illumination variations were also carried out. Additionally, the performance of the algorithms on noisy photos was evaluated. Pros and cons of the proposed face recognition technique along with suggestions for future studies are also given in the dissertation. / by Guan Xin. / Thesis (Ph.D.)--Florida Atlantic University, 2012. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2012. Mode of access: World Wide Web.

In order to facilitate the development, discussion, and advancement of the relatively new subfield of Artificial Intelligence focused on generating narrative content, the author has developed a pattern language for generating narratives, along with a new categorization framework for narrative generation systems. An emphasis and focus is placed on generating the Fabula of the story (the ordered sequence of events that make up the plot). Approaches to narrative generation are classified into one of three categories, and a pattern is presented for each approach. Enhancement patterns that can be used in conjunction with one of the core patterns are also identified. In total, nine patterns are identified - three core narratology patterns, four Fabula patterns, and two extension patterns. These patterns will be very useful to software architects designing a new generation of narrative generation systems. / by Samuel Greene. / Thesis (M.S.C.S.)--Florida Atlantic University, 2012. / Includes bibliography. / Mode of access: World Wide Web. / System requirements: Adobe Reader.

The literature regarding biological motion suggests that people may accurately identify and recognize the gender of others using movement cues in the absence of typical identifiers. This study compared identification and gender judgments of traditional point-light stimuli to skeleton stimuli. Controlling for previous experience and execution of actions, the frequency and familiarity of movements was also considered. Watching action clips, participants learned to identify 4 male and 4 female actors. Participants then identified the corresponding point-light or skeleton displays. Although results indicate higher than chance performance, no difference was observed between stimuli conditions. Analyses did show better gender recognition for common as well as previously viewed actions. This suggests that visual experience influences extraction and application of biological motion. Thus insufficient practice in relying on movement cues for identification could explain the significant yet poor performance in biological motion point-light research. / by Sara Manuel. / Thesis (M.A.)--Florida Atlantic University, 2012. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2012. Mode of access: World Wide Web. / Mode of access: World Wide Web. / System requirements: Adobe Reader.

Made available in DSpace on 2015-03-04T18:57:58Z (GMT). No. of bitstreams: 1 dissertacao_caio_godinho_2014.pdf: 2200918 bytes, checksum: aa8817dd5a147c8a2d55413e1a796132 (MD5) Previous issue date: 2014-03-18 / Mycoplasma hyopneumoniae 7448 e uma bactéria patogênica e parasita obrigatória do trato respiratório de suínos. A compreensão de seus mecanismos de regulação gênica é ainda incompleta e incapaz de explicar a dinâmica observada na expressão de seus genes. Diversos elementos podem exercer funções regulatórias da expressão gênica em bactérias, dentre eles os ncRNAs. Este trabalho reporta a identificação e classificaçãao de 48 regiões no genoma de M. hyopneumoniae 7448 suscetíveis a abrigarem novos genes de ncRNA. Para isso foram utilizadas técnicas de modelagem estocástica e diversas outras ferramentas computacionais. Duas importantes ferramentas foram desenvolvidas no decorrer desta dissertação, sendo uma para a inferência de conservação evolutiva em regiões intergênicas e a outra { denominada FraPS { uma melhoria na delimitação genômica dos candidatos a ncRNA. Os resultados corroboram com a hipótese da existência de ncRNAs como elementos reguladores da expressão gênica na bactéria estudada, exercendo papeis fundamentais na sobrevivência e patogenicidade da mesma. Genes de adesinas, lipoproteínas, e do complexo de transporte ABC foram encontrados entre os possíveis genes-alvo a regulação via ncRNA, resultado que auxilia o planejamento de experimentos moleculares para o estudo da regulação por ncRNAs em micoplasmas.

Shi, Xingang. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (leaves 135-152). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese.

by Tham Yiu-Man. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1993. / Includes bibliographical references. / Abstract --- p.i / Acknowledgements --- p.iii / Table of Contents --- p.iv / Chapter Chapter I --- Introduction / Chapter 1.1 --- Introduction --- p.1-1 / Chapter 1.2 --- Survey on Chinese Character Recognition --- p.1-4 / Chapter 1.3 --- Methodology Adopts in Our System --- p.1-7 / Chapter 1.4 --- Contributions and Organization of the Thesis --- p.1-11 / Chapter Chapter II --- Pre-processing and Stroke Extraction / Chapter 2.1 --- Introduction --- p.2-1 / Chapter 2.2 --- Thinning --- p.2-1 / Chapter 2.2.1 --- Introduction to Thinning --- p.2-1 / Chapter 2.2.2 --- Proposed Thinning Algorithm Cater for Stroke Extraction --- p.2-6 / Chapter 2.2.3 --- Thinning Results --- p.2-9 / Chapter 2.3 --- Stroke Extraction --- p.2-13 / Chapter 2.3.1 --- Introduction to Stroke Extraction --- p.2-13 / Chapter 2.3.2 --- Proposed Stroke Extraction Method --- p.2-14 / Chapter 2.3.2.1 --- Fork point detection --- p.2-16 / Chapter 2.3.2.2 --- 8-connected fork point merging --- p.2-18 / Chapter 2.3.2.3 --- Sub-stroke extraction --- p.2-18 / Chapter 2.3.2.4 --- Fork point merging --- p.2-19 / Chapter 2.3.2.5 --- Sub-stroke connection --- p.2-24 / Chapter 2.3.3 --- Stroke Extraction Accuracy --- p.2-27 / Chapter 2.3.4 --- Corner Detection --- p.2-29 / Chapter 2.3.4.1 --- Introduction to Corner Detection --- p.2-29 / Chapter 2.3.4.2 --- Proposed Corner Detection Formulation --- p.2-30 / Chapter 2.4 --- Concluding Remarks --- p.2-33 / Chapter Chapter III --- Four Corner Code / Chapter 3.1 --- Introduction --- p.3-1 / Chapter 3.2 --- Deletion of Hook Strokes --- p.3-3 / Chapter 3.3 --- Stroke Types Selection --- p.3-5 / Chapter 3.4 --- Probability Formulations of Stroke Types --- p.3-7 / Chapter 3.4.1 --- Simple Strokes --- p.3-7 / Chapter 3.4.2 --- Square --- p.3-8 / Chapter 3.4.3 --- Cross --- p.3-10 / Chapter 3.4.4 --- Upper Right Corner --- p.3-12 / Chapter 3.4.5 --- Lower Left Corner --- p.3-12 / Chapter 3.5 --- Corner Segments Extraction Procedure --- p.3-14 / Chapter 3.5.1 --- Corner Segment Probability --- p.3-21 / Chapter 3.5.2 --- Corner Segment Extraction --- p.3-23 / Chapter 3.6 4 --- C Codes Generation --- p.3-26 / Chapter 3.7 --- Parameters Determination --- p.3-29 / Chapter 3.8 --- Sensitivity Test --- p.3-31 / Chapter 3.9 --- Classification Rate --- p.3-32 / Chapter 3.10 --- Feedback by Corner Segments --- p.3-34 / Chapter 3.11 --- Classification Rate with Feedback by Corner Segment --- p.3-37 / Chapter 3.12 --- Reasons for Mis-classification --- p.3-38 / Chapter 3.13 --- Suggested Solution to the Mis-interpretation of Stroke Type --- p.3-41 / Chapter 3.14 --- Reduce Size of Candidate Set by No.of Input Segments --- p.3-43 / Chapter 3.15 --- Extension to Higher Order Code --- p.3-45 / Chapter 3.16 --- Concluding Remarks --- p.3-46 / Chapter Chapter IV --- Relaxation / Chapter 4.1 --- Introduction --- p.4-1 / Chapter 4.1.1 --- Introduction to Relaxation --- p.4-1 / Chapter 4.1.2 --- Formulation of Relaxation --- p.4-2 / Chapter 4.1.3 --- Survey on Chinese Character Recognition by using Relaxation --- p.4-5 / Chapter 4.2 --- Relaxation Formulations --- p.4-9 / Chapter 4.2.1 --- Definition of Neighbour Segments --- p.4-9 / Chapter 4.2.2 --- Formulation of Initial Probability Assignment --- p.4-12 / Chapter 4.2.3 --- Formulation of Compatibility Function --- p.4-14 / Chapter 4.2.4 --- Formulation of Support from Neighbours --- p.4-16 / Chapter 4.2.5 --- Stopping Criteria --- p.4-17 / Chapter 4.2.6 --- Distance Measures --- p.4-17 / Chapter 4.2.7 --- Parameters Determination --- p.4-21 / Chapter 4.3 --- Recognition Rate --- p.4-23 / Chapter 4.4 --- Reasons for Mis-recognition in Relaxation --- p.4-27 / Chapter 4.5 --- Introduction of No-label Class --- p.4-31 / Chapter 4.5.1 --- No-label Initial Probability --- p.4-31 / Chapter 4.5.2 --- No-label Compatibility Function --- p.4-32 / Chapter 4.5.3 --- Improvement by No-label Class --- p.4-33 / Chapter 4.6 --- Rate of Convergence --- p.4-35 / Chapter 4.6.1 --- Updating Formulae in Exponential Form --- p.4-38 / Chapter 4.7 --- Comparison with Yamamoto et al's Relaxation Method --- p.4-40 / Chapter 4.7.1 --- Formulations in Yamamoto et al's Relaxation Method --- p.4-40 / Chapter 4.7.2 --- Modifications in [YAMAM82] --- p.4-42 / Chapter 4.7.3 --- Performance Comparison with [YAMAM82] --- p.4-43 / Chapter 4.8 --- System Overall Recognition Rate --- p.4-45 / Chapter 4.9 --- Concluding Remarks --- p.4-48 / Chapter Chapter V --- Concluding Remarks / Chapter 5.1 --- Recapitulation and Conclusions --- p.5-1 / Chapter 5.2 --- Limitations in the System --- p.5-4 / Chapter 5.3 --- Suggestions for Further Developments --- p.5-6 / References --- p.R-1 / Appendix User's Guide / Chapter A .l --- System Functions --- p.A-1 / Chapter A.2 --- Platform and Compiler --- p.A-1 / Chapter A.3 --- File List --- p.A-2 / Chapter A.4 --- Directory --- p.A-3 / Chapter A.5 --- Description of Sub-routines --- p.A-3 / Chapter A.6 --- Data Structures and Header Files --- p.A-12 / Chapter A.7 --- Character File charfile Structure --- p.A-15 / Chapter A.8 --- Suggested Program to Implement the System --- p.A-17

Sin Ka Wai. / Thesis (M.Sc.)--Chinese University of Hong Kong, 1992. / Includes bibliographical references (leaves [81]-[82]). / PREFACE / ABSTRACT / CONTENT / Chapter §1. --- INTRODUCTION / Chapter §1.1 --- The Chinese language --- p.1 / Chapter §1.2 --- Chinese information processing system --- p.2 / Chapter §1.3 --- Chinese character recognition --- p.4 / Chapter §1.4 --- Multi-stage tree classifier Vs Single-stage tree classifier in Chinese character recognition --- p.6 / Chapter §1.5 --- Decision Tree / Chapter §1.5.1 --- Basic Terminology of a decision tree --- p.7 / Chapter §1.5.2 --- Structure design of a decision tree --- p.10 / Chapter §1.6 --- Motivation of the project --- p.12 / Chapter §1.7 --- Objects of the project --- p.14 / Chapter §1.8 --- Development environment --- p.14 / Chapter §2. --- APPROACH 1 - UNSUPERVISED LEARNING / Chapter §2.1 --- Idea --- p.15 / Chapter §2.2 --- Feature Extraction / Chapter §2.2.1 --- Feature selection criteria --- p.15 / Chapter §2.2.2 --- 4C code --- p.20 / Chapter §2.2.3 --- Regional code --- p.22 / Chapter §2.2.4 --- Walsh Transform --- p.24 / Chapter §2.2.5 --- Black dot density projection profile --- p.26 / Chapter §2.2.6 --- Corner features --- p.28 / Chapter §2.3 --- Clustering Method -K-MEANS & Other Algorithms --- p.32 / Chapter §2.4 --- Pros & Cons --- p.35 / Chapter §2.5 --- Decision Table --- p.37 / Chapter §2.6 --- The Optimum Classifier & its Implemen- tation difficulties --- p.39 / Chapter §3. --- APPROACH 2 - SUPERVISED LEARNING --- p.43 / Chapter §4. --- POSSIBLE IMPROVEMENT / Chapter §4.1 --- Training and Test Sample Reduction --- p.46 / Chapter §4.2 --- Noise Filtering --- p.46 / Chapter §4.3 --- Decision with Overlapping --- p.52 / Chapter §4.4 --- Back Tracking for Holes --- p.56 / Chapter §4.5 --- Fuzzy Decision with Tolerance Limit --- p.59 / Chapter §4.6 --- Different Tree Architecture --- p.63 / Chapter §4.7 --- Building Decision Tree by Entropy Reduction Method --- p.65 / Chapter §5. --- EXPERIMENTAL RESULTS & THE IMPROVED MULTISTAGE CLASSIFIER / Chapter §5.1 --- Experimental Results --- p.70 / Chapter §5.2 --- Conclusion --- p.81 / Chapter §6. --- IMPROVED MULTISTAGE TREE CLASSIFIER / Chapter §6.1 --- The Optimal Multistage Tree Classifier --- p.83 / Chapter §6.2 --- Performance Analysis --- p.84 / Chapter §7. --- FURTHER DISCRIMINATION BY CONTEXT CONSIDERATION --- p.87 / Chapter §8. --- CONCLUSION / Chapter §8.1 --- Advantage of the Classifier --- p.89 / Chapter §8.2 --- Limitation of the Classifier --- p.90 / Chapter §9. --- AREA OF FUTURE RESEARCH AND IMPROVEMENT / Chapter §9.1 --- Detailed Analysis at Each Terminal Node --- p.91 / Chapter §9.2 --- Improving the Noise Filtering Technique --- p.92 / Chapter §9.3 --- The Use of 4 Corner Code --- p.93 / Chapter §9.4 --- Increase in the Dimension of the Feature Space --- p.95 / Chapter §9.5 --- 1-Tree Protocol with Entropy Reduction --- p.96 / Chapter §9.6 --- The Use of Human Intelligence --- p.97 / APPENDICES / Chapter A.1 --- K-MEANS / Chapter A.2 --- Maximum Distance Algorithm & ISODATA Algorithm / Chapter A.3 --- Approach Two - Supervised Learning / Chapter A.4 --- Theories on Statistical Discriminant Analysis / Chapter A.5 --- An Example of Misclassification Table / Chapter A.6 --- "Listing of the Program ""CHDIS.C""" / Chapter A.7 --- Further Discrimination by Context Consideration / Chapter A.8 --- Passage used in Testing the Performance of the Classifier with Context Consideration / Chapter A.9 --- A Partial List of Semantically Related Chinese Characters / REFERENCE

by Kong Shao-hua. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1992. / Includes bibliographical references (leaves 113-116). / Chapter CHAPTER 1 --- INTRODUCTION --- p.1 / Chapter CHAPTER 2 --- REVIEW OF 3-D OBJECT RECOGNITION SYSTEMS --- p.8 / Chapter CHAPTER 3 --- FEATURE EXTRACTION AND OBJECT REPRESEN- TATION --- p.16 / Chapter 3.1 --- Preprocessing --- p.18 / Chapter 3.2 --- Extraction of Characteristic Points --- p.20 / Chapter 3.3 --- Characterization of Surface Patches --- p.28 / Chapter 3.4 --- Object Representation --- p.37 / Chapter 3.5 --- Model Formation --- p.42 / Chapter CHAPTER 4 --- OBJECT RECOGNITION AND OBJECT LOCATION AND ORIENTATION DETERMINATION --- p.45 / Chapter 4.1 --- RBM-Matching --- p.48 / Chapter 4.1.1 --- Rigid body model (RBM) --- p.48 / Chapter 4.1.2 --- RBM-matching --- p.55 / Chapter 4.2 --- Estimation of the Transformation Parameters --- p.63 / Chapter 4.3 --- Recognition Decision Making --- p.72 / Chapter CHAPTER 5 --- EXPERIMENTATION --- p.80 / Chapter 5.1 --- Automatic Model Building --- p.82 / Chapter 5.2 --- Recognition of Single Objects --- p.88 / Chapter 5.3 --- Recognition of Multiple Objects with Occlusion --- p.103 / Chapter CHAPTER 6 --- CONCLUSION AND DISCUSSION --- p.109 / REFERENCES --- p.113

by Yingli Tian. / Thesis (Ph.D.)--Chinese University of Hong Kong, 1996. / Includes bibliographical references (leaves 202-222). / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Physics-Based Shape Recovery Techniques --- p.3 / Chapter 1.2 --- Proposed Approaches to Shape Recovery in this Thesis --- p.9 / Chapter 1.3 --- Thesis Outline --- p.13 / Chapter 2 --- Camera Model in Color Vision --- p.15 / Chapter 2.1 --- Introduction --- p.15 / Chapter 2.2 --- Spectral Linearization --- p.17 / Chapter 2.3 --- Image Balancing --- p.21 / Chapter 2.4 --- Spectral Sensitivity --- p.24 / Chapter 2.5 --- Color Clipping and Blooming --- p.24 / Chapter 3 --- Extended Light Source Models --- p.27 / Chapter 3.1 --- Introduction --- p.27 / Chapter 3.2 --- A Spherical Light Model in 2D Coordinate System --- p.30 / Chapter 3.2.1 --- Basic Photometric Function for Hybrid Surfaces under a Point Light Source --- p.32 / Chapter 3.2.2 --- Photometric Function for Hybrid Surfaces under the Spher- ical Light Source --- p.34 / Chapter 3.3 --- A Spherical Light Model in 3D Coordinate System --- p.36 / Chapter 3.3.1 --- Radiance of the Spherical Light Source --- p.36 / Chapter 3.3.2 --- Surface Brightness Illuminated by One Point of the Spher- ical Light Source --- p.38 / Chapter 3.3.3 --- Surface Brightness Illuminated by the Spherical Light Source --- p.39 / Chapter 3.3.4 --- Rotating the Source-Object Coordinate to the Camera- Object Coordinate --- p.41 / Chapter 3.3.5 --- Surface Reflection Model --- p.44 / Chapter 3.4 --- Rectangular Light Model in 3D Coordinate System --- p.45 / Chapter 3.4.1 --- Radiance of a Rectangular Light Source --- p.45 / Chapter 3.4.2 --- Surface Brightness Illuminated by One Point of the Rect- angular Light Source --- p.47 / Chapter 3.4.3 --- Surface Brightness Illuminated by a Rectangular Light Source --- p.47 / Chapter 4 --- Shape Recovery from Specular Reflection --- p.54 / Chapter 4.1 --- Introduction --- p.54 / Chapter 4.2 --- Theory of the First Method --- p.57 / Chapter 4.2.1 --- Torrance-Sparrow Reflectance Model --- p.57 / Chapter 4.2.2 --- Relationship Between Surface Shapes from Different Images --- p.60 / Chapter 4.3 --- Theory of the Second Method --- p.65 / Chapter 4.3.1 --- Getting the Depth of a Reference Point --- p.65 / Chapter 4.3.2 --- Recovering the Depth and Normal of a Specular Point Near the Reference Point --- p.67 / Chapter 4.3.3 --- Recovering Local Shape of the Object by Specular Reflection --- p.69 / Chapter 4.4 --- Experimental Results and Discussions --- p.71 / Chapter 4.4.1 --- Experimental System and Results of the First Method --- p.71 / Chapter 4.4.2 --- Experimental System and Results of the Second Method --- p.76 / Chapter 5 --- Shape Recovery from One Sequence of Color Images --- p.81 / Chapter 5.1 --- Introduction --- p.81 / Chapter 5.2 --- Temporal-color Space Analysis of Reflection --- p.84 / Chapter 5.3 --- Estimation of Illuminant Color Ks --- p.88 / Chapter 5.4 --- Estimation of the Color Vector of the Body-reflection Component Kl --- p.89 / Chapter 5.5 --- Separating Specular and Body Reflection Components and Re- covering Surface Shape and Reflectance --- p.91 / Chapter 5.6 --- Experiment Results and Discussions --- p.92 / Chapter 5.6.1 --- Results with Interreflection --- p.93 / Chapter 5.6.2 --- Results Without Interreflection --- p.93 / Chapter 5.6.3 --- Simulation Results --- p.95 / Chapter 5.7 --- Analysis of Various Factors on the Accuracy --- p.96 / Chapter 5.7.1 --- Effects of Number of Samples --- p.96 / Chapter 5.7.2 --- Effects of Noise --- p.99 / Chapter 5.7.3 --- Effects of Object Size --- p.99 / Chapter 5.7.4 --- Camera Optical Axis Not in Light Source Plane --- p.102 / Chapter 5.7.5 --- Camera Optical Axis Not Passing Through Object Center --- p.105 / Chapter 6 --- Shape Recovery from Two Sequences of Images --- p.107 / Chapter 6.1 --- Introduction --- p.107 / Chapter 6.2 --- Method for 3D Shape Recovery from Two Sequences of Images --- p.109 / Chapter 6.3 --- Genetics-Based Method --- p.111 / Chapter 6.4 --- Experimental Results and Discussions --- p.115 / Chapter 6.4.1 --- Simulation Results --- p.115 / Chapter 6.4.2 --- Real Experimental Results --- p.118 / Chapter 7 --- Shape from Shading for Non-Lambertian Surfaces --- p.120 / Chapter 7.1 --- Introduction --- p.120 / Chapter 7.2 --- Reflectance Map for Non-Lambertian Color Surfaces --- p.123 / Chapter 7.3 --- Recovering Non-Lambertian Surface Shape from One Color Image --- p.127 / Chapter 7.3.1 --- Segmenting Hybrid Areas from Diffuse Areas Using Hue Information --- p.127 / Chapter 7.3.2 --- Calculating Intensities of Specular and Diffuse Compo- nents on Hybrid Areas --- p.128 / Chapter 7.3.3 --- Recovering Shape from Shading --- p.129 / Chapter 7.4 --- Experimental Results and Discussions --- p.131 / Chapter 7.4.1 --- Simulation Results --- p.131 / Chapter 7.4.2 --- Real Experimental Results --- p.136 / Chapter 8 --- Shape from Shading under Multiple Extended Light Sources --- p.142 / Chapter 8.1 --- Introduction --- p.142 / Chapter 8.2 --- Reflectance Map for Lambertian Surface Under Multiple Rectan- gular Light Sources --- p.144 / Chapter 8.3 --- Recovering Surface Shape Under Multiple Rectangular Light Sources --- p.148 / Chapter 8.4 --- Experimental Results and Discussions --- p.150 / Chapter 8.4.1 --- Synthetic Image Results --- p.150 / Chapter 8.4.2 --- Real Image Results --- p.152 / Chapter 9 --- Shape from Shading in Unknown Environments by Neural Net- works --- p.167 / Chapter 9.1 --- Introduction --- p.167 / Chapter 9.2 --- Shape Estimation --- p.169 / Chapter 9.2.1 --- Shape Recovery Problem under Multiple Rectangular Ex- tended Light Sources --- p.169 / Chapter 9.2.2 --- Forward Network Representation of Surface Normals --- p.170 / Chapter 9.2.3 --- Shape Estimation --- p.174 / Chapter 9.3 --- Application of the Neural Network in Shape Recovery --- p.174 / Chapter 9.3.1 --- Structure of the Neural Network --- p.174 / Chapter 9.3.2 --- Normalization of the Input and Output Patterns --- p.175 / Chapter 9.4 --- Experimental Results and Discussions --- p.178 / Chapter 9.4.1 --- Results for Lambertian Surface under One Rectangular Light --- p.178 / Chapter 9.4.2 --- Results for Lambertian Surface under Four Rectangular Light Sources --- p.180 / Chapter 9.4.3 --- Results for Hybrid Surface under One Rectangular Light Sources --- p.190 / Chapter 9.4.4 --- Discussions --- p.190 / Chapter 10 --- Summary and Conclusions --- p.191 / Chapter 10.1 --- Summary Results and Contributions --- p.192 / Chapter 10.2 --- Directions of Future Research --- p.199 / Bibliography --- p.202