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1 Computational vision in neural and machine systems 1 I.1 Introduction I 1.2 The CD-ROM 4 References 4 I Dynamical systems 7 2 Exploring contrast-controlled adaptation processes in human vision (with help from Buffy the Vampire Slayer 9 2.1 Dynamics of luminance-controlled adaption processes (light adaptation) 10 12 Dynamics of contrast-controlled adaptation processes 14 2.3 Using Buffy and Regular Steady-State backgrounds 25 2.4 A new kind of complex channel with embedded contrast adaptation (the Buffy channel) 34 25 Where are we now? 42 References 46 3 Image comparison and motion detection by a contrario methods 49 3.1 Introduction 49 3,2 Image comparison 51 33 Motion detection 57 34 Conclusions and perspectives 66 References 67 4 Computer vision in the Mars Exploration Rover (MER) mission 71 4.1 Introduction 71 4.2 Overview of rover hardware and mission operations 72 4.3 Stereo vision-based obstacle detection and avoidance 74 4.4 Visual odometry 77 4.5 Descent Image Motion Estimation System (DIMES) 79 4.6 Discussion 82 References 83 5 Calibration and shape recovery from videos of dynamic scenes 85 5.1 Introduction 85 5.2 Silhouette-based camera network calibration 86 5.3 Articulated shape recovery from video 94 5.4 Conclusions and discussion 100 References 102 6 Specular planar target surface recovery via coded target stereopsis 105 6. 1 Introduction 105 6.2 Exploiting properties of planar specular surfaces 106 6.3 Actively recovering 3D specular surface structure 107 6.4 Discussion 113 References 113 7 Neural construction of objects from parts 115 7.1 Basis function representation of object parts 1 16 7.2 Synthesis of part signals into representations of multi-part configurations 120 References 124 II Attention, motion, and eye movements 127 8 Attention and action 129 8.1 Introduction 129 8.2 Active vision and attention in robots 129 8.3 Attention and saccade generation in biological systems 131 8,4 Covert attention tracking and microsaccades 136 8.5 What is next? 143 References 144 9 Cueing visual search in clutter 149 9. I Background 149 9.2 Detecting smooth motion paths in noise 152 9.3 Self-cueing in contour formation 157 9.4 Uncertainty and collinear facilitation at detection threshold 160 9.5 Conclusions 161 References 162 10 Transsaccadic memory of visual features 167 10.1 Introduction 167 10.2 Methods 168 10.3 Results 172 10.4 Discussion 175 References 178 11 Modeling what attracts human gaze over dynamic natural scenes 183 1 l .1 Introduction and rationale 183 S12 Quantifying surprise 184 1i.3 Computational model 188 11.4 Experimental validation results 194 11.5 Discussion and conclusion 195 References 198 1.1 Stereo 201 12 Global stereo in polynomial time 203 i2.1 Introduction 203 12.2 Dynamic programming stereo and its limits 205 12.3 The max-flow/min-cut formulation 209 12.4 An experiment 215 12.5 Conclusions and future work 217 References 2 218 13 Computational analysis of binocular half occlusions 221 "13.1 Introduction 221 13.2 Technical approach 224 13 3 Empirical evaluation 231 13.4 Summary and conclusions 236 References 236 14 Speed versus quality -- measuring and optimizing stereo for telepresence 241 14.1 Stereo reconstruction of the human form 241 "14.2 Stereo, depth and surfaces 242 14.3 Quality and empirical evaluation 245 14.4 Temporal coherence 250 14.5 Conclusion 254 References 255 15 Binocular combinations: measurements and a model 257 151 Introduction 257 15.2 Methods 262 15.3 Experiment 1. Binocular combination as determined by the interocular contrast ratio, the interocular grating phase difference, and overall contrast level 268 154 Experiment 1. Stimulus duration 273 15.5 Experiment 3. Masking by spatial-frequency noise 279 15.6 Experiment 4. Masking sinewave gratings of different spatial frequencies 281 15.7 Expeiment 5. Temporal frequency masking 284 15.8 Experiment 6. Orientation masking 287 15.9 Model 288 15.10 Discussion 299 15.11 Summary and conclusions 303 References and further reading 305