Introduction to Computer Vision Ronen Basri Michal Irani

Introduction to Computer Vision Ronen Basri, Michal Irani, Shimon Ullman Teaching Assistants Tal Amir, Sima Sabah, Netalee Efrat, Nati Ofir, Yuval Bahat, Itay Kezurer.

Misc. . . · Course website – look under: www. wisdom. weizmann. ac. il/~vision · To be added to course mailing-list: Send email to one of the TAs: <Tal. Amir@weizmann. ac. il>, <Sima. Sabah@weizmann. ac. il>, … · Vision & Robotics Seminar (not for credit): Thursdays at 12: 00 -13: 00 (Ziskind 1) Send email to Amir Gonen: <amir. gonen@weizmann. ac. il> · Hands-on course in deep neural networks for vision: Wednesday at 15: 00 -17: 00 (Ziskind 1) First Lecture: this week (4/11) (Limited number of students)

Applications: · How is an image formed? - Robot navigation (geometryvehicles and photometry) - Autonomous - Guiding tools for blind · How and is anmonitoring image represented? - Security - Object/face recognition; OCR. · What. Applications kind of operations - Medical can we apply to images? - Visualization; NVS - Manufacturing and inspection; QA · What do images tell us - Visual communication about the world? - Digital libraries and video search (analysis & interpretation) - Video manipulation and editing

Digital Image Pixels: 0 = Black 255 = White 137 73 29 65 30 19 42 33 76 255 237 74 64 59 19 72 139 147 111 251 155 239 52 23 43 187 255 108 69 231 160 103 255 16 68 246 244 118 42 235 139 71 237 222 128 246 170 20 244 187 67 195 224 220 217 243 151 37 232 253 81 217 255 240 219 207 225 99 153 235 130 222 255 241 255 205 221 120 74 255 218 75 236 240 255 253 191 220 94 185 233 184 5 59 20 255 250 203 179 138 188 245 50 207 62 119 55 206 188 140 214 255 41 40 4 155 54 248 180 193 116 205 236 9 73 55 158 80 243 168 195 210 127 252 55 31 0 39 176 247 189 173 138 182 147 81 141 91 188 227 140 188 162 203 123 207 11 81 84 245 194 96 193 142 186 197 110 181 9 15 231 137 85

Topics covered • • • Fourier and Applications (2 lessons) Human Vision (1 lesson) Geometry, Stereo, 3 D Structure (4 lessons) Lighting (1 lesson) Motion & video analysis (3 lessons) Object Recognition (2 lessons) · 2 -3 programming exercises (MATLAB) -· 2 -3 theoretical exercises -· EXAM CAN SUBMIT IN PAIRS MUST SUBMIT INDIVIDUALLY

Panoramic Mosaic Image Original video 1. Optical clip Flow 2. Image Alignment 3. Sequence Alignment Generated Mosaic image

Video Removal Original Outliers Synthesized

Photometric Stereo