COMP 9517 Computer Vision Introduction 1212020 COMP 9517

COMP 9517 Computer Vision Introduction 12/1/2020 COMP 9517 S 2, 2009 1

Computer Vision • Computer vision has been around since 1960’s, but it only recently became possible to build useful vision system. • Computer and imaging system have become much cheaper. • Our understanding of basic geometry and physics underlying vision has been improved significantly. • It is a great time to be studying computer vision. 12/1/2020 COMP 9517 S 2, 2009 2

What is Computer Vision? • What are the goals of computer vision? • What are the applications? • What are computer vision processes? 12/1/2020 COMP 9517 S 2, 2009 3

Goals of Computer Vision • Make useful decisions about real physical objects and scenes based on sensed images or sequences of images. • Alternative: goal is the construction of scene descriptions from images. • Use statistical methods to process data using models constructed with the aid of geometry, physics and learning theory. 12/1/2020 COMP 9517 S 2, 2009 4

Computer Vision Topics • Requires a solid understanding of camera and of the physical process of image, • to obtain simple inferences from individual pixel values, • to combine the information available in multiple images into a coherent whole, • to enforce some order on groups of pixels to separate them from each other or infer shape information, • to recognise objects using geometric information or probabilistic techniques. 12/1/2020 COMP 9517 S 2, 2009 5

Critical Issues • Sensing: how do sensors obtain images of the world? • Encoded Information: how do image yield information of the scene, such as color, texture, shape, motion, etc. ? • Representations: what representations are appropriate to describe objects? • Algorithms: what algorithms process image information and construct scene descriptions? 12/1/2020 COMP 9517 S 2, 2009 6

CV Applications • Vision-based HCI – Eye. Mouse: a vision-based eye control system – To use human head and eyes to control computers, so how? – Computer vision and an webcam to track the eyes and head – Shakes and winks to control a mouse pointer on the screen – Face expression recognition – Challenge: clutter and real time • Game Controller: Cam-Trax 12/1/2020 COMP 9517 S 2, 2009 7

CV Applications • Geographical: GIS – Interpreting satellite images – Road detection for creating maps – Edge detection, Road edge classification and linking – Challenge: complex and wide scene, occlusion, low resolution or large data size. 12/1/2020 COMP 9517 S 2, 2009 8

CV Applications • Medical Imaging – Enhance imagery, or identify important phenomena or events, or visualise information obtained by imaging – Parenchymal bands: linear structures touching the lung boundary – Segment and classify candidate regions into positive (parenchymal bands) and negative (others) class – Challenge: • Often attached to other structures, in this case a nodular mass • Similar appearance to blood vessels 12/1/2020 COMP 9517 S 2, 2009 9

CV Applications • Video Surveillance – Traffic Monitoring – Object tracking – Action recognition, driving, stopping, etc – Vehicle speed – Counting – Challenge: occlusion, illumination changes and non-linear speed 12/1/2020 COMP 9517 S 2, 2009 10

CV Applications • Image/video retrieval – Content-based retrieval – Search engine – Challenge: big data volume, semantic 12/1/2020 COMP 9517 S 2, 2009 11

CV Applications • Text Recognition – Converting information from paper documents into digital form – Challenge: semantic interpretation I looked as hard as I could see, beyond 100 plus infinity an object of bright intensity- it was the back of me! 12/1/2020 COMP 9517 S 2, 2009 12

Computer Vision Processes • Low level processes – use little knowledge of image content – include image compression, noise filtering, edge extraction, . . . – use data which resemble the input image, eg. matrix of picture elements • High level processes – based on knowledge, goals, plans – use Artificial Intelligence methods – simulate human cognition and decision making based on information in the image – cognitive processes, geometric models, goals, plans, . . . 12/1/2020 COMP 9517 S 2, 2009 13

Low Level Vision • almost entirely digital image processing – sensing: image capture and digitisation – pre-processing: improve image quality: suppress noise, enhance object features, edge extraction – image segmentation: separate objects from background, partition image into objects of interest – description: compute features which differentiate objectsalso called feature extraction – Classification: assign labels to image segments (regions) 12/1/2020 COMP 9517 S 2, 2009 14

High Level Vision • About knowledge construction, representation and inference – recognition: identification of objects – interpretation: assign meaning to groups of recognized objects – scene analysis 12/1/2020 COMP 9517 S 2, 2009 15