CSCE 5013 Computer Vision Fall 2011

1. CSCE 5013Computer VisionFall 2011 Prof. John Gauch jgauch@uark.edu

2. Overview Application Areas History Course Objectives 01 - Introduction

3. Computer vision is the process of extracting useful information from digital images Finding objects of interest in images Properties of objects (size, shape, color) Recognition of objects Computer vision is also known as machine vision, robot vision, computational vision, or image understanding Overview

4. The fundamental problem of computer vision is that multiple models could fit the image data Fitting a line equation to set of 2D points Calculating 3D coordinates from 2D images Hence we must select the best model that fits the data given the time/space constraints of the application Overview

5. Computer vision is closely related to three other research areas: Image processing (image => image) Computer vision (image => model) Computer graphics (model => image) Computational geometry (model => model) Overview

6. Automated inspection – CV is used to look for defects in manufactured parts and to assist in automated assembly Application Areas

7. Navigation – CV is used to guide a car or robot along roads or paths while avoiding obstacles Application Areas

8. Computer graphics modeling – CV is used to generate natural looking models that bend and move like real objects Application Areas

9. Security and surveillance – CV is used to watch areas of interest to detect suspicious activities in restricted areas Application Areas

10. Medical applications – CV is used to locate, identify and quantify abnormal features in medical images and assist in treatment Application Areas

11. Human Biometrics – CV is used to recognize people via face or fingerprint recognition Application Areas

12. Computer vision is a well established area of computer science and engineering First attempts to model blocks world images were made at MIT in the 1960s In the 1970s early computer vision methods made use of AI techniques to reason about line drawings In the 1980s attempts to “understand everything” in an image were outperformed by task specific techniques History

13. The focus in the 1990s moved towards more physics based image modeling and real time applications such as video content analysis In the 2000s we have seen computer vision methods mature and become widespread in other areas such as computer graphics In the 2010s we will see even wider use of computer vision applications making use of FPGAs and GPUs and mobile devices History

14. Goal of this class is to learn the fundamental techniques used in computer vision Mathematical tools and techniques Algorithms and data structures Existing computer vision software Developing CV applications in C++ Reading current research literature Course Objectives

15. The remainder of this class we will focus on the fundamentals of computer vision Image formation – how digital images are captured and how this knowledge of the scene can be used Image processing – survey of basic techniques to manipulate images prior to detailed analysis Feature detection – methods to extract geometric, chromatic or textural features from images Course Objectives

16. Image segmentation – methods to partition an image into visually sensible regions Feature-based alignment – how image features can be used to align large collections of images Motion estimation – techniques for measuring camera and/or object motion in image sequences Emerging techniques – discussion of recent trends in computer vision and future applications Course Objectives