We're still actively developing this site. If you encounter any issues, please report them! - Report an issue

INTRODUCTION TO COMPUTER VISION

COMPSCI 566
Course Description

Topics include image formation, feature detection, motion estimation, image mosaics, 3D shape reconstruction, and object recognition. Applications of these techniques include building 3D maps, creating virtual characters, organizing photo and video databases, human computer interaction, video surveillance, and automatic vehicle navigation. Broad overview of various computer vision and machine learning techniques and sensing and imaging technologies used in computer vision applications. This is a project-based course.

Prerequisties

COMPSCI 400 and (MATH 320 , MATH 340 , MATH 341 or MATH 375 ) and (STAT 302, STAT 311 , STAT 324 , STAT 340 , STAT 371 , MATH/STAT 309 , MATH/STAT 431 , MATH 331 or MATH 531 ) or graduate/professional standing

Satisfies

This course does not satisfy any prerequisites.

Credits

3

Offered

Not Applicable

Grade Point Average
Not Reported

Could not calculate change

Completion Rate
Not Reported

Could not calculate change

A Rate
Not Reported

Could not calculate change

Class Size
Not Reported

No change from Historical

No data available

Instructors (2025 Fall)

Sorted by ratings from Rate My Professors

Similar Courses

COMPUTATIONAL PHOTOGRAPHY

COMPSCI 534

Study of sensing and computational techniques that enhance or extend the capabilities of digital photography by using methods from computer vision and computer graphics to create new visual representations. Algorithms for analyzing, improving, manipulating, combining, and synthesizing images.

COMPUTER GRAPHICS

COMPSCI 559

Survey of computer graphics. Image representation, formation, presentation, composition and manipulation. Modeling, transformation, and display of geometric objects in two and three dimensions. Representation of curves and surfaces. Rendering, animation, multi-media and visualization. Fluency with vector mathematics (e.g., fromMATH 234or a linear algebra class) is recommended.

INTRODUCTION TO DATA VISUALIZATION

COMPSCI 565

Introduction to topics such as perception, cognition, communication, design, implementation, applications, tools, and evaluation. Provides a broad survey of the field and covers fundamental concepts, theory, and tools in data visualization with opportunities for hands-on activities. Gain real-world experience in designing and evaluating visualizations.

LEARNING BASED METHODS FOR COMPUTER VISION

BMI/COMPSCI 771

Addresses the problems of representation and reasoning for large amounts of visual data, including images and videos, medical imaging data, and their associated tags or text descriptions. Introduces deep learning in the context of computer vision. Covers topics on visual recognition using deep models, such as image classification, object detection, human pose estimation, action recognition, 3D understanding, and medical image analysis. Emphasizes the design of vision and learning algorithms and models, as well as their practical implementations. Strongly recommended to have knowledge in computer vision or machine learning [such asCOMP SCI 540] or medical image analysis [such as B M I /COMP SCI/​B M I  567].

COMPUTER VISION

COMPSCI/ECE 766

Fundamentals of image analysis and computer vision; image acquisition and geometry; image enhancement; recovery of physical scene characteristics; shape-from techniques; segmentation and perceptual organization; representation and description of two-dimensional objects; shape analysis; texture analysis; goal-directed and model-based systems; parallel algorithms and special-purpose architectures. Students are strongly encouraged to have basic proficiency in calculus and linear algebra, such asMATH 340, and basic programming such asCOMP SCI 300.