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COMPUTER VISION

COMPSCI/ECE 766
과목 설명

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.

선수과목

Graduate/professional standing

충족 요건

This course does not satisfy any prerequisites.

학점

미보고

개설 시기

미보고

평점
3.87

1.23% 과거 데이터 대비

수료율
100%

0.43% 과거 데이터 대비

A 비율
87.23%

10.03% 과거 데이터 대비

학급 규모
94

116.4% 과거 데이터 대비

Cumulative Grade Distribution

강사 (2026 Summr)

다음 사이트의 평점순으로 정렬 Rate My Professors

유사 과목

INTRODUCTION TO COMPUTER VISION

COMPSCI 566

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. Project-based.

BIOMEDICAL IMAGE ANALYSIS

BMI/COMPSCI 567

Hands-on introduction to biological and medical image analysis techniques. Topics include medical imaging formats, segmentation, registration, image quantification, and classification.

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.

IMAGE PROCESSING

COMPSCI/ECE 533

Mathematical representation of continuous and digital images; models of image degradation; picture enhancement, restoration, segmentation, and coding; pattern recognition, tomography.

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].