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LEARNING BASED METHODS FOR COMPUTER VISION

BMI/COMPSCI 771
과목 설명

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].

선수과목

Graduate/professional standing

충족 요건

This course does not satisfy any prerequisites.

학점

미보고

개설 시기

미보고

평점
3.91

0.93% 과거 데이터 대비

수료율
100%

과거 데이터 대비 변화 없음

A 비율
97.73%

12.97% 과거 데이터 대비

학급 규모
44

4.76% 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.

LEARNING BASED IMAGE SYNTHESIS AND MANIPULATION

COMPSCI 772

Introduces machine learning based synthesis and manipulation of visual data (images and videos). Both classical (e.g., nearest neighbor, filtering) and modern deep learning based (e.g., ConvNets, GANs, Diffusion Models) algorithms will be presented for image representation, synthesis, and manipulation. Usage of self-developed algorithms for image synthesis and manipulation to understand and analyze state-of-the-art techniques, and to identify interesting open questions and future directions.

COMPUTATIONAL METHODS FOR MEDICAL IMAGE ANALYSIS

BMI/COMPSCI 767

Review of advanced medical image analysis techniques. Covers advanced segmentation and registration methods. Describes the use and extension of statistical and machine learning methods for medical image analysis tasks.

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 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.