我们仍在积极开发此网站。如果您遇到任何问题，请报告给我们！ - 报告问题

ADVANCED DIGITAL IMAGE PROCESSING

ECE 738
课程描述

Deterministic and stochastic spatio-temporal image models, transform domain processing, Markov random fields and anisotropic diffusion; MAP parameter estimation, ill-posed inverse problems, robust statistics and non-linear digital filtering in image processing. Applications to image restoration, motion estimation, (video) image compression (MPEG, JPEG) and tomography. Knowledge of image processing [such asE C E/​COMP SCI  533] strongly encouraged.

先修课程

Graduate/professional standing

满足要求

This course does not satisfy any prerequisites.

学分

未报告

开课时间

未报告

平均绩点
3.44

-10.3% 相比历史数据

完成率
96%

-2.92% 相比历史数据

A率
48%

-46.3% 相比历史数据

班级规模
25

39.66% 相比历史数据

Cumulative Grade Distribution

教师 (2026 Summr)

按评分排序，数据来自 Rate My Professors

相似课程

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.

SIGNAL SYNTHESIS AND RECOVERY TECHNIQUES

ECE 735

Signals and their representation. Signal synthesis subject to constraints on peak voltage, energy, duration-bandwidth product. The theory of alternating projections onto convex sets and applications to inverse problems in signal processing: signal recovery using incomplete data, image recovery in tomography using limited views, phase retrieval in optical astronomy.

PROBABILITY AND RANDOM PROCESSES

ECE 730

Review of basic probability. Advanced probability concepts. Random vectors; linear filtering of random processes; stationarity; power spectral densities; estimation; convergence; Markov chains; Poisson process; Wiener process. Knowledge of basic probability analysis [such asE C E 331,STAT/​MATH  431, orSTAT 311] strongly encouraged.

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.

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.