CS 5330 — Pattern Recognition and Computer Vision

4 semester hoursGraduateLectureusually offered: fall, springBostonOnlinePortland, MaineSeattle, WASilicon Valley, CAVancouver, CanadaHybridLive CastOnlineTraditional

Introduces fundamental techniques for low-level and high-level computer vision. Examines image formation, early processing, boundary detection, image segmentation, texture analysis, shape from shading, photometric stereo, motion analysis via optic flow, object modeling, shape description, and object recognition (classification). Discusses models of human vision (gestalt effects, texture perception, subjective contours, visual illusions, apparent motion, mental rotations, and cyclopean vision). Requires knowledge of linear algebra.

Prerequisites

Offering history

TermSectionsEnrolledCapacityFullOpen seats/section
Fall 20232326053%14.0
Spring 2024215316692%6.5
Fall 2024519123282%8.2
Spring 2025213215685%12.0
Summer A 20251183060%12.0
Fall 20253669669%10.0
Spring 2026323025989%9.7

Snapshots from scheduled scrapes — not live seat availability. "Full" can exceed 100% when sections over-enroll.

Meeting times

Share of recent sections by weekday: M 3% · T 16% · W 44% · Th 24% · F 27%

Common patterns: W (44% of sections), R (24% of sections), TF (14% of sections), F (13% of sections), M (3% of sections), T (2% of sections) — in patterns, R means Thursday

Professors

Fall

Spring

Summer A

Percentages are each professor's average share of the season's enrolled students in recent terms.

Links

Official catalog (CS course descriptions) · Student reviews on RateMyHusky · All CS courses · Plan it at numap.app