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Caixia Jiang

dblp:182/6812 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 1992
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-authorGraphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Multimedia analysis and retrieval · 50% Image and video processing · 50%
Artificial intelligence
1 paper
Segmentation and scene understanding · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Multimedia analysis and retrieval › image analysis
image understanding
0.011992
Shadow identification · CVPR 1992
Image and video processing › image enhancement › shadow detection and removal
shadow detection
0.011992
Shadow identification · CVPR 1992
Computer vision › Segmentation and scene understanding
region analysis
0.011992
Shadow identification · CVPR 1992

Methods — techniques the papers use, named apart from their topics

shadow intensity analysis · 0.0shadow geometry analysis · 0.0multi-level processing · 0.0
YearPublicationVenuePosition
1992 Shadow identification
abstract
A shadow identification and classification method for real images is developed. The method is based on extensive analysis of shadow intensity and shadow geometry. The procedure for identifying shadows is divided into low-level, middle-level, and high-level processes. The low-level extracts dark regions from images. The middle-level process performs feature analysis on dark regions, including detecting vertices on the outlines of dark regions, identifying penumbrae in dark regions, assigning the subregions in dark regions as self-shadows and cast shadows, and finding object regions adjacent to dark regions. The high-level process integrates the information derived from the previous processes and confirms shadows among the dark regions.>
Caixia Jiang, Matthew O. Ward
CVPR1