Ping-Jung Duh

dblp:295/4636 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2021
—ORCID · none

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

Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author · 1 since 2021

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.

Artificial intelligence
1 paper
Robot navigation and mapping · 77% Segmentation and scene understanding · 23%
Human-computer interaction and pervasive computing
1 paper
Accessibility and assistive technology · 100%

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

TopicWeightPapersLastEvidence papers
Robotics › Robot navigation and mapping › localization
global localization
0.512021
V-Eye: A Vision-Based Navigation System for the Visually Impaired · IEEE Trans. Multim. 2021
Accessibility and assistive technology › assistive navigation
navigation assistance for visually impaired
0.512021
V-Eye: A Vision-Based Navigation System for the Visually Impaired · IEEE Trans. Multim. 2021
Computer vision › Segmentation and scene understanding
image segmentation
0.112021
V-Eye: A Vision-Based Navigation System for the Visually Impaired · IEEE Trans. Multim. 2021

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

image segmentation · 1.0VB-GPS · 1.0
YearPublicationVenuePosition
2021 V-Eye: A Vision-Based Navigation System for the Visually Impaired
abstract
Numerous systems for helping visually impaired people navigate in unfamiliar places have been proposed. However, few can detect and warn about moving obstacles, provide correct orientation in real time, or support navigation between indoor and outdoor spaces. Accordingly, this paper proposes V-Eye, which fulfills these needs by utilizing a novel global localization method (VB-GPS) and image-segmentation techniques to achieve better scene understanding with a single camera. Our experiments establish that the proposed system can reliably provide precise locations and orientation information (with a median error of approximately 0.27 m and 0.95°); detect unpredictable obstacles; and support navigating both within and between indoor and outdoor environments. The results of a user-experience study of V-eye further indicate that it helped the participants not only with navigation, but also improved their awareness of obstacles, enhanced their spatial awareness more generally, and led them to feel more secure and independent while walking.
Ping-Jung Duh, Yu-Cheng Sung, Liang-Yu Fan Chiang, Yung-Ju Chang, Kuan-Wen Chen
IEEE Trans. Multim.1