J
James Z. Wang
Researcher at Pennsylvania State University
Publications - 234
Citations - 23185
James Z. Wang is an academic researcher from Pennsylvania State University. The author has contributed to research in topics: Image retrieval & Automatic image annotation. The author has an hindex of 57, co-authored 225 publications receiving 21890 citations. Previous affiliations of James Z. Wang include Penn State College of Information Sciences and Technology & University of Minnesota.
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Proceedings Article
QCHARM: a novel computational and scientific visualization framework for facilitating discovery and improving diagnostic reliability in medicine.
TL;DR: In this article, the authors developed advanced image recognition methods whose goal is to automatically recognize and quantitatively characterize abnormalities in the tissue morphology of larval and adult zebrafish, which can lead to an improved understanding of human development and also drive more targeted development of disease treatments.
Posted Content
An Investigation Into Three Visual Characteristics of Complex Scenes That Evoke Human Emotion
TL;DR: In this article, a large collection of ecologically valid stimuli (i.e., photos that humans regularly encounter on the web), named the EmoSet and containing more than 40,000 images crawled from web albums, was generated using crowdsourcing and subjected to human subject emotion ratings.
Journal ArticleDOI
Low-dose CBCT Imaging for External Beam Radiotherapy
Journal ArticleDOI
Modulation of In Vivo Tumor Radiation Response via Vascular-focused Hyperthermia-characterizing Gold Nanoshells as Integrated Anti-hypoxic and Localized Vascular Disrupting Agents
Parmeswaran Diagaradjane,Anil Shetty,James Z. Wang,Jon A. Schwartz,Hee Chul Park,Amit Deorukhkar,Jason Stafford,S.H. Cho,John D. Hazle,James W. Tunnell,Sunil Krishnan +10 more
Posted Content
Modeling Photographic Composition via Triangles.
TL;DR: This paper proposes a system that can identify prominent triangle arrangements in two major categories of photographs: natural or urban scenes, and portraits, and shows that line analysis is highly advantageous for modeling composition in both categories.