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M. Demi

dblp:43/4168 · DBLP profile ↗
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2ranked-venue papers
1as first author
0since 2021 · last 2005
0000-0002-1472-7863ORCID · corroborated

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

Artificial intelligence and machine learning · 2 · 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
Image and video processing · 67% Visualization and visual analytics · 33%

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

TopicWeightPapersLastEvidence papers
Visualization and visual analytics
hierarchical aggregation
0.011993
An Artificial Vision System for X-ray Images of Human Coronary Trees · IEEE Trans. Pattern Anal. Mach. Intell. 1993
Image and video processing
image segmentation
0.011993
An Artificial Vision System for X-ray Images of Human Coronary Trees · IEEE Trans. Pattern Anal. Mach. Intell. 1993
Image and video processing › image segmentation
medical image segmentation
0.011993
An Artificial Vision System for X-ray Images of Human Coronary Trees · IEEE Trans. Pattern Anal. Mach. Intell. 1993

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

signal detection theory · 0.0bottom-up grouping · 0.0
YearPublicationVenuePosition
2005 On the gray-level central and absolute central moments and the mass center of the gray-level variability in low-level image processing
M. Demi
Comput. Vis. Image Underst.1
1993 An Artificial Vision System for X-ray Images of Human Coronary Trees
abstract
The coronary tree expert (CORTEX) analyzer, which is a vision system for the description of the bidimensional shape and position of coronary vessels using standard nonsubtracted radiographic images, is described. A bottom-up approach was used to deal with the typical characteristics of medical images, such as structural and nonstructural noise and complexity and variability of biological shapes. On these grounds, grouping criteria were utilized to produce intermediate image representations with an increasing complexity in a hierarchical manner (from edge points to curves, segments, bars, and finally to vessels and their mutual relations). In this way, uncertain, inconsistent, and deficient information was efficiently processed. The evaluation of CORTEX segmentation is also performed according to a signal-detection-theory-like approach.>
Giuseppe Coppini, M. Demi, Riccardo Poli, Guido Valli
IEEE Trans. Pattern Anal. Mach. Intell.2