Sadali N. Jayaramamurthy

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4ranked-venue papers

0as first author

0since 2021 · last 1988

—ORCID · none

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

Artificial intelligence and machine learning · 2Systems, architecture and hardware · 2

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		Segmentation and scene understanding · 33% Trustworthy machine learning · 33% Knowledge representation and reasoning · 33%
Computer architecture, parallel and distributed computing, and storage systems 1 paper		Parallel and multicore computing · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

Topic	Papers	Last	Evidence papers
Knowledge, reasoning and agents › Knowledge representation and reasoning › uncertainty reasoning fuzzy sets	1	1986	Representation of Uncertainty in Computer Vision Using Fuzzy Sets · IEEE Trans. Computers 1986
Computer vision › Segmentation and scene understanding scene understanding	1	1986	Representation of Uncertainty in Computer Vision Using Fuzzy Sets · IEEE Trans. Computers 1986
Machine learning › Trustworthy machine learning uncertainty modeling	1	1986	Representation of Uncertainty in Computer Vision Using Fuzzy Sets · IEEE Trans. Computers 1986
Parallel and multicore computing › array processor parallel array processor	1	1972	Automatic Generation of Texture Feature Detectors · IEEE Trans. Computers 1972

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

fuzzy sets · 0.0mccormick-michalski pattern recognition method · 0.0interval generation · 0.0

Year	Publication	Venue	Position
1988	Determination of the optic flow field in the presence of occlusion Terrance L. Huntsberger, Sadali N. Jayaramamurthy	Pattern Recognit. Lett.	2
1987	Determination of the optic flow field using the spatiotemporal deformation of region properties Terrance L. Huntsberger, Sadali N. Jayaramamurthy	Pattern Recognit. Lett.	2
1986	Representation of Uncertainty in Computer Vision Using Fuzzy Sets abstract Uncertainty in computer vision can arise at various levels. It can occur in the low level in the raw sensor input, and extends all the way through intermediate and higher levels. Ideally, at any level where decisions are being made on the basis of previous processing steps, a computer vision system must have sufficient flexibility for representation of uncertainty in any of these levels. Terrance L. Huntsberger, Chandraleka Rangarajan, Sadali N. Jayaramamurthy	IEEE Trans. Computers	3
1972	Automatic Generation of Texture Feature Detectors abstract This correspondence is an informal exposition on a method of pattern recognition recently introduced by McCormick and Michalski. The method, described here by means of a simple example and a biological application, provides mechanized generation of an optimum set of "intervals" which can be interpreted as local position-invariant digital operations for use in picture filtering with a parallel array processor. John S. Read, Sadali N. Jayaramamurthy	IEEE Trans. Computers	2