a Record control number 000178869100014

a Original cataloging agency NEU GrandLibrary

a Language code of text/sound track or separate title eng

a Classification number QA76

b Item number .O68 2002

a Personal name Khashman, Adnan,

h Medium Electronic Document

a Title Optimal scale edge detection utilizing noise within images.

c Statement of responsibility, etc A.Khashman.

c Date of publication, distribution, etc 2002.

b Name of publisher, distributor, etc Int. Inst. Informatics & Systemics,

a Place of publication, distribution, etc Orlando :

a Summary, etc Edge detection techniques have common problems that include poor edge detection in low contrast images, speed of recognition and high computational cost. An efficient solution to the edge detection of objects in low to high contrast images is scale space analysis. However, this approach is time consuming and computationally expensive. These expenses can be marginally reduced if ail optimal scale is found in scale space edge detection. This paper presents a new approach to detecting objects within images using noise within the images. The novel idea is based on selecting one optimal scale for the entire image at which scale space edge detection can be applied. The selection of an ideal scale is based on the hypothesis that "the optimal edge detection scale (ideal scale) depends on the noise within an image". This paper aims at providing the experimental evidence oil the relationship between the optimal scale and the noise within images.

a Topical term or geographic name as entry element Near East University Article

a Topical term or geographic name as entry element Computer science

a Topical term or geographic name as entry element Yakın Doğu Üniversitesi Makale

a Topical term or geographic name as entry element Edge detection

a Topical term or geographic name as entry element Scale space analysis

g Relationship information 2002, p72-76

t Title 6th World Multiconference On Systemics, Cybernetics And Informatics, Vol III, Proceedings: Image, Acoustic, Speech And Signal Processing I