1,720,991 research outputs found
Crack detection by a measure of texture anisotropy
No full textIn this paper, the problem of automatic visual inspection of textured surfaces is addressed. In particular, a technique for crack detection on both regularly and randomly textured images is presented. The technique is based on a new measure of texture anisotropy that allows an easy discrimination between defect pixels and defect-free ones. This technique was used to detect cracks on granite slabs. The reported results confirm its effectiveness
Generation and optimization of certainty factors for remote-sensing image classification
No full textHigh-performance image coding: Integration of different techniques by a knowledge-based recognition system
No full text
"Supervised learning of descriptions for image recognition purposes"
No full textThis study deals with a learning system for generation of descriptions of objects to be recognized in 2-D images. After proposing a framework for handling fuzzy and relational descriptions, we present the system obtained by making such a framework manage a well-known learning methodology. Satisfactory results and comparisons are reporte
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Classifier fusion for multisensor image recognition
No full textClassifier fusion approaches are receiving increasing attention for their capability of improving classification performances. At present, the usual operation mechanism for classifier fusion is the "combination" of classifier outputs. Improvements in performances are related to the degree of "error diversity" among combined classifiers. Unfortunately, in remote-sensing image recognition applications, it may be difficult to design an ensemble that exhibit an high degree of error diversity. Recently, some researchers have pointed out the potentialities of "dynamic classifier selection" (DCS) as an alternative operation mechanism. DCS techniques are based on a function that selects the most appropriate classifier for each input pattern. The assumption of uncorrelated errors is not necessary for DCS because an "optimal" classifier selector always selects the most appropriate classifier for each test pattern. The potentialities of DCS have been motivated so far by experimental results on ensemble of classifiers trained using the same feature set. In this paper, we present an approach to multisensor remote-sensing image classification based on DCS. A selection function is presented aimed at choosing among classifiers created using different feature sets. The experimental results obtained in the classification of remote-sensing images and comparisons with different combination methods are reported
- …
