Attribute Grid Computer based on Qualitative Mapping and its application in pattern Recognition

Author(s):  
Jiali Feng
1972 ◽  
Vol 11 (01) ◽  
pp. 32-37 ◽  
Author(s):  
F. T. DE DOMBAL ◽  
J. C. HORROCKS ◽  
J. R. STANILAND ◽  
P. J. GUILLOU

This paper describes a series of 10,500 attempts at »pattern-recognition« by two groups of humans and a computer based system. There was little difference between the performances of 11 clinicians and 11 other persons of comparable intellectual capability. Both groups’ performances were related to the pattern-size, the accuracy diminishing rapidly as the patterns grew larger. By contrast the computer system increased its accuracy as the patterns increased in size.It is suggested (a) that clinicians are very little better than others at pattem-recognition, (b) that the clinician is incapable of analysing on a probabilistic basis the data he collects during a traditional clinical interview and examination and (c) that the study emphasises once again a major difference between human and computer performance. The implications as - regards human- and computer-aided diagnosis are discussed.


2000 ◽  
Vol 12 (6) ◽  
pp. 1001-1012 ◽  
Author(s):  
Erich Kasten ◽  
Dorothe A. Poggel ◽  
Bernhard A. Sabel

In a previously conducted randomized placebo-controlled trial, we were able to demonstrate significant visual field enlargement induced by restitution therapy in patients with cerebral lesions [Kasten, E., Wuest, S., Behrens-Bamann, W., & Sabel, B. A. (1998c). Computer-based training for the treatment of partial blindness. Nature Medicine, 4, 1083-1087.]. Visual field training was performed on a computer monitor for 1 hr per day over a period of 6 months. Since the procedure included only stimulation with white light, in the present study we investigated if this simple detection training had a transfer effect on color or form recognition in the trained area (i.e., in the absence of modality specific training). Answering this question would be crucial for planning optimal restitution therapy: In case there is no transfer of training effects to other visual modalities, a specific treatment of each visual function must be performed in order to achieve maximum benefit. Therefore, we analyzed the data from 32 patients with visual field defects who had participated in the original trial and whose form and color recognition had been investigated. The experimental group (n = 19, restitution training) experienced not only an increase of 12.8% correctly detected stimuli (PeriMa program, p < .05), but also an improvement of 5.6% in pattern recognition (PeriForm) and of 6.1% in color perception (PeriColor), respectively. In contrast, the placebo group (n = 13, fixation training) showed no significant changes from baseline to final outcome in any of the visual modalities (PeriMa: 0.3%; PeriForm: -0.3%; PeriColor: 0.4%). Conventional perimetry yielded an increase of 7.8% detected stimuli in the experimental group, but only of 1.2% in the placebo group (p < .05). For form recognition and color perception, the differences between the results of the experimental and the placebo groups narrowly missed significance. However, correlations of diagnostic results showed that mainly those patients who had achieved visual field enlargement also improved in color and form perception: r = .67 (p < .05) between PeriMa and PeriForm and r = .32 between PeriMa and PeriColor. We conclude that visual restitution training using a simple white light stimulus has at least some influence on improving other visual functions such as color and pattern recognition. This result supports the “bottleneck theory” of visual restitution, i.e., training effects can be explained as a process of perceptual learning and increased processing of information by residual structures surviving lesions of the primary visual pathways.


1989 ◽  
Vol 28 (01) ◽  
pp. 28-35 ◽  
Author(s):  
Mark A. Musen ◽  
Johan van der Lei

Abstract:Developers of computer-based decision-support tools frequently adopt either pattern recognition or artificial intelligence techniques as the basis for their programs. Because these developers often choose to accentuate the differences between these alternative approaches, the more fundamental similarities are frequently overlooked. The principal challenge in the creation of any clinical consultation program - regardless of the methodology that is used - lies in creating a computational model of the application domain. The difficulty in generating such a model manifests itself in symptoms that workers in the expert systems community have labeled “the knowledge-acquisition bottleneck” and “the problem of brittleness”. This paper explores these two symptoms and shows how the development of consultation programs based on pattern-recognition techniques is subject to analogous difficulties. The expert systems and pattern recognition communities must recognize that they face similar challenges, and must unite to develop methods that assist with the process of building of models of complex application tasks.


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