Interactive search and retrieval methods using automatic information displays

1969 ◽  
Author(s):  
M. E. Lesk ◽  
G. Salton
Keyword(s):  
Interactive Search ◽  
Information Displays ◽  
Automatic Information ◽  
Search And Retrieval

A method to measure pores and fissures in geologic materials under S.E.M. by digital image processing

1978 ◽  
Vol 36 (1) ◽  
pp. 212-213
Author(s):  
L. Montoto ◽  
M. Montoto ◽  
A. Bel-Lan
Keyword(s):  
Image Processing ◽  
Optical Microscopy ◽  
Digital Image Processing ◽  
Digital Image ◽  
Digital Processing ◽  
Free Surfaces ◽  
Geologic Materials ◽  
Automatic Information ◽  
Detector Output ◽  
Rock Specimens

INTRODUCTION.- The physical properties of rock masses are greatly influenced by their internal discontinuities, like pores and fissures. So, these need to be measured as a basis for interpretation. To avoid the basic difficulties of measurement under optical microscopy and analogic image systems, the authors use S.E.M. and multiband digital image processing. In S.E.M., analog signal processing has been used to further image enhancement (1), but automatic information extraction can be achieved by simple digital processing of S.E.M. images (2). The use of multiband image would overcome difficulties such as artifacts introduced by the relative positions of sample and detector or the typicals encountered in optical microscopy.DIGITAL IMAGE PROCESSING.- The studied rock specimens were in the form of flat deformation-free surfaces observed under a Phillips SEM model 500. The SEM detector output signal was recorded in picture form in b&w negatives and digitized using a Perkin Elmer 1010 MP flat microdensitometer.

Coordinating Medical Literature with Patient Care

1977 ◽  
Vol 16 (04) ◽  
pp. 234-240 ◽  
Author(s):  
Joann Gustafson ◽  
J. Nelson ◽  
Ann Buller
Keyword(s):  
Health Care ◽  
Medical Staff ◽  
Medical Information ◽  
Medical Literature ◽  
Medical Information System ◽  
Staff Members ◽  
Information Displays ◽  
Medical Displays ◽  
Computerized Medical Information ◽  
The Given

The contribution of a special library project to a computerized problem-oriented medical information system (PROMIS) is discussed. Medical information displays developed by the PROMIS medical staff are accessible to the health care provider via touch screen cathode terminals. Under PROMIS, members of the library project developed two information services, one concerned with the initial building of the medical displays and the other with the updating of this information. Information from 88 medical journals is disseminated to physicians involved in the building of the medical displays. Articles meeting predetermined selection criteria are abstracted and the abstracts are made available by direct selective dissemination or via a problem-oriented abstract file. The updating service involves comparing the information contained in the selected articles with the computerized medical displays on the given topic. Discrepancies are brought to the attention of PROMIS medical staff members who evaluate the information and make appropriate changes in the displays. Thus a feedback loop is maintained which assures the completeness, accuracy, and currency of the computerized medical information. The development of this library project and its interface with the computerized health care system thus attempts to deal with the problems in the generation, validation, dissemination, and application of medical literature.

Ensemble Visual Content based Search and Retrieval for Natural Scene Images

2019 ◽  
Vol 12 ◽  
Author(s):  
Pushpendra Singh ◽  
P.N. Hrisheekesha ◽  
Vinai Kumar Singh
Keyword(s):  
Template Matching ◽  
Feature Descriptor ◽  
Natural Scene ◽  
Visual Content ◽  
Other Information ◽  
Similar Images ◽  
State Of Art ◽  
Natural Scene Images ◽  
Hybrid Methodology ◽  
Search And Retrieval

Content based image retrieval (CBIR) is one of the field for information retrieval where similar images are retrieved from database based on the various image descriptive parameters. The image descriptor vector is used by machine learning based systems to store, learn and template matching. These feature descriptor vectors locally or globally demonstrate the visual content present in an image using texture, color, shape, and other information. In past, several algorithms were proposed to fetch the variety of contents from an image based on which the image is retrieved from database. But, the literature suggests that the precision and recall for the gained results using single content descriptor is not significant. The main vision of this paper is to categorize and evaluate those algorithms, which were proposed in the interval of last 10 years. In addition, experiment is performed using a hybrid content descriptors methodology that helps to gain the significant results as compared with state-of-art algorithms. The hybrid methodology decreases the error rate and improves the precision and recall for large natural scene images dataset having more than 20 classes.

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