The Logical Rules of Commonsense Reasoning

2010 ◽  
pp. 88-101
Author(s):  
Xenia Naidenova
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Ore Deposits ◽  
Spectral Information ◽  
Human Reasoning ◽  
Commonsense Reasoning ◽  
Inference Mechanism ◽  
Testing Data ◽  
Information Image ◽  
Logical Rules

In this chapter we describe a model of commonsense reasoning that has been acquired from our numerous investigations on the human reasoning modes used by experts for solving diagnostic problems in diverse areas such as pattern recognition of natural objects (rocks, ore deposits, types of trees, types of clouds etc.), analysis of multi-spectral information, image processing, interpretation of psychological testing data, medicine diagnosis and so on. The principal aspects of this model coincide with the rulebased inference mechanism that is embodied in the KADS system (Ericson, et al., 1992), (Gappa, & Poeck, 1992). More details related to our model of reasoning and its implementation can be found in (Naidenova, & Syrbu, 1984; Naidenova, & Polegaeva, 1985a; 1985b).

The Examples of Human Commonsense Reasoning Processes

2010 ◽  
pp. 102-121
Author(s):  
Xenia Naidenova
Keyword(s):  
Pattern Recognition ◽  
Human Reasoning ◽  
Commonsense Reasoning ◽  
Scientific Investigations

In this chapter, we concentrate our attention on analyzing and modeling natural human reasoning in solving different tasks: pattern recognition in scientific investigations, logical games, and investigation of crimes.

scholarly journals Review of local binary pattern operators in image feature extraction

2020 ◽  
Vol 19 (1) ◽  
pp. 23
Author(s):  
Shihab Hamad Khaleefah ◽  
Salama A. Mostafa ◽  
Aida Mustapha ◽  
Mohammad Faidzul Nasrudin
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Feature Extraction ◽  
Local Binary Pattern ◽  
Image Feature ◽  
Image Feature Extraction ◽  
Classification Image ◽  
Current Trends ◽  
Segmentation Pattern ◽  
Information Image

<span>With the substantial expansion of image information, image processing and computer vision have significant roles in several applications, including image classification, image segmentation, pattern recognition, and image retrieval. An important feature that has been applied in many image applications is texture. Texture is the characteristic of a set of pixels that form an image. Therefore, analyzing texture has a significant impact on segmenting an image or detecting important portions of an image. This paper provides a review on LBP and its modifications. The aim of this review is to show the current trends for using, modifying and adapting LBP in the domain of image processing.</span>

Software pattern recognition applied to nanodiffraction patterns from a STEM instrument

1986 ◽  
Vol 44 ◽  
pp. 694-695
Author(s):  
G.Y. Fan ◽  
J.M. Cowley
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Computer Software ◽  
Processing System ◽  
Light Level ◽  
Host Computer ◽  
Low Light ◽  
Image Processing System ◽  
Recent Developments ◽  
On Line

In recent developments, the ASU HB5 has been modified so that the timing, positioning, and scanning of the finely focused electron probe can be entirely controlled by a host computer. This made the asynchronized handshake possible between the HB5 STEM and the image processing system which consists of host computer (PDP 11/34), DeAnza image processor (IP 5000) which is interfaced with a low-light level TV camera, array processor (AP 400) and various peripheral devices. This greatly facilitates the pattern recognition technique initiated by Monosmith and Cowley. Software called NANHB5 is under development which, instead of employing a set of photo-diodes to detect strong spots on a TV screen, uses various software techniques including on-line fast Fourier transform (FFT) to recognize patterns of greater complexity, taking advantage of the sophistication of our image processing system and the flexibility of computer software.

Computer Vision and robotics in postal automation

1999 ◽  
Vol 18 (3-4) ◽  
pp. 265-273
Author(s):  
Giovanni B. Garibotto
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Pattern Recognition ◽  
Material Handling ◽  
State Of The Art ◽  
Short Description ◽  
The Other ◽  
Functional Requirements ◽  
Postal Automation ◽  
And Robotics

The paper is intended to provide an overview of advanced robotic technologies within the context of Postal Automation services. The main functional requirements of the application are briefly referred, as well as the state of the art and new emerging solutions. Image Processing and Pattern Recognition have always played a fundamental role in Address Interpretation and Mail sorting and the new challenging objective is now off-line handwritten cursive recognition, in order to be able to handle all kind of addresses in a uniform way. On the other hand, advanced electromechanical and robotic solutions are extremely important to solve the problems of mail storage, transportation and distribution, as well as for material handling and logistics. Finally a short description of new services of Postal Automation is referred, by considering new emerging services of hybrid mail and paper to electronic conversion.

scholarly journals Application of Supervised Machine Learning to Recognize Competent Level and Mixed Antinuclear Antibody Patterns Based on ICAP International Consensus

Diagnostics ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 642
Author(s):  
Yi-Da Wu ◽  
Ruey-Kai Sheu ◽  
Chih-Wei Chung ◽  
Yen-Ching Wu ◽  
Chiao-Chi Ou ◽  
...  
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Excellent Agreement ◽  
Antinuclear Antibody ◽  
Disease Diagnosis ◽  
Machine Learning Algorithms ◽  
Observer Agreement ◽  
Supervised Machine Learning ◽  
International Consensus ◽  
Testing Data

Background: Antinuclear antibody pattern recognition is vital for autoimmune disease diagnosis but labor-intensive for manual interpretation. To develop an automated pattern recognition system, we established machine learning models based on the International Consensus on Antinuclear Antibody Patterns (ICAP) at a competent level, mixed patterns recognition, and evaluated their consistency with human reading. Methods: 51,694 human epithelial cells (HEp-2) cell images with patterns assigned by experienced medical technologists collected in a medical center were used to train six machine learning algorithms and were compared by their performance. Next, we choose the best performing model to test the consistency with five experienced readers and two beginners. Results: The mean F1 score in each classification of the best performing model was 0.86 evaluated by Testing Data 1. For the inter-observer agreement test on Testing Data 2, the average agreement was 0.849 (?) among five experienced readers, 0.844 between the best performing model and experienced readers, 0.528 between experienced readers and beginners. The results indicate that the proposed model outperformed beginners and achieved an excellent agreement with experienced readers. Conclusions: This study demonstrated that the developed model could reach an excellent agreement with experienced human readers using machine learning methods.

A μc-Si:H P-I-N Imager For 2-D Pattern Recognition

1999 ◽  
Vol 558 ◽  
Author(s):  
J. Martins ◽  
M. Fernandes ◽  
F. Sousa ◽  
P. Louro ◽  
A. MaçArico ◽  
...  
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Image Enhancement ◽  
Image Representation ◽  
Rectangular Grid ◽  
Sensing Element ◽  
Coplanar Electrodes ◽  
Basic Image ◽  
Image Processing Algorithms ◽  
Processing Algorithms

ABSTRACTA TCO/ μc-p-i-n Si:H/AI imager is presented and analyzed. The μc-p-i-n Si:H photodiode acts as a sensing element. Contacts are used as an electrical interface. The image is acquired by a scan-out process. Sampling is performed on a rectangular grid, and the read-out of the photogenerated charges is achieved by measuring simultaneously both transverse photovoltages at the coplanar electrodes. The image representation in gray-tones is obtained by using low level processing algorithms. Basic image processing algorithms are developed for image enhancement and restoration.

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