Références divers

Dendral was an early artificial intelligence system developed in the 1960s at Stanford University to help chemists identify unknown organic molecules. It used mass spectrometry data and knowledge of chemistry to generate possible molecular structures and test them against the data. Dendral consisted of two subprograms: Heuristic Dendral, which produced potential structures, and Meta Dendral, which learned to explain the correlation between structures and spectra. The system pioneered the use of heuristics, knowledge engineering, and the plan-generate-test problem-solving paradigm in expert systems.

Chapter 9 morphological image processing

Ahmed Daoud

This document provides an overview of mathematical morphology and its applications to image processing. Some key points: - Mathematical morphology uses concepts from set theory and uses structuring elements to probe and extract image properties. It provides tools for tasks like noise removal, thinning, and shape analysis. - Basic operations include erosion, dilation, opening, and closing. Erosion shrinks objects while dilation expands them. Opening and closing combine these to smooth contours or fill gaps. - Hit-or-miss transforms allow detecting specific shapes. Skeletonization reduces objects to 1-pixel wide representations. - Morphological operations can be applied to binary or grayscale images. Structuring elements are used to specify the neighborhood of pixels

Image segmentation 3 morphology

Rumah Belajar

The document discusses morphological image operations and mathematical morphology. It provides examples of basic morphological operations like dilation, erosion, opening and closing. It also discusses morphological algorithms for tasks like boundary extraction, region filling, connected component extraction, skeletonization, and using morphological operations for applications like detecting foreign objects. The key concepts covered are binary morphological operations, connectivity in images, and algorithms for thinning, boundary detection, and segmentation.

Expert System MYCIN

Rached Krim

MYCIN was an early expert system developed in the 1970s and 1980s to diagnose bacterial infections and recommend antibiotics. It used a production rule-based approach with a static knowledge base of rules and a dynamic knowledge base to represent patient-specific information. MYCIN could explain its reasoning and allow knowledge engineers to update its rules through an interactive dialogue interface. The system demonstrated competence comparable to human experts in bacterial infection diagnosis and treatment selection.

Expert system mycin

university of education,Lahore

This document discusses the development and testing of the MYCIN expert system. MYCIN was developed in 1976 at Stanford University to diagnose and recommend treatment for bacterial infections. It was tested against physicians and found to be as or more accurate in its diagnoses and treatment recommendations. However, MYCIN was never fully implemented in clinical practice due to legal liability concerns if it provided incorrect diagnoses.

morphological image processing

Anubhav Kumar

The document discusses various morphological image processing techniques including binary morphology, grayscale morphology, dilation, erosion, opening, closing, boundary extraction, region filling, connected components, hit-or-miss, thinning, thickening, and skeletonization. Morphological operations can be used for tasks like edge detection, noise removal, image enhancement, and image segmentation. The key morphological operations of dilation and erosion expand and shrink binary images using a structuring element, while opening and closing combine these operations to remove noise or fill holes.

Mycin 016

Nidhi Singh

MYCIN was an early expert system developed in the 1970s to diagnose infectious diseases. It used a knowledge base of rules provided by experts and an inference engine to identify bacteria causing infections, determine appropriate antibiotics, and provide treatments. MYCIN could also explain its reasoning. It demonstrated that expert systems could perform competently in domains requiring specialized knowledge.

Traitement d'imageAnissa Teyeb

Qu'est-ce que le design interactif ?

Wif

Introduction au traitement d'images

Abdelouahed Abdou

Traffic jam detection using image processing

Sai As Sharman

This document presents a traffic jam detection system using image processing. The system uses cameras to capture video frames of traffic at regular intervals. The frames are analyzed using image processing techniques like grayscale conversion, erosion, and dilation to detect vehicles and motion. An android application is also developed to provide users with real-time traffic density information for different locations based on the image analysis. The proposed system aims to provide a low-cost and reliable alternative to existing magnetic and infrared-based traffic detection methods.

Fondamentaux java

Ines Ouaz

Chap 5, Traitement d'images

Tunisie collège

Pourquoi choisir la franchise ? Avantages

Teemster

Anissa & thierrycocktaildefree

Demande en mariage de maximecocktaildefree