This document provides an overview of associative memories and discrete Hopfield networks. It begins with introductions to basic concepts like autoassociative and heteroassociative memory. It then describes linear associative memory, which uses a Hebbian learning rule to form associations between input-output patterns. Next, it covers Hopfield's autoassociative memory, a recurrent neural network for associating patterns to themselves. Finally, it discusses performance analysis of recurrent autoassociative memories. The document presents key concepts in associative memory theory and different models like linear associative memory and Hopfield networks.
Defuzzification is the process of producing a quantifiable result in Crisp logic, given fuzzy sets and corresponding membership degrees. It is the process that maps a fuzzy set to a crisp set. It is typically needed in fuzzy control systems.
A Threshold Logic Unit (TLU) is a basic processing unit with inputs, weights, and a threshold that determines its binary output. TLUs can represent some Boolean functions geometrically but are limited in their capabilities. Networks of multiple TLUs can overcome these limitations and represent any Boolean function by decomposing it into linearly separable components.
The document provides an overview of fuzzy logic concepts including types of fuzzy systems, membership functions, fuzzy inference, fuzzification and defuzzification methods. It discusses knowledge-based and rule-based fuzzy systems, types of membership functions like triangular, trapezoidal and Gaussian. Examples of fuzzy logic applications in autonomous driving cars and methods for defuzzification like weighted average, centroid, max-membership and centre of sums are also summarized.
Homomorphic filtering is a technique used to remove multiplicative noise from images by transforming the image into the logarithmic domain, where the multiplicative components become additive. This allows the use of linear filters to separate the illumination and reflectance components, with a high-pass filter used to remove low-frequency illumination variations while preserving high-frequency reflectance edges. The filtered image is then transformed back to restore the original domain. Homomorphic filtering is commonly used to correct non-uniform illumination and simultaneously enhance contrast in grayscale images.
This document provides an overview of mathematical morphology and its applications in image processing. Some key points:
- Mathematical morphology uses concepts from set theory and uses structuring elements to probe and modify binary and grayscale images.
- Basic morphological operations include erosion, dilation, opening, closing, hit-or-miss transformation, thinning, thickening, and skeletonization.
- Erosion shrinks objects and removes small details while dilation expands objects and fills small holes. Opening and closing combine these to smooth contours or fuse breaks.
- Morphological operations have many applications including boundary extraction, region filling, component labeling, convex hulls, pruning, and more. Grayscale images extend these concepts using minimum/maximum
Fuzzy logic is a flexible machine learning technique that mimics human thought by allowing intermediate values between true and false. It provides a mechanism for interpreting and executing commands based on approximate or uncertain reasoning. Unlike binary logic which can only have true or false values, fuzzy logic uses linguistic variables and degrees of membership to represent concepts that may have a partial truth. Fuzzy systems find applications in automatic control, prediction, diagnosis and user interfaces.
1) Markov Chain Monte Carlo (MCMC) methods use Markov chains to sample from complex probability distributions and are useful for problems that cannot be solved efficiently using other methods.
2) Common MCMC algorithms include Metropolis-Hastings, which samples from a target distribution using a proposal distribution, and Gibbs sampling, which efficiently samples multidimensional distributions by updating variables sequentially.
3) MCMC methods like simulated annealing can find global maxima of probability distributions and have applications in statistical mechanics, optimization, and Bayesian inference.
This document provides an overview of associative memories and discrete Hopfield networks. It begins with introductions to basic concepts like autoassociative and heteroassociative memory. It then describes linear associative memory, which uses a Hebbian learning rule to form associations between input-output patterns. Next, it covers Hopfield's autoassociative memory, a recurrent neural network for associating patterns to themselves. Finally, it discusses performance analysis of recurrent autoassociative memories. The document presents key concepts in associative memory theory and different models like linear associative memory and Hopfield networks.
Defuzzification is the process of producing a quantifiable result in Crisp logic, given fuzzy sets and corresponding membership degrees. It is the process that maps a fuzzy set to a crisp set. It is typically needed in fuzzy control systems.
A Threshold Logic Unit (TLU) is a basic processing unit with inputs, weights, and a threshold that determines its binary output. TLUs can represent some Boolean functions geometrically but are limited in their capabilities. Networks of multiple TLUs can overcome these limitations and represent any Boolean function by decomposing it into linearly separable components.
The document provides an overview of fuzzy logic concepts including types of fuzzy systems, membership functions, fuzzy inference, fuzzification and defuzzification methods. It discusses knowledge-based and rule-based fuzzy systems, types of membership functions like triangular, trapezoidal and Gaussian. Examples of fuzzy logic applications in autonomous driving cars and methods for defuzzification like weighted average, centroid, max-membership and centre of sums are also summarized.
Homomorphic filtering is a technique used to remove multiplicative noise from images by transforming the image into the logarithmic domain, where the multiplicative components become additive. This allows the use of linear filters to separate the illumination and reflectance components, with a high-pass filter used to remove low-frequency illumination variations while preserving high-frequency reflectance edges. The filtered image is then transformed back to restore the original domain. Homomorphic filtering is commonly used to correct non-uniform illumination and simultaneously enhance contrast in grayscale images.
This document provides an overview of mathematical morphology and its applications in image processing. Some key points:
- Mathematical morphology uses concepts from set theory and uses structuring elements to probe and modify binary and grayscale images.
- Basic morphological operations include erosion, dilation, opening, closing, hit-or-miss transformation, thinning, thickening, and skeletonization.
- Erosion shrinks objects and removes small details while dilation expands objects and fills small holes. Opening and closing combine these to smooth contours or fuse breaks.
- Morphological operations have many applications including boundary extraction, region filling, component labeling, convex hulls, pruning, and more. Grayscale images extend these concepts using minimum/maximum
Fuzzy logic is a flexible machine learning technique that mimics human thought by allowing intermediate values between true and false. It provides a mechanism for interpreting and executing commands based on approximate or uncertain reasoning. Unlike binary logic which can only have true or false values, fuzzy logic uses linguistic variables and degrees of membership to represent concepts that may have a partial truth. Fuzzy systems find applications in automatic control, prediction, diagnosis and user interfaces.
1) Markov Chain Monte Carlo (MCMC) methods use Markov chains to sample from complex probability distributions and are useful for problems that cannot be solved efficiently using other methods.
2) Common MCMC algorithms include Metropolis-Hastings, which samples from a target distribution using a proposal distribution, and Gibbs sampling, which efficiently samples multidimensional distributions by updating variables sequentially.
3) MCMC methods like simulated annealing can find global maxima of probability distributions and have applications in statistical mechanics, optimization, and Bayesian inference.
Computer Vision: Correlation, Convolution, and GradientAhmed Gad
The document provides an overview of computer vision techniques including correlation, convolution, and gradient filtering. It discusses how correlation can be used to match a template to an image region by calculating a similarity measure as the template is passed over the image. Convolution is explained as similar to correlation but with the signs of the variables flipped in the formula. Implementing these techniques in Python code is also covered generically for squared and odd-sized templates.
This document discusses fidelity criteria in image compression. It defines fidelity as the degree of exactness of reproduction and identifies two types of fidelity criteria: objective and subjective. Objective criteria measure information loss mathematically between original and compressed images, using metrics like root mean square error and peak signal-to-noise ratio. Subjective criteria involve human evaluations of compressed image quality based on rating scales. The document also describes the basic components of image compression systems, including encoders, decoders, mappers, quantizers and symbol coders.
Deep generative models can be either generative or discriminative. Generative models directly model the joint distribution of inputs and outputs, while discriminative models directly model the conditional distribution of outputs given inputs. Common deep generative models include restricted Boltzmann machines, deep belief networks, variational autoencoders, generative adversarial networks, and deep convolutional generative adversarial networks. These models use different network architectures and training procedures to generate new examples that resemble samples from the training data distribution.
A polygon is a closed two-dimensional shape with straight or curved sides. It can be defined by an ordered sequence of vertices and edges connecting consecutive vertices. The scan line polygon fill algorithm uses an odd-even rule to determine if a point is inside or outside the polygon by counting edge crossings along a scan line from that point to infinity. Boundary fill and flood fill are two area filling algorithms that color the interior of a polygon or region by recursively filling neighboring pixels of the same color.
Intro to AI STRIPS Planning & Applications in Video-games Lecture6-Part1Stavros Vassos
This is a short course that aims to provide an introduction to the techniques currently used for the decision making of non-player characters (NPCs) in commercial video games, and show how a simple deliberation technique from academic artificial intelligence research can be employed to advance the state-of-the art.
For more information and downloading the supplementary material please use the following links:
http://stavros.lostre.org/2012/05/19/video-games-sapienza-roma-2012/
http://tinyurl.com/AI-NPC-LaSapienza
Fuzzy ARTMAP is a neural network architecture that uses fuzzy logic and adaptive resonance theory (ART) for supervised learning. It incorporates two fuzzy ART modules, ART-a and ART-b, linked together by an inter-ART module called the MAP field. This allows the network to form predictive associations between categories and track matches using a mechanism called match tracking. The match tracking recognizes category structures to avoid repeating predictive errors on subsequent inputs. Fuzzy ARTMAP is trained until it can correctly classify all training data by increasing the vigilance parameter of ART-a in response to predictive mismatches at ART-b.
This document discusses feature selection techniques for classification problems. It begins by outlining class separability measures like divergence, Bhattacharyya distance, and scatter matrices. It then discusses feature subset selection approaches, including scalar feature selection which treats features individually, and feature vector selection which considers feature sets and correlations. Examples are provided to demonstrate calculating class separability measures for different feature combinations on sample datasets. Exhaustive search and suboptimal techniques like forward, backward, and floating selection are discussed for choosing optimal feature subsets. The goal of feature selection is to select a subset of features that maximizes class separation.
This presentation briefs about International Collegiate Programming Contest(ICPC) which is organized by ACM and sponsored by IBM.
This is delivered at VB Siddardha Colleges, Vijayawada on 10th Mar 2015. Somehow Indian participation is not attractive. I am encouraging Indian students to participate in this competition by delivering lectures like this.
This presentation provides an introduction to the Ant Colony Optimization topic, it shows the basic idea of ACO, advantages, limitations and the related applications.
This document discusses morphological operations in image processing. It describes how morphological operations like erosion, dilation, opening, and closing can be used to extract shapes and boundaries from binary and grayscale images. Erosion shrinks foreground regions while dilation expands them. Opening performs erosion followed by dilation to remove noise, and closing does the opposite to join broken parts. The hit-and-miss transform is also introduced to detect patterns in binary images using a structuring element containing foreground and background pixels. Examples are provided to illustrate each morphological operation.
Unit 2 discusses knowledge representation, which is important for intelligent systems to achieve useful tasks. It cannot be done without a large amount of domain-specific knowledge. Humans tackle problems using their knowledge resources, so knowledge must be represented inside computers for AI programs to manipulate. The document then defines knowledge representation as the part of AI concerned with how agents think and how thinking enables intelligent behavior. It represents real-world information so computers can understand and utilize knowledge to solve complex problems.
APPLICATION OF CNN MODEL ON MEDICAL IMAGEIRJET Journal
The document discusses using convolutional neural network (CNN) models to detect diseases from medical images such as chest X-rays. It describes how CNN models can be trained on large labeled datasets of chest X-rays to learn patterns and features that indicate diseases. The document then evaluates several CNN architectures - including VGG-16, ResNet, DenseNet, and InceptionNet - for classifying chest X-rays as normal or infected. It finds these models achieve high accuracy, with metrics like accuracy over 89% and AUC over 0.94. In conclusion, deep learning models show promising results for automated disease detection from medical images.
This document discusses various methods for defuzzification, which is the process of converting a fuzzy quantity into a crisp quantity. It describes seven common defuzzification methods: 1) max membership principle, 2) centroid method, 3) weighted average method, 4) mean max membership, 5) center of sums, 6) centre of largest area, and 7) first of maxima, last of maxima. For each method, it provides details on the calculation approach and formulas used to determine the defuzzified crisp value. The centroid method is noted as the most commonly used defuzzification technique.
At the end of this lesson, you should be able to;
describe the energy and the EM spectrum.
describe image acquisition methods.
discuss image formation model.
express sampling and quantization.
define dynamic range and image representation.
Adaline and Madaline are adaptive linear neuron models. Adaline is a single linear neuron that can be trained with the least mean square algorithm or stochastic gradient descent. Madaline is a network of multiple Adalines that can be trained with Madaline Rule II to perform non-linear functions like XOR. Madaline has applications in tasks like echo cancellation, signal prediction, adaptive beamforming antennas, and translation-invariant pattern recognition. Conjugate gradient descent converges faster than gradient descent for minimizing quadratic functions.
Natural language processing involves parsing text using a lexicon, categorization of parts of speech, and grammar rules. The parsing process involves determining the syntactic tree and label bracketing that represents the grammatical structure of sentences. Evaluation measures for parsing include precision, recall, and F1-score. Ambiguities from multiple word senses, anaphora, indexicality, metonymy, and metaphor make parsing challenging.
The document discusses fuzzy measures and belief theory. It begins by defining fuzzy sets and fuzzy measures, which assign a degree of membership between 0 and 1 to subsets of a universal set. Belief and plausibility measures are then introduced as generalizations of probability measures that satisfy additional axioms. Combining evidence from multiple sources is discussed, along with deriving a basic assignment from a belief measure and combining basic assignments using Dempster's rule of combination. An example combines the assessments of two experts examining a painting to derive joint belief. Marginal basic assignments are also briefly mentioned.
This document discusses dictionary-based compression techniques, specifically LZ77. It explains that LZ77 uses a sliding window approach with a search buffer and look-ahead buffer. To encode a sequence, it searches the search buffer for a match to the look-ahead buffer and outputs a triplet with the offset, length and next symbol. An example demonstrates encoding a message using this approach by outputting triplets.
Computer Vision: Correlation, Convolution, and GradientAhmed Gad
The document provides an overview of computer vision techniques including correlation, convolution, and gradient filtering. It discusses how correlation can be used to match a template to an image region by calculating a similarity measure as the template is passed over the image. Convolution is explained as similar to correlation but with the signs of the variables flipped in the formula. Implementing these techniques in Python code is also covered generically for squared and odd-sized templates.
This document discusses fidelity criteria in image compression. It defines fidelity as the degree of exactness of reproduction and identifies two types of fidelity criteria: objective and subjective. Objective criteria measure information loss mathematically between original and compressed images, using metrics like root mean square error and peak signal-to-noise ratio. Subjective criteria involve human evaluations of compressed image quality based on rating scales. The document also describes the basic components of image compression systems, including encoders, decoders, mappers, quantizers and symbol coders.
Deep generative models can be either generative or discriminative. Generative models directly model the joint distribution of inputs and outputs, while discriminative models directly model the conditional distribution of outputs given inputs. Common deep generative models include restricted Boltzmann machines, deep belief networks, variational autoencoders, generative adversarial networks, and deep convolutional generative adversarial networks. These models use different network architectures and training procedures to generate new examples that resemble samples from the training data distribution.
A polygon is a closed two-dimensional shape with straight or curved sides. It can be defined by an ordered sequence of vertices and edges connecting consecutive vertices. The scan line polygon fill algorithm uses an odd-even rule to determine if a point is inside or outside the polygon by counting edge crossings along a scan line from that point to infinity. Boundary fill and flood fill are two area filling algorithms that color the interior of a polygon or region by recursively filling neighboring pixels of the same color.
Intro to AI STRIPS Planning & Applications in Video-games Lecture6-Part1Stavros Vassos
This is a short course that aims to provide an introduction to the techniques currently used for the decision making of non-player characters (NPCs) in commercial video games, and show how a simple deliberation technique from academic artificial intelligence research can be employed to advance the state-of-the art.
For more information and downloading the supplementary material please use the following links:
http://stavros.lostre.org/2012/05/19/video-games-sapienza-roma-2012/
http://tinyurl.com/AI-NPC-LaSapienza
Fuzzy ARTMAP is a neural network architecture that uses fuzzy logic and adaptive resonance theory (ART) for supervised learning. It incorporates two fuzzy ART modules, ART-a and ART-b, linked together by an inter-ART module called the MAP field. This allows the network to form predictive associations between categories and track matches using a mechanism called match tracking. The match tracking recognizes category structures to avoid repeating predictive errors on subsequent inputs. Fuzzy ARTMAP is trained until it can correctly classify all training data by increasing the vigilance parameter of ART-a in response to predictive mismatches at ART-b.
This document discusses feature selection techniques for classification problems. It begins by outlining class separability measures like divergence, Bhattacharyya distance, and scatter matrices. It then discusses feature subset selection approaches, including scalar feature selection which treats features individually, and feature vector selection which considers feature sets and correlations. Examples are provided to demonstrate calculating class separability measures for different feature combinations on sample datasets. Exhaustive search and suboptimal techniques like forward, backward, and floating selection are discussed for choosing optimal feature subsets. The goal of feature selection is to select a subset of features that maximizes class separation.
This presentation briefs about International Collegiate Programming Contest(ICPC) which is organized by ACM and sponsored by IBM.
This is delivered at VB Siddardha Colleges, Vijayawada on 10th Mar 2015. Somehow Indian participation is not attractive. I am encouraging Indian students to participate in this competition by delivering lectures like this.
This presentation provides an introduction to the Ant Colony Optimization topic, it shows the basic idea of ACO, advantages, limitations and the related applications.
This document discusses morphological operations in image processing. It describes how morphological operations like erosion, dilation, opening, and closing can be used to extract shapes and boundaries from binary and grayscale images. Erosion shrinks foreground regions while dilation expands them. Opening performs erosion followed by dilation to remove noise, and closing does the opposite to join broken parts. The hit-and-miss transform is also introduced to detect patterns in binary images using a structuring element containing foreground and background pixels. Examples are provided to illustrate each morphological operation.
Unit 2 discusses knowledge representation, which is important for intelligent systems to achieve useful tasks. It cannot be done without a large amount of domain-specific knowledge. Humans tackle problems using their knowledge resources, so knowledge must be represented inside computers for AI programs to manipulate. The document then defines knowledge representation as the part of AI concerned with how agents think and how thinking enables intelligent behavior. It represents real-world information so computers can understand and utilize knowledge to solve complex problems.
APPLICATION OF CNN MODEL ON MEDICAL IMAGEIRJET Journal
The document discusses using convolutional neural network (CNN) models to detect diseases from medical images such as chest X-rays. It describes how CNN models can be trained on large labeled datasets of chest X-rays to learn patterns and features that indicate diseases. The document then evaluates several CNN architectures - including VGG-16, ResNet, DenseNet, and InceptionNet - for classifying chest X-rays as normal or infected. It finds these models achieve high accuracy, with metrics like accuracy over 89% and AUC over 0.94. In conclusion, deep learning models show promising results for automated disease detection from medical images.
This document discusses various methods for defuzzification, which is the process of converting a fuzzy quantity into a crisp quantity. It describes seven common defuzzification methods: 1) max membership principle, 2) centroid method, 3) weighted average method, 4) mean max membership, 5) center of sums, 6) centre of largest area, and 7) first of maxima, last of maxima. For each method, it provides details on the calculation approach and formulas used to determine the defuzzified crisp value. The centroid method is noted as the most commonly used defuzzification technique.
At the end of this lesson, you should be able to;
describe the energy and the EM spectrum.
describe image acquisition methods.
discuss image formation model.
express sampling and quantization.
define dynamic range and image representation.
Adaline and Madaline are adaptive linear neuron models. Adaline is a single linear neuron that can be trained with the least mean square algorithm or stochastic gradient descent. Madaline is a network of multiple Adalines that can be trained with Madaline Rule II to perform non-linear functions like XOR. Madaline has applications in tasks like echo cancellation, signal prediction, adaptive beamforming antennas, and translation-invariant pattern recognition. Conjugate gradient descent converges faster than gradient descent for minimizing quadratic functions.
Natural language processing involves parsing text using a lexicon, categorization of parts of speech, and grammar rules. The parsing process involves determining the syntactic tree and label bracketing that represents the grammatical structure of sentences. Evaluation measures for parsing include precision, recall, and F1-score. Ambiguities from multiple word senses, anaphora, indexicality, metonymy, and metaphor make parsing challenging.
The document discusses fuzzy measures and belief theory. It begins by defining fuzzy sets and fuzzy measures, which assign a degree of membership between 0 and 1 to subsets of a universal set. Belief and plausibility measures are then introduced as generalizations of probability measures that satisfy additional axioms. Combining evidence from multiple sources is discussed, along with deriving a basic assignment from a belief measure and combining basic assignments using Dempster's rule of combination. An example combines the assessments of two experts examining a painting to derive joint belief. Marginal basic assignments are also briefly mentioned.
This document discusses dictionary-based compression techniques, specifically LZ77. It explains that LZ77 uses a sliding window approach with a search buffer and look-ahead buffer. To encode a sequence, it searches the search buffer for a match to the look-ahead buffer and outputs a triplet with the offset, length and next symbol. An example demonstrates encoding a message using this approach by outputting triplets.