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Character Recognition Using Matlab: ..

These features are shown to improve the recognition rate using simple classification algorithms so they are used to train a Neural Network and test its performance on .

Index Terms: Matlab, source, code, ocr, optical character recognition, scanned text, written text, ascii, isolated character.

Character Recognition Using Neural Networks Thesis …

Applies competing theories of the nature of law and legal reasoning to evaluate decisions of the U.S. Supreme Court in controversial areas of constitutional law such as free speech, privacy, sexual conduct, affirmative action, and political campaign contributions.

shape recognition using matlab in the context of image processing

The paper presents a case of the estimation of evapotranspiration using a free source tool – ILWIS Open.

An introduction to the lexical, syntactic, semantic, and pragmatic characteristics of the Java language for experienced programmers. Emphasis on object-oriented programming, using standard libraries, and programming with automatic garbage collection.

An introduction to the lexical, syntactic, semantic, and pragmatic characteristics of the C/C++ languages for experienced programmers. Emphasis on object-oriented programming, using standard libraries, and programming with manual garbage collection. Formerly ICS 65.

Python Projects | 1000 Projects

Hassan, Optical Bangla Character Recognition using Chain Code, IEEE/OSA/IAPR International Conference on Informatics, Electronics & Vision, 2012

From the experimental results it is observed that RDWT method provides better information quality for SD and SNR metric and the Contourlet Transform method provides better information quality using EN metric.

Key words: Contourlet Transform,Entropy, SD, SNR.

[1] Firooz Sadjadi " Comparative Image Fusion Analysis" , lEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol.

The present paper deals with the dynamic analysis and design sensitivity analysis of such a marine engine foundation system considering it as a two degree freedom system.

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efg's Image Processing: Algorithms

Introduces the concepts and principles of good scientific writing, demonstrates them by examples drawn from the literature, and uses a hands-on approach to apply them to documents being written by the participants.

Links to many different image processing algorithms

Probabilistic graphical modeling languages for representing complex domains, algorithms for reasoning using these representations, and learning these representations from data. Topics include: Bayesian and Markov networks, extensions to temporal modeling such as hidden Markov models and dynamic Bayesian networks, exact and approximate probabilistic inference algorithms, and methods for learning models from data. Also included are sample applications to various domains including speech recognition, biological modeling and discovery, medical diagnosis, message encoding, vision, and robot motion planning. Prerequisites: basic probability theory and algorithm design and analysis.

Index of Packages Matching 'tensorflow' - Python …

The main aim of this project is to develop isolated spoken word recognition system using Hidden Markov Model (HMM) with a good accuracy at all the possible frequency range of human voice. Here ten different words are recorded by different speakers including male and female and results are compared with different feature extraction methods. Earlier work includes recognition of seven small utterances using HMM with the use only one feature extraction method. This spoken word recognition system mainly divided into two major blocks. First includes recording data base and feature extraction of recorded signals. Here we use Mel frequency cepstral coefficients, linear cepstral coefficients and fundamental frequency as feature extraction methods. To obtain Mel frequency cepstral coefficients signal should go through the following: pre emphasis, framing, applying window function, Fast Fourier transform, filter bank and then discrete cosine transform, where as a linear frequency cepstral coefficients does not use Mel frequency. Second part describes HMM used for modeling and recognizing the spoken words. All the raining samples are clustered using K-means algorithm. Gaussian mixture containing mean, variance and weight are modeling parameters. Here Baum Welch algorithm is used for training the samples and re-estimate the parameters. Finally Viterbi algorithm recognizes best sequence that exactly matches for given sequence there is given spoken utterance to be recognized. Here all the simulations are done by the MATLAB tool and Microsoft window 7 operating system.

Cognitive Science Courses - University of California San …

Recent breakthroughs in high-throughput genomic and biomedical data are transforming biological sciences into "big data" disciplines. In parallel, progress in deep neural networks are revolutionizing fields such as image recognition, natural language processing and, more broadly, AI. This course explores the exciting intersection between these two advances. The course will start with an introduction to deep learning and overview the relevant background in genomics and high-throughput biotechnology, focusing on the available data and their relevance. It will then cover the ongoing developments in deep learning (supervised, unsupervised and generative models) with the focus on the applications of these methods to biomedical data, which are beginning to produced dramatic results. In addition to predictive modeling, the course emphasizes how to visualize and extract interpretable, biological insights from such models. Recent papers from the literature will be presented and discussed. Students will be introduced to and work with popular deep learning software frameworks. Students will work in groups on a final class project using real world datasets. Prerequisites: College calculus, linear algebra, basic probability and statistics such as CS109, and basic machine learning such as CS229. No prior knowledge of genomics is necessary.
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