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SVBiComm: Sign-Voice Bidirectional Communication System for Normal, 'Deaf/Dumb' and Blind People based on machine learning 1

SVBiComm: Sign-Voice Bidirectional Communication System for ...

引用

1st international conference on Computer Applications and Information Security, ICCAIS 2018

作者： Reda, Mariam Moustafa Mohammed, Nada Gamal Abdel Azeem Abul Seoud, Rania Ahmed Department of Computer Science Faculty of Computers and Information Fayoum University Egypt Department of Electrical Engineering Communication and Electronics Section Faculty of Engineering Fayoum University Fayoum Egypt

ISBN: (纸本)9781538644263

'deaf/dumb' and blind people face problems in communicating with others with difficulties in dealing with the communication technology. The goal of this paper is to design a desktop human computer interface application that is used to facilitate communication between normal, 'deaf/dumb' and blind people. SVBiComm system helps blind person to hear voice saying the word gestured by the' deaf/dumb' while the deaf will receive a gesture representing the word said by the blind. SVBiComm works in two directions, the first direction is processing from video to speech. The animated word gestures are mapped with language knowledge base into text. Then, the relevant audio is generated using Text-to-Speech (TTS) API. The second direction is processing from speech to video. The voice from blind is converted into its corresponding text using Speech-to-Text (stT) API. Then, the natural language is mapped from the database to 'deaf/dumb' in a relevant sign language form by using a 3D graphical model. The system was evaluated using a set of 113 sentences with 244 signs. In voice recognition;system recognized words with a percentage of 90% from 19 different persons. For image recognition the system recognized images with a percentage of 84 % for 21 different persons. SVBiComm system provides many facilities with low cost that could be used in many areas. © 2018 IEEE.

关键词： image recognition

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Multi-resolution processing of colored texture

Multi-resolution processing of colored texture

引用

4th international conference on Computer Science and Informatics, JCIS 1998, 1st international Workshop on High Performance, 1st international Workshop on Computer Vision, pattern recognition and image processing Volume 4

作者： Liang, Yufeng Lei, Zhibin Wilder, Joseph Ganapathy, S.K. Bell Labs. Lucent Technologies 700 Mountain Ave Murray Hill NJ 07974

This paper presents a new method for processing color texture images at multi-resolution levels. It combines the spectral information with the spatial information of an image and processes them in an unified way. The method utilizes the power spectra of the image sequence at consequent resolutions to construct the heirachical structure of the texture and decides at which resolution the texture should be processed. The colored texture feature extraction is then conducted at multiple resolutions. During classification, texture features are compared at multiple levels and the distance metric is a combination of the distances at all the levels.

关键词： image processing

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A feature analysis approach to mass detection in mammography based on RF-SVM

A feature analysis approach to mass detection in mammography...

引用

IEEE international conference on image processing (ICIP 2007)

作者： Wang, Ying Gao, Xinbo Li, Jie Xidian Univ Sch Elect Engn Xian 710071 Peoples R China

ISBN: (纸本)9781424414369

A new approach to mass detection in mammography is presented. The main obstacle of building a mass detection system is the similar appearance between masses and density tissues in breast. Hence, the various features of the extracted regions of interest (ROIs) are analyzed by synthesis. Then the support vector machine (SVM), which is designed later to distinguish masses from normal areas, is employed to classify these ROIs exactly. To further improve the performance of SVM, the relevance feedback (RF) is introduced to filter out the false positives. The experimental results illustrate that SVM classifier can effectively detect the mass areas, and the RF-SVM scheme can be efficiently incorporated into this learning framework to further improve detection performance.

关键词： image analysis feature extraction pattern recognition relevance feedback

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A chosen plaintext steganalysis of Hide4PGP v 2.0

引用

1st international conference on pattern recognition and machine Intelligence, PReMI 2005

作者： Mazumdar, Debasis Mitra, Soma Dhali, Sonali Pal, Sankar K. CDAC Kolkata Plot E2/1 Block - GP Sector V Salt Lake City Kolkata - 700091 Machine Intelligence Unit Indian Statistical Institute Kolkata - 700108 India

ISBN: (纸本)3540305068

A chosen plaintext steganalysis algorithm is described to isolate the corrupted bits in an image tampered with Hide4PGP V 2.0. The method is developed from the notion of representation of two dimensional image data in terms of a linear bit stream consisting of a set of basic building blocks. Its performance for message extraction is demonstrated on different 24 bit BMP images. © Springer-Verlag Berlin Heidelberg 2005.

关键词： image processing

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Federated learning Based Prediction of Chronic Kidney Diseases 1

Federated Learning Based Prediction of Chronic Kidney Diseas...

引用

1st international conference on Computational Science and Technology, ICCst 2022

作者： Nandhini, J.M. Joshi, Soshya Anuratha, K. Sri Sai Ram Institute of Technology India

ISBN: (纸本)9781665476553

Chronic Kidney diseases are increasing at an alarming rate in present days. The prediction of kidney disease consume more time as it involves a lot of laboratory tests. Traditionally, the health care domain relies on the centralized data, which may lead the system to have significant risks and difficulties. The proposed model involves uses federated learning, a machine learning technique that involves training an algorithm on multiple decentralized data. The model uses image processing technique, where the scanned image of the kidney is given as the input. The image, upon various pre-processing techniques identifies the affected area of the kidney. The affected area is also marked in color and the values of accuracy, efficiency, specificity and sensitivity are found to be higher than the existing systems. The output of the single image is again given to training dataset to increase the accuracy precisely, which is a part of the federated learning. © 2022 IEEE.

关键词： image processing

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Deep learning Based Indian Currency Coin recognition 1

Deep Learning Based Indian Currency Coin Recognition

引用

1st international conference on Advances in Communication and Computing Technology (ICACCT)

作者： Tajane, A. U. Patil, J. M. Shahane, A. S. Dhulekar, P. A. Gandhe, S. T. Phade, G. M. Maharashtra Savitribai Phule Pune Univ Sandip Inst Technol & Res Ctr Dept Elect & Telecommun Engn Pune Maharashtra India

ISBN: (纸本)9781538609262

Automatic coin recognition and identification systemplays vital role in vending machine, slot machine and in several banking related equipment's. Most of the existing coin recognitions systems are based on physical parameters of the coins whereas image processing methodologies relies on extraction of color, shape and edge features. For recognition and detection of Indian coin we have proposed Deep learning approach in this paper. Pretrained convolutional neural network i.e. AlexNet is trained by using the features such as textures, colors and shape. The model is trained on more than 1600 images and can classify images into four object categories like one, two, five and ten rupees coins. The trained model is tested on various standard and own recorded datasets consist of rotational, translated and shifted images. The parameters used to calibrate the performance system are recognition accuracy and response time. Obtained results shown the outperformance of proposed methodology over conventional systems.

关键词： Coin recognition Deep learning Convolutional Neural network AlexNet

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Facial Age Estimation Using Clustered Multi-task Support Vector Regression machine

Facial Age Estimation Using Clustered Multi-task Support Vec...

引用

21st international conference on pattern recognition (ICPR)

作者： Gao, Peter Xiang Univ Waterloo Waterloo ON N2L 3G1 Canada

ISBN: (纸本)9784990644109;9781467322164

Automatic age estimation is the process of using a computer to predict the age of a person automatically based on a given facial image. While this problem has numerous real-world applications, the high variability of aging patterns and the sparsity of available data present challenges for model training. Here, instead of training one global aging function, we train an individual function for each person by a multi-task learning approach so that the variety of human aging processes can be modelled. To deal with the sparsity of training data, we propose a similarity measure for clustering the aging functions. During the testing stage, which involves a new person with no data used for model training, we propose a feature-based similarity measure for characterizing the test case. We conduct simulation experiments on the FG-NET and MORPH databases and compared our method with other state-of-the-art methods.

关键词： pattern recognition

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Deep learning based Automated Rice Plant Disease recognition and Classification Model 1

Deep Learning based Automated Rice Plant Disease Recognition...

引用

1st international conference on Advances in Electrical, Electronics and Computational Intelligence, ICAEECI 2023

作者： Rose Anandhi, D. Felicia Sathiamoorthy, S. Annamalai University Faculty of Science Department of Computer and Information Science India Annamalai University PG Extension Centre Villupuram India

ISBN: (纸本)9798350342796

Rice plant disease detection and classification include employing methods such as Computer Vision (CV) and machine learning (ML) for classifying and recognizing diseases affecting rice crops. These techniques could help agricultural experts and farmers in rapidly managing and diagnosing diseases, eventually resulting in food security and best crop yield. Rice plant disease classification and recognition utilizing Deep learning (DL) has emerged as an effective strategy to address the problems related to automatic disease detection from the rice plants. DL technique, a subregion of Artificial Intelligence (AI), focuses on training neural networks with different layers to automatically learn intricate representations and patterns from information. This study leverages the Deep learning-based Automated Rice Plant Disease recognition and Classification (DL-ARPDRC) approach. The DL-ARPDRC technique encompasses different processes to improve accuracy and diagnosis performance. The primary level encompasses pre-processing stage which includes image resizing and Gaussian Filter (GF) to improve image quality. Next, Otsu's threshold-based segmentation can be used for the segmentation of the lesion from the background, isolating the region of interest. Feature extraction is accomplished through the utilization of the VGG-19 architecture. Finally, the extracted features are fed into an XGBoost classification model for distinguishing between different types of diseases. A detailed experimental result analysis portrayed that the DL-ARPDRC technique reaches better performance compared to other recent approaches. © 2023 IEEE.

关键词： Computer vision Deep learning machine learning Precision agriculture Rice plant disease

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An efficient multiple faces tracking system

An efficient multiple faces tracking system

引用

1st international Congress on image and Signal processing

作者： Shen, Shuhan Liu, Yuncai Shanghai Jiao Tong Univ Inst Image Proc & Pattern Recognit Shanghai 200240 Peoples R China

ISBN: (纸本)9780769531199

A multiple faces tracking system was presented based on Relevance Vector machine (RVM) and Boosting learning. In this system, a face detector based on Boosting learning is used to detect faces at the first frame, and the face motion model and color model are created. In the tracking process different tracking methods are used according to different states of faces, and the states are changed according to the tracking results. When the full image search condition is satisfied, a full image search is started in order to find new coming faces and former occluded faces. In the full image search and local search, the similarity matrix is introduced to help matching faces efficiently. Experimental results demonstrate the capability and efficiency of the proposed system.

关键词： Tracking (position)

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Word-Spotting approach using transfer deep learning of a CNN network 1

Word-Spotting approach using transfer deep learning of a CNN...

引用

1st international conference on Communications, Control Systems and Signal processing, CCSSP 2020

作者： Benabdelaziz, Ryma Gaceb, Djamel Haddad, Mohammed UMBB Boumerdes Algeria University of Claude Bernard Lyon 1 Lab LIRIS UMR CNRS 5205 VilleurbanneF-69622 France

ISBN: (纸本)9781728158358

Convolutional Neural Networks (CNNs) are deep learning models that are trained to automatically extract the most discriminating features directly from an input image to be used for visual classification tasks. Recently, CNNs attracted a lot of interest thanks to their effectiveness in many computer vision applications (medical imaging, video surveillance, biometrics, pattern recognition, OCR, etc.). Transfer learning is an optimization method that uses a pretrained network to speed up the training of another related task or application. This helps speed up and improve the training process on a new dataset. In this paper, we propose a new approach of handwritten word retrieval based on deep learning and transfer learning. We compared the performance between two types of extracted features based on transfer learning: from a pre-trained model and a fine-tuned network. Experiments are performed using six different CNN architectures and three similarity measures on the presegmented Bentham dataset of the ICDAR competition. The obtained results demonstrate the effectiveness of our proposed approach compared to existing methods, evaluated in this competition. © 2020 IEEE.

关键词： Convolutional neural networks

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