scholarly journals Sign Language Detection Using Image Processing

2021 ◽  
pp. 100-103
Author(s):  
Vinnayak Sharma ◽  
Khushbu Gupta ◽  
Dr. Krishna Singh
Keyword(s):  
Image Processing ◽  
Sign Language ◽  
Effective Means ◽  
Structural Features ◽  
The Other ◽  
Svm Classifier ◽  
Instruction Set ◽  
Language Detection

Sign language is an effective means of communication for the deaf and dumb. But those who cannot understand this language find it almost impossible to get a grip of what the other person is trying to communicate. The proposed research helps non-sign-language speakers in identifying gestures used in Sign Language. The methodology described in this paper is implemented using MATLAB. In this method, firstly, the signs are captured with the help of a webcam. The images captured are then processed further and the features are extracted from the captured images using different structural features. Comparison of the features is done using SVM Classifier can use this document as both an instruction set and as a template into which you can type your own text.

PERANGKAT LUNAK

2020 ◽  
Author(s):  
nadila shabira
Keyword(s):  
Image Processing ◽  
Computer System ◽  
Word Processing ◽  
The Other ◽  
Instruction Set ◽  
Daily Work ◽  
Processing Elements ◽  
Electronic Network ◽  
Input Processing ◽  
Software And Hardware

A computer system is an electronic network consisting of software and hardware that performs certain tasks (receiving input, processing input, storing commands, and providing output in the form of information). Besides that it can also be interpreted as elements that are related to carry out an activity using a computer. Computers can help humans in their daily work, jobs such as: word processing, number processing, and image processing. Elements of a computer system consist of human (brainware), software (software), instruction set (instruction set), and hardware (hardware). Thus these components are elements that are involved in a computer system. Of course hardware doesn't mean anything if there isn't one of the other two (software and brainware).

Breast cancer diagnosis based on guided Water Strider Algorithm

2021 ◽  
pp. 095441192110390
Author(s):  
Dezhong Bi ◽  
Yuxi Liu ◽  
Naser Youssefi ◽  
Dan Chen ◽  
Yuexiang Ma
Keyword(s):  
Breast Cancer ◽  
Image Processing ◽  
Cancer Diagnosis ◽  
Breast Cancer Diagnosis ◽  
Final Diagnosis ◽  
Structural Features ◽  
Svm Classifier ◽  
Water Strider ◽  
Early Diagnosis Of Cancer ◽  
Comparison Of The Results

Breast cancer is one of the main cancers that effect of the women’s health. This cancer is one of the most important health issues in the world and because of that, diagnosis in the beginning and appropriate cure is very effective in the recovery and survival of patients, so image processing as a decision-making tool can assist physicians in the early diagnosis of cancer. Image processing mechanisms are simple and non-invasive methods for identifying cancer cells that accelerate early detection and ultimately increase the chances of cancer patients surviving. In this study, a pipeline methodology is proposed for optimal diagnosis of the breast cancer area in the mammography images. Based on the proposed method, after image preprocessing and filtering for noise reduction, a simple and fast tumors mass segmentation based on Otsu threshold segmentation and mathematical morphology is proposed. Afterward, for simplifying the final diagnosis, a feature extraction based on 22 structural features is utilized. To reduce and pruning the useless features, an optimized feature selection based on a new developed design of Water Strider Algorithm (WSA), called Guided WSA (GWSA). Finally, the features injected to an optimized SVM classifier based on GWSA for optimal cancer diagnosis. Simulations of the suggested method are applied to the DDSM database. A comparison of the results with several latest approaches are performed to indicate the method higher effectiveness.

PERANGKAT LUNAK

2020 ◽  
Author(s):  
nadila shabira
Keyword(s):  
Image Processing ◽  
Computer System ◽  
Word Processing ◽  
The Other ◽  
Instruction Set ◽  
Daily Work ◽  
Processing Elements ◽  
Electronic Network ◽  
Input Processing ◽  
Software And Hardware

Abstract A computer system is an electronic network consisting of software and hardware that performs certain tasks (receiving input, processing input, storing commands, and providing output in the form of information). Besides that it can also be interpreted as elements that are related to carry out an activity using a computer. Computers can help humans in their daily work, jobs such as: word processing, number processing, and image processing. Elements of a computer system consist of human (brainware), software (software), instruction set (instruction set), and hardware (hardware). Thus these components are elements that are involved in a computer system. Of course hardware doesn't mean anything if there isn't one of the other two (software and brainware).

Perangkat lunak

2020 ◽  
Author(s):  
nadila shabira
Keyword(s):  
Image Processing ◽  
Computer System ◽  
Word Processing ◽  
The Other ◽  
Instruction Set ◽  
Daily Work ◽  
Processing Elements ◽  
Electronic Network ◽  
Input Processing ◽  
Software And Hardware

A computer system is an electronic network consisting of software and hardware that performs certain tasks (receiving input, processing input, storing commands, and providing output in the form of information). Besides that it can also be interpreted as elements that are related to carry out an activity using a computer. Computers can help humans in their daily work, jobs such as: word processing, number processing, and image processing. Elements of a computer system consist of human (brainware), software (software), instruction set (instruction set), and hardware (hardware). Thus these components are elements that are involved in a computer system. Of course hardware doesn't mean anything if there isn't one of the other two (software and brainware).

Efficient Computing in Image Processing and DSPs with ASIP Based Multiplier

2019 ◽  
Vol 13 (2) ◽  
pp. 174-180
Author(s):  
Poonam Sharma ◽  
Ashwani Kumar Dubey ◽  
Ayush Goyal
Keyword(s):  
Image Processing ◽  
High Speed ◽  
Computation Time ◽  
Digital Signal ◽  
Instruction Set ◽  
Computationally Efficient ◽  
Specific Instruction ◽  
Processor Core ◽  
Speed Performance ◽  
Application Specific

Background: With the growing demand of image processing and the use of Digital Signal Processors (DSP), the efficiency of the Multipliers and Accumulators has become a bottleneck to get through. We revised a few patents on an Application Specific Instruction Set Processor (ASIP), where the design considerations are proposed for application-specific computing in an efficient way to enhance the throughput. Objective: The study aims to develop and analyze a computationally efficient method to optimize the speed performance of MAC. Methods: The work presented here proposes the design of an Application Specific Instruction Set Processor, exploiting a Multiplier Accumulator integrated as the dedicated hardware. This MAC is optimized for high-speed performance and is the application-specific part of the processor; here it can be the DSP block of an image processor while a 16-bit Reduced Instruction Set Computer (RISC) processor core gives the flexibility to the design for any computing. The design was emulated on a Xilinx Field Programmable Gate Array (FPGA) and tested for various real-time computing. Results: The synthesis of the hardware logic on FPGA tools gave the operating frequencies of the legacy methods and the proposed method, the simulation of the logic verified the functionality. Conclusion: With the proposed method, a significant improvement of 16% increase in throughput has been observed for 256 steps iterations of multiplier and accumulators on an 8-bit sample data. Such an improvement can help in reducing the computation time in many digital signal processing applications where multiplication and addition are done iteratively.

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.

American Sign Language Detection Using Instance-Based Segmentation

2021 ◽  
Author(s):  
Narayana Darapaneni ◽  
Prasad Gandole ◽  
Sureshkumar Ramasamy ◽  
Yashraj Tambe ◽  
Anshuman Dwivedi ◽  
...  
Keyword(s):  
American Sign Language ◽  
Sign Language ◽  
American Sign ◽  
Language Detection

scholarly journals Analysis of the Nosema Cells Identification for Microscopic Images

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3068
Author(s):  
Soumaya Dghim ◽  
Carlos M. Travieso-González ◽  
Radim Burget
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Image Processing ◽  
Deep Learning ◽  
The Other ◽  
Support Vector ◽  
Learning Approaches ◽  
Microscopic Images ◽  
Trained Neural Network ◽  
Nosema Disease

The use of image processing tools, machine learning, and deep learning approaches has become very useful and robust in recent years. This paper introduces the detection of the Nosema disease, which is considered to be one of the most economically significant diseases today. This work shows a solution for recognizing and identifying Nosema cells between the other existing objects in the microscopic image. Two main strategies are examined. The first strategy uses image processing tools to extract the most valuable information and features from the dataset of microscopic images. Then, machine learning methods are applied, such as a neural network (ANN) and support vector machine (SVM) for detecting and classifying the Nosema disease cells. The second strategy explores deep learning and transfers learning. Several approaches were examined, including a convolutional neural network (CNN) classifier and several methods of transfer learning (AlexNet, VGG-16 and VGG-19), which were fine-tuned and applied to the object sub-images in order to identify the Nosema images from the other object images. The best accuracy was reached by the VGG-16 pre-trained neural network with 96.25%.

scholarly journals Global-Local Enhancement Network for NMF-Aware Sign Language Recognition

2021 ◽  
Vol 17 (3) ◽  
pp. 1-19
Author(s):  
Hezhen Hu ◽  
Wengang Zhou ◽  
Junfu Pu ◽  
Houqiang Li
Keyword(s):  
Sign Language ◽  
Daily Life ◽  
The Other ◽  
Challenging Problem ◽  
Language Recognition ◽  
Local Enhancement ◽  
Sign Language Recognition ◽  
Vocabulary Size ◽  
Hand Gestures ◽  
Fine Grained

Sign language recognition (SLR) is a challenging problem, involving complex manual features (i.e., hand gestures) and fine-grained non-manual features (NMFs) (i.e., facial expression, mouth shapes, etc .). Although manual features are dominant, non-manual features also play an important role in the expression of a sign word. Specifically, many sign words convey different meanings due to non-manual features, even though they share the same hand gestures. This ambiguity introduces great challenges in the recognition of sign words. To tackle the above issue, we propose a simple yet effective architecture called Global-Local Enhancement Network (GLE-Net), including two mutually promoted streams toward different crucial aspects of SLR. Of the two streams, one captures the global contextual relationship, while the other stream captures the discriminative fine-grained cues. Moreover, due to the lack of datasets explicitly focusing on this kind of feature, we introduce the first non-manual-feature-aware isolated Chinese sign language dataset (NMFs-CSL) with a total vocabulary size of 1,067 sign words in daily life. Extensive experiments on NMFs-CSL and SLR500 datasets demonstrate the effectiveness of our method.

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