sign language recognition using HMM
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This document discusses sign language recognition using hidden Markov models. It outlines preprocessing techniques like noise reduction and hand detection using skin detection and optical flow analysis. Feature extraction is then performed on the training data to create feature vectors for each sign. Hidden Markov models will be trained on these features to classify signs in new data, allowing computers to recognize basic American Sign Language letters and numbers through vision-based techniques. The current progress includes data collection, preprocessing, hand detection and feature extraction, while the remaining work is training the hidden Markov model for classification.
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By
Alaa Tharwat1,3
And
Tarek Gaber2,3
1Faculty of Eng. Suez Canal University, Ismailia, Egypt
2Faculty of Computers & Informatics , Suez Canal University, Ismailia, Egypt
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Sign Language hand formations:
Hand shape
Hand location
Hand movement
Hand orientation
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Proposed Method: General Framework
Training phase
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Representing each image by one feature vector.
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Proposed Method: Feature Extraction
Proposed Method: Feature Extraction
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SVM is one of the classifers which deals with a problem of high dimensional datasets and gives very good results.
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To prove that our proposed method is robust against rotation
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Experimental Results
Experimental Results
Experimental Results
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And
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1Faculty of Eng. Suez Canal University, Ismailia, Egypt
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What is ArSL?
Translating ArSL to spoken language, i.e. translate hand gestures to Arabic characters
Sign Language hand formations:
Hand shape
Hand location
Hand movement
Hand orientation
Introduction: Types of ArSL
Proposed Method: General Framework
Proposed Method: General Framework
Training phase
Collecting all training images (i.e. gestures of Arabic Sign Language).
Extracting the features using SIFT
Representing each image by one feature vector.
Applying a dimensionality reduction (e.g, LDA) to reduce the number features in the vector
Proposed Method: Feature Extraction
Proposed Method: Feature Extraction
Proposed Method: Feature Extraction
Proposed Method: Classification Techniques
We have used the following classifiers assess their performance with our approach :
SVM is one of the classifers which deals with a problem of high dimensional datasets and gives very good results.
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The images are collected in different illumination, rotation, quality levels, and image partiality.
Experimental Scenarios
To select the most suitable parameters.
To understand the effect of changing the number of training images.
To prove that our proposed method is robust against rotation
To prove that our proposed method is robust against occlusion.
Experimental Results
Experimental Results
Experimental Results
Experimental Results
Experimental Results
Conclusions
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Achieve an excellent accuracy to identify ArSL from 2D images
Robust against to rotation images with different angels and occluded images horizontally or vertically.
Robust against many previous ArSL approaches.
Performance of this approach is measured by
Using captured images with Matlab implementation
Comparison with related work
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Increase the size of the dataset to check its scalability.
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This document provides an overview of gesture recognition technology. It begins with introducing gestures as a form of non-verbal communication and defines gesture recognition as interpreting human gestures through mathematical algorithms. It then discusses the motivation for gesture recognition, including its naturalness and applications in overcoming interaction problems with traditional input devices. The document outlines different types of gestures, input devices like gloves and cameras, challenges like developing standardized gesture languages, and uses like sign language recognition, virtual controllers, and assisting disabled individuals. It concludes with references for further reading.Hearing loss ppt final
Hearing loss ppt finalCentennial College
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Here are the key steps an audiologist would take to help Lily and her family:
1. The doctor would refer Lily to an audiologist for hearing tests to determine the severity and type of her hearing loss.
2. The audiologist would perform behavioral and objective tests to assess Lily's hearing ability and identify if she has a partial, mild, severe or profound hearing loss.
3. If needed, the audiologist would fit Lily for hearing aids and work with the family to help Lily gradually adjust to using the aids. This would require periodic follow-ups to monitor progress.
4. The audiologist can provide guidance on communication strategies and connect the family to additionalWhat Is Sign Language
What Is Sign LanguageArun Rao
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Sign language is a visual method of communication that uses manual gestures and body language to convey meaning. It naturally develops in deaf communities as their primary means of communicating with each other when oral communication is ineffective. While sign languages share common properties, there is no single universal sign language and different communities develop their own, with regional variations. Sign languages have their own grammar and syntax distinct from spoken languages.Project development cycle
Project development cycleAswin prakash i , Xantus Technologies
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The document discusses the project life cycle and its phases. It begins by introducing systems development cycles and how projects fit within the first three phases of conception, definition, and execution. It then describes the typical phases a project passes through as initiation, planning, execution, monitoring/controlling, and closing. Finally, it explains the significance of segmenting projects into phases and how it allows for management of work, resources, deliverables, costs, and risks throughout the project.Similar to sign language recognition using HMM (20)
Nikppt
NikpptNikith Kumar Reddy
油
Gestures are an important form of non-verbal communication that involve visible bodily motions. The document discusses the history and development of gesture recognition technologies, describing early data gloves and videoplace systems as well as current technologies like Cepal and ADITI that help people with disabilities control devices with gestures. It also outlines the key components of a gesture recognition system including modeling, analysis, and recognition of gestures and discusses classification methods like HMMs and MLPs. Applications discussed include virtual keyboards, navigaze, and Sixth Sense technology.Gesture Recognition Technology
Gesture Recognition TechnologyNikith Kumar Reddy
油
Gestures are an important form of non-verbal communication that involve visible bodily motions. They can be used to control devices through gesture recognition systems. Such systems work by modeling, analyzing, and recognizing gestures based on features extracted from images of body movements. Various technologies have been developed to recognize both static and dynamic gestures through methods like contour analysis, Hidden Markov models, and others. Gesture recognition has applications in areas like human-computer interaction, rehabilitation robotics, and interactive gaming.Real Time Vision Hand Gesture Recognition Based Media Control via LAN & Wirel...
Real Time Vision Hand Gesture Recognition Based Media Control via LAN & Wirel...IJMER
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International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.Computer Based Human Gesture Recognition With Study Of Algorithms
Computer Based Human Gesture Recognition With Study Of AlgorithmsIOSR Journals
油
This document discusses computer-based human gesture recognition algorithms. It begins with an introduction to gesture recognition and its uses in human-computer interaction. It then describes two main approaches to gesture recognition: appearance-based and 3D model-based. For appearance-based recognition, it discusses active appearance models and histogram-of-motion words. For 3D model-based recognition, it discusses using 3D image data to achieve invariance to viewpoint. It also discusses representing gestures as sequences of motion primitives to achieve viewpoint independence. Finally, it discusses skeletal algorithms that represent body pose as joint configurations and angles.
Gesture Technology
Gesture TechnologyBugRaptors
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Gesture recognition technology uses mathematical algorithms to interpret human gestures and enable interaction with machines without physical devices. It has various applications including sign language recognition, interpreting facial expressions, and electrical field sensing of body proximity. Vision-based and device-based techniques use cameras, gloves, or other sensors to detect gestures. Challenges include varying lighting and background items that can reduce accuracy. The future potential is vast across entertainment, home automation, education, medicine and security.Complex Weld Seam Detection Using Computer Vision Linked In
Complex Weld Seam Detection Using Computer Vision Linked Inglenn_silvers
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This document discusses a project to use computer vision and a Microsoft Kinect sensor to enable real-time gesture control of a welding robot. The project aims to detect and track a user's hand gestures to control robot movement, and to define the weld seam region of interest to allow for seam detection. The plan involves accessing Kinect data, detecting and tracking the hand in 3D space, recognizing gestures for robot movement commands, extracting color values from the hand for skin detection, and using the hand position to define the seam region of interest. The work so far has successfully defined the hand and fingers, tracked hand motion, and extracted the seam region. Further work is needed to finalize the gesture commands and integrate control of the robot.Development of Sign Signal Translation System Based on Alteras FPGA DE2 Board
Development of Sign Signal Translation System Based on Alteras FPGA DE2 BoardWaqas Tariq
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The main aim of this paper is to build a system that is capable of detecting and recognizing the hand gesture in an image captured by using a camera. The system is built based on Alteras FPGA DE2 board, which contains a Nios II soft core processor. Image processing techniques and a simple but effective algorithm are implemented to achieve this purpose. Image processing techniques are used to smooth the image in order to ease the subsequent processes in translating the hand sign signal. The algorithm is built for translating the numerical hand sign signal and the result are displayed on the seven segment display. Alteras Quartus II, SOPC Builder and Nios II EDS software are used to construct the system. By using SOPC Builder, the related components on the DE2 board can be interconnected easily and orderly compared to traditional method that requires lengthy source code and time consuming. Quartus II is used to compile and download the design to the DE2 board. Then, under Nios II EDS, C programming language is used to code the hand sign translation algorithm. Being able to recognize the hand sign signal from images can helps human in controlling a robot and other applications which require only a simple set of instructions provided a CMOS sensor is included in the system.Real time human-computer interaction
Real time human-computer interactionijfcstjournal
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At the present time, hand gestures recognition system could be used as a more expected and useable
approach for human computer interaction. Automatic hand gesture recognition system provides us a new
tactic for interactive with the virtual environment. In this paper, a face and hand gesture recognition
system which is able to control computer media player is offered. Hand gesture and human face are the key
element to interact with the smart system. We used the face recognition scheme for viewer verification and
the hand gesture recognition in mechanism of computer media player, for instance, volume down/up, next
music and etc. In the proposed technique, first, the hand gesture and face location is extracted from the
main image by combination of skin and cascade detector and then is sent to recognition stage. In
recognition stage, first, the threshold condition is inspected then the extracted face and gesture will be
recognized. In the result stage, the proposed technique is applied on the video dataset and the high
precision ratio acquired. Additional the recommended hand gesture recognition method is applied on static
American Sign Language (ASL) database and the correctness rate achieved nearby 99.40%. also the
planned method could be used in gesture based computer games and virtual reality.
Gesture Recogntion Technology
Gesture Recogntion TechnologyMohit Sipani
油
this slide lets you know about all the information you need to know about the gesture recognition technology and their advancements.Gesture final report new
Gesture final report newchithiracyriac
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The document describes a digital pen system for gesture recognition and control of devices. It contains an accelerometer to capture gesture data and a microcontroller to process the data and control output devices. The system can recognize digits 0-7 written in air and uses gestures to control a fan and light. It aims to provide an alternative interface for human-machine interaction, especially for disabled users. The hardware includes an ATmega32 microcontroller, accelerometer module, LCD display, and output devices. The system works by measuring acceleration data during gestures and identifying the gestures to control external devices.Major_Project_Group 03_KIIT-1.pptx
Major_Project_Group 03_KIIT-1.pptxSubhrajyotiPayra
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This document describes a hand gesture recognition project using computer vision techniques. The project uses background subtraction, motion detection, thresholding, and contour extraction to segment the hand region from a video stream in real-time. A convolutional neural network is then used to classify different hand gestures. The objectives are to understand how to approach computer vision problems step-by-step and implement concepts like background subtraction, thresholding, and contours using OpenCV and Python. The methodology used background subtraction to separate the foreground hand from the static background, thresholding to isolate the hand region, and contour extraction to find the largest contour corresponding to the hand. The project aims to recognize a limited set of gestures but could be expanded to recognize more gestures and deal with challengesVision based dynamic gesture recognition in Indian sign Language
Vision based dynamic gesture recognition in Indian sign LanguageUnnikrishnan Parameswaran
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This document proposes a novel method for dynamic gesture recognition of Indian Sign Language (ISL) using computer vision. It extracts both global and local features from input video captured by a Microsoft Kinect sensor. For global feature extraction, it uses a new method called Axis of Least Inertia (ALI) to represent hand motion. For local feature extraction, it uses Principle Component Analysis (PCA) to represent finger movements. The extracted features are used to recognize ISL gestures for applications that help communication for the deaf and dumb.Hand gesture recognition using support vector machine
Hand gesture recognition using support vector machinetheijes
油
1) The document describes a system for hand gesture recognition using support vector machines. It uses Canny's edge detection algorithm and histogram of gradients (HOG) for feature extraction from input images of hand gestures.
2) The system is trained using a dataset of predefined hand gestures. During testing, it compares the features extracted from new input images to those in the training dataset and classifies the gesture using an SVM classifier.
3) Experimental results found the system could accurately recognize 20 different static hand gestures in complex backgrounds. However, the authors note that future work could focus on real-time gesture recognition and reducing complexity for faster processing.
GESTURE RECOGNITION TECHNOLOGY
GESTURE RECOGNITION TECHNOLOGYjinal thakrar
油
Gesture recognition technology allows for control of devices through hand and body motions. It works by using cameras, sensors and algorithms to interpret gestures and movements. Key applications include controlling smart TVs with hand motions, sign language translation, and assisting disabled individuals. Challenges include variations between individuals, reading motions accurately due to lighting and noise, and lack of standardized gesture languages.IRJET- Survey on Sign Language and Gesture Recognition System
IRJET- Survey on Sign Language and Gesture Recognition SystemIRJET Journal
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This document summarizes several research papers on sign language and gesture recognition systems. It discusses various techniques that have been used to convert sign language and gestures into understandable formats for hearing people. Vision-based and sensor-based approaches are described. Specific papers summarized include those using 7Hu moments and KNN classification achieving 82% accuracy, a system using gloves with flex and inertial sensors recognizing Taiwanese sign language with 94% accuracy, and a vision-based system using convex hull and defects to control computer functions. The document concludes by describing a system using a sensor glove to detect gestures from British and Indian sign languages and output text and audio.Real Time Sign Language Detection
Real Time Sign Language DetectionIRJET Journal
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The document describes a project to develop a real-time sign language detection system using computer vision and deep learning techniques. The researchers collected over 500 images of 5 different signs and trained a convolutional neural network model using transfer learning with a pre-trained SSD MobileNet V2 model. The model takes input from a webcam video stream and classifies each frame in real-time to detect the sign language. Some key applications of this system include improving communication for deaf individuals and teaching sign language. The researchers achieved reliable detection results under controlled lighting conditions and aim to expand the dataset and model capabilities in future work.Soli sensor
Soli sensorPratik Hiremath
油
The Soli sensor developed by Google's ATAP group uses radar technology to detect hand gestures without the need for cameras or external devices. It can detect up to 10 gestures by capturing tiny hand movements at a rate of 1000 frames per second, much faster than cameras. The sensor works by emitting electromagnetic waves and analyzing properties of the reflected signal such as energy, time delay, and frequency shift to distinguish finger movements and shapes within its range. Developers are using convolutional neural networks to train the sensor to recognize complex hand gestures and patterns for applications in gaming, home automation, and vehicle control.Sign Language Recognition using Machine Learning
Sign Language Recognition using Machine LearningIRJET Journal
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This document describes a study on sign language recognition using machine learning. The researchers developed a convolutional neural network model to detect hand movements and classify them as letters of the alphabet from sign language. They used a dataset of images of American Sign Language letters and trained their CNN model on this data. Their model was able to accurately recognize the letters in real-time using input from a webcam. The document also discusses using background subtraction and other techniques to improve the model's performance at sign language recognition.sign language recognition using HMM
- 1. Sign Language RecognitionUsingHidden Markov ModelPresented by:VipulAgarwal - 070905060
- 5. SKIN AND HAND DETECTION
- 7. FEATURE EXTRACTION FOR TRAINING DATA
- 8. HIDDEN MARKOV MODEL & ITS USE
- 10. DEMONSTRATIONIntroductionInteraction with computers may often not be a comfortable experience.
- 11. Computers should be able to communicate with people with body language.
- 12. Hand gesture recognition becomes important Interactive human-machine interface and virtual environment
- 13. IntroductionTwo common technologies for hand gesture recognitionGLOVE-BASED METHODUsing special glove-based device to extract hand postureVISION-BASED METHOD3D hand/arm modelingAppearance modeling
- 14. Introduction3D hand/arm modelingHighly computational complexity Using many approximation processAppearance modelingLow computational complexityReal-time processing
- 15. Sign LanguageRely on the hearing societyTwo main elements:Low and simple level signed alphabet, mimics the letters of the spoken language.Higher level signed language, using actions to mimic the meaning or description of the sign.The project aim is to make the computer recognize low and simple level American Sign Language.
- 16. Sign LanguageAmerican Sign Language26 signs to denote the alphabets.10 signs to denote numbers
- 17. Pre - ProcessingThe video sequence used has a lot of noise due to:Low quality of the webcam Improper lighting conditionsBackground
- 18. Pre - ProcessingPre-processing involves reducing the noise and illumination problems.The morphological operations used for reducing the noise involves:DilationStatistical Elimination
- 19. Pre - ProcessingDILATION>A disc shaped region is traversed over every blob and the ones which do not fit the disc are removed completely.
- 20. Pre - ProcessingSTATISTICAL ELIMINATION>For every region the area is computed. Since hand is the one with the largest area, all blobs having less than a specified area are removed.
- 21. Hand DetectionFirst all the noise is removed in the pre-processing stage.Now we assume that the hand is the largest skin blob in our video sequence.We calculate the area of every blob and take the one with the largest area.We also calculate the bounding box of the region containing the hand for further analysis
- 22. Hand Detection
- 23. Optical Flow AnalysisDEFINITION:Optical flow is the pattern of apparent motion of objects, surfaces, and edges in a visual scene caused by the relative motion between an observer (an eye or a camera) and the scene.
- 24. Optical Flow AnalysisWhy Optical Flow Analysis?Till now the system is just able to detect the hand and follow the bounding box as the hand moves.The problem now is that we need to define a way to take a snapshot of the hand when the hand is not moving.
- 25. Optical Flow AnalysisUsing this technique we find the motion in the hand. When the hand has stabilized, we assume that the gesture is ready. We then take a snapshot of the hand and perform the recognition on that image.
- 26. Feature ExtractionFor training the network with test images we perform the following feature extraction technique:-Thresholding of the test handConverting to a binary imageFinding the centroid of the hand and orientation of the minor axis.Making feature vectors using a predefined number of features.
- 27. Feature ExtractionExtracting the intersection of the feature vectors with the boundary points.Finding the scalar length of the vectors from the centroid.Normalising the lengths in a scale of 1 to 100 to make it scaling invariant.
- 29. Hidden Markov Model (HMM)HMMs allow you to estimate probabilities of unobserved eventsGiven plain text, which underlying parameters generated the surface
- 30. HMMs and their UsageHMMs are very common in Computational Linguistics:GESTURE RECOGNITION (observed: image, hidden: alphabets)
- 31. Progress ReportWORK COMPLETED:Data CollectionPre-processing Skin And Hand DetectionOptical Flow AnalysisFeature Extraction For Training DataWORK REMAINING:Training The Hidden Markov Model
- 32. Any Questions ?