The communication gap between the deaf and hearing population is clearly noticed. To make possible the communication between the Deaf and the hearing population and to overpass the gap in access to next generation Human Computer Interfaces, automated sign language analysis is highly crucial. Conversely, an enhanced solution is to build up a conversion system that translates a sign language gestures to text or speech. Exploration and experimentation of an efficient methodology based on facet features analysis. For a recognition system that can recognize gestures from video which can be used as a translation, A methodology has been proposed that extracts candidate hand gestures from sequence of video frames and collect hand features. The system has three separate parts namely: Hand Detection, Shape Matching and Hu moments comparison. The Hand Detection section detects hand through skin detection and by finding contours. It also includes processing of video frames. The procedure of shape matching is attained by comparing the histograms. The values of Hu moments of candidate hand region is identified using contour region analysis and compared to run matches and identify the particular sign language alphabet. Experimental analysis supports the efficiency of the proposed methodology on benchmark data.
Inhaltsverzeichnis (Table of Contents)
- Abstract
- Chapter 1: Introduction
- 1.1 Background
- 1.2 Problem Statement
- 1.3 Proposed Solution
- 1.4 Thesis Organization
- Chapter 2: Literature Review
- Chapter 3: System Design and Implementation
- 3.1 Hand Detection
- 3.2 Shape Matching
- 3.3 Hu Moments Comparison
- Chapter 4: Experimental Analysis and Results
- Chapter 5: Conclusion and Future Work
Zielsetzung und Themenschwerpunkte (Objectives and Key Themes)
The objective of this dissertation is to propose and evaluate a methodology for vision-based sign language identification using facet analysis. The goal is to bridge the communication gap between deaf and hearing individuals by developing a system that translates sign language gestures into text or speech. This involves efficient hand gesture recognition from video sequences.
- Vision-based sign language recognition
- Facet feature analysis for gesture identification
- Hand detection and segmentation from video
- Shape matching and Hu moments comparison for gesture classification
- System design and implementation for real-time sign language translation
Zusammenfassung der Kapitel (Chapter Summaries)
Abstract: This abstract introduces the communication gap between deaf and hearing individuals and highlights the importance of automated sign language analysis. It outlines a proposed methodology for a sign language recognition system based on facet feature analysis, encompassing hand detection, shape matching, and Hu moments comparison. The system's efficiency is supported by experimental analysis on benchmark data.
Chapter 1: Introduction: This chapter sets the stage by discussing the communication challenges faced by the deaf community and the need for advanced Human-Computer Interfaces. It clearly defines the problem of limited access to communication for deaf individuals and introduces the proposed solution: a vision-based sign language identification system. The chapter also outlines the structure and organization of the entire thesis.
Chapter 2: Literature Review: (Note: Since the provided text does not contain Chapter 2, a summary cannot be provided. This section would typically review existing sign language recognition systems, different approaches to hand detection and feature extraction, and discuss the relevant literature on image processing and pattern recognition techniques used in similar applications.)
Chapter 3: System Design and Implementation: This chapter details the design and implementation of the proposed vision-based sign language identification system. It breaks down the system into three main components: hand detection (using skin detection and contour finding to isolate the hand in each video frame), shape matching (comparing histograms to find similar shapes), and Hu moments comparison (using contour region analysis and comparing Hu moments to identify specific signs). The chapter explains the process flow, algorithms used in each component, and the integration of these components into a cohesive system.
Chapter 4: Experimental Analysis and Results: This chapter presents the experimental results obtained by testing the proposed system on a benchmark dataset. It would describe the dataset used, the evaluation metrics employed (e.g., accuracy, precision, recall), and a detailed analysis of the performance achieved. The chapter would likely include tables and graphs visualizing the results and a discussion on the system's strengths and limitations based on the experimental findings. (Note: Since the provided text only mentions experimental analysis without details, a more detailed summary cannot be provided).
Schlüsselwörter (Keywords)
Contours, Skin Detection, Shape Matching, Gesture Recognition, Hu Moments Comparison, Sign Language Identification System, Facet Analysis, Human-Computer Interaction, Deaf Communication.
Frequently Asked Questions: Vision-Based Sign Language Identification System
What is the main objective of this dissertation?
The dissertation aims to propose and evaluate a methodology for vision-based sign language identification using facet analysis. The goal is to bridge the communication gap between deaf and hearing individuals by developing a system that translates sign language gestures into text or speech. This involves efficient hand gesture recognition from video sequences.
What are the key themes explored in this dissertation?
Key themes include vision-based sign language recognition, facet feature analysis for gesture identification, hand detection and segmentation from video, shape matching and Hu moments comparison for gesture classification, and system design and implementation for real-time sign language translation.
What are the main components of the proposed sign language identification system?
The system comprises three main components: hand detection (using skin detection and contour finding), shape matching (comparing histograms), and Hu moments comparison (using contour region analysis and comparing Hu moments). These components work together to identify sign language gestures.
How does the system perform hand detection?
Hand detection is achieved using skin detection and contour finding techniques to isolate the hand in each video frame.
How does the system perform shape matching?
Shape matching is done by comparing histograms to find similar shapes.
How does the system utilize Hu moments for gesture classification?
The system uses contour region analysis and compares Hu moments to identify specific signs.
What is the process of the system?
The system processes video sequences, detects hands, matches shapes, compares Hu moments, and finally classifies the gestures into corresponding signs. This involves several steps of image processing and pattern recognition.
What chapters are included in the dissertation?
The dissertation includes an abstract, introduction, literature review, system design and implementation, experimental analysis and results, and conclusion and future work chapters.
What kind of experimental analysis was conducted?
The dissertation mentions experimental analysis on a benchmark dataset to evaluate the system's performance using metrics such as accuracy, precision, and recall. Specific details of the dataset and results are not provided in the preview.
What are the keywords associated with this research?
Keywords include: Contours, Skin Detection, Shape Matching, Gesture Recognition, Hu Moments Comparison, Sign Language Identification System, Facet Analysis, Human-Computer Interaction, Deaf Communication.
What is the overall goal of this research in terms of improving communication?
The ultimate goal is to improve communication between deaf and hearing individuals by providing a more accessible and efficient method of sign language translation.
- Quote paper
- Faryal Amber (Author), 2013, Vision Based Sign Language Identification System Using Facet Analysis, Munich, GRIN Verlag, https://www.grin.com/document/276571
