Description
Gesture Recognition using CNN | Opencv and Python
A hand gesture recognition system provides a natural, innovative, and modern way of nonverbal communication. It has a wide area of application in human-computer interaction and sign language. The intention of this paper is to discuss a novel approach to hand gesture recognition based on the detection of some shape-based features. The setup consists of a single camera to capture the gesture formed by the user and take this hand image as an input to the proposed algorithm. The overall algorithm is divided into some main steps, which include segmentation, feature extraction, and CNN. The proposed algorithm is independent of user characteristics. It does not require any kind of training in sample data
Introduction
Challenge to involve humans and robots interact. The problem is that robot does not understand human? language directly and HRI requires media for communication which can be both understood by robots and easily done by humans, especially to help deaf people, patients, and old people, therefore gesture recognition as communication media is needed to give order to Robot. Machine learning is a part of Artificial Intelligence (AI) which discusses the development of a system that depends on information or data This paper discusses hand gesture recognition as input using two methods, DWT, feature extraction and CNN techniques proposed t
The way of clustering some data which applying the Euclidean idea of the distance between all data elements. The method is done by using feature extraction and feature matching concepts. We are going to these techniques and get clear output at the end of this project.
System Analysis
Existing Systems
- SVM
- KNN
- PCA
Drawback:
- Difficulties are there to find the optimal gradient
- Poor Edge detection.
- Time-consuming
- Less accuracy
Proposed Systems
- Feature extraction
- DWT
- CNN
Advantages
- Better efficiency and less sensitive to noise
- Highly Security
- Automated? recognition and replication system.
- High accuracy
Block diagram:
Preprocessing
Image Pre-processing? and video processing is a common names for operations with? images/video frames? at the lowest level of abstraction. Its input and output are intensity? images. The aim of?pre-processing?is an improvement on the? image? or data that suppresses unwanted distortions or enhances some images? features important for further processing.
Image restoration is the operation of taking a corrupted/noisy image and estimating the clean original image. Corruption may come in many forms such as motion blur, noise, and camera misfocus.? Image restoration is different from image enhancement in that the latter is designed to emphasize features of the image that make the image more pleasing to the observer, but not necessarily to produce realistic data from a scientific point of view. Image enhancement techniques (like contrast stretching or de-blurring by the nearest neighbor procedure) provided by “Imaging packages” use no a priori model of the process that created the image.? With image, enhancement noise can effectively be removed by sacrificing some resolution, but this is not acceptable in many applications. In a Fluorescence Microscope resolution in the z-direction is as bad as it is. More advanced image processing techniques must be applied to recover the object.? De-Convolution is an example of an image restoration method. It is capable of Increasing resolution, especially in the axial direction removing noise and increasing contrast.
Discrete Wavelet Transform (DWT)
The discrete wavelet remodels (DWT) became superior to using the wavelet rework to the digital international. Filter banks are used to approximate the behavior of the non-prevent wavelet remodels. The sign is decomposed with an immoderate-skip smooth out and a low-bypass clear out. The coefficients of these filters are computed using mathematical evaluation and made to be had to you. See Appendix B for more records about those computations.
2.2 Discrete Wavelet Transform
Where,
LP d: Low Pass Decomposition Filter
HP d: High Pass Decomposition Filter
LP r: Low Pass Reconstruction Filter
HP r: High Pass Reconstruction Filter
Convolution neural networks (CNN):
Convolutional neural networks (CNN) and General Regression Neural Networks (GRNN) have similar architectures, but there is a fundamental difference: Probabilistic networks perform classification where the target variable is categorical, whereas general regression neural networks perform regression where the target variable is continuous. If you select a CNN/GRNN network, DTREG will automatically select the correct type of network based on the type of target variable.
The architecture of a CNN:
All CNN networks have four layers:
Hardware Requirements
- system
- 4 GB of RAM
- 500 GB of Hard disk
SOFTWARE REQUIREMENTS:
- Python Idle
- Library Packages
Gesture Recognition using CNN | Opencv and Python
Reference:
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