Human machine interaction using Hand gesture recognition
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The seminar presented by Mr. M.D. Harsule at G.H. Raisoni College of Engineering focuses on hand gesture recognition (HGR) as a natural form of human-machine interaction and its applications without the need for additional devices. It outlines a systematic approach to processing images using MATLAB and embedded C, including steps for feature extraction and processing for effective gesture recognition. Emphasis is placed on the progression from image acquisition to edge detection, feature extraction, and utilization of neural networks for gesture input processing.
Human machine interaction using Hand gesture recognition
- 1. G.H. Raisoni College of Engineering & Management, Amravati SEMINAR ON HUMAN-MACHINE INTERACTION USING HAND GESTURE Presented by Mr. M.D. Harsule M.E.(II year -Electronics & Telecommunication) GUIDED BY Dr. P.V. Ingole (Principal, G.H. Raisoni College of Engineering & Management, Amravati)
- 2. INTRODUCTION: Hand gestures recognition (HGR) is one of the main areas of research for the engineers, scientists and bioinformatics. HGR is the natural way of Human Machine interaction and today many researchers in the World are working on different application to make interactions more easy, natural and convenient without wearing any extra device. If by using HGR there is interaction between Human and computer further HGR can be used to control various equipments or can be used to instruct a computer.
- 3. MATLAB R2010a Embedded C
- 4. Camera Nowadays Digital cameras are easily available and cheaper device Image processing became a convenient method for applications Microcontroller based Home appliances switching system
- 6. Train set: Some training sets of images, each one containing three or more images. Each set originates from a single image for testing. Test Set: From databases and they can be tested extensively Feature extraction is very important
- 8. Step1 The first thing for the program to do is to read the image database. A for loop is used to read an entire folder of images and store them in MATLAB’s memory. The folder is selected by the user from menus. A menu will firstly pop-up asking user whether he want to run the algorithm on test or train sets
- 9. Step2 Resize all the images that were read in Step1 to 150x140 pixels. This size seems the optimal for offering enough detail while keeping the processing time low.
- 10. Step3. to find the edges. For that filters may be used feature extracted from the images it had to offer enough discrimination among them operators threshold values and some other parameters can be set as well
- 11. Step 4 Dividing the two resulting matrices (images) dx and dy element by element taking the atan ( tan−1 ). This will give the gradient orientation
- 12. Step 5 Then the MATLAB function im2col is called to rearrange the image blocks into columns. This is not a necessary step Using because want to display identified gesture
- 13. Step 6 Converting the column matrix with the radian values to degrees. This way we can scan the vector for values ranging from 0ο to 90ο . This is because for real elements of X, atan(X) is in the range - 180 to +180. At the same time we are using the neural network itself as this vector would be the input to the network. The smaller the vector the faster the processing.