DEMONSTRATION</li></li></ul><li>Introduction<br /><ul><li>Interaction with computers may often not be a comfortable experience.
Computers should be able to communicate with people with body language.
Hand gesture recognition becomes important …</li></ul>Interactive human-machine interface and virtual environment <br />
Introduction<br />Two common technologies for hand gesture recognition<br />GLOVE-BASED METHOD<br />Using special glove-based device to extract hand posture<br />VISION-BASED METHOD<br />3D hand/arm modeling<br />Appearance modeling<br />
Sign Language<br />Rely on the hearing society<br />Two main elements:<br />Low and simple level signed alphabet, mimics the letters of the spoken language.<br />Higher level signed language, using actions to mimic the meaning or description of the sign.<br />The project aim is to make the computer recognize low and simple level American Sign Language.<br />
Sign Language<br />American Sign Language<br />26 signs to denote the alphabets.<br />10 signs to denote numbers<br />
Pre - Processing<br />The video sequence used has a lot of noise due to:<br />Low quality of the webcam <br />Improper lighting conditions<br />Background<br />
Pre - Processing<br />Pre-processing involves reducing the noise and illumination problems.<br />The morphological operations used for reducing the noise involves:<br />Dilation<br />Statistical Elimination<br />
Pre - Processing<br />DILATION><br />A disc shaped region is traversed over every blob and the ones which do not fit the disc are removed completely.<br />
Pre - Processing<br />STATISTICAL ELIMINATION><br />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.<br />
Hand Detection<br />First all the noise is removed in the pre-processing stage.<br />Now we assume that the hand is the largest skin blob in our video sequence.<br />We calculate the area of every blob and take the one with the largest area.<br />We also calculate the bounding box of the region containing the hand for further analysis<br />
Optical Flow Analysis<br />DEFINITION:<br />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.<br />
Optical Flow Analysis<br />Why Optical Flow Analysis?<br />Till now the system is just able to detect the hand and follow the bounding box as the hand moves.<br />The problem now is that we need to define a way to take a snapshot of the hand when the hand is not moving.<br />
Optical Flow Analysis<br />Using 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.<br />
Feature Extraction<br />For training the network with test images we perform the following feature extraction technique:-<br />Thresholding of the test hand<br />Converting to a binary image<br />Finding the centroid of the hand and orientation of the minor axis.<br />Making feature vectors using a predefined number of features.<br />
Feature Extraction<br />Extracting the intersection of the feature vectors with the boundary points.<br />Finding the scalar length of the vectors from the centroid.<br />Normalising the lengths in a scale of 1 to 100 to make it scaling invariant.<br />