Abstract-In this paper, a novel real time robot control system using human facial expressions is presented. The proposed system mainly consists of three modules: face detection, facial expression recognition and robot control. The first module aims to find the user's face from the image captured from a live video through a series of steps including skin color classification, edge detection and mathematical morphology, while the second module analyses the detected face to recognize the facial expressions like happiness, sadness, surprise, anger and neutral using Principal Component Analysis (PCA) and Euclidian distance calculation. Finally, the detected facial expressions are used as controlling commands for the robot. Experiments were conducted for respective modules. 150 images under different lightening conditions and complex background were employed. In the experiment on detecting the facial region, the rate of detection was 99.3% and on recognizing the facial expression, the recognition rate was 97.3%. The robot was then controlled autonomously with an accuracy of 100%. Combining the above three rates, the overall rate of success of our system was 97.3%.
This paper proposes Enhanced Fuzzy C-means technique (EFCM) based infrared image segmentation and its broad application in Automatic detection systems. The EFCM based image segmentation is able to approximate the exact number of clusters present in the image. EFCM based segmentation is applied on various infrared images that can be used for automatic detection systems and compared with widely used clustering techniques such as K-means and EM. Clustering performance has been compared in terms of well-proven and widely accepted validation indices, Global Silhouette Index and Separation Index. The segments or clusters obtained from above mentioned clustering methods have been assessed visually. Automatic Detection Systems based on EFCM can help in reducing complexities present in conventional systems.
As robotics is increasing its influence over the technological developments, the need for autonomous robots capable of performing various tasks has increased. These task include the operations in which they detect the obstacles and navigate themselves in an unknown environment .Here we present two algorithms for wireless detection and depth calculation of obstacles. These algorithms are implemented using image processing techniques. The sensor assembly used for wireless sensing of the obstacle comprises of a LASER source and a camera.
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