Abstract A Novel methodology has been proposed in this paper that enabled us to track objects of interest in video sequences at a much higher rates of 25-30 frames per second. This is particularly useful in applications where a real time intervention is of paramount importance for e.g. in safety critical applications and in video surveillance environment. This simplistic but very... A Novel methodology has been proposed in this paper that enabled us to track objects of interest in video sequences at a much higher rates of 25-30 frames per second. This is particularly useful in applications where a real time intervention is of paramount importance for e.g. in safety critical applications and in video surveillance environment. This simplistic but very robust tracking algorithm is based on static and dynamic particle like sensors that also march along the object in video frames. The direction of propagation of these sensors is associated with different components including previous motion history and the maneuverability of the object in question and the track characteristics. A group of particles each of a pixel size bunch together to form a super particle and its mean position in the frame is used to direct other subordinate particles to explore the search space further to determine the exact boundary profiles of the object of interest. The super particle position is similar to groups best velocity profile in Particle Swarm Optimization. The main difference in this approach is that the birth of particles takes place in a circular like search space around the groups best position. This evolutionary approach inspired by biological life forms like flock of birds and a bee hive outperforms the standard particle filter based approach where the state dynamics are predetermined and are usually influenced by Newtonian Physics. Our approach dramatically reduces the number of iteration required in each frame and needs even less than 30 particles (300-500 in standard particle filter) to effectively track object of interest in a frame of medium resolution. This approach also recovered the object effectively in real time if it was partially or fully occluded for a significant number of frames and when it underwent large maneuverability which original mean shift algorithm had been unable to cope with. Read More Read Less

Abstract We have already proposed a new approach to modeling the humanoid robot [1], the direct and inverse geometric model of the humanoid robot RH-ARP [2] This result complements the first study and is used for the practical realization of this prototype. The work presented in this paper is the kinematic modeling of robot RH-ARP, with 18 degrees of freedom (DOF),... We have already proposed a new approach to modeling the humanoid robot [1], the direct and inverse geometric model of the humanoid robot RH-ARP [2] This result complements the first study and is used for the practical realization of this prototype. The work presented in this paper is the kinematic modeling of robot RH-ARP, with 18 degrees of freedom (DOF), three degrees of freedom in each arm and six degrees of freedom per foot. Read More Read Less

Abstract So far many biometric systems such as fingerprint, palm print and iris have been developed for several years. Nowadays, many researchers are interested in developing new and more efficient biometric systems by using alternative features. In line with this, a newer characteristic that is dorsal hand vein patterns are used to identify an individual because its uniqueness, reliability, permanence and... So far many biometric systems such as fingerprint, palm print and iris have been developed for several years. Nowadays, many researchers are interested in developing new and more efficient biometric systems by using alternative features. In line with this, a newer characteristic that is dorsal hand vein patterns are used to identify an individual because its uniqueness, reliability, permanence and difficulty to forge. To develop a dorsal hand vein biometric security system, the hand vein images are first captured using an appropriate setup. Several preprocessing techniques are then applied to obtain a thinned version of the image. One challenging phase in biometric security system is the feature extraction phase. In this work, three feature extraction and representation techniques namely Hough lines transform, Pixel by Pixel Method and Directional Coding Method have been explored and implemented. These techniques are applied on 500 images obtained from 100 individuals of different age. For matching, Mahalanobis Distance and Correlation Percentage have been used. From the experimental results, it was deduced that Pixel by Pixel Method proved to be the best feature extraction technique with a False Rejection Rate (FRR) of 0.03%. Read More Read Less

Abstract Articulation via target-oriented approaches have been commonly used in robotics. Movement of a robotic arm can involve targeting via a forward or inverse kinematics approach to reach the target. We attempted to transform the task of controlling the motor articulation to a machine learning approach. Towards this goal, we built an online robotic arm to extract articulation datasets and have... Articulation via target-oriented approaches have been commonly used in robotics. Movement of a robotic arm can involve targeting via a forward or inverse kinematics approach to reach the target. We attempted to transform the task of controlling the motor articulation to a machine learning approach. Towards this goal, we built an online robotic arm to extract articulation datasets and have used SVM and Naïve Bayes techniques to predict multi-joint articulation. For controlling the preciseness and efficiency, we developed pick and place tasks based on pre-marked positions and extracted training datasets which were then used for learning. We have used classification as a scheme to replace prediction-correction approach as usually attempted in traditional robotics. This study reports significant classification accuracy and efficiency on real and synthetic datasets generated by the device. The study also suggests SVM and Naïve Bayes algorithms as alternatives for computational intensive prediction-correction learning schemes for articulator movement in laboratory environments. Read More Read Less