CVDec 15, 2013
Face Detection from still and Video Images using Unsupervised Cellular Automata with K means clustering algorithmP. Kiran Sree, I. Ramesh Babu
Pattern recognition problem rely upon the features inherent in the pattern of images. Face detection and recognition is one of the challenging research areas in the field of computer vision. In this paper, we present a method to identify skin pixels from still and video images using skin color. Face regions are identified from this skin pixel region. Facial features such as eyes, nose and mouth are then located. Faces are recognized from color images using an RBF based neural network. Unsupervised Cellular Automata with K means clustering algorithm is used to locate different facial elements. Orientation is corrected by using eyes. Parameters like inter eye distance, nose length, mouth position, Discrete Cosine Transform (DCT) coefficients etc. are computed and used for a Radial Basis Function (RBF) based neural network. This approach reliably works for face sequence with orientation in head, expressions etc.
MADec 10, 2013
Cellular Automata based Feedback Mechanism in Strengthening biological Sequence Analysis Approach to Robotic SoccerP. Kiran Sree, G. V. S. Raju, S. Viswandha Raju et al.
This paper reports on the application of sequence analysis algorithms for agents in robotic soccer and a suitable representation is proposed to achieve this mapping. The objective of this research is to generate novel better in-game strategies with the aim of faster adaptation to the changing environment. A homogeneous non-communicating multi-agent architecture using the representation is presented. To achieve real-time learning during a game, a bucket brigade algorithm is used to reinforce Cellular Automata Based Classifier. A technique for selecting strategies based on sequence analysis is adopted.
IRNov 18, 2013
CAVDM: Cellular Automata Based Video Cloud Mining Framework for Information RetrievalP. Kiran Sree, Inampudi Ramesh Babu, SSSN Usha Devi N
Cloud Mining technique can be applied to various documents. Acquisition and storage of video data is an easy task but retrieval of information from video data is a challenging task. So video Cloud Mining plays an important role in efficient video data management for information retrieval. This paper proposes a Cellular Automata based framework for video Cloud Mining to extract the information from video data. This includes developing the technique for shot detection then key frame analysis is considered to compare the frames of each shot to each others to define the relationship between shots. Cellular automata based hierarchical clustering technique is adopted to make a group of similar shots to detect the particular event on some requirement as per user demand.