This paper presents a scheme to support curtailing illegal activities that are carried out with the help of computers. The paper focuses on determining criminal character of a user by analyzing user’s interactions with the computer at the operating system level. Doing this at the operating system level gives an advantage of catching all interactions of the user with the computer. User interaction information is obtained during the system use and this information is classified using neural network. Neural network does the processing to obtain the criminal character of the user. A sample test was conducted on 200 different users (50 criminal users and 150 normal users). The results reported show that the proposed system is practical and accurate.
In many critical applications such as airport operations (for capacity planning), military simulations (for tactical training and planning), and medical simulations (for the planning of medical treatment and surgical operations), it is very useful to conduct simulations within physically accurate and visually realistic settings that are represented by real video imaging sequences. Furthermore, it is important that the simulated entities conduct autonomous actions which are realistic and which follow plans of action or intelligent behavior in reaction to current situations. We describe the research we have conducted to incorporate synthetic objects in a visually realistic manner in video sequences representing a real scene. We also discuss how the synthetic objects can be designed to conduct intelligent behavior within an augmented reality setting. The paper discusses both the computer vision aspects that we have addressed and solved, and the issues related to the insertion of intelligent autonomous objects within an augmented reality simulation.
We propose a method for learning and generating image textures based on learning the weights of a recurrent Multiple Class Random Neural Network (MCRNN) from the color texture image. The network we use has a neuron which corresponds to each image pixel, and the local connectivity of the neurons reflects the adjacent structure of neighboring neurons. The same trained recurrent network is then used to generate a synthetic texture that imitates the original one. The proposed texture learning technique is efficient and its computation time is small. Texture generation is also fast. This work is a refinement and extension of our earlier work where we considered learning of grey-level textures and the generation of grey level or color textures. We have tested our method with different synthetic and natural textures. The experimental results show that the MCRNN can efficiently model a large category of color homogeneous microtextures. Statistical feature extracted from the co-occurrence matrix of the original and the MCRNN based texture are used to confirm the quality of fit of our approach.
Modern video encoding techniques generate variable bit rates, because they take advantage of different rates of motion in scenes, in addition to using lossy compression within individual frames. We have introduced a novel method for video compression based on temporal subsampling of video frames, and for video frame reconstruction using neural network based function approximations. In this paper we describe another method using wavelets for still image compression of frames, and function approximations for the reconstruction of subsampled frames. We evaluated the performance of the method in terms of observed traffic characteristics for the resulting compressed and subsampled frames, and in terms of quality versus compression ratio curves with real video image sequences. Comparisons are presented with other standard methods.
This paper presents a novel technique for texture modeling and synthesis using the random neural network (RNN). This technique is based on learning the weights of a recurrent network directly from the texture image. The same trained recurrent network is then used to generate a synthetic texture that imitates the original one. The proposed texture learning technique is very efficient and its computation time is much smaller than that of approaches using Markov Random Fields. Texture generation is also very fast. We have tested our method with different synthetic and natural textures. The experimental results show that the RNN can efficiently model a large category of homogeneous microtextures. Statistical features extracted from the co- occurrence matrix of the original and the RNN based texture are used to evaluate the quality of fit of the RNN based approach.