Novel hybrid framework for image compression for supportive hardware design of boosting compression

Performing the image compression over the resource constrained hardware is quite a challenging task. Although, there has been various approaches being carried out towards image compression considering the hardware aspect of it, but still there are problems associated with the memory acceleration associated with the entire operation that downgrade the performance of the hardware device. Therefore, the proposed approach presents a cost effective image compression mechanism which offers lossless compression using a unique combination of the non-linear filtering, segmentation, contour detection, followed by the optimization. The compression mechanism adapts analytical approach for significant image compression. The execution of the compression mechanism yields faster response time, reduced mean square error, improved signal quality and significant compression ratio performance

A simplified machine learning approach for recognizing human activity

With the wide ranges of real-time event feed capturing devices, there has been significant progress in the area of digital image processing towards activity detection and recognition. Irrespective of the presence of various such devices, they are not adequate to meet dynamic monitoring demands of the visual surveillance system, and their features are highly limited towards complex human activity recognition system.  Review of existing system confirms that still there is a large scope of enhancement as they lack applicability to real-life events and also doesn't offer optimal system performance. Therefore, the proposed manuscript presents a model for activity recognition system where the accuracy of recognition operation and system performance are retained with good balance. The study presents a simplified feature extraction process from spatial and temporal traits of the event feeds that is further subjected to the machine learning mechanism for boosting recognition performanc

An Efficient Activity Detection System based on Skeleton Joints Identification

The increasing criminal activities in the current world has drawn lot of interest activity recognition techniques which helps to perform the sophistical analytical operations on human activity and also helps to interface the human and computer interactions. From the existing review analysis it is found that most of the existing systems are not emphasize on computational performance but are more application specific by identifying specific problems. Hence, it is found that all the features are not required for accurate and cost effective human activity detection. Thus, the human skelton action can be considered and presented a simple and accurate process to identify the significant joints only. From the outcomes it is found that the proposed system is cost effective and computational efficient activity recognition technique for human actions

Pattern Approximation Based Generalized Image Noise Reduction Using Adaptive Feedforward Neural Network

The problem of noise interference with the image always occurs irrespective of whatever precaution is taken. Challenging issues with noise reduction are diversity of characteristics involved with source of noise and in result; it is difficult to develop a universal solution. This paper has proposed neural network based generalize solution of noise reduction by mapping the problem as pattern approximation. Considering the statistical relationship among local region pixels in the noise free image as normal patterns, feedforward neural network is applied to acquire the knowledge available within such patterns. Adaptiveness is applied in the slope of transfer function to improve the learning process. Acquired normal patterns knowledge is utilized to reduce the level of different type of noise available within an image by recorrection of noisy patterns through pattern approximation. The proposed restoration method does not need any estimation of noise model characteristics available in the image not only that it can reduce the mixer of different types of noise efficiently. The proposed method has high processing speed along with simplicity in design. Restoration of gray scale image as well as color image has done, which has suffered from different types of noise like, Gaussian noise, salt &peper, speckle noise and mixer of it

Determining adsorption geometry, bonding, and translational pathways of a metal-organic complex on an oxide surface: Co-salen on NiO(001)

Individual molecules of Co-Salen, a small chiral paramagnetic metal-organic complex, deposited on NiO(001) were imaged with noncontact atomic force microscopy (NC-AFM) using metallic Cr coated tips. Experimentally, we simultaneously resolve both the molecule and the individual surface ions. Images recorded at low temperatures show that the Co-Salen molecules are aligned slightly away from the ⟨110⟩ directions of the surface and that the Co center of the molecule is located above a bright spot in atomically resolved images of the surface. Density functional theory calculations predict that the molecule adsorbs with the central Co atom on top of an oxygen ion and is in its lowest energy configuration aligned either + or −4° away from the ⟨110⟩ directions, dependent on the chirality of the molecule. Combining theoretical predictions and experimental data allows us to identify bright spots in NC-AFM images as oxygen sites on NiO(001) and hence determine the exact adsorption geometry and position of the molecule. Additionally, we observed tip-induced translations of the Co-Salen molecules along ⟨110⟩ directions on the substrate, which corresponds to the lowest energy pathway for diffusion. A comparison of these results with theoretical calculations and previously published experimental data for Co-Salen on the (001) surface of bulk NaCl highlights differences in the character of adsorption of individual molecules and the ensuing growth of Co-Salen thin films on these substrates. © 2012 American Chemical Society