A study of the feature‐based digital image stabilization system

Abstract

This article presents a digital image stabilization scheme that uses image processing techniques to compensate for undesirable image jitter due to vehicle or platform vibration to obtain a stabilized image display. The developed digital image stabilization system is based on the image combination approach combined with advanced image selection, feature detection, feature matching and pre‐rotation processing to produce crisp images. Using the Sobel edge detector, we calculate the magnitude of edge response as the judgment criteria for image transformation. To demonstrate the validity of the proposed technique, experimental results are given for some synthetic images.     

A broad review about face recognition – feature extraction and recognition techniques

Abstract

Face recognition (FR) throws open a vast horizon of challenging tasks in the arena of facial image processing applications and computer visualisation, and hence has riveted keen interest during the last few years on account of its versatile applications in numerous spheres. Creating a useful facial design from initial face images is a very important gradient for victorious facial expression detection. Here, we furnish a report of several feature extraction and recognition methods which find themselves employed in the method of FR. The major aim of this survey is to assess the diverse FR methods according to their feature extraction and recognition techniques. From the analysis, we come to know about the feature extraction and recognition methods which have been elegant utilised in the FR procedure. They also vividly establish the technique which has performed excellently yielding superior FR precision by detecting face images more exactly. Moreover our study draws a concise picture of the feature extraction and recognition techniques and acts as a lodestar to the incoming intriguing investigators intending to increase their information about this innovative technique.     

Development of machine learning‐based real time scheduling systems: using ensemble based on wrapper feature selection approach

There are two items that significantly enhance the generalisation ability (i.e. classification accuracy) of machine learning‐based classifiers: feature selection (including parameter optimisation) and an ensemble of the classifiers. Accordingly, the objective in this study is to develop an ensemble of classifiers based on a genetic algorithm (GA) wrapper feature selection approach for real time scheduling (RTS). The proposed approach can better enhance the generalisation ability of the RTS knowledge base (i.e. classifier) in comparison with three classical machine learning‐based classifier RTS systems, including the GA‐based wrapper feature selection mechanism, in terms of the prediction accuracy of 10‐fold cross validation as measured according to all the performance criteria. The proposed ensemble classifier RTS also provides better system performance than the three machine learning‐based RTS systems, including the GA‐based wrapper feature selection mechanism and heuristic dispatching rules, under all the performance criteria, over a long period in a flexible manufacturing system (FMS) case study.     

Synthesis and characterization of maghemite iron oxide (γ-Fe2O3) nanofibers: novel semiconductor with magnetic feature

Among the reported nanostructural shapes, nanofibers have special interest due to the long axial ratio which has a distinct impact on many chemical and physical properties. In this study, synthesis of the desirable maghemite iron oxide (γ-Fe₂O₃) nanofibers is introduced. Calcination of electrospun mats composed of ferrous acetate and poly(vinyl alcohol) in argon atmosphere resulted in producing maghemite nanofibers. Detailed characterization affirmed that the obtained γ-Fe₂O₃ nanofibers are free of other iron oxides. Due to the axial ratio impact, the synthesized nanofibers which have an average diameter of ~70 nm do have magnetic properties resemble γ-Fe₂O₃ nanoparticles having an average diameter of ~5 nm. Accordingly, the produced nanofibers are considerable candidate for biomagnetic separation of the biomaterials. The prepared γ-Fe₂O₃ nanofibers can be easily handled as they were obtained in the form of strong mats. Electrical properties study indicated that the introduced nanofibers behave as a semiconducting material. Moreover, the synthesized γ-Fe₂O₃ nanofibers have band gap energy of ~4.2 eV. Based on the simplicity, effectiveness, high-yield, and low-cost features of the utilized preparation process and the studied physiochemical properties of the obtained product, the synthesized γ-Fe₂O₃ nanofibers might have considerable application fields.