Spatial frequency based video stream analysis for object classification and recognition in clouds

The recent rise in multimedia technology has made it easier to perform a number of tasks. One of these tasks is monitoring where cheap cameras are producing large amount of video data. This video data is then processed for object classification to extract useful information. However, the video data obtained by these cheap cameras is often of low quality and results in blur video content. Moreover, various illumination effects caused by lightning conditions also degrade the video quality. These effects present severe challenges for object classification. We present a cloud-based blur and illumination invariant approach for object classification from images and video data. The bi-dimensional empirical mode decomposition (BEMD) has been adopted to decompose a video frame into intrinsic mode functions (IMFs). These IMFs further undergo to first order Reisz transform to generate monogenic video frames. The analysis of each IMF has been carried out by observing its local properties (amplitude, phase and orientation) generated from each monogenic video frame. We propose a stack based hierarchy of local pattern features generated from the amplitudes of each IMF which results in blur and illumination invariant object classification. The extensive experimentation on video streams as well as publically available image datasets reveals that our system achieves high accuracy from 0.97 to 0.91 for increasing Gaussian blur ranging from 0.5 to 5 and outperforms state of the art techniques under uncontrolled conditions. The system also proved to be scalable with high throughput when tested on a number of video streams using cloud infrastructure

Extrema Points Application In Determining Iris Region Of Interest

Extrema points are usually applied to solve everyday problems, for example, to determine the potential of a created tool and for optimisation. In this study, extrema points were used to help determine the region of interest (ROI) for the iris in iris recognition systems. Iris recognitionis an automated method of biometric identification that uses mathematical pattern-recognition techniques on the images of one or both irises of an individual' seyes, where the complex patterns are unique, stable, and can be seen from a distance. In order to obtain accurate results, the iris must be localised correctly. Hence, to address this issue, this paper proposed a method of iris localisation in the case of ideal and non-ideal iris images. In this study, the algorithm was based on finding the classification for the region of interest (ROI) with the help of a Support Vector Machine (SVM) by applying a histogram of grey level values as a descriptor in each region from the region growing technique. The valid ROI was found from the probabilities graph of the SVM obtained by looking at the global minimum conditions determined by a second derivative model in a graph of functions. Furthermore, the model from the global minimum condition values was used in the test phase, and the results showed that the ROI image obtained helped in the elimination of sensitive noise with the involvement of fewer computations, while reserving relevant information