Showing papers by "Muhammad Murtaza Khan published in 2015"

A Multivariate Empirical Mode DecompositionBased Approach to Pansharpening

Abstract: We propose a novel class of schemes for the pansharpening of multispectral (MS) images using a multivariate empirical mode decomposition (MEMD) algorithm. MEMD is an extension of the empirical mode decomposition (EMD) algorithm, which enables the decomposition of multivariate data into its intrinsic oscillatory scales. The ability of MEMD to process multichannel data directly by performing data-driven, local, and multiscale analysis makes it a perfect match for pansharpening applications, a task for which standard univariate EMD is ill-equipped due to the nonuniqueness, mode-mixing, and mode-misalignment issues. We show that MEMD overcomes the limitations of standard EMD and yields improved spatial and spectral performance in the context of pansharpening of MS images. The potential of the proposed schemes is further demonstrated through comparative analysis against a number of standard pansharpening algorithms on both simulated Pleiades and real-world IKONOS data sets.

Saliency based visualization of hyper-spectral images

Abstract: The problem with visualization of hyper-spectral images on tri-stimulus displays arises from the fact that they contain hundreds of spectral bands while generally used display devices support only three bands/channels namely blue, green and red. Therefore, for visualization a hyper-spectral (HS) image has to be reduced to three bands. The main challenge while performing this band reduction is to retain and display the maximum information available in a hyper-spectral image. Human visual system focuses attention on certain regions in images called “salient regions”. Therefore to provide a comprehensive representation of hyper-spectral data on tri-stimulus displays we propose to use a weighted fusion method of saliency maps and hyper-spectral bands. The efficacy of the proposed algorithm has been demonstrated by tests on both urban and countryside images of AVIRIS and ROSIS sensors.

Quality Assessment of Images Projected Using Multiple Projectors

Abstract: Multiple projectors with partially overlapping regions can be used to project a seamless image on a large projection surface. With the advent of high-resolution photography, such systems are gaining popularity. Experts set up such projection systems by subjectively identifying the types of errors induced by the system in the projected images and rectifying them by optimizing (correcting) the parameters associated with the system. This requires substantial time and effort, thus making it difficult to set up such systems. Moreover, comparing the performance of different multi-projector display (MPD) systems becomes difficult because of the subjective nature of evaluation. In this work, we present a framework to quantitatively determine the quality of an MPD system and any image projected using such a system. We have divided the quality assessment into geometric and photometric qualities. For geometric quality assessment, we use Feature Similarity Index (FSIM) and distance-based Scale Invariant Feature Transform (SIFT). For photometric quality assessment, we propose to use a measure incorporating Spectral Angle Mapper (SAM), Intensity Magnitude Ratio (IMR) and Perceptual Color Difference (ΔE). We have tested the proposed framework and demonstrated that it provides an acceptable method for both quantitative evaluation of MPD systems and estimation of the perceptual quality of any image projected by them.

Low Cost Semi-Autonomous Agricultural Robots In Pakistan-Vision Based Navigation Scalable methodology for wheat harvesting.

Abstract: Robots have revolutionized our way of life in recent years.One of the domains that has not yet completely benefited from the robotic automation is the agricultural sector. Agricultural Robotics should complement humans in the arduous tasks during different sub-domains of this sector. Extensive research in Agricultural Robotics has been carried out in Japan, USA, Australia and Germany focusing mainly on the heavy agricultural machinery. Pakistan is an agricultural rich country and its economy and food security are closely tied with agriculture in general and wheat in particular. However, agricultural research in Pakistan is still carried out using the conventional methodologies. This paper is an attempt to trigger the research in this modern domain so that we can benefit from cost effective and resource efficient autonomous agricultural methodologies. This paper focuses on a scalable low cost semi-autonomous technique for wheat harvest which primarily focuses on the farmers with small land holdings. The main focus will be on the vision part of the navigation system deployed by the proposed robot.

A computationally efficient heart rate measurement system using video cameras

Abstract: Video cameras are increasingly being used to measure human heart rates non-invasively, without contact. Such systems find applications in tele-medicine, remote monitoring of quarantined patients and premature neonates and are also useful for health conscious consumers. Several algorithms have been reported in the literature for measuring the heart rate from videos of human subjects. These algorithms use offline, computationally involved techniques such as Independent Component Analysis (ICA) or Principal Component Analysis (PCA) which render the algorithms unfeasible for implementation on real-time embedded systems. We conducted experiments to find an optimal colorspace for measuring the heart rate. We subsequently used a novel means of optical filtering of this colorspace to develop an accurate real-time algorithm, without the need for ICA or PCA, using ordinary standard definition (SD) web cameras. In this paper we present our algorithm and also compare it with existing state of the art algorithms.