Abstract
In this paper, we address the problem of tracking feature points along image sequences efficiently. Thus, to estimate the undergoing movement we use an approach based on Kalman filtering, which performs the prediction and correction of the features' movement in every image frame. Measured data is incorporated by optimizing the global association set built on efficient approximations of the Mahalanobis distance (MD). We analyze the difference between the usage in the tracking results of the original MD formulation and its more efficient approximation, as well as the related computational costs. Experimental results which validate our approach are presented.

Abstract
Scenes in real world have dynamic range of radiation that cannot be captured by conventional cameras. High dynamic range imaging is a technique to capture detail images where, in the field of image, intensity variation is extreme. This technique is very useful for biological imaging where the samples have very bright and very dark regions and both parts have useful information. In this article we propose a novel high dynamic range imaging technique based on compressive imaging that uses one single detector instead of camera (array of detectors) to capture an image. Combination of high dynamic range imaging and compressive imaging benefits from imaging with high dynamic range of radiation and advantages of compressive sampling; namely, imaging at regions of optical spectrum where conventional cameras are not readily available and single detectors are available. Additionally, as its name suggests, this technique requires less number of samples (compared to raster scanning). Our experimental results show that high dynamic range compressive imaging system is capable of capturing images with large intensity contrast.

Abstract
Some scenes and objects have a wide range of brightness that cannot be captured with a conventional camera. This limitation, which degrades the dynamic range of an imaged scene or object, is addressed by use of high dynamic range (HDR) imaging techniques. With HDR imaging techniques, images of a very broad range of intensity can be obtained with conventional cameras. Another limitation of conventional cameras is the range of wavelength that they can capture. Outside the visible wavelengths, the responsivity of conventional cameras drops; therefore, a conventional camera cannot capture images in nonvisible wavelengths. Compressive imaging is a solution for this problem. Compressive imaging reduces the number of pixels of a camera to one, so a camera can be replaced by a detector with one pixel. The range of wavelengths to which such detectors are responsive is much wider than that of a conventional camera. A combination of HDR imaging and compressive imaging is introduced and is benefitted from the advantages of both techniques. An algorithm that combines these two techniques is proposed, and results are presented. (C) 2012 Society of Photo-Optical Instrumentation Engineers (SPIE). [DOI: 10.1117/1.OE.51.7.071407]

Abstract
Until recent years most research involving the capture and analysis of biometric and/or physiological signals have been limited to a laboratory or otherwise controlled environment. Wearable technologies introduced a refinement to personal signal capturing by permitting a long-term onperson approach. Sensors, integrated circuits, textile integration and other elements are directly responsible for advancements in this area; however, in spite of the present progress there are still a number of obstacles to overcome for truly achieving seamless wearable monitoring technology (WMT). This article presents an overview of a generic monitoring systems architecture based on designs found in recent literature and commercially available solutions. A custom implementation based on commercially available components and evaluation boards is also presented, including some obtained data in varying body locations and/or activities. © 2010 IEEE.

Abstract
In this paper we present an evaluation of optical flow techniques applied to a case study in the perception of visual motion. This case study is being conducted in a project for the evaluation of human factors in road traffic, specifically, concerning the processing of visual information. We present the goals of the case study, discuss the need to apply optical flow techniques to synthesized image sequences and evaluate some limitations encountered in their use. © 1996 IEEE.

Abstract
Optical flow algorithms generally demand for high computational power and huge storage capacities. This paper is a contribution for real-time implementation of an optical flow algorithm on a pipeline machine. This overall optical flow computation methodology is presented and evaluated on a set of synthetic and real image sequences. Results are compared to other implementations using as measures the average angular error, the optical flow density and the root mean square error. The proposed implementation achieves very low computation delays, allowing operation at standard video frame-rate and resolution. It compares favorably to recent implementations in standard microprocessors and in parallel hardware.