A holographic system for subsea recording and analysis of plankton and other marine particles

We report here details of the design, development, initial testing and field-deployment of the HOLOMAR system for in-situ subsea holography and analysis of marine plankton and nonliving particles. HOLOMAR comprises a submersible holographic camera ("HoloCam") able to record in-line and off-axis holograms at depths down to 100 m, together with specialised reconstruction hardware ("HoloScan") linked to custom image processing and classification software. The HoloCam consists of a laser and power supply, holographic recording optics and holographic plate holders, a water-tight housing and a support frame. It utilises two basic holographic geometries, in-line and off-axis such that a wide range of species, sizes and concentrations can be recorded. After holograms have been recorded and processed they are reconstructed in full three-dimensional detail in air in a dedicated replay facility. A computer-controlled microscope, using video cameras to record the image at a given depth, is used to digitise the scene. Specially written software extracts a binarised image of an object in its true focal plane and is classified using a neural network. The HoloCam was deployed on two separate cruises in a Scottish sea loch (Loch Etive) to a depth of 100 m and over 300 holograms were recorded

High-resolution in situ holographic recording and analysis of marine organisms and particles (HOLOMAR)

We report on the development of a fully- unctioning, prototype, underwater holographic camera (holo-camera) for holographic recording of large-volumes of sea water containing marine plankton and seston within the upper water column The overriding benefit of holographic imaging over other measurement techniques is that it allows non-intrusive and non-destructive, in-situ, recording of living organisms and inanimate particles in their natural environment. Because of the inherently high resolution of holography, its threedimensional imaging properties and the ability to perform "optical sectioning" on the image, it allows identification of particular organisms together with the extraction of sue and relative positional information This information, in turn, affords the ability to gain knowledge of the behaviour of marine biological communities, their relationship with each other and with the particles with which they interact

A novel framework for intelligent surveillance system based on abnormal human activity detection in academic environments

Abnormal activity detection plays a crucial role in surveillance applications, and a surveillance system thatcan perform robustly in an academic environment has become an urgent need. In this paper, we propose a novel framework for an automatic real-time video-based surveillance system which can simultaneously perform the tracking, semantic scene learning, and abnormality detection in an academic environment. To develop our system, we have divided the work into three phases: preprocessing phase, abnormal human activity detection phase, and content-based image retrieval phase. For motion object detection, we used the temporal-differencing algorithm and then located the motions region using the Gaussian function.Furthermore, the shape model based on OMEGA equation was used as a filter for the detected objects (i.e.,human and non-human). For object activities analysis, we evaluated and analyzed the human activities of the detected objects. We classified the human activities into two groups:normal activities and abnormal activities based on the support vector machine. The machine then provides an automatic warning in case of abnormal human activities. It also embeds a method to retrieve the detected object from the database for object recognition and identification using content-based image retrieval.Finally,a software-based simulation using MATLAB was performed and the results of the conducted experiments showed an excellent surveillance system that can simultaneously perform the tracking, semantic scene learning, and abnormality detection in an academic environment with no human intervention

Iron Behaving Badly: Inappropriate Iron Chelation as a Major Contributor to the Aetiology of Vascular and Other Progressive Inflammatory and Degenerative Diseases

The production of peroxide and superoxide is an inevitable consequence of aerobic metabolism, and while these particular "reactive oxygen species" (ROSs) can exhibit a number of biological effects, they are not of themselves excessively reactive and thus they are not especially damaging at physiological concentrations. However, their reactions with poorly liganded iron species can lead to the catalytic production of the very reactive and dangerous hydroxyl radical, which is exceptionally damaging, and a major cause of chronic inflammation. We review the considerable and wide-ranging evidence for the involvement of this combination of (su)peroxide and poorly liganded iron in a large number of physiological and indeed pathological processes and inflammatory disorders, especially those involving the progressive degradation of cellular and organismal performance. These diseases share a great many similarities and thus might be considered to have a common cause (i.e. iron-catalysed free radical and especially hydroxyl radical generation). The studies reviewed include those focused on a series of cardiovascular, metabolic and neurological diseases, where iron can be found at the sites of plaques and lesions, as well as studies showing the significance of iron to aging and longevity. The effective chelation of iron by natural or synthetic ligands is thus of major physiological (and potentially therapeutic) importance. As systems properties, we need to recognise that physiological observables have multiple molecular causes, and studying them in isolation leads to inconsistent patterns of apparent causality when it is the simultaneous combination of multiple factors that is responsible. This explains, for instance, the decidedly mixed effects of antioxidants that have been observed, etc...Comment: 159 pages, including 9 Figs and 2184 reference

Learning and Classification of Suspicious events for advanced visual-based surveillance

The recent evolution of advanced visual-based surveillance (AVS) systems has allowed to introduce digital image processing and computer vision techniques in several application domains where a human operator has to observe multiple images provided by complex remote environments. The main goal of an AVS system is to generate automatically focus-of-attention messages in order to help the human operator to concentrate his decision capabilities on possible danger situations. In this way, possible human failures are expected to be overcome and better surveillance performances should be obtained [1]

PTZ network configuration for optimal 3D coverage

2nonenonePiciarelli C; Foresti GLPiciarelli, Claudio; Foresti, Gian Luc

Detecting moving people in video streams

The detection of moving people is an important task for video surveillance systems. This paper presents a motion segmentation algorithm for detecting people moving in indoor environments. The proposed algorithm works with mobile cameras and it is composed of two main parts. In the first part, a frame-by-frame procedure is applied to compute the difference image, and a neural network is used to classify whether the resulting image represents a static scene or a scene containing mobile objects. The second part tries to reduce the detection errors in terms of both false or missed alarms. A finite state automaton has been designed to give a robust classification and to reduce the number of false or missed blobs. Finally, a bounding ellipse is computed for each detected blob in order to isolate moving people. (c) 2005 Elsevier B.V. All rights reserved