Samer M. Abdallah

Mortality rates among nuclear industry workers at Lucas Heights Science and Technology Centre

Objectives: To assess whether workers at Lucas Heights Science and Technology Centre (LHSTC) have different levels of mortality from the New South Wales (NSW) and Australian populations.

Tree Trunks as Landmarks for Outdoor Vision SLAM

Simultaneous Localization and Mapping (SLAM) of robots is the process of building a map of the robot milieu, while simultaneously localizing the robot inside that map. Cameras have been recently proposed, as a replacement for laser range finders, for the purpose of detecting and localizing landmarks around the navigating robot. Vision SLAM is either Interest Point (IP) based, where landmarks are images saliencies, or object-based where real objects are used as landmarks. The contribution of this paper is two prong: first, it details an approach based on Perceptual Organization (PO) to detect and track trees in a sequence of images, thereby promoting the use of a camera as a viable exteroceptive sensor for object-based SLAM; second,it demonstrates the superiority of the suggested PO system over two appearance-based algorithms in segmenting trees from difficult settings. Experiments conducted on a database of 873 images containing approximately 2008 tree trunks, show that the proposed system correctly classifies trees at 81 % with a false positive rate of 30%.

Cancer Incidence among Australian Nuclear Industry Workers

Cancer Incidence among Australian Nuclear Industry Workers: Rima R. Habib, et al. Faculty of Health Sciences, American University of Beirut, Lebanon—To assess whether workers at Lucas Heights Science and Technology Centre (LHSTC) had different levels of cancer incidence from the New South Wales (NSW) population in Australia. A retrospective cohort study was undertaken at LHSTC. Data on 7,076 workers employed between 1957–98 were abstracted from personnel, dosimetry, and medical files. An inception cohort was defined which included 4,523 workers in employment between 1972–96 to examine cancer incidence. Cancer registrations in the inception cohort were identified to 1996 through electronic linkage of records with the NSW and the Australian national registers of cancer incidence. Allcancer incidence in workers at LHSTC was 15% below the NSW rates [SIR=0.85; 95% CI=(0.75, 0.95)]. Of 37 specific cancers and groups of cancers examined, statistically significant excesses relative to NSW rates were observed only for pleural cancer incidence [SIR=17.71; 95%=(7.96, 39.43)], and for incidence of cancer of the small intestine [SIR=4.34; 95% CI=(1.40, 13.46)]. This study gives little evidence of an increased risk of cancers associated with radiation exposure in a cohort of nuclear workers in Australia. The observed increase in the risk of cancer of the pleura was probably due to unmeasured exposures, given the lack of an established association with radiation exposure, and the strong link to asbestos exposure. Findings for cancers of the small intestine were based on small numbers and were likely to be due to chance.

Towards benchmarks for vision SLAM algorithms

SLAM in an outdoor environment using natural landmarks stands as the holy grail of SLAM algorithms. Segmenting landmarks from background clutter in such environments is difficult and vision, rather than laser, has a higher potential to perform such tasks due to the higher bandwidth of information it carries. There is a need to establish a benchmark upon which emerging vision SLAM algorithms can be assessed and compared. Towards this objective, this paper proposes the infrastructure for such a benchmark and discusses the issues involved in compiling it. Ego-motion information is extracted via a strap-down inertial measurement unit (IMU). Synchronized Global Positioning System (GPS), IMU, and surrounding images of an outdoor park environment are compiled into a database. IMU data in tested on an inertial navigation system (INS) dead-reckoning algorithm. The adequacy of the stereo image database is validated by extracting disparity maps of each stereo image in the database. IMU simulations show the necessity for visual SLAM to improve pose estimation. The complete data set, including GPS, IMU, and stereo images is available for downloading purposes

A benchmark for outdoor vision SLAM systems

ALSPACH DL, 1972, IEEE T AUTOMAT CONTR, VAC17, P439, DOI 10.1109-TAC.1972.1100034; ASMAR DC, 2006, IEEE P COMP VIS PATT; BAILEY T, 2003, IEEE P INT C ROB AUT; Bryson M., 2005, P AUSTR C ROB AUT SY; DAVISON AJ, 2003, IEEE P C PATT AN MAC; DAVISON AJ, 2003, P C COMP VIS NIC FRA; DAVISON AJ, 2001, IEEE P C COMP VIS PA; Deans M., 2000, INT S EXP ROB; Dissanayake G, 2001, IEEE T ROBOTIC AUTOM, V17, P731, DOI 10.1109-70.964672; Dufournaud Y, 2000, PROC CVPR IEEE, P612, DOI 10.1109-CVPR.2000.855876; FITZGIBBONS T, 2004, THESIS U SYDNEY AUST; Harris C., 1988, 4 ALV VIS C, P147; JUNG IK, 2004, THESIS CNRS TOULOUSE; KIM JH, 2003, IEEE P C ROB AUT TAI; KWOK NM, 2003, P AUSTR C ROB AUT BR; KWOK NM, 2005, IEEE P C ROB AUT; KWOK NM, 2004, IEEE P C INT ROB SYS; LEMAIRE T, 2005, IEEE P C INT ROB SYS; MALLET A, 2000, IEEE INT C ROB AUT S, P3519; NEBOT E, 2004, NAVIGATION SYSTEM DE; Nebot E, 1999, J ROBOTIC SYST, V16, P81, DOI 10.1002-(SICI)1097-4563(199902)16:281::AID-ROB23.0.CO;2-9; NIETO J, 2000, SIMULTANEOUS LOCALIZ; Olson CF, 2001, IEEE INT CONF ROBOT, P1099; PANZIERI S, 2001, VISION BASED NAVIGAT; ROY S, 1998, IEEE P INT C COMP VI; Se S, 2002, INT J ROBOT RES, V21, P735, DOI 10.1177-027836402761412467; SHI J, 1994, IEEE C COMP VIS PATT, P593; SOLA J, 2005, IEEE RSJ P C INT ROB; SORENSON HW, 1971, AUTOMATICA, V7, P465, DOI 10.1016-0005-1098(71)90097-5; *U WAT, 2005, GEOGR DEP; Wald A., 1947, SEQUENTIAL ANAL; WALD A, 1948, ANN MATH STAT, V19, P326, DOI 10.1214-aoms-1177730197; WILLIAMS SB, 2001, P 3 INT C FIELD SERV, P315

Effect of systemic insulin and angiotensin II receptor subtype-1 antagonist on endothelin-1 receptor subtype(s) regulation and binding in diabetic rat heart.

This study reports on the regulation and remodeling role of endothelin-1 (ET-1) and its receptor subtypes, ET(A)-Rs/ET(B)-Rs, at the coronary endothelium (CE) and cardiomyocyte (CM) sites. It is carried out in normal and normotensive rats with streptozotocin-induced diabetes mellitus receiving different treatment modalities. Normal rats were divided into two groups, namely a placebo (N) and a losartan-treated (NL), and diabetic rats into four groups receiving placebo (D), insulin-treated (DI), losartan-treated (DL), and insulin/losartan-treated (DIL) respectively. Binding kinetics of ET-1 to ET(A)-Rs/ET(B)-Rs on CE and CMs were assessed in the above groups to try to explain the effect of therapeutic doses of an angiotensin II receptor subtype-1 blocker on the dynamics of this ligand and its receptor in insulin supplemented diabetic animals. Each group was divided into two subgroups: CHAPS-untreated and CHAPS-treated rat hearts perfused with [125I]ET-1 to respectively estimate ET-1 binding affinity (tau = 1/k-n) to its receptor subtype(s) on CE and CMs using mathematical modeling describing a 1:1 reversible binding stoichiometry. Heart perfusion results revealed that insulin treatment significantly decreased tau on CE but not on CMs in diabetic rats. In diabetics treated with losartan, an increase in tau value on CE but not on CMs was noted. Cotreatment of diabetic rats with insulin and losartan normalized tau on CE but decreased it on CMs. Western blot, using snap-frozen heart tissues, revealed increase in ET(A)-R density in all diabetic groups. However, significant decrease in ET(B)-R density was observed in all groups compared to the normal, and was reconfirmed by immunohistochemical analysis. In conclusion, coadministration of insulin and losartan in nonhypertensive animals suffering from diabetes type 1 may offer new cardiac protection benefits by improving coronary blood flow and cardiomyocyte contractility through modulating ET-1 receptor subtypes density and affinity at CE and CM sites.

SmartSLAM: localization and mapping across multi-environments

In the absence of absolute localization tools such as GPS, a robot can still successfully navigate by conducting simultaneous localization and mapping (SLAM). All SLAM algorithms to date can only be applied in one environment at a time. In this paper we propose to extend SLAM to multi-environments. In SmartSLAM, the robot first classifies its entourage using environment recognition code and then performs SLAM using landmarks that are appropriate for its surrounding milieu. One thousand images of various indoor and outdoor environments were collected and used as training data for a three-layered feedforward backpropagation neural network. This neural network was then tested on two sets of query images of indoor environments and another two sets of outdoor environments, yielding 83% and 95% correct classification rates for the indoor images and 80% and 79% success rates for the outdoor images.

Seeing the trees before the forest [natural object detection]

In this paper, we propose an algorithm that detects and locates natural objects in an outdoor environment using local descriptors. Interest points inside images are detected with a difference of Gaussian (DoG) filter and are then represented using scale invariant local descriptors. Our algorithm learns objects in a weakly supervised manner by clustering similar descriptors together and using those clusters as object classifiers. The intent is to identify stable objects to be used as landmarks for simultaneous localization and mapping (SLAM) of robots. The robot milieu is first identified using a fast environment recognition algorithm and then landmarks are suggested for SLAM that are appropriate for that environment. In our experiments we test our theory on the detection of trees that belong to the plantae pinophyta (pine family). Initial results show that out of 200 test images, our classification yields 85 correct positives, 15 false negatives, 73 correct negatives and 27 false positives.

Vision system for fast 3D obstacle detection via sterovision matching

Image matching is one of the fundamental problems of computer vision. Various approaches exist. They differ essentially by extracted primitives, by the best match search strategy, and by final applications. Feature based dense matching methods use such geometric primitives as raw pixels, edges, interest points, etc. Some of the correlation based matching methods involve a distance calculation. A time consuming operation. Its enhancement adds pixel complex photometric characteristics such as gradient direction, local curvature and luminosity local disparity, what increases the matching time, but they are usually very noisy. The matching method noise dependency and data volume can be reduced when improving the interest point robustness. This paper proposes to add to interest point primitive a set (vector) of simple characteristics (geometric and photometric), which are invariant to geometric plan transforms. A matching method based upon these enriched pixels and accumulation array concept is presented as well. These elements are useful for 3D obstacle detection in the ongoing project intelligent glasses, our final application. The intelligent glasses is a vision system for humanoid robot and for blind/visually impaired persons under joint development by Rouen University and Robotics Laboratory in Paris.

Vision SLAM Maps: Towards Richer Content

Simultaneous Localization and Mapping (SLAM) is a recursive probabilistic inferencing process for concurrently building a map of a robot’s surroundings and localizing that robot within this map. The ultimate goal of SLAM is to operate anywhere, allowing a robot to navigate autonomously and producing a meaningful purposeful map. Research in SLAM to date has focused on improving the localization part of SLAM, while lagging in the ability to produce useful maps. Indeed, all feature-based SLAM maps are built from either low level features such as points or lines or from artificial beacons; such maps have little use other than to perform SLAM. There are benefits in building maps from real natural objects that are indigenous of the environment for operations such as surveying of remote areas or as a guide for human navigation in dangerous settings. To investigate the potential of SLAM to produce such maps, an Inertial-Visual SLAM system is designed and used here which relies on inertial measurements to predict ego-motion and a digital camera to collect images of natural landmarks about the scene. Experiments conducted on a mobile vehicle show encouraging results and highlight the potential for Vision SLAM to generate meaningful maps which agree with ground truth. The Computer Vision system is capable of recognizing the environment type, of detecting trees within this environment, and recognizing different trees based on clusters of distinctive visual features.