A near real-time algorithm for flood detection in urban and rural areas using high resolution Synthetic Aperture Radar images

A near real-time flood detection algorithm giving a synoptic overview of the extent of flooding in both urban and rural areas, and capable of working during night-time and day-time even if cloud was present, could be a useful tool for operational flood relief management and flood forecasting. The paper describes an automatic algorithm using high resolution Synthetic Aperture Radar (SAR) satellite data that assumes that high resolution topographic height data are available for at least the urban areas of the scene, in order that a SAR simulator may be used to estimate areas of radar shadow and layover. The algorithm proved capable of detecting flooding in rural areas using TerraSAR-X with good accuracy, and in urban areas with reasonable accuracy.

An improved lesion detection approach based on similarity measurement between fuzzy intensity segmentation and spatial probability maps

The application of automatic segmentation methods in lesion detection is desirable. However, such methods are restricted by intensity similarities between lesioned and healthy brain tissue. Using multi-spectral magnetic resonance imaging (MRI) modalities may overcome this problem but it is not always practicable. In this article, a lesion detection approach requiring a single MRI modality is presented, which is an improved method based on a recent publication. This new method assumes that a low similarity should be found in the regions of lesions when the likeness between an intensity based fuzzy segmentation and a location based tissue probabilities is measured. The usage of a normalized similarity measurement enables the current method to fine-tune the threshold for lesion detection, thus maximizing the possibility of reaching high detection accuracy. Importantly, an extra cleaning step is included in the current approach which removes enlarged ventricles from detected lesions. The performance investigation using simulated lesions demonstrated that not only the majority of lesions were well detected but also normal tissues were identified effectively. Tests on images acquired in stroke patients further confirmed the strength of the method in lesion detection. When compared with the previous version, the current approach showed a higher sensitivity in detecting small lesions and had less false positives around the ventricle and the edge of the brain

Near real-time flood detection in urban and rural areas using high resolution Synthetic Aperture Radar images

A near real-time flood detection algorithm giving a synoptic overview of the extent of flooding in both urban and rural areas, and capable of working during night-time and day-time even if cloud was present, could be a useful tool for operational flood relief management. The paper describes an automatic algorithm using high resolution Synthetic Aperture Radar (SAR) satellite data that builds on existing approaches, including the use of image segmentation techniques prior to object classification to cope with the very large number of pixels in these scenes. Flood detection in urban areas is guided by the flood extent derived in adjacent rural areas. The algorithm assumes that high resolution topographic height data are available for at least the urban areas of the scene, in order that a SAR simulator may be used to estimate areas of radar shadow and layover. The algorithm proved capable of detecting flooding in rural areas using TerraSAR-X with good accuracy, classifying 89% of flooded pixels correctly, with an associated false positive rate of 6%. Of the urban water pixels visible to TerraSAR-X, 75% were correctly detected, with a false positive rate of 24%. If all urban water pixels were considered, including those in shadow and layover regions, these figures fell to 57% and 18% respectively.

Automatic prediction of functional site regions in low-resolution protein structures

World-wide structural genomics initiatives are rapidly accumulating structures for which limited functional information is available. Additionally, state-of-the art structural prediction programs are now capable of generating at least low resolution structural models of target proteins. Accurate detection and classification of functional sites within both solved and modelled protein structures therefore represents an important challenge. We present a fully automatic site detection method, FuncSite, that uses neural network classifiers to predict the location and type of functionally important sites in protein structures. The method is designed primarily to require only backbone residue positions without the need for specific side-chain atoms to be present. In order to highlight effective site detection in low resolution structural models FuncSite was used to screen model proteins generated using mGenTHREADER on a set of newly released structures. We found effective metal site detection even for moderate quality protein models illustrating the robustness of the method.

Robust abandoned object detection integrating wide area visual surveillance and social context

This paper presents a video surveillance framework that robustly and efficiently detects abandoned objects in surveillance scenes. The framework is based on a novel threat assessment algorithm which combines the concept of ownership with automatic understanding of social relations in order to infer abandonment of objects. Implementation is achieved through development of a logic-based inference engine based on Prolog. Threat detection performance is conducted by testing against a range of datasets describing realistic situations and demonstrates a reduction in the number of false alarms generated. The proposed system represents the approach employed in the EU SUBITO project (Surveillance of Unattended Baggage and the Identification and Tracking of the Owner).

ATD: a multiplatform for semiautomatic 3-D detection of kidneys and their pathology in real time

This research presents a novel multi-functional system for medical Imaging-enabled Assistive Diagnosis (IAD). Although the IAD demonstrator has focused on abdominal images and supports the clinical diagnosis of kidneys using CT/MRI imaging, it can be adapted to work on image delineation, annotation and 3D real-size volumetric modelling of other organ structures such as the brain, spine, etc. The IAD provides advanced real-time 3D visualisation and measurements with fully automated functionalities as developed in two stages. In the first stage, via the clinically driven user interface, specialist clinicians use CT/MRI imaging datasets to accurately delineate and annotate the kidneys and their possible abnormalities, thus creating “3D Golden Standard Models”. Based on these models, in the second stage, clinical support staff i.e. medical technicians interactively define model-based rules and parameters for the integrated “Automatic Recognition Framework” to achieve results which are closest to that of the clinicians. These specific rules and parameters are stored in “Templates” and can later be used by any clinician to automatically identify organ structures i.e. kidneys and their possible abnormalities. The system also supports the transmission of these “Templates” to another expert for a second opinion. A 3D model of the body, the organs and their possible pathology with real metrics is also integrated. The automatic functionality was tested on eleven MRI datasets (comprising of 286 images) and the 3D models were validated by comparing them with the metrics from the corresponding “3D Golden Standard Models”. The system provides metrics for the evaluation of the results, in terms of Accuracy, Precision, Sensitivity, Specificity and Dice Similarity Coefficient (DSC) so as to enable benchmarking of its performance. The first IAD prototype has produced promising results as its performance accuracy based on the most widely deployed evaluation metric, DSC, yields 97% for the recognition of kidneys and 96% for their abnormalities; whilst across all the above evaluation metrics its performance ranges between 96% and 100%. Further development of the IAD system is in progress to extend and evaluate its clinical diagnostic support capability through development and integration of additional algorithms to offer fully computer-aided identification of other organs and their abnormalities based on CT/MRI/Ultra-sound Imaging.

An automatic correction method for the heel effect in digitized mammography images

The most significant radiation field nonuniformity is the well-known Heel effect. This nonuniform beam effect has a negative influence on the results of computer-aided diagnosis of mammograms, which is frequently used for early cancer detection. This paper presents a method to correct all pixels in the mammography image according to the excess or lack on radiation to which these have been submitted as a result of the this effect. The current simulation method calculates the intensities at all points of the image plane. In the simulated image, the percentage of radiation received by all the points takes the center of the field as reference. In the digitized mammography, the percentages of the optical density of all the pixels of the analyzed image are also calculated. The Heel effect causes a Gaussian distribution around the anode-cathode axis and a logarithmic distribution parallel to this axis. Those characteristic distributions are used to determine the center of the radiation field as well as the cathode-anode axis, allowing for the automatic determination of the correlation between these two sets of data. The measurements obtained with our proposed method differs on average by 2.49 mm in the direction perpendicular to the anode-cathode axis and 2.02 mm parallel to the anode-cathode axis of commercial equipment. The method eliminates around 94% of the Heel effect in the radiological image and the objects will reflect their x-ray absorption. To evaluate this method, experimental data was taken from known objects, but could also be done with clinical and digital images.

Motion Detection for Video Surveillance

This thesis is related to the broad subject of automatic motion detection and analysis in videosurveillance image sequence. Besides, proposing the new unique solution, some of the previousalgorithms are evaluated, where some of the approaches are noticeably complementary sometimes.In real time surveillance, detecting and tracking multiple objects and monitoring their activities inboth outdoor and indoor environment are challenging task for the video surveillance system. Inpresence of a good number of real time problems limits scope for this work since the beginning. Theproblems are namely, illumination changes, moving background and shadow detection.An improved background subtraction method has been followed by foreground segmentation, dataevaluation, shadow detection in the scene and finally the motion detection method. The algorithm isapplied on to a number of practical problems to observe whether it leads us to the expected solution.Several experiments are done under different challenging problem environment. Test result showsthat under most of the problematic environment, the proposed algorithm shows the better qualityresult.

A New Approach for Nontechnical Losses Detection Based on Optimum-Path Forest

Nowadays, fraud detection is important to avoid nontechnical energy losses. Various electric companies around the world have been faced with such losses, mainly from industrial and commercial consumers. This problem has traditionally been dealt with using artificial intelligence techniques, although their use can result in difficulties such as a high computational burden in the training phase and problems with parameter optimization. A recently-developed pattern recognition technique called optimum-path forest (OPF), however, has been shown to be superior to state-of-the-art artificial intelligence techniques. In this paper, we proposed to use OPF for nontechnical losses detection, as well as to apply its learning and pruning algorithms to this purpose. Comparisons against neural networks and other techniques demonstrated the robustness of the OPF with respect to commercial losses automatic identification.

A comparison between overweight cutoff points for detection of high blood pressure in adolescents

Objectives: To evaluate the accuracy of three different cutoff points for the detection of high blood pressure in adolescents, given the strong relationship between overweight and high blood pressure levels.Methods: A total of 1,021 adolescents of both sexes were enrolled in the study, selected at random from public and private schools in Londrina, Brazil. Their body weight was measured using a digital balance, and their height with a portable stadiometer with a maximum extension of 2 meters. Arterial blood pressure was measured using an automatic apparatus. The capacity of body mass index to detect high blood pressure was gauged using ROC curves and their parameters (sensitivity, specificity, and area under the curve).Results: The cutoff points proposed in a Brazilian standard exhibited greater accuracy (males: 0.636 +/- 0.038; females: 0.585 +/- 0.043) than the cutoff points proposed in an international (males: 0.594 +/- 0.040; females: 0.570 +/- 0.044) and a North-American standard (males: 0.612 +/- 0.039; females: 0.578 +/- 0.044).Conclusions: The Brazilian proposal offered greatest accuracy for indicating high blood pressure levels.

Semi-automatic modeling of buildings from digital surface models

Semi-automatic building detection and extraction is a topic of growing interest due to its potential application in such areas as cadastral information systems, cartographic revision, and GIS. One of the existing strategies for building extraction is to use a digital surface model (DSM) represented by a cloud of known points on a visible surface, and comprising features such as trees or buildings. Conventional surface modeling using stereo-matching techniques has its drawbacks, the most obvious being the effect of building height on perspective, shadows, and occlusions. The laser scanner, a recently developed technological tool, can collect accurate DSMs with high spatial frequency. This paper presents a methodology for semi-automatic modeling of buildings which combines a region-growing algorithm with line-detection methods applied over the DSM.

Automatic Orientation of Multi-Scale Terrestrial Images for 3D Reconstruction

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Development of a CAD system for automatic classification of microcalcifications based on FPGA

This paper presents a Computer Aided Diagnosis (CAD) system that automatically classifies microcalcifications detected on digital mammograms into one of the five types proposed by Michele Le Gal, a classification scheme that allows radiologists to determine whether a breast tumor is malignant or not without the need for surgeries. The developed system uses a combination of wavelets and Artificial Neural Networks (ANN) and is executed on an Altera DE2-115 Development Kit, a kit containing a Field-Programmable Gate Array (FPGA) that allows the system to be smaller, cheaper and more energy efficient. Results have shown that the system was able to correctly classify 96.67% of test samples, which can be used as a second opinion by radiologists in breast cancer early diagnosis. (C) 2013 The Authors. Published by Elsevier B.V.

Development of a high sensitive automatic setup for screening of microcystins in surface waters by employing a LED-based photometric detector

In this manuscript, an automatic setup for screening of microcystins in surface waters by employing photometric detection is described. Microcystins are toxins delivered by cyanobacteria within an aquatic environment, which have been considered strongly poisonous for humans. For that reason, the World Health Organization (WHO) has proposed a provisional guideline value for drinking water of 1 mu g L-1. In this work, we developed an automated equipment setup, which allows the screening of water for concentration of microcystins below 0.1 mu g V. The photometric method was based on the enzyme-linked immunosorbent assay (ELISA) and the analytical signal was monitored at 458 nm using a homemade LED-based photometer. The proposed system was employed for the detection of microcystins in rivers and lakes waters. Accuracy was assessed by processing samples using a reference method and applying the paired t-test between results. No significant difference at the 95% confidence level was observed. Other useful features including a linear response ranging from 0.05 up to 2.00 mu g L-1 (R-2 =0.999) and a detection limit of 0.03 mu g L-1 microcystins were achieved. (C) 2011 Elsevier B.V. All rights reserved.

Optical Design for Automatic Identification and Portable Systems

This thesis proposes design methods and test tools, for optical systems, which may be used in an industrial environment, where not only precision and reliability but also ease of use is important. The approach to the problem has been conceived to be as general as possible, although in the present work, the design of a portable device for automatic identification applications has been studied, because this doctorate has been funded by Datalogic Scanning Group s.r.l., a world-class producer of barcode readers. The main functional components of the complete device are: electro-optical imaging, illumination and pattern generator systems. For what concerns the electro-optical imaging system, a characterization tool and an analysis one has been developed to check if the desired performance of the system has been achieved. Moreover, two design tools for optimizing the imaging system have been implemented. The first optimizes just the core of the system, the optical part, improving its performance ignoring all other contributions and generating a good starting point for the optimization of the whole complex system. The second tool optimizes the system taking into account its behavior with a model as near as possible to reality including optics, electronics and detection. For what concerns the illumination and the pattern generator systems, two tools have been implemented. The first allows the design of free-form lenses described by an arbitrary analytical function exited by an incoherent source and is able to provide custom illumination conditions for all kind of applications. The second tool consists of a new method to design Diffractive Optical Elements excited by a coherent source for large pattern angles using the Iterative Fourier Transform Algorithm. Validation of the design tools has been obtained, whenever possible, comparing the performance of the designed systems with those of fabricated prototypes. In other cases simulations have been used.