Pulse processing routines for neutron time-of-flight data

A pulse shape analysis framework is described, which was developed for n_TOF-Phase3, the third phase in the operation of the n_TOF facility at CERN. The most notable feature of this new framework is the adoption of generic pulse shape analysis routines, characterized by a minimal number of explicit assumptions about the nature of pulses. The aim of these routines is to be applicable to a wide variety of detectors, thus facilitating the introduction of the new detectors or types of detectors into the analysis framework. The operational details of the routines are suited to the specific requirements of particular detectors by adjusting the set of external input parameters. Pulse recognition, baseline calculation and the pulse shape fitting procedure are described. Special emphasis is put on their computational efficiency, since the most basic implementations of these conceptually simple methods are often computationally inefficient.Comment: 13 pages, 10 figures, 5 table

Biomimetic Design for Efficient Robotic Performance in Dynamic Aquatic Environments - Survey

This manuscript is a review over the published articles on edge detection. At first, it provides theoretical background, and then reviews wide range of methods of edge detection in different categorizes. The review also studies the relationship between categories, and presents evaluations regarding to their application, performance, and implementation. It was stated that the edge detection methods structurally are a combination of image smoothing and image differentiation plus a post-processing for edge labelling. The image smoothing involves filters that reduce the noise, regularize the numerical computation, and provide a parametric representation of the image that works as a mathematical microscope to analyze it in different scales and increase the accuracy and reliability of edge detection. The image differentiation provides information of intensity transition in the image that is necessary to represent the position and strength of the edges and their orientation. The edge labelling calls for post-processing to suppress the false edges, link the dispread ones, and produce a uniform contour of objects

Computer-assisted detection of lung cancer nudules in medical chest X-rays

Diagnostic medicine was revolutionized in 1895 with Rontgen's discovery of x-rays. X-ray photography has played a very prominent role in diagnostics of all kinds since then and continues to do so. It is true that more sophisticated and successful medical imaging systems are available. These include Magnetic Resonance Imaging (MRI), Computerized Tomography (CT) and Positron Emission Tomography (PET). However, the hardware instalment and operation costs of these systems remain considerably higher than x-ray systems. Conventional x-ray photography also has the advantage of producing an image in significantly less time than MRI, CT and PET. X-ray photography is still used extensively, especially in third world countries. The routine diagnostic tool for chest complaints is the x-ray. Lung cancer may be diagnosed by the identification of a lung cancer nodule in a chest x-ray. The cure of lung cancer depends upon detection and diagnosis at an early stage. Presently the five-year survival rate of lung cancer patients is approximately 10%. If lung cancer can be detected when the tumour is still small and localized, the five-year survival rate increases to about 40%. However, currently only 20% of lung cancer cases are diagnosed at this early stage. Giger et al wrote that "detection and diagnosis of cancerous lung nodules in chest radiographs are among the most important and difficult tasks performed by radiologists"

Review of Person Re-identification Techniques

Person re-identification across different surveillance cameras with disjoint fields of view has become one of the most interesting and challenging subjects in the area of intelligent video surveillance. Although several methods have been developed and proposed, certain limitations and unresolved issues remain. In all of the existing re-identification approaches, feature vectors are extracted from segmented still images or video frames. Different similarity or dissimilarity measures have been applied to these vectors. Some methods have used simple constant metrics, whereas others have utilised models to obtain optimised metrics. Some have created models based on local colour or texture information, and others have built models based on the gait of people. In general, the main objective of all these approaches is to achieve a higher-accuracy rate and lowercomputational costs. This study summarises several developments in recent literature and discusses the various available methods used in person re-identification. Specifically, their advantages and disadvantages are mentioned and compared.Comment: Published 201

A Review on Edge Detection Algorithms in Digital Image Processing Applications

Edge detection is one of the major step in Image segmentation, image enhancement, image detection and recognition applications. The main goal of edge detection is that to localize the variation in the intensity of an image to identify the phenomena of physical properties which produced by the capturing device. An edge might be characterized as a set of neighborhood pixels that forms a boundary between two different regions. Detecting the edges is an essential technique for segmenting the image in to various regions based on their discontinuity in the pixels. Edge detection has very important applications in image processing and computer vison. It is broadly used technique and quick feature extraction technique hence used in various feature extraction and feature detection techniques. There exists several methods in the literature for edge detection such as Canny, Prewitt, Sobel, Maar Hildrith, Robert etc. In this paper we have studied and compared Prewitt, Sobel, and Canny detection operators. Our experimental study shows that the canny operator is giving better results for different kinds of images and has numerous advantages than the other operators such as the nature of adaptive, works better for noisy images and providing the sharp edges with low probability of false detection edges

From Stereogram to Surface: How the Brain Sees the World in Depth

When we look at a scene, how do we consciously see surfaces infused with lightness and color at the correct depths? Random Dot Stereograms (RDS) probe how binocular disparity between the two eyes can generate such conscious surface percepts. Dense RDS do so despite the fact that they include multiple false binocular matches. Sparse stereograms do so even across large contrast-free regions with no binocular matches. Stereograms that define occluding and occluded surfaces lead to surface percepts wherein partially occluded textured surfaces are completed behind occluding textured surfaces at a spatial scale much larger than that of the texture elements themselves. Earlier models suggest how the brain detects binocular disparity, but not how RDS generate conscious percepts of 3D surfaces. A neural model predicts how the layered circuits of visual cortex generate these 3D surface percepts using interactions between visual boundary and surface representations that obey complementary computational rules.Air Force Office of Scientific Research (F49620-01-1-0397); National Science Foundation (EIA-01-30851, SBE-0354378); Office of Naval Research (N00014-01-1-0624

Melhorias na segmentação de pele humana em imagens digitais

Orientador: Hélio PedriniDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Segmentação de pele humana possui diversas aplicações nas áreas de visão computacional e reconhecimento de padrões, cujo propósito principal é distinguir regiões de pele e não pele em imagens. Apesar do elevado número de métodos disponíveis na literatura, a segmentação de pele com precisão ainda é uma tarefa desafiadora. Muitos métodos contam somente com a informação de cor, o que não discrimina completamente as regiões da imagem devido a variações nas condições de iluminação e à ambiguidade entre a cor da pele e do plano de fundo. Dessa forma, há ainda a demanda em melhorar a segmentação. Este trabalho apresenta três contribuições com respeito a essa necessidade. A primeira é um método autocontido para segmentação adaptativa de pele que faz uso de análise espacial para produzir regiões nas quais a cor da pele é estimada e, dessa forma, ajusta o padrão da cor para uma imagem em particular. A segunda é a introdução da detecção de saliência para, combinada com detectores de pele baseados em cor, realizar a remoção do plano de fundo, o que elimina muitas regiões de não pele. A terceira é uma melhoria baseada em textura utilizando superpixels para capturar energia de regiões na imagem filtrada, que é então utilizada para caracterizar regiões de não pele e assim eliminar a ambiguidade da cor adicionando um segundo voto. Resultados experimentais obtidos em bases de dados públicas comprovam uma melhoria significativa nos métodos propostos para segmentação de pele humana em comparação com abordagens disponíveis na literaturaAbstract: Human skin segmentation has several applications on computer vision and pattern recognition fields, whose main purpose is to distinguish skin and non-skin regions. Despite the large number of methods available in the literature, accurate skin segmentation is still a challenging task. Many methods rely only on color information, which does not completely discriminate the image regions due to variations in lighting conditions and ambiguity between skin and background color. Therefore, there is still demand to improve the segmentation process. Three main contributions toward this need are presented in this work. The first is a self-contained method for adaptive skin segmentation that makes use of spatial analysis to produce regions from which the overall skin color can be estimated and such that the color model is adjusted to a particular image. The second is the combination of saliency detection with color skin segmentation, which performs a background removal to eliminate non-skin regions. The third is a texture-based improvement using superpixels to capture energy of regions in the filtered image, employed to characterize non-skin regions and thus eliminate color ambiguity adding a second vote. Experimental results on public data sets demonstrate a significant improvement of the proposed methods for human skin segmentation over state-of-the-art approachesMestradoCiência da ComputaçãoMestre em Ciência da Computaçã