Comparative Performance Study and Analysis on Different Edge based Image Segmentation Techniques of Thermal Images

In this work, authors have been tried to analyse the edge-based approach for thermal image segmentation. Here, author?s have used different thermal images for the edge based analysis of image segmentation. Author?s have given studies regarding different edge operators like Prewitt, Sobel, LoG, and Canny edge detection operators for segmentation purposes and analyze their performance. This paper compares each of these operators by the manner of checking Peak signal to Noise Ratio (PSNR) and Mean Squared Error (MSE) of resultant image. It evaluates the performance of each algorithm using image quality analysis. This paper presents a comparative analysis of different edge based thermal image segmentation techniques

Edge Detection with Sub-pixel Accuracy Based on Approximation of Edge with Erf Function

Edge detection is an often used procedure in digital image processing. For some practical applications is desirable to detect edges with sub-pixel accuracy. In this paper we present edge detection method for 1-D images based on approximation of real image function with Erf function. This method is verified by simulations and experiments for various numbers of samples of simulated and real images. Results of simulations and experiments are also used to compare proposed edge detection scheme with two often used moment-based edge detectors with sub-pixel precision

Computer Aided Multi-Data Fusion Dismount Modeling

Recent research efforts strive to address the growing need for dismount surveillance, dismount tracking and characterization. Current work in this area utilizes hyperspectral and multispectral imaging systems to exploit spectral properties in order to detect areas of exposed skin and clothing characteristics. Because of the large bandwidth and high resolution, hyperspectral imaging systems pose great ability to characterize and detect dismounts. A multi-data dismount modeling system where the development and manipulation of dismount models is a necessity. This thesis demonstrates a computer aided multi-data fused dismount model, which facilitates studies of dismount detection, characterization and identification. The system is created by fusing: pixel mapping, signature attachment, and pixel mixing algorithms. The developed multi-data dismount model produces simulated hyperspectral images that closely represent an image collected by a hyperspectral imager. The dismount model can be modified to fit the researcher\u27s needs. The multi-data model structure allows the employment of a database of signatures acquired from several sources. The model is flexible enough to allow further exploitation, enhancement and manipulation. The multi-data dismount model developed in this effort fulfills the need for a dismount modeling tool in a hyperspectral imaging environment

Safety related object and pattern recognition for autonomous machines

Automation is nowadays everywhere, from automated machines in factories that build goods, to robots cleaning your own house. Everyday new ideas about automation come out, trying to make life easier for every person and worker. Work automation is the process where production tasks, that are usually performed by humans, are transferred to some sort of technological element. This change normally comes with optimization of the use of resources, as energy and materials, besides incrementing the quality and precision of the product, while reducing the time needed to make it. This project's goal is to achieve safety measures during the positioning of a vehicle handling cargo, in order to pick up a demountable. To perform such process, several devices are used, a 3D sensor that calculates shape and distance of the objects in front of it using time of flight measurements and a video camera, it will be used to perform the search of the objects, for example, pedestrian detection in this project, in order to give the extra information needed for the software to adapt the movement of the vehicle until its position is the required one to hook the cargo and load it on the vehicle without taking any risk

Mejora de un algoritmo de segmentación de iris mediante contornos activos

En el presento Proyecto Fin de Carrera se exploran los fundamentos de la biometría, una de las más potentes tecnologías en el ámbito de seguridad y de la medicina. Esta tecnología cada vez está más presente en la sociedad actual. En concreto se centra en el estudio de una técnica novedosa y hasta ahora poco empleada como son los contornos activos. A través de dicha tecnología el proyecto tiene como objetivo el reconocimiento biométrico del iris. En los algoritmos biométricos por iris uno de los módulos fundamentales es el preprocesado. Este módulo se encarga de encontrar y aislar el iris dentro de una imagen. La mayor parte de los algoritmos existentes para este módulo se basan en detectar círculos dentro una imagen, asemejando el iris a una corona circular, sin embargo, en muchos casos ni el iris ni la pupila son círculos perfectos. En este proyecto se propone una mejora de estos algoritmos para que partiendo de los datos proporcionados por estos, el sistema sea capaz de detectar perfectamente la pupila y el borde exterior del iris, independientemente de la forma que estos tengan. Para llevar a cabo este proyecto, nos basamos en varios métodos además de los snakes, el algoritmo de Canny, GVF, método de Otsu e imagen binaria. Todos ellos se describen teóricamente, además podemos ver un amplio estudio práctico para cada uno de los métodos utilizados a lo largo de este proyecto. Se ha implementado en C#. Se ha utilizado el programa Microsoft Visual Studio 2008.Ingeniería Técnica en Telemátic

2D and 3D segmentation of medical images.

"Cardiovascular disease is one of the leading causes of the morbidity and mortality in the western world today. Many different imaging modalities are in place today to diagnose and investigate cardiovascular diseases. Each of these, however, has strengths and weaknesses. There are different forms of noise and artifacts in each image modality that combine to make the field of medical image analysis both important and challenging. The aim of this thesis is develop a reliable method for segmentation of vessel structures in medical imaging, combining the expert knowledge of the user in such a way as to maintain efficiency whilst overcoming the inherent noise and artifacts present in the images. We present results from 2D segmentation techniques using different methodologies, before developing 3D techniques for segmenting vessel shape from a series of images. The main drive of the work involves the investigation of medical images obtained using catheter based techniques, namely Intra Vascular Ultrasound (IVUS) and Optical Coherence Tomography (OCT). We will present a robust segmentation paradigm, combining both edge and region information to segment the media-adventitia, and lumenal borders in those modalities respectively. By using a semi-interactive method that utilizes "soft" constraints, allowing imprecise user input which provides a balance between using the user's expert knowledge and efficiency. In the later part of the work, we develop automatic methods for segmenting the walls of lymph vessels. These methods are employed on sequential images in order to obtain data to reconstruct the vessel walls in the region of the lymph valves. We investigated methods to segment the vessel walls both individually and simultaneously, and compared the results both quantitatively and qualitatively in order obtain the most appropriate for the 3D reconstruction of the vessel wall. Lastly, we adapt the semi-interactive method used on vessels earlier into 3D to help segment out the lymph valve. This involved the user interactive method to provide guidance to help segment the boundary of the lymph vessel, then we apply a minimal surface segmentation methodology to provide segmentation of the valve.

Shadow segmentation and tracking in real-world conditions

Visual information, in the form of images and video, comes from the interaction of light with objects. Illumination is a fundamental element of visual information. Detecting and interpreting illumination effects is part of our everyday life visual experience. Shading for instance allows us to perceive the three-dimensional nature of objects. Shadows are particularly salient cues for inferring depth information. However, we do not make any conscious or unconscious effort to avoid them as if they were an obstacle when we walk around. Moreover, when humans are asked to describe a picture, they generally omit the presence of illumination effects, such as shadows, shading, and highlights, to give a list of objects and their relative position in the scene. Processing visual information in a way that is close to what the human visual system does, thus being aware of illumination effects, represents a challenging task for computer vision systems. Illumination phenomena interfere in fact with fundamental tasks in image analysis and interpretation applications, such as object extraction and description. On the other hand, illumination conditions are an important element to be considered when creating new and richer visual content that combines objects from different sources, both natural and synthetic. When taken into account, illumination effects can play an important role in achieving realism. Among illumination effects, shadows are often integral part of natural scenes and one of the elements contributing to naturalness of synthetic scenes. In this thesis, the problem of extracting shadows from digital images is discussed. A new analysis method for the segmentation of cast shadows in still and moving images without the need of human supervision is proposed. The problem of separating moving cast shadows from moving objects in image sequences is particularly relevant for an always wider range of applications, ranging from video analysis to video coding, and from video manipulation to interactive environments. Therefore, particular attention has been dedicated to the segmentation of shadows in video. The validity of the proposed approach is however also demonstrated through its application to the detection of cast shadows in still color images. Shadows are a difficult phenomenon to model. Their appearance changes with changes in the appearance of the surface they are cast upon. It is therefore important to exploit multiple constraints derived from the analysis of the spectral, geometric and temporal properties of shadows to develop effective techniques for their extraction. The proposed method combines an analysis of color information and of photometric invariant features to a spatio-temporal verification process. With regards to the use of color information for shadow analysis, a complete picture of the existing solutions is provided, which points out the fundamental assumptions, the adopted color models and the link with research problems such as computational color constancy and color invariance. The proposed spatial verification does not make any assumption about scene geometry nor about object shape. The temporal analysis is based on a novel shadow tracking technique. On the basis of the tracking results, a temporal reliability estimation of shadows is proposed which allows to discard shadows which do not present time coherence. The proposed approach is general and can be applied to a wide class of applications and input data. The proposed cast shadow segmentation method has been evaluated on a number of different video data representing indoor and outdoor real-world environments. The obtained results have confirmed the validity of the approach, in particular its ability to deal with different types of content and its robustness to different physically important independent variables, and have demonstrated the improvement with respect to the state of the art. Examples of application of the proposed shadow segmentation tool to the enhancement of video object segmentation, tracking and description operations, and to video composition, have demonstrated the advantages of a shadow-aware video processing