Studies on Colour Image Segmentation Method Based on Finite Left Truncated Bivariate Gaussian Mixture Model with K-Means

Colour Image segmentation is one of the prime requisites for computer vision and analysis. Much work has been reported in literature regarding colour image segmentation under HSI colour space and Gaussian mixture model (GMM). Since the Hue and Saturation values of the pixel in the image are non-negative. And may not be meso-kurtic, it is needed left truncate the Gaussian variate and is used to represent these two features of the colour image. The effect of truncation can not be ignored in developing the model based colour image segmentation. Hence in this paper a left truncated bivariate Gaussian mixture model is utilized to segment the colour image. The correlation between Hue and Saturation plays a predominant role in segmenting the colour images which is observed through experimental results. The expectation maximization (EM) algorithm is used for estimating model parameters. The number of image segments can be initialization of the model parameters are done with K-means algorithm. The performance of the proposed algorithm is studied by calculating the segmentation performance techniques like probabilistic rand index (PRI), global consistency error (GCE) and variation of information (VOI). The utility of the estimated joint probability density function of feature vector of the image is demonstrated through image retrievals. The image quality measures obtained for six images taken from Berkeley image dataset reveals that the proposed algorithm outperforms the existing algorithms in image segmentation and retrievals

Optimal Depth Estimation and Extended Depth of Field from Single Images by Computational Imaging using Chromatic Aberrations

The thesis presents a thorough analysis of a computational imaging approach to estimate the optimal depth, and the extended depth of field from a single image using axial chromatic aberrations. To assist a camera design process, a digital camera simulator is developed which can efficiently simulate different kind of lenses for a 3D scene. The main contribution in the simulator is the fast implementation of space variant filtering and accurate simulation of optical blur at occlusion boundaries. The simulator also includes sensor modeling and digital post processing to facilitate a co-design of optics and digital processing algorithms. To estimate the depth from color images, which are defocused to different amount due to axial chromatic aberrations, a low cost algorithm is developed. Due to varying contrast across colors, a local contrast independent blur measure is proposed. The normalized ratios between the blur measure of all three colors (red, green and blue) are used to estimate the depth for a larger distance range. The analysis of depth errors is performed, which shows the limitations of depth from chromatic aberrations, especially for narrowband object spectra. Since the blur changes over the field and hence depth, therefore, a simple calibration procedure is developed to correct the field varying behavior of estimated depth. A prototype lens is designed with optimal amount of axial chromatic aberrations for a focal length of 4 mm and F-number 2.4. The real captured and synthetic images show the depth measurement with the root mean square error of 10% in the distance range of 30 cm to 2 m. Taking the advantage of chromatic aberrations and estimated depth, a method is proposed to extend the depth of field of the captured image. An imaging sensor with white (W) pixel along with red, green and blue (RGB) pixels with a lens exhibiting axial chromatic aberrations is used to overcome the limitations of previous methods. The proposed method first restores the white image with depth invariant point spread function, and then transfers the sharpness information of the sharpest color or white image to blurred colors. Due to broadband color filter responses, the blur of each RGB color at its focus position is larger in case of chromatic aberrations as compared to chromatic aberrations corrected lens. Therefore, restored white image helps in getting a sharper image for these positions, and also for the objects where the sharpest color information is missing. An efficient implementation of the proposed algorithm achieves better image quality with low computational complexity. Finally, the performance of the depth estimation and extended depth of field is studied for different camera parameters. The criteria are defined to select optimal lens and sensor parameters to acquire desired results with the proposed digital post processing algorithms

Modeling and applications of the focus cue in conventional digital cameras

El enfoque en cámaras digitales juega un papel fundamental tanto en la calidad de la imagen como en la percepción del entorno. Esta tesis estudia el enfoque en cámaras digitales convencionales, tales como cámaras de móviles, fotográficas, webcams y similares. Una revisión rigurosa de los conceptos teóricos detras del enfoque en cámaras convencionales muestra que, a pasar de su utilidad, el modelo clásico del thin lens presenta muchas limitaciones para aplicación en diferentes problemas relacionados con el foco. En esta tesis, el focus profile es propuesto como una alternativa a conceptos clásicos como la profundidad de campo. Los nuevos conceptos introducidos en esta tesis son aplicados a diferentes problemas relacionados con el foco, tales como la adquisición eficiente de imágenes, estimación de profundidad, integración de elementos perceptuales y fusión de imágenes. Los resultados experimentales muestran la aplicación exitosa de los modelos propuestos.The focus of digital cameras plays a fundamental role in both the quality of the acquired images and the perception of the imaged scene. This thesis studies the focus cue in conventional cameras with focus control, such as cellphone cameras, photography cameras, webcams and the like. A deep review of the theoretical concepts behind focus in conventional cameras reveals that, despite its usefulness, the widely known thin lens model has several limitations for solving different focus-related problems in computer vision. In order to overcome these limitations, the focus profile model is introduced as an alternative to classic concepts, such as the near and far limits of the depth-of-field. The new concepts introduced in this dissertation are exploited for solving diverse focus-related problems, such as efficient image capture, depth estimation, visual cue integration and image fusion. The results obtained through an exhaustive experimental validation demonstrate the applicability of the proposed models

Entwurf von Computational-Imaging-Systemen am Beispiel der monokularen Tiefenschätzung

Computational-Imaging-Systeme kombinieren optische und digitale Signalverarbeitung um Information aus dem Licht einer Szene zu extrahieren. In dieser Arbeit wird das Raytracing-Verfahren als Simulationswerkzeug genutzt, um Computational-Imaging-Systeme ganzheitlich zu beschreiben, bewerten und optimieren. Am Beispiel der monokularen Tiefenschätzung wird die Simulation mit einem realen Prototyp einer Kamera mit programmierbarer Apertur verglichen und die vorgestellten Methoden evaluiert