Método de selección automática de algoritmos de correspondencia estéreo en ausencia de ground truth

La correspondencia estéreo es un campo ampliamente estudiado que ha recibido una atención notable en las últimas tres décadas. Es posible encontrar en la literatura un número considerable de propuestas para resolver el problema de correspondencia estéreo. En contraste, las propuestas para evaluar cuantitativamente la calidad de los mapas de disparidad obtenidos a partir de los algoritmos de correspondencia estéreo son relativamente escasas. La selección de un algoritmo de correspondencia estéreo y sus respectivos parámetros para un caso de aplicación particular es un problema no trivial dada la dependencia entre la calidad de la estimación de un mapa de disparidad y el contenido de la escena de interés. Este trabajo de investigación propone una estrategia de selección de algoritmos de correspondencia estéreo a partir de los mapas de disparidad estimados, por medio de un proceso de evaluación en ausencia de ground truth. El método propuesto permitiría a un sistema de visión estéreo adaptarse a posibles cambios en las escenas al ser aplicados a problemas en el mundo real. Esta investigación es de interés para investigadores o ingenieros aplicando visión estéreo en campos de aplicación como la industria.Abstract: The stereo correspondence problem has received significant attention in literature during approximately three decades. A plethora of stereo correspondence algorithms can be found in literature. In contrast, the amount of methods to objectively and quantitatively evaluate the accuracy of disparity maps estimated from stereo correspondence algorithms is relatively low. The application of stereo correspondence algorithms on real world applications is not a trivial problem, mainly due to the existing dependence between the estimated disparity map quality, the algorithms parameter definition and the contents on the assessed scene. In this research a stereo correspondence algorithms selection method is proposed by assessing the quality of estimated disparity maps in absence of ground truth. The proposed method could be used in a stereo vision to increase the system robustness by adapting it to possible changes in real world applications. The contribution of this work is relevant to researchers and engineers applying stereo vision in fields such as industryMaestrí

Novel Approaches in Structured Light Illumination

Among the various approaches to 3-D imaging, structured light illumination (SLI) is widely spread. SLI employs a pair of digital projector and digital camera such that the correspondences can be found based upon the projecting and capturing of a group of designed light patterns. As an active sensing method, SLI is known for its robustness and high accuracy. In this dissertation, I study the phase shifting method (PSM), which is one of the most employed strategy in SLI. And, three novel approaches in PSM have been proposed in this dissertation. First, by regarding the design of patterns as placing points in an N-dimensional space, I take the phase measuring profilometry (PMP) as an example and propose the edge-pattern strategy which achieves maximum signal to noise ratio (SNR) for the projected patterns. Second, I develop a novel period information embedded pattern strategy for fast, reliable 3-D data acquisition and reconstruction. The proposed period coded phase shifting strategy removes the depth ambiguity associated with traditional phase shifting patterns without reducing phase accuracy or increasing the number of projected patterns. Thus, it can be employed for high accuracy realtime 3-D system. Then, I propose a hybrid approach for high quality 3-D reconstructions with only a small number of illumination patterns by maximizing the use of correspondence information from the phase, texture, and modulation data derived from multi-view, PMP-based, SLI images, without rigorously synchronizing the cameras and projectors and calibrating the device gammas. Experimental results demonstrate the advantages of the proposed novel strategies for 3-D SLI systems

Performance Metrics and Test Data Generation for Depth Estimation Algorithms

This thesis investigates performance metrics and test datasets used for the evaluation of depth estimation algorithms. Stereo and light field algorithms take structured camera images as input to reconstruct a depth map of the depicted scene. Such depth estimation algorithms are employed in a multitude of practical applications such as industrial inspection and the movie industry. Recently, they have also been used for safety-relevant applications such as driver assistance and computer assisted surgery. Despite this increasing practical relevance, depth estimation algorithms are still evaluated with simple error measures and on small academic datasets. To develop and select suitable and safe algorithms, it is essential to gain a thorough understanding of their respective strengths and weaknesses. In this thesis, I demonstrate that computing average pixel errors of depth estimation algorithms is not sufficient for a thorough and reliable performance analysis. The analysis must also take into account the specific requirements of the given applications as well as the characteristics of the available test data. I propose metrics to explicitly quantify depth estimation results at continuous surfaces, depth discontinuities, and fine structures. These geometric entities are particularly relevant for many applications and challenging for algorithms. In contrast to prevalent metrics, the proposed metrics take into account that pixels are neither spatially independent within an image nor uniformly challenging nor equally relevant. Apart from performance metrics, test datasets play an important role for evaluation. Their availability is typically limited in quantity, quality, and diversity. I show how test data deficiencies can be overcome by using specific metrics, additional annotations, and stratified test data. Using systematic test cases, a user study, and a comprehensive case study, I demonstrate that the proposed metrics, test datasets, and visualizations allow for a meaningful quantitative analysis of the strengths and weaknesses of different algorithms. In contrast to existing evaluation methodologies, application-specific priorities can be taken into account to identify the most suitable algorithms