Proposta de uma plataforma de imageamento microscópico portátil baseada em holografia digital inline

Orientador : Prof. Dr. Aldo Von WangenheimTese (doutorado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa: Curitiba, 29/01/2015Inclui referênciasResumo: Nesta tese o desenvolvimento de tecnologias de imageamento sem-lentes utilizando o conceito de holografia digital inline para o enfoque microscópico é apresentado. Na formação de imagem sem lentes, o princípio do imageamento por sombras é utilizado, onde as amostras são iluminadas por uma fonte de luz, e sombras contendo as assinaturas das partículas no plano-objeto são projetadas e capturadas em um meio detector eletrônico. Pelo uso de um sistema espacialmente e temporalmente coerente de iluminação, o princípio da difração de onda é aplicado, e assinaturas de padrões de interferências holográficas são projetadas ao invés de somente sombras com assinaturas espaciais. Este conceito denomina-se de holografia digital inline, e métodos numéricos são requeridos para a conversão do sinal holográfico em informação morfológica do plano-objeto. Embora explorada bastante recentemente, a holografia digital inline possibilita horizontes para uma vasta gama de aplicações que envolvem a miniaturização de microscópios em plataformas portáteis, que por sua vez são facilmente integradas com recursos computacionais. Nesta tese de doutorado são propostas 2 plataformas distintas baseadas em holografia digital inline: (i) plataforma holográfica com melhoramento da resolução das assinaturas holográficas por meio de técnicas de super-resolução, (ii) plataforma holográfica para o processamento de vídeo em altas taxas de amostragem. A primeira plataforma é baseada na premissa do deslocamento da fonte luminosa, realizando a computação de uma imagem holográfica em alta-resolução a partir de um conjunto de múltiplas observações da mesma cena. Métodos de registro das imagens holográficas são utilizados, seguidos por um procedimento de otimização do alinhamento inspirado em modelos variacionais de energia. Um framework bio-inspirado é utilizado para minimizar a função em relação a um termo de fidelidade e uma medida de sharpness, e encontrar a solução de ótimo global de alinhamento das imagens. A cena é aproximadamente planar, e somente amostras estáticas são utilizadas neste contexto. Para os hologramas em alta-resolução, métodos computacionais numéricos baseados em recuperação de fase são aplicados para difração do sinal de onda e recuperar as informações geométricas da amostra analisada. A plataforma holográfica foi validada com amostras biológicas de células humanas reprodutoras masculinas, onde a confirmação microscópica foi obtida através de um microscópio óptico convencional, apresentando alta correlação com a imagem de campo de brilho (bright-field ). Os resultados obtidos mostram uma resolução espacial de ?1?m sobre um campo máximo de visão de ?30mm2. Diferentemente das abordagens descritas da literatura, somente uma fonte de iluminação é utilizada para melhoramento da resolução, bem como os métodos de registro e minimização são especificamente designados para incrementar o sinal holográfico da propagação da onda. A segunda plataforma apresenta como enfoque a visualização de amostras biológicas in-situ, como uma ferramenta de propósito geral de imageamento em meio fluídico. A plataforma para vídeo holográfico é mais simples que a plataforma com melhoramento de resolução, e esta pode ser miniaturizada em apenas alguns centímetros cúbicos com resolução de algumas frações de micrômetros. Os experimentos nesta plataforma foram conduzidos principalmente na inspeção de micro-organismos existentes em amostras de água, onde uma série de espécimens podem ser verificados. A vantagem de uma plataforma holográfica em vídeo é o imageamento de estruturas em 4D (volume e tempo), onde um simples vídeo pode ser analisado repetidamente em diferentes pontos de difração. Palavras-chave: Holografia Digital inline, Super-Resolução em Multi-Frame, Vídeo Holográfico, Plataformas de Diagnóstico em Point-of-Care, Imageamento Sem-Lentes.Abstract: In this doctoral's dissertation, lensless imaging technologies based on the digital inline holography concept were developed for the biological microscopy context. From a lensless image formation point-of-view, the shadow imaging principle is used, where the samples are illuminated by a light-source, and shadows containing particle's signatures are projected from the object-plane to an electronic detector. By using a temporal and spatial coherent illumination system, the diffraction-wave principle is applied, and interference holographic patterns are projected, instead of simple spatial signature shadows only. This concept is the so-called digital inline holography, and numerical methods should be used to convert holographic signals into morphological details of the object-plane. Digital inline holography has been explored very recently, opening a wide range of new applications involving the miniaturization of imaging devices into portable platforms, that can be easily integrated with computational resources. In this doctoral's dissertation two distinct platforms based on digital inline holography are presented: (i) holographic platform based on resolution improvement of the holographic signatures, (ii) holographic platform for video processing using high-frame rate sampling. The first platform is based on the premise of computing a higher resolution holographic image from a set of multiple observations of the scene. Image registration methods for the holographic images are used, followed by an optimization approach inspired on variational models of energy equation. A bio-inspired framework is used to minimize this function based on a fidelity term and a sharpness measure, and its minimization is used to find a global optimum solution for the alignment problem. The scene is approximately planar, and static samples can be imaged in this platform. For the high-resolution holograms, numerical diffraction methods are used to recover the phase information, and consequently the morphological information of the analyzed sample. The holographic platform was validated using biological human samples corresponding to reproductive male cells (sperm) using a conventional optical microscope, showing a high correlation with the bright-field microscopy image. The obtained results show a spatial resolution of ?1?m in a field-of-view corresponding to ?30mm2. Differently from the state-of-the-art approaches, this method is based on a single shifted light-source where arbitrary displacements are registered into a single frame, using a combined optimization approach. The second approach has the biological visualization of in-situ specimens in focus. This platform is presented as a generic tool for the visualization of live microorganism in a fluidic mean, being simpler than the previously described platform. On the other hand, this platform can be easily miniaturized into a few cubic centimetres, having resolution of a few microns. Experimental results in this platform were conducted based on the inspection of free-living microorganism in water samples, where several specimens can be observed. A goal of this video holographic platform is the 4D imaging (volume over time), where a single captured video can be repeatedly analyzed for different object-plane diffraction distances. Keywords: Digital Inline Holography, Multi-Frame Super-Resolution, Holographic Video, Point-of-Care Diagnostic Platforms, Lensless Imaging

Face Tracking: An approach for face identification, recognition and tracking for low-cost environments

Abstract. Applicable facial identification and recognition are great trends nowadays, representing a powerful tool needed in various application domains. Its use varies from authentication purposes, business intelligence, emotion recognition, and security services. For this last one, a very desirable application is the use of several cameras geographically sparse, where specific individuals can be recognized for crowded environments. This paper proposes the use of face recognition and identification to help in the development of a low-cost tool. A dataset with several face photos was built to simulate our experimental environment. The obtained preliminary results demonstrate the feasibility of the proposed approach for security applications

Vehicle Traffic and Routing Identification for Road Planning Optimization in Smart Cities Using UAV Videos

Abstract. Unmanned Aerial Vehicles (UAVs) can be used for several applications such as: precision agriculture, environmental monitoring and management, smart cities, planning, and others. Specifically for smart cities, a very desirable application is the identification and tracking of vehicles in roads for planning and traffic optimization. In this paper, a computational approach is proposed to address this problem. Our solution has the proposed pipeline: (a) background subtraction is applied to extract the vehicles in motion; (b) tracking method is used to keep a history of trajectories for each vehicle. The preliminary results were obtained from an UAV prototype, showing promising results for traffic counting and flow analysis

Can the Use of nonlinear Color Metrics systematically improve Segmentation?

Image segmentation is a procedure where an image is split into its constituent parts, according to some criterion. In the literature, there are different well-known approaches for segmentation, such as clustering, thresholding, graph theory and region growing. Such approaches, additionally, can be combined with color distance metrics, playing an important role for color similarity computation. Aiming to investigate general approaches able to enhance the performance of segmentation methods, this work presents an empirical study of the effect of a nonlinear color metric on segmentation procedures. For this purpose, three algorithms were  chosen: Mumford-Shah, Color Structure Code and Felzenszwalb and Huttenlocher Segmentation. The color similarity metric employed by these algorithms (L2-norm) was replaced by the Polynomial Mahalanobis Distance. This metric is an extension of the statistical Mahalanobis Distance used to measure the distance between coordinates and distribution centers. An evaluation based upon automated comparison of segmentation results against ground truths from the Berkeley Dataset was performed. All three segmentation approaches were compared to their traditional implementations, against each other and also to a large set of other segmentation methods. The statistical analysis performed has indicated a systematic improvement of segmentation results for all three segmentation approaches when the nonlinear metric was employed

Comparison of Classical Computer Vision vs. Convolutional Neural Networks for Weed Mapping in Aerial Images

In this paper, we present a comparison between convolutional neural networks and classicalcomputer vision approaches, for the specific precision agriculture problem of weed mapping on sugarcane fields aerial images. A systematic literature review was conducted to find which computer vision methods are being used on this specific problem. The most cited methods were implemented, as well as four models of convolutional neural networks. All implemented approaches were tested using the same dataset, and their results were quantitatively and qualitatively analyzed. The obtained results were compared to a human expert made ground truth, for validation. The results indicate that the convolutional neural networks present better precision and generalize better than the classical model

Comparison between low-cost passive and active vision for obstacle depth

Obstacle detection is a key issue in many current applications, especially in applications that have been increasingly highlighted such as: advanced driver assistance systems (ADAS), simultaneous localization and mapping (SLAM) and autonomous navigation system. This can be achieved by active and passive acquisition vision systems, for example: laser and cameras respectively. In this paper we present a comparison between low-cost active and passive devices, more specifically LIDAR and two cameras. To this comparison a disparity map is created by stereo correspondence through two images and a point cloud map created by LIDAR data values (distances measures). The obtained results shown that passive vision can be as good as or even better than active vision in low cost scenarios

Comparison between low-cost passive and active vision for obstacle depth

Obstacle detection is a key issue in many current applications, especially in applications that have been increasingly highlighted such as: advanced driver assistance systems (ADAS), simultaneous localization and mapping (SLAM) and autonomous navigation system. This can be achieved by active and passive acquisition vision systems, for example: laser and cameras respectively. In this paper we present a comparison between low-cost active and passive devices, more specifically LIDAR and two cameras. To this comparison a disparity map is created by stereo correspondence through two images and a point cloud map created by LIDAR data values (distances measures). The obtained results shown that passive vision can be as good as or even better than active vision in low cost scenarios

Comparison between low-cost passive and active vision for obstacle depth

Obstacle detection is a key issue in many current applications, especially in applications that have been increasingly highlighted such as: advanced driver assistance systems (ADAS), simultaneous localization and mapping (SLAM) and autonomous navigation system. This can be achieved by active and passive acquisition vision systems, for example: laser and cameras respectively. In this paper we present a comparison between low-cost active and passive devices, more specifically LIDAR and two cameras. To this comparison a disparity map is created by stereo correspondence through two images and a point cloud map created by LIDAR data values (distances measures). The obtained results shown that passive vision can be as good as or even better than active vision in low cost scenarios

Crop Row Identification for UAV Images Based on Local Features Descriptors

Abstract. This paper proposes an automated curvatureinvariant method for crop row identification in images, acquired from Unmanned Aerial Vehicles. The proposed approach is composed of three steps: Step (i)image segmentation is performed based on the combination of Excess of Green Index and Otsu’s method to obtain the region of interest. Step (ii)local feature extraction computes the local orientation using a radial search descriptor employed to detect the predominant orientation of perpendicular row signals. Step (iii)lines are detected by linking the responses for each window using a gradient’s peak arrangement method. The preliminary results indicates the robustness and effectiveness of the approach for crop row identification