Binary Adaptive Semi-Global Matching Based on Image Edges

Image-based modeling and rendering is currently one of the most challenging topics in Computer Vision and Photogrammetry. The key issue here is building a set of dense correspondence points between two images, namely dense matching or stereo matching. Among all dense matching algorithms, Semi-Global Matching (SGM) is arguably one of the most promising algorithms for real-time stereo vision. Compared with global matching algorithms, SGM aggregates matching cost from several (eight or sixteen) directions rather than only the epipolar line using Dynamic Programming (DP). Thus, SGM eliminates the classical “streaking problem” and greatly improves its accuracy and efficiency. In this paper, we aim at further improvement of SGM accuracy without increasing the computational cost. We propose setting the penalty parameters adaptively according to image edges extracted by edge detectors. We have carried out experiments on the standard Middlebury stereo dataset and evaluated the performance of our modified method with the ground truth. The results have shown a noticeable accuracy improvement compared with the results using fixed penalty parameters while the runtime computational cost was not increased

Delaunay triangulation based image enhancement for echocardiography images

A novel image enhancement approach for automatic echocardiography image processing is proposed. The main steps include undecimated wavelet based speckle noise reduction, edge detection, followed by a regional enhancement process that employs Delaunay triangulation based thresholding. The edge detection is performed using a fuzzy logic based center point detection and a subsequent radial search based fuzzy multiscale edge detection. The edges obtained are used as the vertices for Delaunay triangulation for enhancement purposes. This method enhances the heart wall region in the echo image. This technique is applied to both synthetic and real image sets that were obtained from a local hospital

Measurement of Micro-bathymetry with a GOPRO Underwater Stereo Camera Pair

A GO-PRO underwater stereo camera kit has been used to measure the 3D topography (bathymetry) of a patch of seafloor producing a point cloud with a spatial data density of 15 measurements per 3 mm grid square and an standard deviation of less than 1 cm A GO-PRO camera is a fixed focus, 11 megapixel, still-frame (or 1080p high-definition video) camera, whose small form-factor and water-proof housing has made it popular with sports enthusiasts. A stereo camera kit is available providing a waterproof housing (to 61 m / 200 ft) for a pair of cameras. Measures of seafloor micro-bathymetrycapable of resolving seafloor features less than 1 cm in amplitude were possible from the stereoreconstruction. Bathymetric measurements of this scale provide important ground-truth data and boundary condition information for modeling of larger scale processes whose details depend on small-scale variations. Examples include modeling of turbulent water layers, seafloor sediment transfer and acoustic backscatter from bathymetric echo sounders

The research for shape-based visual recognition of object categories

摘要 视觉目标类识别旨在识别图像中特定的某类目标，基于形状的目标类识别是目前计算机视觉研究的热点之一。真实图像中物体姿态的多样性以及环境的复杂性，给目标的形状提取和识别带来巨大挑战。本文借鉴生物视觉机制的研究成果，对基于形状的目标类识别算法进行研究。主要研究内容如下： 1. 研究与形状认知相关的视觉机制，分析形状知觉整体性的生理基础及其生理模型。以形状知觉整体性为基础，建立基于形状的目标类识别系统框架。框架既重视整体性在自下而上的特征加工中的作用，也重视整体约束在自上而下的识别中的作用。 2. 受生物视觉上的整合野模型启发，本文提出了一个三阶段轮廓检测算法。阶段1利用结构自适应滤波器平滑...Categorical object detection addresses determining the number of instances of a particular object category in an image, and localizing those instances in space and scale. The shape-based visual recognition of object categories is one of hot topics in computer vision. The diversity of poses of targets and complexity of the environment in real images bring huge challenges to shape extraction and obj...学位：工学博士院系专业：信息科学与技术学院自动化系_控制理论与控制工程学号：2322006015337

Two and three dimensional segmentation of multimodal imagery

The role of segmentation in the realms of image understanding/analysis, computer vision, pattern recognition, remote sensing and medical imaging in recent years has been significantly augmented due to accelerated scientific advances made in the acquisition of image data. This low-level analysis protocol is critical to numerous applications, with the primary goal of expediting and improving the effectiveness of subsequent high-level operations by providing a condensed and pertinent representation of image information. In this research, we propose a novel unsupervised segmentation framework for facilitating meaningful segregation of 2-D/3-D image data across multiple modalities (color, remote-sensing and biomedical imaging) into non-overlapping partitions using several spatial-spectral attributes. Initially, our framework exploits the information obtained from detecting edges inherent in the data. To this effect, by using a vector gradient detection technique, pixels without edges are grouped and individually labeled to partition some initial portion of the input image content. Pixels that contain higher gradient densities are included by the dynamic generation of segments as the algorithm progresses to generate an initial region map. Subsequently, texture modeling is performed and the obtained gradient, texture and intensity information along with the aforementioned initial partition map are used to perform a multivariate refinement procedure, to fuse groups with similar characteristics yielding the final output segmentation. Experimental results obtained in comparison to published/state-of the-art segmentation techniques for color as well as multi/hyperspectral imagery, demonstrate the advantages of the proposed method. Furthermore, for the purpose of achieving improved computational efficiency we propose an extension of the aforestated methodology in a multi-resolution framework, demonstrated on color images. Finally, this research also encompasses a 3-D extension of the aforementioned algorithm demonstrated on medical (Magnetic Resonance Imaging / Computed Tomography) volumes

Aplicação de uma métrica de similaridade não linear em algoritmos de segmentação

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Ciência da Computação, 2015.Um dos principais processos utilizados no campo de processamento digital de imagens é a segmentação, processo no qual a imagem é separada em seus elementos ou partes constituintes. Na literatura, existem diferentes e bem conhecidos métodos usados para segmentação, tais como clusterização, limiarização, segmentação com redes neurais e segmentação por crescimento de regiões . No intuito de melhorar de melhorar o desempenho dos algoritmos de segmentação, um estudo sobre o efeito da aplicação de uma métrica não linear em algoritmos de segmentação foi realizado neste trabalho. Foram selecionados três algoritmos de segmentação (Mumford-Shah, Color Structure Code e Felzenszwalb and Huttenlocher) provenientes do método de crescimento de regiões e nestes se alterou a parte de análise de similaridade utilizando para tal uma métrica não linear. A métrica não linear utilizada, denominada Polinomial Mahalanobis, é uma variação da distância de Mahalanobis utilizada para medir a distância estatística entre distribuições. Uma avaliação qualitativa e uma análise empírica foram realizadas neste trabalho para comparar os resultados obtidos em termos de eficácia. Os resultados desta comparação, apresentados neste estudo, apontam uma melhoria nos resultados de segmentação obtidos pela abordagem proposta. Em termos de eficiência, foram analisados os tempos de execução dos algoritmos com e sem o aprimoramento e os resultados desta análise mostraram um aumento do tempo de execução dos algoritmos com abordagem proposta.Abstract : One of the main procedures used on digital image processing is segmentation,where the image is split into its constituent parts or objects. In the literature,there are different well-known methods used for segmentation, suchas clustering, thresholding, segmentation using neural network and segmentationusing region growing. Aiming to improve the performance of the segmentationalgorithms, a study off the effect of the application of a non-linearmetric on segmentation algorithms was performed in this work. Three segmentationalgorithms were chosen (Mumford-Shah, Color Structure Code,Felzenszwalb and Huttenlocher) originating from region growing techniques,and on those the similarity metric was enhanced with a non-linear metric.The non-linear metric used, known as Polynomial Mahalanobis, is a variationfrom the statistical Mahalanobis distance used for measure the distancebetween distributions. A qualitative evaluation and empirical analysis wasperformed in this work to compare the obtained results in terms of efficacy.The results from these comparison, presented in this study, indicate an improvementon the segmentation result obtained by the proposed approach. Interms of efficiency, the execution time of the algorithms with and without theproposed improvement were analyzed and the result of this analysis showedan increase of the execution time for the algorithms with the proposed approach