Research on structure adaptive multi-atoms matching pursuit algorithm of image sparse representation

图像内容的有效表示是图像处理领域的基本问题。图像的稀疏表示是指用相对较少的数据来表示出目标图像的主要信息。稀疏表示能够更有效地对图像建模，已成为带动压缩感知与图像处理、信号处理、通信等领域发展的核心技术之一，是当前图像处理领域的研究热点与难点，受到国内外学者的广泛关注。本文主要围绕图像稀疏表示理论中过完备字典设计和快速稀疏分解算法两个方面进行了详细和深入的研究，取得的主要研究成果及创新点如下： 1)根据图像的几何结构特性，参考哺乳类动物的视觉系统感知特性，选取二维Gabor函数作为过完备字典的生成函数，建立了可以匹配多种图像结构的Gabor多成分过完备字典。该字典包含平滑、边缘轮廓与纹理三种...Efficient representation of image is the basic problem in digital image processing. Image sparse representation can capture significant information of the original image with relatively less data. Because sparse representation model can effectively represent the image, it becomes one of the core technologies which drive the development of many subjects, such as Compressed Sensing, Signal Processin...学位：工程硕士院系专业：信息科学与技术学院计算机科学系_计算机技术学号：2302009115270

A Human Visual System-Driven Image Segmentation Algorithm

This paper presents a novel image segmentation algorithm driven by human visual system (HVS) properties. Quality metrics for evaluating the segmentation result, from both region-based and boundary-based perspectives, are integrated into an objective function. The objective function encodes the HVS properties into a Markov random fields (MRF) framework, where the just-noticeable difference (JND) model is employed when calculating the difference between the image contents. Experiments are carried out to compare the performances of three variations of the presented algorithm and several representative segmentation algorithms available in the literature. Results are very encouraging and show that the presented algorithms outperform the state-of-the-art image segmentation algorithms

Effect of Customer Heterogeneity on Online Pricing: Just Noticeable Differences in a Competitive Service Industry

Online sales for both products and services are on the rise globally and are projected to increase by 10% annually to $370 billion by 2017 (Lomas 2013). Price is a key management lever for firm performance (McKinsey 2002) and key determinant of purchasing decision for a consumer (Bishop 1984, Doyle 1984, Sawyer and Dickson 1984, Schechter 1984). However, customers do not remember exact price but have a band of prices that are acceptable (Monroe 1973) (Olson 1976)(Monroe 1969). This research uses Just Noticeable Difference (JND) theory as the theoretical lens to study online pricing thresholds in a retail service industry. This quantitative field study uses three and half years of non-contractual transactional and customer level data from a B2C company to evaluate the hypotheses. Two phased investigations are conducted. Study 1 empirically determines the pricing threshold range for the service industry. Study 2 examines the effect of pricing action on purchase frequency based on customer heterogeneity and competitive prices. Contributions are three-fold. Theoretically, the study furthers the conceptual understanding of the pricing thresholds in the digital marketplace by using real customer level data. Second, the application of JND theory in a non-contractual B2C sector confirms that pricing thresholds for the service industry are higher than consumer goods industry. Third, this research confirms the varying effects of customer attributes (loyalty, motivation, and online purchase channel) on pricing thresholds. These findings are key to implementing a differentiated pricing strategy across channels and customer types to maximize firm performance and increase customer retention

The Impact of Graph Layouts on the Perception of Graph Properties

abstract: Graphs are commonly used visualization tools in a variety of fields. Algorithms have been proposed that claim to improve the readability of graphs by reducing edge crossings, adjusting edge length, or some other means. However, little research has been done to determine which of these algorithms best suit human perception for particular graph properties. This thesis explores four different graph properties: average local clustering coefficient (ALCC), global clustering coefficient (GCC), number of triangles (NT), and diameter. For each of these properties, three different graph layouts are applied to represent three different approaches to graph visualization: multidimensional scaling (MDS), force directed (FD), and tsNET. In a series of studies conducted through the crowdsourcing platform Amazon Mechanical Turk, participants are tasked with discriminating between two graphs in order to determine their just noticeable differences (JNDs) for the four graph properties and three layout algorithm pairs. These results are analyzed using previously established methods presented by Rensink et al. and Kay and Heer.The average JNDs are analyzed using a linear model that determines whether the property-layout pair seems to follow Weber's Law, and the individual JNDs are run through a log-linear model to determine whether it is possible to model the individual variance of the participant's JNDs. The models are evaluated using the R2 score to determine if they adequately explain the data and compared using the Mann-Whitney pairwise U-test to determine whether the layout has a significant effect on the perception of the graph property. These tests indicate that the data collected in the studies can not always be modelled well with either the linear model or log-linear model, which suggests that some properties may not follow Weber's Law. Additionally, the layout algorithm is not found to have a significant impact on the perception of some of these properties.Dissertation/ThesisMasters Thesis Computer Science 201

Noise-Enhanced and Human Visual System-Driven Image Processing: Algorithms and Performance Limits

This dissertation investigates the problem of image processing based on stochastic resonance (SR) noise and human visual system (HVS) properties, where several novel frameworks and algorithms for object detection in images, image enhancement and image segmentation as well as the method to estimate the performance limit of image segmentation algorithms are developed. Object detection in images is a fundamental problem whose goal is to make a decision if the object of interest is present or absent in a given image. We develop a framework and algorithm to enhance the detection performance of suboptimal detectors using SR noise, where we add a suitable dose of noise into the original image data and obtain the performance improvement. Micro-calcification detection is employed in this dissertation as an illustrative example. The comparative experiments with a large number of images verify the efficiency of the presented approach. Image enhancement plays an important role and is widely used in various vision tasks. We develop two image enhancement approaches. One is based on SR noise, HVS-driven image quality evaluation metrics and the constrained multi-objective optimization (MOO) technique, which aims at refining the existing suboptimal image enhancement methods. Another is based on the selective enhancement framework, under which we develop several image enhancement algorithms. The two approaches are applied to many low quality images, and they outperform many existing enhancement algorithms. Image segmentation is critical to image analysis. We present two segmentation algorithms driven by HVS properties, where we incorporate the human visual perception factors into the segmentation procedure and encode the prior expectation on the segmentation results into the objective functions through Markov random fields (MRF). Our experimental results show that the presented algorithms achieve higher segmentation accuracy than many representative segmentation and clustering algorithms available in the literature. Performance limit, or performance bound, is very useful to evaluate different image segmentation algorithms and to analyze the segmentability of the given image content. We formulate image segmentation as a parameter estimation problem and derive a lower bound on the segmentation error, i.e., the mean square error (MSE) of the pixel labels considered in our work, using a modified Cramér-Rao bound (CRB). The derivation is based on the biased estimator assumption, whose reasonability is verified in this dissertation. Experimental results demonstrate the validity of the derived bound

Visualising lighting simulations for automotive design evaluations using emerging technologies

Automotive design visualisation is at a turning point with the commercial development of immersive technologies such as virtual reality, among other displays and visual interfaces. A fundamental objective of this research is to assess how seamlessly the integration of emerging visualisation technologies can be implemented into the new product development methodologies, with the use of lighting simulation, design review applications and the use of immersive hardware and software. Optical automotive considerations such as display legibility, veiling glare, and perceived quality among other current processes of Systemic Optical Failure (SOF) modes are analysed, to determine how the application of new immersive visualisation technologies could improve the efficiency of new product development, in particular reducing time and cost in early stages while improving decision making and quality. Different hardware and software combinations were investigated in terms of their ability to realistically represent design intent. Following on from this investigation, a user study was carried out with subjects from various automotive engineering disciplines, to evaluate a range of potential solutions. Recommendations are then made as to how these solutions could be deployed within the automotive new product development process to deliver maximum value