Scalable exploration of highly detailed and annotated 3D models

With the widespread availability of mobile graphics terminals andWebGL-enabled browsers, 3D graphics over the Internet is thriving. Thanks to recent advances in 3D acquisition and modeling systems, high-quality 3D models are becoming increasingly common, and are now potentially available for ubiquitous exploration. In current 3D repositories, such as Blend Swap, 3D Café or Archive3D, 3D models available for download are mostly presented through a few user-selected static images. Online exploration is limited to simple orbiting and/or low-fidelity explorations of simplified models, since photorealistic rendering quality of complex synthetic environments is still hardly achievable within the real-time constraints of interactive applications, especially on on low-powered mobile devices or script-based Internet browsers. Moreover, navigating inside 3D environments, especially on the now pervasive touch devices, is a non-trivial task, and usability is consistently improved by employing assisted navigation controls. In addition, 3D annotations are often used in order to integrate and enhance the visual information by providing spatially coherent contextual information, typically at the expense of introducing visual cluttering. In this thesis, we focus on efficient representations for interactive exploration and understanding of highly detailed 3D meshes on common 3D platforms. For this purpose, we present several approaches exploiting constraints on the data representation for improving the streaming and rendering performance, and camera movement constraints in order to provide scalable navigation methods for interactive exploration of complex 3D environments. Furthermore, we study visualization and interaction techniques to improve the exploration and understanding of complex 3D models by exploiting guided motion control techniques to aid the user in discovering contextual information while avoiding cluttering the visualization. We demonstrate the effectiveness and scalability of our approaches both in large screen museum installations and in mobile devices, by performing interactive exploration of models ranging from 9Mtriangles to 940Mtriangles

Selected Topics in Bayesian Image/Video Processing

In this dissertation, three problems in image deblurring, inpainting and virtual content insertion are solved in a Bayesian framework.;Camera shake, motion or defocus during exposure leads to image blur. Single image deblurring has achieved remarkable results by solving a MAP problem, but there is no perfect solution due to inaccurate image prior and estimator. In the first part, a new non-blind deconvolution algorithm is proposed. The image prior is represented by a Gaussian Scale Mixture(GSM) model, which is estimated from non-blurry images as training data. Our experimental results on a total twelve natural images have shown that more details are restored than previous deblurring algorithms.;In augmented reality, it is a challenging problem to insert virtual content in video streams by blending it with spatial and temporal information. A generic virtual content insertion (VCI) system is introduced in the second part. To the best of my knowledge, it is the first successful system to insert content on the building facades from street view video streams. Without knowing camera positions, the geometry model of a building facade is established by using a detection and tracking combined strategy. Moreover, motion stabilization, dynamic registration and color harmonization contribute to the excellent augmented performance in this automatic VCI system.;Coding efficiency is an important objective in video coding. In recent years, video coding standards have been developing by adding new tools. However, it costs numerous modifications in the complex coding systems. Therefore, it is desirable to consider alternative standard-compliant approaches without modifying the codec structures. In the third part, an exemplar-based data pruning video compression scheme for intra frame is introduced. Data pruning is used as a pre-processing tool to remove part of video data before they are encoded. At the decoder, missing data is reconstructed by a sparse linear combination of similar patches. The novelty is to create a patch library to exploit similarity of patches. The scheme achieves an average 4% bit rate reduction on some high definition videos

Prioritizing Content of Interest in Multimedia Data Compression

Image and video compression techniques make data transmission and storage in digital multimedia systems more efficient and feasible for the system's limited storage and bandwidth. Many generic image and video compression techniques such as JPEG and H.264/AVC have been standardized and are now widely adopted. Despite their great success, we observe that these standard compression techniques are not the best solution for data compression in special types of multimedia systems such as microscopy videos and low-power wireless broadcast systems. In these application-specific systems where the content of interest in the multimedia data is known and well-defined, we should re-think the design of a data compression pipeline. We hypothesize that by identifying and prioritizing multimedia data's content of interest, new compression methods can be invented that are far more effective than standard techniques. In this dissertation, a set of new data compression methods based on the idea of prioritizing the content of interest has been proposed for three different kinds of multimedia systems. I will show that the key to designing efficient compression techniques in these three cases is to prioritize the content of interest in the data. The definition of the content of interest of multimedia data depends on the application. First, I show that for microscopy videos, the content of interest is defined as the spatial regions in the video frame with pixels that don't only contain noise. Keeping data in those regions with high quality and throwing out other information yields to a novel microscopy video compression technique. Second, I show that for a Bluetooth low energy beacon based system, practical multimedia data storage and transmission is possible by prioritizing content of interest. I designed custom image compression techniques that preserve edges in a binary image, or foreground regions of a color image of indoor or outdoor objects. Last, I present a new indoor Bluetooth low energy beacon based augmented reality system that integrates a 3D moving object compression method that prioritizes the content of interest.Doctor of Philosoph

Bayesian Optimization for Image Segmentation, Texture Flow Estimation and Image Deblurring

Ph.DDOCTOR OF PHILOSOPH

Inter-modality image synthesis and recognition.

跨模態圖像的合成和識別已成為計算機視覺領域的熱點。實際應用中存在各種各樣的圖像模態，比如刑偵中使用的素描畫和光照不變人臉識別中使用的近紅外圖像。由於某些模態的圖像很難獲得，模態間的轉換和匹配是一項十分有用的技術，為計算機視覺的應用提供了很大的便利。本論文研究了三個應用：人像素描畫的合成，基於樣本的圖像風格化和人像素描畫識別。我們將人像素描畫的合成的前沿研究擴展到非可控條件下的合成。以前的工作都只能在嚴格可控的條件下從照片合成素描畫。我們提出了一種魯棒的算法，可以從有光照和姿態變化的人臉照片合成素描畫。該算法用多尺度馬爾可夫隨機場來合成局部素描圖像塊。對光照和姿態的魯棒性通過三個部分來實現：基於面部器官的形狀先驗可以抑制缺陷和扭曲的合成效果，圖像塊的特征描述子和魯棒的距離測度用來選擇素描圖像塊，以及像素灰度和梯度的一致性來有效地匹配鄰近的素描圖像塊。在CUHK人像素描數據庫和網上的名人照片上的實驗結果表明我們的算法顯著提高了現有算法的效果。針對基於樣本的圖像風格化，我們提供了一種將模板圖像的藝術風格傳遞到照片上的有效方法。大多數已有方法沒有考慮圖像內容和風格的分離。我們提出了一種通過頻段分解的風格傳遞算法。一幅圖像被分解成低頻、中頻和高頻分量，分別描述內容、主要風格和邊緣信息。接著中頻和高頻分量中的風格從模板傳遞到照片，這一過程用馬爾可夫隨機場來建模。最後我們結合照片中的低頻分量和獲得的風格信息重建出藝術圖像。和其它算法相比，我們的方法不僅合成了風格，而且很好的保持了原有的圖像內容。我們通過圖像風格化和個性化藝術合成的實驗來驗證了算法的有效性。我們為人像素描畫的識別提出了一個從數據中學習人臉描述子的新方向。最近的研究都集中在轉換照片和素描畫到相同的模態，或者設計復雜的分類算法來減少從照片和素描畫提取的特征的模態間差異。我們提出了一種新穎的方法：在提取特征的階段減小模態間差異。我們用一種基於耦合信息論編碼的人臉描述子來獲取有判別性的局部人臉結構和有效的匹配照片和素描畫。通過最大化在量化特征空間的照片和素描畫的互信息，我們設計了耦合信息論投影森林來實現耦合編碼。在世界上最大的人像素描畫數據庫上的結果表明我們的方法和已有最好的方法相比有顯著提高。Inter-modality image synthesis and recognition has been a hot topic in computer vision. In real-world applications, there are diverse image modalities, such as sketch images for law enforcement and near infrared images for illumination invariant face recognition. Therefore, it is often useful to transform images from a modality to another or match images from different modalities, due to the difficulty of acquiring image data in some modality. These techniques provide large flexibility for computer vision applications.In this thesis we study three problems: face sketch synthesis, example-based image stylization, and face sketch recognition.For face sketch synthesis, we expand the frontier to synthesis from uncontrolled face photos. Previous methods only work under well controlled conditions. We propose a robust algorithm for synthesizing a face sketch from a face photo with lighting and pose variations. It synthesizes local sketch patches using a multiscale Markov Random Field (MRF) model. The robustness to lighting and pose variations is achieved with three components: shape priors specific to facial components to reduce artifacts and distortions, patch descriptors and robust metrics for selecting sketch patch candidates, and intensity compatibility and gradient compatibility to match neighboring sketch patches effectively. Experiments on the CUHK face sketch database and celebrity photos collected from the web show that our algorithm significantly improves the performance of the state-of-the-art.For example-based image stylization, we provide an effective approach of transferring artistic effects from a template image to photos. Most existing methods do not consider the content and style separately. We propose a style transfer algorithm via frequency band decomposition. An image is decomposed into the low-frequency (LF), mid-frequency (MF), and highfrequency( HF) components, which describe the content, main style, and information along the boundaries. Then the style is transferred from the template to the photo in the MF and HF components, which is formulated as MRF optimization. Finally a reconstruction step combines the LF component of the photo and the obtained style information to generate the artistic result. Compared to the other algorithms, our method not only synthesizes the style, but also preserves the image content well. We demonstrate that our approach performs excellently in image stylization and personalized artwork in experiments.For face sketch recognition, we propose a new direction based on learning face descriptors from data. Recent research has focused on transforming photos and sketches into the same modality for matching or developing advanced classification algorithms to reduce the modality gap between features extracted from photos and sketches. We propose a novel approach by reducing the modality gap at the feature extraction stage. A face descriptor based on coupled information-theoretic encoding is used to capture discriminative local face structures and to effectively match photos and sketches. Guided by maximizing the mutual information between photos and sketches in the quantized feature spaces, the coupled encoding is achieved by the proposed coupled information-theoretic projection forest. Experiments on the largest face sketch database show that our approach significantly outperforms the state-of-the-art methods.Detailed summary in vernacular field only.Detailed summary in vernacular field only.Detailed summary in vernacular field only.Detailed summary in vernacular field only.Detailed summary in vernacular field only.Zhang, Wei.Thesis (Ph.D.)--Chinese University of Hong Kong, 2012.Includes bibliographical references (leaves 121-137).Abstract also in Chinese.Abstract --- p.iAcknowledgement --- p.vChapter 1 --- Introduction --- p.1Chapter 1.1 --- Multi-Modality Computer Vision --- p.1Chapter 1.2 --- Face Sketches --- p.4Chapter 1.2.1 --- Face Sketch Synthesis --- p.6Chapter 1.2.2 --- Face Sketch Recognition --- p.7Chapter 1.3 --- Example-based Image Stylization --- p.9Chapter 1.4 --- Contributions and Summary of Approaches --- p.10Chapter 1.5 --- Thesis Road Map --- p.13Chapter 2 --- Literature Review --- p.14Chapter 2.1 --- Related Works in Face Sketch Synthesis --- p.14Chapter 2.2 --- Related Works in Example-based Image Stylization --- p.17Chapter 2.3 --- Related Works in Face Sketch Recognition --- p.21Chapter 3 --- Lighting and Pose Robust Sketch Synthesis --- p.27Chapter 3.1 --- The Algorithm --- p.31Chapter 3.1.1 --- Overview of the Method --- p.32Chapter 3.1.2 --- Local Evidence --- p.34Chapter 3.1.3 --- Shape Prior --- p.40Chapter 3.1.4 --- Neighboring Compatibility --- p.42Chapter 3.1.5 --- Implementation Details --- p.43Chapter 3.1.6 --- Acceleration --- p.45Chapter 3.2 --- Experimental Results --- p.47Chapter 3.2.1 --- Lighting and Pose Variations --- p.49Chapter 3.2.2 --- Celebrity Faces from the Web --- p.54Chapter 3.3 --- Conclusion --- p.54Chapter 4 --- Style Transfer via Band Decomposition --- p.58Chapter 4.1 --- Introduction --- p.58Chapter 4.2 --- Algorithm Overview --- p.63Chapter 4.3 --- Image Style Transfer --- p.64Chapter 4.3.1 --- Band Decomposition --- p.64Chapter 4.3.2 --- MF and HF Component Processing --- p.67Chapter 4.3.3 --- Reconstruction --- p.74Chapter 4.4 --- Experiments --- p.76Chapter 4.4.1 --- Comparison to State-of-the-Art --- p.76Chapter 4.4.2 --- Extended Application: Personalized Artwork --- p.82Chapter 4.5 --- Conclusion --- p.84Chapter 5 --- Coupled Encoding for Sketch Recognition --- p.86Chapter 5.1 --- Introduction --- p.86Chapter 5.1.1 --- Related work --- p.89Chapter 5.2 --- Information-Theoretic Projection Tree --- p.90Chapter 5.2.1 --- Projection Tree --- p.91Chapter 5.2.2 --- Mutual Information Maximization --- p.92Chapter 5.2.3 --- Tree Construction with MMI --- p.94Chapter 5.2.4 --- Randomized CITP Forest --- p.102Chapter 5.3 --- Coupled Encoding Based Descriptor --- p.103Chapter 5.4 --- Experiments --- p.106Chapter 5.4.1 --- Descriptor Comparison --- p.108Chapter 5.4.2 --- Parameter Exploration --- p.109Chapter 5.4.3 --- Experiments on Benchmarks --- p.112Chapter 5.5 --- Conclusions --- p.115Chapter 6 --- Conclusion --- p.116Bibliography --- p.12

A Parametric Sound Object Model for Sound Texture Synthesis

This thesis deals with the analysis and synthesis of sound textures based on parametric sound objects. An overview is provided about the acoustic and perceptual principles of textural acoustic scenes, and technical challenges for analysis and synthesis are considered. Four essential processing steps for sound texture analysis are identifi ed, and existing sound texture systems are reviewed, using the four-step model as a guideline. A theoretical framework for analysis and synthesis is proposed. A parametric sound object synthesis (PSOS) model is introduced, which is able to describe individual recorded sounds through a fi xed set of parameters. The model, which applies to harmonic and noisy sounds, is an extension of spectral modeling and uses spline curves to approximate spectral envelopes, as well as the evolution of parameters over time. In contrast to standard spectral modeling techniques, this representation uses the concept of objects instead of concatenated frames, and it provides a direct mapping between sounds of diff erent length. Methods for automatic and manual conversion are shown. An evaluation is presented in which the ability of the model to encode a wide range of di fferent sounds has been examined. Although there are aspects of sounds that the model cannot accurately capture, such as polyphony and certain types of fast modulation, the results indicate that high quality synthesis can be achieved for many different acoustic phenomena, including instruments and animal vocalizations. In contrast to many other forms of sound encoding, the parametric model facilitates various techniques of machine learning and intelligent processing, including sound clustering and principal component analysis. Strengths and weaknesses of the proposed method are reviewed, and possibilities for future development are discussed

3D Shape Descriptor-Based Facial Landmark Detection: A Machine Learning Approach

Facial landmark detection on 3D human faces has had numerous applications in the literature such as establishing point-to-point correspondence between 3D face models which is itself a key step for a wide range of applications like 3D face detection and authentication, matching, reconstruction, and retrieval, to name a few. Two groups of approaches, namely knowledge-driven and data-driven approaches, have been employed for facial landmarking in the literature. Knowledge-driven techniques are the traditional approaches that have been widely used to locate landmarks on human faces. In these approaches, a user with sucient knowledge and experience usually denes features to be extracted as the landmarks. Data-driven techniques, on the other hand, take advantage of machine learning algorithms to detect prominent features on 3D face models. Besides the key advantages, each category of these techniques has limitations that prevent it from generating the most reliable results. In this work we propose to combine the strengths of the two approaches to detect facial landmarks in a more ecient and precise way. The suggested approach consists of two phases. First, some salient features of the faces are extracted using expert systems. Afterwards, these points are used as the initial control points in the well-known Thin Plate Spline (TPS) technique to deform the input face towards a reference face model. Second, by exploring and utilizing multiple machine learning algorithms another group of landmarks are extracted. The data-driven landmark detection step is performed in a supervised manner providing an information-rich set of training data in which a set of local descriptors are computed and used to train the algorithm. We then, use the detected landmarks for establishing point-to-point correspondence between the 3D human faces mainly using an improved version of Iterative Closest Point (ICP) algorithms. Furthermore, we propose to use the detected landmarks for 3D face matching applications