Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Humans perceive the three-dimensional structure of the world with apparent ease. However, despite all of the recent advances in computer vision research, the dream of having a computer interpret an image at the same level as a two-year old remains elusive. Why is computer vision such a challenging problem and what is the current state of the art? Computer Vision: Algorithms and Applications explores the variety of techniques commonly used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both for specialized applications such as medical imaging, and for fun, consumer-level tasks such as image editing and stitching, which students can apply to their own personal photos and videos. More than just a source of recipes, this exceptionally authoritative and comprehensive textbook/reference also takes a scientific approach to basic vision problems, formulating physical models of the imaging process before inverting them to produce descriptions of a scene. These problems are also analyzed using statistical models and solved using rigorous engineering techniques Topics and features: structured to support active curricula and project-oriented courses, with tips in the Introduction for using the book in a variety of customized courses; presents exercises at the end of each chapter with a heavy emphasis on testing algorithms and containing numerous suggestions for small mid-term projects; provides additional material and more detailed mathematical topics in the Appendices, which cover linear algebra, numerical techniques, and Bayesian estimation theory; suggests additional reading at the end of each chapter, including the latest research in each sub-field, in addition to a full Bibliography at the end of the book; supplies supplementary course material for students at the associated website, http://szeliski.org/Book/. Suitable for an upper-level undergraduate or graduate-level course in computer science or engineering, this textbook focuses on basic techniques that work under real-world conditions and encourages students to push their creative boundaries. Its design and exposition also make it eminently suitable as a unique reference to the fundamental techniques and current research literature in computer vision.

/pdf/computer-vision-algorithms-and-applications-25dn6wu83j.pdf

Computer Vision: Algorithms and Applications

From the Publisher: 
The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

A new algorithm is proposed for removing large objects from digital images. The challenge is to fill in the hole that is left behind in a visually plausible way. In the past, this problem has been addressed by two classes of algorithms: 1) "texture synthesis" algorithms for generating large image regions from sample textures and 2) "inpainting" techniques for filling in small image gaps. The former has been demonstrated for "textures"-repeating two-dimensional patterns with some stochasticity; the latter focus on linear "structures" which can be thought of as one-dimensional patterns, such as lines and object contours. This paper presents a novel and efficient algorithm that combines the advantages of these two approaches. We first note that exemplar-based texture synthesis contains the essential process required to replicate both texture and structure; the success of structure propagation, however, is highly dependent on the order in which the filling proceeds. We propose a best-first algorithm in which the confidence in the synthesized pixel values is propagated in a manner similar to the propagation of information in inpainting. The actual color values are computed using exemplar-based synthesis. In this paper, the simultaneous propagation of texture and structure information is achieved by a single , efficient algorithm. Computational efficiency is achieved by a block-based sampling process. A number of examples on real and synthetic images demonstrate the effectiveness of our algorithm in removing large occluding objects, as well as thin scratches. Robustness with respect to the shape of the manually selected target region is also demonstrated. Our results compare favorably to those obtained by existing techniques.

/pdf/region-filling-and-object-removal-by-exemplar-based-image-4pegss3e0m.pdf

Region filling and object removal by exemplar-based image inpainting

In this paper, a new technique for modeling textured 3D faces is introduced. 3D faces can either be generated automatically from one or more photographs, or modeled directly through an intuitive user interface. Users are assisted in two key problems of computer aided face modeling. First, new face images or new 3D face models can be registered automatically by computing dense one-to-one correspondence to an internal face model. Second, the approach regulates the naturalness of modeled faces avoiding faces with an ''unlikely'' appearance Starting from an example set of 3D face models, we derive a morphable face model by transforming the shape and texture of the examples into a vector space representation. New faces and expressions can be modeled by forming linear combinations of the prototypes. Shape and texture constraints derived from the statistics of our example faces are used to guide manual modeling or automated matching algorithms We show 3D face reconstructions from single images and their applications for photo-realistic image manipulations. We also demonstrate face manipulations according to complex parameters such as gender, fullness of a face or its distinctiveness.

A morphable model for the synthesis of 3D faces

In this paper we present a novel method of simulating the elegant brushstrokes found in calligraphic lettering and painting. Such simulations have earlier been attempted with physically based or texture-mapped approaches, methods that succeeded in producing aesthetic, but also computationally intensive, results. We introduce a brushstroke model based on a parametric curve, the interval spline. By defining brush effects as mathematical constraints between knots, we generate artistic strokes that render faster than earlier methods and are also resistant to scaling. More significantly, because the construction of the interval spline curve is based on changes in the shape of the brush as it moves along a path, variations within a single stroke not fully modeled with earlier attempts are inherently captured by this representation. We present a system for painting with interval spline strokes and discuss a number of examples created with this method.

/pdf/simulating-artistic-brushstrokes-using-interval-splines-2pi8me6wxj.pdf

Simulating artistic brushstrokes using interval splines

In this paper, we present a new approach for automatically generating a life-like portrait from a frontal face image. We learn the portraiture from a set of real artwork examples. Different from previous texture synthesis and image synthesis works that assumed modeling is homogeneous, Inhomogeneous Markov Random Field Model is employed as the statistical model, and a non-parametric sampling scheme is used to capture the complex statistical characteristics of face image and corresponding artist drawing in this paper. In our strategy, only those pixels corresponding to a portrait point are sampled. Such a strategy is crucial for maintaining facial structure and guaranteeing coherence of portrait lines. Experimental results demonstrate the effectiveness and life-likeness of our approach.

/pdf/example-based-automatic-portraiture-4qqmr2oiqn.pdf

Example-based Automatic Portraiture

Panning and zooming interfaces for exploring very large images containing billions of pixels (gigapixel images) have recently appeared on the internet. This paper addresses issues that arise when creating and rendering auditory and textual annotations for such images. In particular, we define a distance metric between each annotation and any view resulting from panning and zooming on the image. The distance then informs the rendering of audio annotations and text labels. We demonstrate the annotation system on a number of panoramic images.

/pdf/annotating-gigapixel-images-4ogwk1dqlf.pdf

Annotating gigapixel images

A non-linear beam tracing technique that supports full non-linear beam tracing effects including multiple reflections and refractions for computer graphics applications. The technique introduces non-linear beam tracing to render non-linear ray tracing effects such as curved mirror reflection, refraction, caustics, and shadows. Beams are allowed to be non-linear where rays within the same beam are not parallel or do not intersect at a single point. Such is the case when a primary beam bounces off of a surface and spawns one or more secondary rays or beams. Secondary beams can be rendered in a similar manner to primary rays or beams via polygon streaming. Beyond smooth ray bundles, the technique can also be applied to incoherent ray bundles which is useful for rendering bump mapped surfaces.

Non-linear beam tracing for computer graphics

Recently there is a resurgent interest in example based texture analysis and synthesis in both computer vision and computer graphics. While study in computer vision is concerned with learning accurate texture models, research in graphics is aimed at effective algorithms for texture synthesis without necessarily obtaining explicit texture model. This paper makes three contributions to this recent excitement. First, we introduce a theoretical framework for designing and analyzing texture sampling algorithms. This framework, built upon the mathematical definition of textures, measures a texture sampling algorithm using admissibility, effectiveness, and sampling speed. Second, we compare and analyze texture sampling algorithms based on admissibility and effectiveness. In particular, we propose different design criteria for texture analysis algorithms in computer vision and texture synthesis algorithms in computer graphics. Finally, we develop a novel texture synthesis algorithm which samples from a subset of the Julesz ensemble by pasting texture patches from the sample texture. A key feature of our algorithm is that it can synthesize high-quality textures extremely fast. On a mid-level PC we can synthesize a 512 512 texture from a 64 64 sample in just 0.03 second. This algorithm has been tested through extensive experiments and we report sample results from our experiments.

/pdf/asymptotically-admissible-texture-synthesis-4t3in5rwle.pdf

Asymptotically Admissible Texture Synthesis

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