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 morphable model for the synthesis of 3D faces

Recently, methods based on local image features have shown promise for texture and object recognition tasks. This paper presents a large-scale evaluation of an approach that represents images as distributions (signatures or histograms) of features extracted from a sparse set of keypoint locations and learns a Support Vector Machine classifier with kernels based on two effective measures for comparing distributions, the Earth Movers Distance and the ÷2 distance. We first evaluate the performance of our approach with different keypoint detectors and descriptors, as well as different kernels and classifiers. We then conduct a comparative evaluation with several state-of-the-art recognition methods on 4 texture and 5 object databases. On most of these databases, our implementation exceeds the best reported results and achieves comparable performance on the rest. Finally, we investigate the influence of background correlations on recognition performance.

/pdf/local-features-and-kernels-for-classification-of-texture-and-4gz5noavwm.pdf

Local Features and Kernels for Classification of Texture and Object Categories: A Comprehensive Study

This paper introduces a texture representation suitable for recognizing images of textured surfaces under a wide range of transformations, including viewpoint changes and nonrigid deformations. At the feature extraction stage, a sparse set of affine Harris and Laplacian regions is found in the image. Each of these regions can be thought of as a texture element having a characteristic elliptic shape and a distinctive appearance pattern. This pattern is captured in an affine-invariant fashion via a process of shape normalization followed by the computation of two novel descriptors, the spin image and the RIFT descriptor. When affine invariance is not required, the original elliptical shape serves as an additional discriminative feature for texture recognition. The proposed approach is evaluated in retrieval and classification tasks using the entire Brodatz database and a publicly available collection of 1,000 photographs of textured surfaces taken from different viewpoints.

/pdf/a-sparse-texture-representation-using-local-affine-regions-1j5ryjdvdh.pdf

A sparse texture representation using local affine regions

We investigate texture classification from single images obtained under unknown viewpoint and illumination. A statistical approach is developed where textures are modelled by the joint probability distribution of filter responses. This distribution is represented by the frequency histogram of filter response cluster centres (textons). Recognition proceeds from single, uncalibrated images and the novelty here is that rotationally invariant filters are used and the filter response space is low dimensional.

A Statistical Approach to Texture Classification from Single Images

A bidirectional texture function (BTF) describes image texture as it varies with viewing and illumination direction. Many real world surfaces such as skin, fur, gravel, etc. exhibit fine-scale geometric surface detail. Accordingly, variations in appearance with viewing and illumination direction may be quite complex due to local foreshortening, masking and shadowing. Representations of surface texture that support robust recognition must account for these effects. We construct a representation which captures the underlying statistical distribution of features in the image texture as well as the variations in this distribution with viewing and illumination direction. The representation combines clustering to learn characteristic image features and principle components analysis to reduce the space of feature histograms. This representation is based on a core image set as determined by a quantitative evaluation of importance of individual images in the overall representation. The result is a compact representation and a recognition method where a single novel image of unknown viewing and illumination direction can be classified efficiently. The CUReT (Columbia-Utrecht reflectance and texture) database is used as a test set for evaluation of these methods.

https://www.ece.rutgers.edu/~kdana/Publications/cula2001.pdf

Compact representation of bidirectional texture functions

Textured surfaces are an inherent constituent of the natural surroundings, therefore efficient real-world applications of computer vision algorithms require precise surface descriptors. Often textured surfaces present not only variations of color or reflectance, but also local height variations. This type of surface is referred to as a 3D texture. As the lighting and viewing conditions are varied, effects such as shadowing, foreshortening and occlusions, give rise to significant changes in texture appearance. Accounting for the variation of texture appearance due to changes in imaging parameters is a key issue in developing accurate 3D texture models. The bidirectional texture function (BTF) is observed image texture as a function of viewing and illumination directions. In this work, we construct a BTF-based surface model which captures the variation of the underlying statistical distribution of local structural image features, as the viewing and illumination conditions are changed. This 3D texture representation is called the bidirectional feature histogram (BFH). Based on the BFH, we design a 3D texture recognition method which employs the BFH as the surface model, and classifies surfaces based on a single novel texture image of unknown imaging parameters. Also, we develop a computational method for quantitatively evaluating the relative significance of texture images within the BTF. The performance of our methods is evaluated by employing over 6200 texture images corresponding to 40 real-world surface samples from the CUReT (Columbia-Utrecht reflectance and texture) database. Our experiments produce excellent classification results, which validate the strong descriptive properties of the BFH as a 3D texture representation.

https://www.ece.rutgers.edu/~kdana/Publications/cula2004a.pdf

3D Texture Recognition Using Bidirectional Feature Histograms

Quantitative characterization of skin appearance is an important but difficult task. The skin surface is a detailed landscape, with complex geometry and local optical properties. In addition, skin features depend on many variables such as body location (e.g. forehead, cheek), subject parameters (age, gender) and imaging parameters (lighting, camera). As with many real world surfaces, skin appearance is strongly affected by the direction from which it is viewed and illuminated. Computational modeling of skin texture has potential uses in many applications including realistic rendering for computer graphics, robust face models for computer vision, computer-assisted diagnosis for dermatology, topical drug efficacy testing for the pharmaceutical industry and quantitative comparison for consumer products. In this work we present models and measurements of skin texture with an emphasis on faces. We develop two models for use in skin texture recognition. Both models are image-based representations of skin appearance that are suitably descriptive without the need for prohibitively complex physics-based skin models. Our models take into account the varied appearance of the skin with changes in illumination and viewing direction. We also present a new face texture database comprised of more than 2400 images corresponding to 20 human faces, 4 locations on each face (forehead, cheek, chin and nose) and 32 combinations of imaging angles. The complete database is made publicly available for further research.

Skin Texture Modeling

In this paper, we present a method of skin imaging called bidirectional imaging that captures significantly more properties of appearance than standard imaging. The observed structure of the skin's surface is greatly dependent on the angle of incident illumination and the angle of observation. Specific protocols to achieve bidirectional imaging are presented and used to create the Rutgers Skin Texture Database (clinical component). This image database is the first of its kind in the dermatology community. Skin images of several disorders under multiple controlled illumination and viewing directions are provided publicly for research and educational use. Using this skin texture database, we employ computational surface modeling to perform automated skin texture classification. The classification experiments demonstrate the usefulness of the modeling and measurement methods.

/pdf/bidirectional-imaging-and-modeling-of-skin-texture-28mcq6yywu.pdf

Bidirectional imaging and modeling of skin texture

Texture as a surface representation is the subject of a wide body of computer vision and computer graphics literature. While texture is always associated with a form of repetition in the image, the repeating quantity may vary. The texture may be a color or albedo variation as in a checkerboard, a paisley print or zebra stripes. Very often in real-world scenes, texture is instead due to a surface height variation, e.g. pebbles, gravel, foliage and any rough surface. Such surfaces are referred to here as 3D textured surfaces. Standard texture recognition algorithms are not appropriate for 3D textured surfaces because the appearance of these surfaces changes in a complex manner with viewing direction and illumination direction. Recent methods have been developed for recognition of 3D textured surfaces using a database of surfaces observed under varied imaging parameters. One of these methods is based on 3D textons obtained using K-means clustering of multiscale feature vectors. Another method uses eigen-analysis originally developed for appearance-based object recognition. In this work we develop a hybrid approach that employs both feature grouping and dimensionality reduction. The method is tested using the Columbia-Utrecht texture database and provides excellent recognition rates. The method is compared with existing recognition methods for 3D textured surfaces. A direct comparison is facilitated by empirical recognition rates from the same texture data set. The current method has key advantages over existing methods including requiring less prior information on both the training and novel images.

http://eceweb1.rutgers.edu/~kdana/Publications/cula2001a.pdf

Oana G. Cula

Papers

Compact representation of bidirectional texture functions

3D Texture Recognition Using Bidirectional Feature Histograms

Skin Texture Modeling

Bidirectional imaging and modeling of skin texture

Recognition methods for 3D textured surfaces