Unifying statistical texture classification frameworks

TL;DR: The experimental results on representative databases show that the proposed LBPV operator and global matching scheme can achieve significant improvement, sometimes more than 10% in terms of classification accuracy, over traditional locally rotation invariant LBP method.

TL;DR: It is demonstrated that materials can be classified using the joint distribution of intensity values over extremely compact neighborhoods (starting from as small as 3times3 pixels square) and that this can outperform classification using filter banks with large support.

TL;DR: An approach to object retrieval which searches for and localizes all the occurrences of an object in a video, given a query image of the object, and investigates retrieval performance with respect to different quantizations of region descriptors and compares the performance of several ranking measures.

TL;DR: The utility of image processing tools that analyze fiber morphology in SHG images of breast and ovarian cancer in human tissues and animal models is summarized and methods that exploit the SHG physical underpinnings that are effective in delineating normal and malignant tissues are described.

TL;DR: This paper investigates the properties of a metric between two distributions, the Earth Mover's Distance (EMD), for content-based image retrieval, and compares the retrieval performance of the EMD with that of other distances.

TL;DR: A new information-theoretic approach is presented for finding the pose of an object in an image that works well in domains where edge or gradient-magnitude based methods have difficulty, yet it is more robust than traditional correlation.

TL;DR: A unified model to construct a vocabulary of prototype tiny surface patches with associated local geometric and photometric properties, represented as a set of linear Gaussian derivative filter outputs, under different lighting and viewing conditions is provided.

TL;DR: A new texture representation called the BTF (bidirectional texture function) which captures the variation in texture with illumination and viewing direction is discussed, and a BTF database with image textures from over 60 different samples, each observed with over 200 different combinations of viewing and illumination directions is presented.