We propose bilinear models, a recognition architecture that consists of two feature extractors
whose outputs are multiplied using outer product at each location of the image and pooled …

Research in texture recognition often concentrates on the problem of material recognition in
uncluttered conditions, an assumption rarely met by applications. In this work we conduct a …

Texture is a fundamental characteristic of many types of images, and texture representation
is one of the essential and challenging problems in computer vision and pattern recognition …

Patterns and textures are key characteristics of many natural objects: a shirt can be striped,
the wings of a butterfly can be veined, and the skin of an animal can be scaly. Aiming at …

Local binary patterns (LBP) are considered among the most computationally efficient high-
performance texture features. However, the LBP method is very sensitive to image noise and …

Abstract Local Binary Patterns (LBP) have emerged as one of the most prominent and
widely studied local texture descriptors. Truly a large number of LBP variants has been …

Recognizing materials in real-world images is a challenging task. Real-world materials have
rich surface texture, geometry, lighting conditions, and clutter, which combine to make the …

Early diagnosis of interstitial lung diseases is crucial for their treatment, but even
experienced physicians find it difficult, as their clinical manifestations are similar. In order to …

An ever-growing number of real-world computer vision applications require classification,
segmentation, retrieval, or realistic rendering of genuine materials. However, the …

Abstract We propose a Deep Texture Encoding Network (TEN) with a novel Encoding Layer
integrated on top of convolutional layers, which ports the entire dictionary learning and …