Texture analysis describes a variety of image analysis techniques that quantify the variation
in intensity and pattern. This paper provides an overview of several texture analysis …

Deep generative models have become increasingly effective at producing realistic images
from randomly sampled seeds, but using such models for controllable manipulation of …

Transfer learning is a cornerstone of computer vision, yet little work has been done to
evaluate the relationship between architecture and transfer. An implicit hypothesis in …

Automated detection of cancer metastases in lymph nodes has the potential to improve the
assessment of prognosis for patients. To enable fair comparison between the algorithms for …

Under typical viewing conditions, human observers effortlessly recognize materials and infer
their physical, functional, and multisensory properties at a glance. Without touching …

Texture analysis is used in a very broad range of fields and applications, from texture
classification (eg, for remote sensing) to segmentation (eg, in biomedical imaging), passing …

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 …

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 …

Abstract Convolutional Neural Networks (CNNs) with Bilinear Pooling, initially in their full
form and later using compact representations, have yielded impressive performance gains …

Bilinear pooling of Convolutional Neural Network (CNN) features [22, 23], and their compact
variants [10], have been shown to be effective at fine-grained recognition, scene …