Describing Textures in the Wild

Patterns and textures are defining 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 supporting this analytical dimension in image understanding, we address the challenging problem of describing textures with semantic attributes. We identify a rich vocabulary of forty-seven texture terms and use them to describe a large dataset of patterns collected in the wild.The resulting Describable Textures Dataset (DTD) is the basis to seek for the best texture representation for recognizing describable texture attributes in images. We port from object recognition to texture recognition the Improved Fisher Vector (IFV) and show that, surprisingly, it outperforms specialized texture descriptors not only on our problem, but also in established material recognition datasets. We also show that the describable attributes are excellent texture descriptors, transferring between datasets and tasks; in particular, combined with IFV, they significantly outperform the state-of-the-art by more than 8 percent on both FMD and KTHTIPS-2b benchmarks. We also demonstrate that they produce intuitive descriptions of materials and Internet images.Comment: 13 pages; 12 figures Fixed misplaced affiliatio

Deep filter banks for texture recognition, description, and segmentation

Visual textures have played a key role in image understanding because they convey important semantics of images, and because texture representations that pool local image descriptors in an orderless manner have had a tremendous impact in diverse applications. In this paper we make several contributions to texture understanding. First, instead of focusing on texture instance and material category recognition, we propose a human-interpretable vocabulary of texture attributes to describe common texture patterns, complemented by a new describable texture dataset for benchmarking. Second, we look at the problem of recognizing materials and texture attributes in realistic imaging conditions, including when textures appear in clutter, developing corresponding benchmarks on top of the recently proposed OpenSurfaces dataset. Third, we revisit classic texture representations, including bag-of-visual-words and the Fisher vectors, in the context of deep learning and show that these have excellent efficiency and generalization properties if the convolutional layers of a deep model are used as filter banks. We obtain in this manner state-of-the-art performance in numerous datasets well beyond textures, an efficient method to apply deep features to image regions, as well as benefit in transferring features from one domain to another.Comment: 29 pages; 13 figures; 8 table

A High-Order Kernel Method for Diffusion and Reaction-Diffusion Equations on Surfaces

In this paper we present a high-order kernel method for numerically solving diffusion and reaction-diffusion partial differential equations (PDEs) on smooth, closed surfaces embedded in $\mathbb{R}^d$. For two-dimensional surfaces embedded in $\mathbb{R}^3$, these types of problems have received growing interest in biology, chemistry, and computer graphics to model such things as diffusion of chemicals on biological cells or membranes, pattern formations in biology, nonlinear chemical oscillators in excitable media, and texture mappings. Our kernel method is based on radial basis functions (RBFs) and uses a semi-discrete approach (or the method-of-lines) in which the surface derivative operators that appear in the PDEs are approximated using collocation. The method only requires nodes at "scattered" locations on the surface and the corresponding normal vectors to the surface. Additionally, it does not rely on any surface-based metrics and avoids any intrinsic coordinate systems, and thus does not suffer from any coordinate distortions or singularities. We provide error estimates for the kernel-based approximate surface derivative operators and numerically study the accuracy and stability of the method. Applications to different non-linear systems of PDEs that arise in biology and chemistry are also presented

The role of picture book storytelling used with Realia in the EFL YLs´ vocabulary acquisition

113 páginas incluye diagramasEn este artículo se reporta un estudio desarrollado para ayudar a un grupo de 12 niños estudiantes de Inglés como Lengua Extranjera a incrementar su rango de vocabulario, a través de la lectura de Pictolibros acompañados con “Realia”, una estrategia de enseñanza en donde objetos reales son traídos al salón de clases como ejemplos o como ayudas para hablar o escribir. Esta investigación tuvo una duración de 8 semanas y fue realizada en la escuela privada “Fundación Educativa Rochester” ubicada en Chía (Cundinamarca), a las afueras de Bogotá, la ciudad capital de Colombia. Los siguientes instrumentos fueron implementados con el fin de medir el impacto de esta investigación: un único examen aplicado al inicio y al final del tratamiento, diarios de campo y cuatro listas de chequeo utilizadas durante el tratamiento. Se emplearon tabulación cruzada y codificación para el análisis de datos. Los resultados revelaron que el uso de Pictolibros acompañados con “Realia”, puede tener un impacto positivo en niños estudiantes de inglés como Lengua Extranjera con habilidades lingüísticas limitadas. Adicionalmente, se encontró que permitir a los niños tomar decisiones sobre algunos elementos de las actividades propuestas para la clase de inglés como lengua extranjera, promueve aprendizaje autónomo, ya que fue evidente como los participantes se apropiaron de su propio proceso de aprendizaje.

3D Alchemy: a guide to 3D realistic computer graphics

Last year, many films and commercials took advantage of computer technology to create astonishing 3D animations. Examples such as the Listerine commercial series, the NBA logo on TV, and the Chip & Pepper TV cartoon, featured unique and vibrant computer images. Among the various animations, some were made by high end computer systems, but some simply by personal computers. Small, fast, and more capable personal computers are now performing professional-level video production roles and, in fact, they are a staple of many feature film productions and broadcast television facilities