70,484 research outputs found
Perception Driven Texture Generation
This paper investigates a novel task of generating texture images from
perceptual descriptions. Previous work on texture generation focused on either
synthesis from examples or generation from procedural models. Generating
textures from perceptual attributes have not been well studied yet. Meanwhile,
perceptual attributes, such as directionality, regularity and roughness are
important factors for human observers to describe a texture. In this paper, we
propose a joint deep network model that combines adversarial training and
perceptual feature regression for texture generation, while only random noise
and user-defined perceptual attributes are required as input. In this model, a
preliminary trained convolutional neural network is essentially integrated with
the adversarial framework, which can drive the generated textures to possess
given perceptual attributes. An important aspect of the proposed model is that,
if we change one of the input perceptual features, the corresponding appearance
of the generated textures will also be changed. We design several experiments
to validate the effectiveness of the proposed method. The results show that the
proposed method can produce high quality texture images with desired perceptual
properties.Comment: 7 pages, 4 figures, icme201
A Self-Organizing Neural System for Learning to Recognize Textured Scenes
A self-organizing ARTEX model is developed to categorize and classify textured image regions. ARTEX specializes the FACADE model of how the visual cortex sees, and the ART model of how temporal and prefrontal cortices interact with the hippocampal system to learn visual recognition categories and their names. FACADE processing generates a vector of boundary and surface properties, notably texture and brightness properties, by utilizing multi-scale filtering, competition, and diffusive filling-in. Its context-sensitive local measures of textured scenes can be used to recognize scenic properties that gradually change across space, as well a.s abrupt texture boundaries. ART incrementally learns recognition categories that classify FACADE output vectors, class names of these categories, and their probabilities. Top-down expectations within ART encode learned prototypes that pay attention to expected visual features. When novel visual information creates a poor match with the best existing category prototype, a memory search selects a new category with which classify the novel data. ARTEX is compared with psychophysical data, and is benchmarked on classification of natural textures and synthetic aperture radar images. It outperforms state-of-the-art systems that use rule-based, backpropagation, and K-nearest neighbor classifiers.Defense Advanced Research Projects Agency; Office of Naval Research (N00014-95-1-0409, N00014-95-1-0657
Haptic Experience and the Design of Drawing Interfaces
Haptic feedback has the potential to enhance users’ sense of being engaged and creative in their artwork. Current work on providing haptic feedback in computer-based drawing applications has focused mainly on the realism of the haptic sensation rather than the users’ experience of that sensation in the context of their creative work. We present a study that focuses on user experience of three haptic drawing interfaces. These interfaces were based on two different haptic metaphors, one of which mimicked familiar drawing tools (such as pen, pencil or crayon on smooth or rough paper) and the other of which drew on abstract descriptors of haptic experience (roughness, stickiness, scratchiness and smoothness). It was found that users valued having control over the haptic sensation; that each metaphor was preferred by approximately half of the participants; and that the real world metaphor interface was considered more helpful than the abstract one, whereas the abstract interface was considered to better support creativity. This suggests that future interfaces for artistic work should have user-modifiable interaction styles for controlling the haptic sensation
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
Coding of visual object features and feature conjunctions in the human brain
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