Topic
Texel
About: Texel is a research topic. Over the lifetime, 672 publications have been published within this topic receiving 12102 citations. The topic is also known as: Texel Island & Texel (Nizozemsko : ostrov).
Papers published on a yearly basis
Papers
1 Jan 1980
TL;DR: In this article, texture energy is measured by filtering with small masks, typically 5x5, then with a moving-window average of the absolute image values, leading to a simple class of texture energy transforms, which perform better than any of the preceding methods.
Abstract: : The problem of image texture analysis is introduced, and existing approaches are surveyed. An empirical evaluation method is applied to two texture measurement systems, co-occurrence statistics and augmented correlation statistics. A spatial-statistical class of texture measures is then defined and evaluated. It leads to a simple class of texture energy transforms, which perform better than any of the preceding methods. These transforms are very fast, and can be made invariant to changes in luminance, contrast, and rotation without histogram equalization or other preprocessing. Texture energy is measured by filtering with small masks, typically 5x5, then with a moving-window average of the absolute image values. This method, similar to human visual processing, is appropriate for textures with short coherence length or correlation distance. The filter masks are integer-valued and separable, and can be implemented with one-dimensional or 3x3 convolutions. The averaging operation is also very fast, with computing time independent of window size. Texture energy planes may be linearly combined to form a smaller number of discriminant planes. These principal component planes seem to represent natural texture dimensions, and to be more reliable texture measures than the texture energy planes. Texture segmentation or classification may be accomplished using either texture energy or principal component planes as input. This study classified 15x15 blocks of eight natural textures. Accuracies of 72% were achieved with co- occurrence statistics, 65% with augmented correlation statistics, and 94% with texture energy statistics.
978 citations
1 Aug 2001
TL;DR: A new form of texture mapping that produces increased photorealism, and several reflectance function transformations that act as contrast enhancement operators are presented that are useful in the study of ancient archeological clay and stone writings.
Abstract: In this paper we present a new form of texture mapping that produces increased photorealism. Coefficients of a biquadratic polynomial are stored per texel, and used to reconstruct the surface color under varying lighting conditions. Like bump mapping, this allows the perception of surface deformations. However, our method is image based, and photographs of a surface under varying lighting conditions can be used to construct these maps. Unlike bump maps, these Polynomial Texture Maps (PTMs) also capture variations due to surface self-shadowing and interreflections, which enhance realism. Surface colors can be efficiently reconstructed from polynomial coefficients and light directions with minimal fixed-point hardware. We have also found PTMs useful for producing a number of other effects such as anisotropic and Fresnel shading models and variable depth of focus. Lastly, we present several reflectance function transformations that act as contrast enhancement operators. We have found these particularly useful in the study of ancient archeological clay and stone writings.
766 citations
23 Oct 1995
TL;DR: This paper describes a method for synthesizing images that match the texture appearance of a given digitized sample that is based on a model of human texture perception, and has potential to be a practically useful tool for image processing and graphics applications.
Abstract: This paper describes a method for synthesizing images that match the texture appearance of a given digitized sample This synthesis is completely automatic and requires only the "target" texture as input It allows generation of as much texture as desired so that any object can be covered The approach is based on a model of human texture perception, and has potential to be a practically useful tool for image processing and graphics applications
687 citations
TL;DR: In this article, a number of numerical techniques used to enhance and classify imagery produced by SeaMARC II, a long-range combination side scan sonar, and bathymetric seafloor mapping system are documented.
Abstract: The recent growth in the production rate of digital side scan sonar images, coupled with the rapid expansion of systematic seafloor exploration programs, has created a need for fast and quantitative means of processing seafloor imagery. Computer-aided analytical techniques fill this need. A number of numerical techniques used to enhance and classify imagery produced by SeaMARC II, a long-range combination side scan sonar, and bathymetric seafloor mapping system are documented. Three categories of techniques are presented: (1) preprocessing corrections (radiometric and geometric), (2) feature extraction, and (3) image segmentation and classification. An introduction to the concept of “feature vectors” is provided, along with an explanation of the method of evaluation of a texture feature vector based upon gray-level co-occurrence matrices (GLCM). An alternative to the a priori texel (texture element) subdivision of images is presented in the form of region growing and texture analysis (REGATA). This routine provides a texture map of spatial resolution superior to that obtainable with arbitrarily assigned texel boundaries and minimizes the possibility of mixed texture signals due to the combination of two or more textures in an arbitrarily assigned texel. Computer classification of these textural features extracted via the GLCM technique results in transformation of images into maps of image texture. These maps may either be interpreted in terms of the theoretical relationships shown between texture signatures and wavelength or converted to geologic maps by correlation of texture signatures with ground truth data. These techniques are applied to SeaMARC II side scan sonar imagery from a variety of geologic environments, including lithified and nonlithified sedimentary formations, volcanic and sedimentary debris flows, and crystalline basaltic outcrops. Application of the above processing steps provided not only superior images for both subjective and quantitative analysis but also the critical ability to discriminate between outcrops with distinct lithologies but similar image intensity.
186 citations
TL;DR: A method is presented for identifying texture elements while simultaneously recovering the orientation of textured surfaces, using a multiscale region detector based on measurements in a Del /sup 2/G (Laplacian-of-Gaussian) scale space.
Abstract: A method is presented for identifying texture elements while simultaneously recovering the orientation of textured surfaces. A multiscale region detector, based on measurements in a Del /sup 2/G (Laplacian-of-Gaussian) scale space, is used to construct a set of candidate texture elements. True elements are selected from the set of candidate elements by finding the planar surface that best predicts the observed areas of the latter. Results are shown for a variety of natural textures, including waves, flowers, rocks, clouds, and dirt clods. >
161 citations
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2025 | 9 |
| 2024 | 11 |
| 2023 | 19 |
| 2022 | 22 |
| 2021 | 6 |
| 2020 | 10 |