A Hybrid Texture Coding Method for Fast Texture Mapping

An efficient texture compression method is proposed based on a block matching process between the current block and the previously encoded blocks. Texture mapping is widely used to improve the quality of rendering results in real-time applications. For fast texture mapping, it is important to find an optimal trade-off between compression efficiency and computational complexity. Low-complexity methods (e.g., ETC1 and DXT1) have often been adopted in real-time rendering applications because conventional compression methods (e.g., JPEG) achieve a high compression ratio at the cost of high complexity. We propose a block matching-based compression method that can achieve a higher compression ratio than ETC1 and DXT1 while maintaining computational complexity lower than that of JPEG. Through a comparison between the proposed method and existing compression methods, we confirm our expectations on the performance of the proposed method

Surface Defect Classification for Hot-Rolled Steel Strips by Selectively Dominant Local Binary Patterns

Developments in defect descriptors and computer vision-based algorithms for automatic optical inspection (AOI) allows for further development in image-based measurements. Defect classification is a vital part of an optical-imaging-based surface quality measuring instrument. The high-speed production rhythm of hot continuous rolling requires an ultra-rapid response to every component as well as algorithms in AOI instrument. In this paper, a simple, fast, yet robust texture descriptor, namely selectively dominant local binary patterns (SDLBPs), is proposed for defect classification. First, an intelligent searching algorithm with a quantitative thresholding mechanism is built to excavate the dominant non-uniform patterns (DNUPs). Second, two convertible schemes of pattern code mapping are developed for binary encoding of all uniform patterns and DNUPs. Third, feature extraction is carried out under SDLBP framework. Finally, an adaptive region weighting method is built for further strengthening the original nearest neighbor classifier in the feature matching stage. The extensive experiments carried out on an open texture database (Outex) and an actual surface defect database (Dragon) indicates that our proposed SDLBP yields promising performance on both classification accuracy and time efficiencyPeer reviewe

VolumeEVM: A new surface/volume integrated model

Volume visualization is a very active research area in the field of scien-tific visualization. The Extreme Vertices Model (EVM) has proven to be a complete intermediate model to visualize and manipulate volume data using a surface rendering approach. However, the ability to integrate the advantages of surface rendering approach with the superiority in visual exploration of the volume rendering would actually produce a very complete visualization and edition system for volume data. Therefore, we decided to define an enhanced EVM-based model which incorporates the volumetric information required to achieved a nearly direct volume visualization technique. Thus, VolumeEVM was designed maintaining the same EVM-based data structure plus a sorted list of density values corresponding to the EVM-based VoIs interior voxels. A function which relates interior voxels of the EVM with the set of densities was mandatory to be defined. This report presents the definition of this new surface/volume integrated model based on the well known EVM encoding and propose implementations of the main software-based direct volume rendering techniques through the proposed model.Postprint (published version

A Self-Organizing System for Classifying Complex Images: Natural Textures and Synthetic Aperture Radar

A self-organizing architecture is developed for image region classification. The system consists of a preprocessor that utilizes multi-scale filtering, competition, cooperation, and diffusion to compute a vector of image boundary and surface properties, notably texture and brightness properties. This vector inputs to a system that incrementally learns noisy multidimensional mappings and their probabilities. The architecture is applied to difficult real-world image classification problems, including classification of synthetic aperture radar and natural texture images, and outperforms a recent state-of-the-art system at classifying natural texturns.Office of Naval Research (N00014-95-1-0409, N00014-95-1-0657, N00014-91-J-4100); Advanced Research Projects Agency (N00014-92-J-4015); Air Force Office of Scientific Research (F49620-92-J-0225, F49620-92-J-0334); National Science Foundation (IRI-90-00530, IRI-90-24877

A Self-Organizing System for Classifying Complex Images: Natural Textures and Synthetic Aperture Radar

A self-organizing architecture is developed for image region classification. The system consists of a preprocessor that utilizes multi-scale filtering, competition, cooperation, and diffusion to compute a vector of image boundary and surface properties, notably texture and brightness properties. This vector inputs to a system that incrementally learns noisy multidimensional mappings and their probabilities. The architecture is applied to difficult real-world image classification problems, including classification of synthetic aperture radar and natural texture images, and outperforms a recent state-of-the-art system at classifying natural texturns.Office of Naval Research (N00014-95-1-0409, N00014-95-1-0657, N00014-91-J-4100); Advanced Research Projects Agency (N00014-92-J-4015); Air Force Office of Scientific Research (F49620-92-J-0225, F49620-92-J-0334); National Science Foundation (IRI-90-00530, IRI-90-24877

Exploiting Deep Features for Remote Sensing Image Retrieval: A Systematic Investigation

Remote sensing (RS) image retrieval is of great significant for geological information mining. Over the past two decades, a large amount of research on this task has been carried out, which mainly focuses on the following three core issues: feature extraction, similarity metric and relevance feedback. Due to the complexity and multiformity of ground objects in high-resolution remote sensing (HRRS) images, there is still room for improvement in the current retrieval approaches. In this paper, we analyze the three core issues of RS image retrieval and provide a comprehensive review on existing methods. Furthermore, for the goal to advance the state-of-the-art in HRRS image retrieval, we focus on the feature extraction issue and delve how to use powerful deep representations to address this task. We conduct systematic investigation on evaluating correlative factors that may affect the performance of deep features. By optimizing each factor, we acquire remarkable retrieval results on publicly available HRRS datasets. Finally, we explain the experimental phenomenon in detail and draw conclusions according to our analysis. Our work can serve as a guiding role for the research of content-based RS image retrieval

Generalized Completed Local Binary Patterns for Time-Efficient Steel Surface Defect Classification

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted ncomponent of this work in other works.Efficient defect classification is one of the most important preconditions to achieve online quality inspection for hot-rolled strip steels. It is extremely challenging owing to various defect appearances, large intraclass variation, ambiguous interclass distance, and unstable gray values. In this paper, a generalized completed local binary patterns (GCLBP) framework is proposed. Two variants of improved completed local binary patterns (ICLBP) and improved completed noise-invariant local-structure patterns (ICNLP) under the GCLBP framework are developed for steel surface defect classification. Different from conventional local binary patterns variants, descriptive information hidden in nonuniform patterns is innovatively excavated for the better defect representation. This paper focuses on the following aspects. First, a lightweight searching algorithm is established for exploiting the dominant nonuniform patterns (DNUPs). Second, a hybrid pattern code mapping mechanism is proposed to encode all the uniform patterns and DNUPs. Third, feature extraction is carried out under the GCLBP framework. Finally, histogram matching is efficiently accomplished by simple nearest-neighbor classifier. The classification accuracy and time efficiency are verified on a widely recognized texture database (Outex) and a real-world steel surface defect database [Northeastern University (NEU)]. The experimental results promise that the proposed method can be widely applied in online automatic optical inspection instruments for hot-rolled strip steel.Peer reviewe

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