Fast Texture Research Articles

A Novel LOD Rendering Method with Multi-level Structure Keeping Mesh Simplification and Fast Texture Alignment for Realistic 3D Models

A Novel LOD Rendering Method with Multi-level Structure Keeping Mesh Simplification and Fast Texture Alignment for Realistic 3D Models

Generating High-Fidelity Texture in RGB-D Reconstruction using Patches Density Regularization

Generating High-Fidelity Texture in RGB-D Reconstruction using Patches Density Regularization

Towards understanding and controlling of the surface texture pattern in 5-axis ball-end milling using fast texture simulation

Surface texture is critical for the functionality of industrial parts. This work aims to effectively control the surface texture by applying the phase difference angle between adjacent cutting passes using 5-axis ball-end milling. Because the texture pattern is complicated and is formed by combining feed-interval and step-interval scallops, we propose a level-contour-based fast simulation method to effectively generate the surface texture by inputting the phase difference angle with tilt and lead angles of the cutter. After clarifying the influence of these three parameters on the texture pattern, the desired texture was controlled by modifying the tool path according to the simulated phase difference angle. As a result, the milled surface texture corresponded well with the simulation. The details of the cutting features were efficiently and comprehensively simulated. The mechanism underlying the sudden change in the texture ridge direction was clarified as the major-minor ridge transition when the phase difference angle increased to a certain level, wherein the surface roughness could be decreased because the rearrangement of the cutting region reduced the scallop height. The proposed fast simulation method was also applied to the optimization process with a case study to demonstrate the benefits it can bring to the surface texture design and control.

Fast Texture Coding Based on Spatial, Temporal and Inter-View Correlations for 3D Video Coding

3D-HEVC (The 3D Extension of High Efficiency Video Coding), the latest international standard for 3D video coding, supports multiview plus depth 3D video format to enrich multimedia applications. For the texture coding, 3D-HEVC utilizes not only the information of temporal and spatial domains but also that of inter-view domain. However, the time consumption and complexity of 3D-HEVC also increase significantly. In this paper, fast texture coding algorithm for 3D-HEVC is proposed. We individually calculate the Pearson correlation coefficients by the rate-distortion costs (RD-costs) of coding tree units (CTUs) in the temporal, spatial and inter-view domains to analyze the correlations for independent view and dependent view. The proposed coding algorithm is based on the coding information of CTUs with higher correlations. The fast algorithm predicts and dynamically adjusts the depth range of the coding unit (CU). The prediction unit (PU) mode decision is proposed according to the complexity and partition direction of the best PU modes obtained from the highly correlated CTUs. The search range is adaptively adjusted for motion estimation. In addition, the RD-cost threshold is estimated to early terminate the CU split. Experimental results show that the proposed fast texture coding algorithm reduces the texture coding time significantly and outperforms numerous previous works.

Fast texture classification of denoised SAR image patches using GLCM on Spark

Classification of a synthetic aperture radar (SAR) image is an essential process for SAR image analysis and interpretation. Recent advances in imaging technologies have allowed data sizes to grow, and a large number of applications in many areas have been generated. However, analysis of high-resolution SAR images, such as classification, is a time-consuming process and high-speed algorithms are needed. In this study, classification of high-speed denoised SAR image patches by using Apache Spark clustering framework is presented. Spark is preferred due to its powerful open-source cluster-computing framework with fast, easy-to-use, and in-memory analytics. Classification of SAR images is realized on patch level by using the supervised learning algorithms embedded in the Spark machine learning library. The feature vectors used as the classifier input are obtained using gray-level cooccurrence matrix which is chosen to quantitatively evaluate textural parameters and representations. SAR image patches used to construct the feature vectors are first applied to the noise reduction algorithm to obtain a more accurate classification accuracy. Experimental studies were carried out using naive Bayes, decision tree, and random forest algorithms to provide comparative results, and significant accuracies were achieved. The results were also compared with a state-of-the-art deep learning method. TerraSAR-X images of high-resolution real-world SAR images were used as data.

Fast Texture Synthesis for Discrete Example-Based Elements

Considering the problem of discrete texture synthesis and the time for texturing, this paper proposes a novel framework for synthesizing texture images based on discrete example-based elements. We start with extracting texture feature distribution from exemplars and then produce discrete elements based on the cluster algorithm. After initializing a texture image, we propose a texture optimization algorithm based on heuristic searching to improve the quality of the texture image. Final, we use a texture transfer method based on Convolutional Neural Network (CNN) to stylize the optimized texture image. Our results show that the proposed texture synthesis method can significantly improve the quality of discrete texture synthesis and effectively shorten the time for texture generation.

A novel framework for inverse procedural texture modeling

Procedural textures are powerful tools that have been used in graphics for decades. In contrast to the alternative exemplar-based texture synthesis techniques, procedural textures provide user control and fast texture generation with low-storage cost and unlimited texture resolution. However, creating procedural models for complex textures requires a time-consuming process of selecting a combination of procedures and parameters. We present an example-based framework to automatically select procedural models and estimate parameters. In our framework, we consider textures categorized by commonly used high level classes. For each high level class we build a data-driven inverse modeling system based on an extensive collection of real-world textures and procedural texture models in the form of node graphs. We use unsupervised learning on collected real-world images in a texture class to learn sub-classes. We then classify the output of each of the collected procedural models into these sub-classes. For each of the collected models we train a convolutional neural network (CNN) to learn the parameters to produce a specific output texture. To use our framework, a user provides an exemplar texture image within a high level class. The system first classifies the texture into a sub-class, and selects the procedural models that produce output in that sub-class. The pre-trained CNNs of the selected models are used to estimate the parameters of the texture example. With the predicted parameters, the system can generate appropriate procedural textures for the user. The user can easily edit the textures by adjusting the node graph parameters. In a last optional step, style transfer augmentation can be applied to the fitted procedural textures to recover details lost in the procedural modeling process. We demonstrate our framework for four high level classes and show that our inverse modeling system can produce high-quality procedural textures for both structural and non-structural textures.

Low-Complexity Texture Video Coding Based on Motion Homogeneity for 3D-HEVC

Three-dimensional extension of the high efficiency video coding (3D-HEVC) is an emerging international video compression standard for multiview video system applications. Similar to HEVC, a computationally expensive mode decision is performed using all depth levels and prediction modes to select the least rate-distortion (RD) cost for each coding unit (CU). In addition, new tools and intercomponent prediction techniques have been introduced to 3D-HEVC for improving the compression efficiency of the multiview texture videos. These techniques, despite achieving the highest texture video coding efficiency, involve extremely high-complex procedures, thus limiting 3D-HEVC encoders in practical applications. In this paper, a fast texture video coding method based on motion homogeneity is proposed to reduce 3D-HEVC computational complexity. Because the multiview texture videos instantly represent the same scene at the same time (considering that the optimal CU depth level and prediction modes are highly multiview content dependent), it is not efficient to use all depth levels and prediction modes in 3D-HEVC. The motion homogeneity model of a CU is first studied according to the motion vectors and prediction modes from the corresponding CUs. Based on this model, we present three efficient texture video coding approaches, such as the fast depth level range determination, early SKIP/Merge mode decision, and adaptive motion search range adjustment. Experimental results demonstrate that the proposed overall method can save 56.6% encoding time with only trivial coding efficiency degradation.

Multimedia document image retrieval based on regional correlation fusion texture feature FDPC

In order to realize the retrieval efficiency and detection precision of digital library collection resources, a new clustering algorithm (Fast density peak clustering,FDPC) based on fast texture density peak is proposed. Firstly, a framework of document image retrieval based on content description is given, based on median filter and direct-square equalization strategy, denoising and background processing of input document image are introduced, then density peak clustering (DPC) is used to classify image, and the convergence performance of DPC algorithm is improved by using dynamic truncation distance mode. Finally, based on the library standard test library (Corel), the performance of the proposed algorithm is validated experimentally, and the experimental results show that the proposed method has higher retrieval efficiency and retrieval accuracy.

Determination of diffraction time and influence of strip moving speeds on texture measurement using X-ray area detector

Abstract In this study, the two most important factors, diffraction time and the influence of the strip moving speed on diffraction accuracy, are analyzed using a laboratory two-dimensional X-ray diffraction system. The results indicate that 2 s is a diffraction time that satisfies both requirements of detection speed and accuracy. The strip moving speed has a certain influence on the detection accuracy, but the influence of the strip moving speed on the diffraction accuracy can be neglected within a strip moving speed of 0.65 m/s. The measurement of online texture and mechanical properties, combined with the fast texture detection theory and mechanical properties calculation model, can be realized in a steel production factory.

Fusion of Polarimetric Features and Structural Gradient Tensors for VHR PolSAR Image Classification

This paper proposes a fast texture based supervised classification framework for fully polarimetric synthetic aperture radar (PolSAR) images with very high spatial resolution (VHR). With the development of recent polarimetric radar remote sensing technologies, the acquired images contain not only rich polarimetric characteristics but also high spatial content. Thus, the notion of geometrical structures and heterogeneous textures within VHR PolSAR data becomes more and more significant. Moreover, when the spatial resolution is increased, we need to deal with large-size image data. In this paper, our motivation is to characterize textures by incorporating (fusing) both polarimetric and structural features, and then use them for classification purpose. First, polarimetric features from the weighted coherency matrix and local geometric information based on the Di Zenzo structural tensors are extracted and fused using the covariance approach. Then, supervised classification task is performed by using Riemannian distance measure relevant for covariance-based descriptors. In order to accelerate the computational time, we propose to perform texture description and classification only on characteristic points, not all pixels from the image. Experiments conducted on the VHR F-SAR data as well as the AIRSAR Flevoland image using the proposed framework provide very promising and competitive results in terms of terrain classification and discrimination.

Fast Texture Mapping Adjustment via Local/Global Optimization.

This paper deals with the texture mapping of a triangular mesh model given a set of calibrated images. Different from the traditional approach of applying projective texture mapping with model parameterizations, we develop an image-space texture optimization scheme that aims to reduce visible seams or misalignment at texture or depth boundaries. Our novel scheme starts with an efficient local (and parallel) texture adjustment scheme at these boundaries, followed by a global correction step to rectify potential texture distortions caused by the local movement. Our phased optimization scheme achieves 50$\sim$∼100 times speed up on GPU (or 6× on CPU) compared to previous state-of-the-art methods. Experiments on a variety of models showed that we achieve this significant speed-up without sacrificing texture quality. Our approach significantly improves resilience to modeling and calibration errors, thereby allowing fast and fully automatic creation of textured models using commodity depth sensors by untrained users.

Fast CU partition strategy for HEVC based on Haar wavelet

As the latest video coding standard, high-efficiency video coding (HEVC) achieves better performance and supports higher resolution compared with the predecessor standard, H.264/advanced video coding (AVC). Intra-coding is an important feature in HEVC standard, which reduces the spatial redundancy significantly, due to the flexible coding structure, and high density of angular prediction modes. However, the improvement on coding efficiency is obtained at the expense of the extraordinary computation complexity. This study presents a novel coding unit (CU) partitioning technique for HEVC. By using a fast texture complexity detection method, which is based on two-dimensional Haar wavelet transform, texture complexity for each CU can be extracted. According to the Haar wavelet coefficients obtained, an early CU splitting termination is proposed to decide whether a CU should be decomposed into four lower dimensions CUs or not. Experimental results demonstrate that the fast CU partition strategy achieves better trade-off between rate-distortion performance and complexity reduction than the previous algorithms. Compared with the reference software HM16.7, the proposed algorithm can lessen the encoding time up to 46.22% on average, with a negligible bit rate increase of 0.45%, and quality losses lower than 0.04 dB, respectively.

A fast texture segmentation scheme based on active contours and discrete cosine transform

Abstract This paper presents a novel and efficient parametric active contour model based on the texture describing properties derived from coefficients of the two-dimensional Discrete Cosine Transform (2-D DCT) for segmenting texture objects against complex backgrounds. Block based 2-D DCT is applied in the local neighbourhood of all the contour control points and some randomly chosen object points. DCT coefficients in the horizontal, vertical and diagonal directions which represent the dominant textures are used to construct the histograms those exhibit different structures for different texture regions. Heterogeneity of these histograms corresponding to contour and object points is measured by Wasserstein distance metric. Finally, DC coefficient which represents the energy of the corresponding block modulated by the calculated Wasserstein distance is employed as the external energy of the proposed active contour model. Experiments on synthetic and natural texture images show the efficiency of our model in terms of accuracy and speed.

Fast Exemplar-Based Image Inpainting Using a New Pruning Technique

This paper presents a new algorithm for fast exemplar-based image inpainting by using image pyramid and a novel pruning scheme. The proposed technique separates the inpainting process into three major steps. First the model determines which patch belonging to the inpainting region should be filled first. Then the pruning system analyzes the selected patch and weeds out regions having no chance to be the best matching patch. Finally, it performs a fast texture synthesis based on greedy search method in the remaining regions to fill the selected patch. Compared with previous methods, the proposed algorithm is much more efficient computationally. Experiments on synthetic and natural images show the advantages of the proposed algorithm, where it is shown to compare favorably with contemporary state-of-the-art schemes.

Efficient volume rendering methods for out-of-Core datasets by semi-adaptive partitioning

Volume rendering methods are widely used for the high-quality visualization of various 3D datasets, especially scalar field datasets (e.g., 3D images). However, when rendering datasets with ultra-high spatial resolutions, which occupy massive (out-of-core) storage space, some traditional in-core volume rendering algorithms cannot function because the large input data can hardly be handled in the main memory. Simple modifications based on disk cache I/O do not perform well because of the overheads associated with external memory access. To solve this problem, this paper describes a semi-adaptive partitioning strategy and an efficient out-of-core visualization framework with improved volume rendering algorithms. Under this new partitioning strategy, an out-of-core dataset is spatially divided into small sub-blocks of different sizes, which are organized by a binary space partitioning (BSP) tree. Each sub-block can be loaded into the fast texture memory of the graphics hardware to be rendered by our improved volume rendering algorithms. The final result is obtained by composing the projection images of all sub-blocks after traveling the BSP tree according to the viewpoint. Experimental results indicate that the new methods are effective and efficient for visualizing out-of-core 3D scalar field datasets.

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.

Filling Disocclusions in Extrapolated Virtual Views Using Hybrid Texture Synthesis

For glasses-free 3-D viewing, autostereoscopic displays provide a dense set of views showing a scene from slightly different viewpoints. As the number of views and their spatial positions are display dependent, a generic depth-enhanced multiview video plus depth format is used for coding and transmission. From this, the required views are generated via depth image-based rendering (DIBR) techniques by using original views and associated depth information. A critical problem in the DIBR techniques is that texture regions, which are covered by foreground objects in the original image, may become visible in the synthesized view, especially in extrapolation scenarios. To cover these disoccluded areas, a novel hybrid texture synthesis method is described in this paper. Our approach combines a new patch-based and a novel fast parametric texture synthesis method in an innovative way to compute the disoccluded image areas. The proposed method outperforms state-of-the-art approaches in terms of peak signal-to-noise ratio, structural similarity, visual saliency induced index, feature similarity index (color), and run-time.

Fast Background Subtraction for Microorganism Detection in Wireless Visual Sensor Networks

With the emergency and development of portable microscopes, applying wireless network and image analysis techniques to real-time and intelligent surveillance in sewage treatment system has paid much attention recently. However, the resource limitation and processing capacity are the main constraints in wireless visual sensor networks (WVSNs), which make many background subtraction algorithms for moving object detection unable to work. In this paper, a more efficient and effective detection algorithm based on background subtraction has been proposed. Firstly, a new codebook model is initialized by selecting randomly in the neighbor of spatial domain from the first frame of video sequence, which greatly reduces the modeling time. Secondly, a new color space and a fast local directional pattern texture operator have been introduced into this model to increase the target contrast and neighborhood dependency. Finally, a self-adaptive model updating strategy based on statistical parameter estimation has been proposed for adding new code words and deleting noisy code words from models quickly and effectively. Experimental results have demonstrated that our method is able to achieve a better balance between effectiveness and efficiency compared with the state-of-the-art background subtraction algorithms.

Effects of sputtering and assisting ions on the orientation of titanium nitride films fabricated by ion beam assisted sputtering deposition from metal target

Abstract Ion beam assisted titanium nitride (TiN) film has attracted much attention because its fast texture and high conductivity can be effectively applied in all-conductive superconducting coated conductor. In this work, TiN films were prepared by ion beam sputtering deposition from a metal titanium target. Effects of sputtering ion energy, assisting ion energy, assisting ion current and deposition temperature on the orientation and surface morphology were analyzed. The results indicate that assisting ion is an important factor in orientation selection, and high assisting ions and low assisting ion current could enhance the crystallinity. However, too high assisting ion energy and current can destroy the crystallinity in IBAD–TiN. This orientation selection can be attributed to the energy exchange between assisting ions and adatoms.