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The purpose of this paper is to propose a color-texture classification approach which uses color sensor information and texture features jointly. High accuracy, low noise sensitivity and low computational complexity are specified aims for this proposed approach.

One of the efficient texture analysis operations is local binary patterns (LBP). The proposed approach includes two steps. First, a noise resistant version of color LBP is proposed to decrease its sensitivity to noise. This step is evaluated based on combination of color sensor information using AND operation. In a second step, a significant points selection algorithm is proposed to select significant LBPs. This phase decreases final computational complexity along with increasing accuracy rate.

The proposed approach is evaluated using Vistex, Outex and KTH-TIPS-2a data sets. This approach has been compared with some state-of-the-art methods. It is experimentally demonstrated that the proposed approach achieves the highest accuracy. In two other experiments, results show low noise sensitivity and low computational complexity of the proposed approach in comparison with previous versions of LBP. Rotation invariant, multi-resolution and general usability are other advantages of our proposed approach.

In the present paper, a new version of LBP is proposed originally, which is called hybrid color local binary patterns (HCLBP). HCLBP can be used in many image processing applications to extract color/texture features jointly. Also, a significant point selection algorithm is proposed for the first time to select key points of images.

The purpose of this paper is to propose a generic framework based on the colour and the texture features for colour‐textured image segmentation. The framework can be applied to any real‐world applications for appropriate interpretation.

The framework derives the contributions of colour and texture in image segmentation. Local binary pattern and an unsupervised k‐means clustering are used to cluster pixels in the chrominance plane. An unsupervised segmentation method is adopted. A quantitative estimation of colour and texture performance in segmentation is presented. The proposed method is tested using different mosaic and natural images and other image database used in computer vision. The framework is applied to three different applications namely, Irish script on screen images, skin cancer images and sediment profile imagery to demonstrate the robustness of the framework.

The inclusion of colour and texture as distributions of regions provided a good discrimination of the colour and the texture. The results indicate that the incorporation of colour information enhanced the texture analysis techniques and the methodology proved effective and efficient.

The novelty lies in the development of a generic framework using both colour and texture features for image segmentation and the different applications from various fields.

The application of colour for an improved presentation of 3‐D structures with finite elements is reported. Also shown is how the hidden‐surface technique can be used for: (1) generating pictures in the hidden line alike mode, (2) generating photo‐alike pictures by shading the surfaces according to Lambert's cosine law, (3) showing the regions of different materials or properties by distinct colouring, (4) the presentation of temperature and stress fields by colouring. This colouring is done with smooth colour transitions and delivers pictures similar to those gained by thermography or stress optics. Furthermore, (5) it is possible to generate contour lines on the remaining visible surfaces. The problems arising with the attachment of a colour hardcopier are also considered.

The purpose of this paper is to study a feasible visualization of large-size three-dimension (3D) color models which are beyond the maximum print size of newest paper-based 3D printer used 3D cutting-bonding frame (3D-CBF) and evaluate the effects of cutting angle and layout method on printing time of designed models.

Sixteen models, including cuboid model, cylinder model, hole model and sphere model with different shape features, were divided into two symmetric parts and printed by the Mcor IRIS HD 3D printer. Before printing, two sub-parts were rearranged in one of three layout methods. Nine scaled sizes of original models were printed to find the quantitative relationship between printing time and scale values in each type. For the 0.3 times of original models, six cutting angles were evaluated in detail.

The correlation function about colorization time and printed pages was proposed. Based on 3D-CBF, the correlation between printing time and scale size is statistically defined. Optimization parameters of designed parts visualization about cutting angel and layout method were found, even if their statistical results were difficult to model their effects on printing time of specimens.

The research is comparative and limited to the special models and used procedures.

The paper provides a feasible visualization and printing speed optimization methods for the further industrialization of 3D paper-based printing technology in cultural creative field.

This study aims to introduce a novel noise filter module designed for LiDAR simultaneous localization and mapping (SLAM) systems. The primary objective is to enhance pose estimation accuracy and improve the overall system performance in outdoor environments.

Distinct from traditional approaches, MCFilter emphasizes enhancing point cloud data quality at the pixel level. This framework hinges on two primary elements. First, the D-Tracker, a tracking algorithm, is grounded on multiresolution three-dimensional (3D) descriptors and adeptly maintains a balance between precision and efficiency. Second, the R-Filter introduces a pixel-level attribute named motion-correlation, which effectively identifies and removes dynamic points. Furthermore, designed as a modular component, MCFilter ensures seamless integration into existing LiDAR SLAM systems.

Based on rigorous testing with public data sets and real-world conditions, the MCFilter reported an increase in average accuracy of 12.39% and reduced processing time by 24.18%. These outcomes emphasize the method’s effectiveness in refining the performance of current LiDAR SLAM systems.

In this study, the authors present a novel 3D descriptor tracker designed for consistent feature point matching across successive frames. The authors also propose an innovative attribute to detect and eliminate noise points. Experimental results demonstrate that integrating this method into existing LiDAR SLAM systems yields state-of-the-art performance.

The purpose of this paper, on innovative design of traditional weft-backed woven fabric, is to investigate a design principle and method for full-backed structure with double-faced shading effect to realize two types of double-faced shading effects for traditional weft-backed fabric that are impossible to be realized under plane design mode. In addition, the study on the color rendering law is conducive to the design application, and the effectiveness of the design method has been verified by the design practices.

This paper presents a design method for full-backed structure with two shaded weave databases (SWDs) by selecting two primary weaves (PWs), establishing the corresponding SWDs, selecting the proper compound structures for database of full-backed structure with double-faced shading effect. Color card fabric with 544 specimens is produced and their color values are measured, their color difference and variance are analyzed to evaluate the color rendering characteristics. Finally, double-faced weft-backed fabrics are produced under layered-combination design mode to verify the practicality of full-backed structure with double-faced shading effect.

Weft-backed woven fabrics with “SPDC” (same pattern and different color) and “DPDC” (different pattern and different color) shading effects can be produced using full-backed structure with double-faced shading effect. The color expression is extremely enhanced (136 compound structures on one side for one color weft). In the shading process, two sets of wefts do not affect each other, and stable and ideal color shading effect with high color purity can be expressed according to the analyses on the L* (lightness) values, color purity, color differences (0.47–3.20) and variance (0.25–1.21) of the color card fabric.

Breaking through the structural limitations and achieving the double-faced shading effects that cannot be expressed in plane design mode. The research on two weft-backed fabric with the most basic weft-backed structure provides not only a theoretical base for further study on weft-backed structures, but also some references for structure innovation design of traditional weft-backed woven fabrics.

This paper aims to propose a biologically inspired processing architecture to recognize and classify fabrics with respect to the weave pattern (fabric texture) and yarn color (fabric color).

By using the fabric weave patterns image identification system, this study analyzed the fabric image based on the Hierarchical-MAX (HMAX) model of computer vision, to extract feature values related to texture of fabric. Red Green Blue (RGB) color descriptor based on opponent color channels simulating the single opponent and double opponent neuronal function of the brain is incorporated in to the texture descriptor to extract yarn color feature values. Finally, support vector machine classifier is used to train and test the algorithm.

This two-stage processing architecture can be used to construct a system based on computer vision to recognize fabric texture and to increase the system reliability and accuracy. Using this method, the stability and fault tolerance (invariance) was improved.

Traditionally, fabric texture recognition is performed manually by visual inspection. Recent studies have proposed automatic fabric texture identification based on computer vision. In the identification process, the fabric weave patterns are recognized by the warp and weft floats. However, due to the optical environments and the appearance differences of fabric and yarn, the stability and fault tolerance (invariance) of the computer vision method are yet to be improved. By using our method, the stability and fault tolerance (invariance) was improved.

In the past few decades, the content-based image retrieval (CBIR), which focuses on the exploration of image feature extraction methods, has been widely investigated. The term of feature extraction is used in two cases: application-based feature expression and mathematical approaches for dimensionality reduction. Feature expression is a technique of describing the image color, texture and shape information with feature descriptors; thus, obtaining effective image features expression is the key to extracting high-level semantic information. However, most of the previous studies regarding image feature extraction and expression methods in the CBIR have not performed systematic research. This paper aims to introduce the basic image low-level feature expression techniques for color, texture and shape features that have been developed in recent years.

First, this review outlines the development process and expounds the principle of various image feature extraction methods, such as color, texture and shape feature expression. Second, some of the most commonly used image low-level expression algorithms are implemented, and the benefits and drawbacks are summarized. Third, the effectiveness of the global and local features in image retrieval, including some classical models and their illustrations provided by part of our experiment, are analyzed. Fourth, the sparse representation and similarity measurement methods are introduced, and the retrieval performance of statistical methods is evaluated and compared.

The core of this survey is to review the state of the image low-level expression methods and study the pros and cons of each method, their applicable occasions and certain implementation measures. This review notes that image peculiarities of single-feature descriptions may lead to unsatisfactory image retrieval capabilities, which have significant singularity and considerable limitations and challenges in the CBIR.

A comprehensive review of the latest developments in image retrieval using low-level feature expression techniques is provided in this paper. This review not only introduces the major approaches for image low-level feature expression but also supplies a pertinent reference for those engaging in research regarding image feature extraction.

As laser scanning technology becomes readily available and affordable, there is an increasing demand of using point cloud data collected from a laser scanner to create as-built building information modeling (BIM) models for quality assessment, schedule control and energy performance within construction projects. To enhance the as-built modeling efficiency, this study explores an integrated system, called Auto-Scan-To-BIM (ASTB), with an aim to automatically generate a complete Industry Foundation Classes (IFC) model consisted of the 3D building elements for the given building based on its point cloud without requiring additional modeling tools.

ASTB has been developed with three function modules. Taking the scanned point data as input, Module 1 is built on the basis of the widely used region segmentation methodology and expanded with enhanced plane boundary line detection methods and corner recalibration algorithms. Then, Module 2 is developed with a domain knowledge-based heuristic method to analyze the features of the recognized planes, to associate them with corresponding building elements and to create BIM models. Based on the spatial relationships between these building elements, Module 3 generates a complete IFC model for the entire project compatible with any BIM software.

A case study validated the ASTB with an application with five common types of building elements (e.g. wall, floor, ceiling, window and door).

First, an integrated system, ASTB, is developed to generate a BIM model from scanned point cloud data without using additional modeling tools. Second, an enhanced plane boundary line detection method and a corner recalibration algorithm are developed in ASTB with high accuracy in obtaining the true surface planes. At last, the research contributes to develop a module, which can automatically convert the identified building elements into an IFC format based on the geometry and spatial relationships of each plan.

The purpose of this paper is to find automatic post‐processing scheme to give textures and motion data to three dimensional (3D) body scan data.

Semi‐implicit particle‐based method was applied to post‐processing of 3D body scan data. The template avatar mesh was draped onto the target scan data and the texture/motion data were transferred to regenerated body. Automatic body feature detection was used to correlate the template body with the target body.

Using semi‐implicit particle method, there are advantages in both computational stability and accuracy. The calculation is done in a few minutes and even data with many holes could be used.

There are several researches for body feature detection and scan body regeneration but this paper aims for fully automatic method which needs no human intervention. The semi‐implicit particle method, which is popularly used for cloth simulation, is applied to body data regeneration. The conventional 3D body scan data, which had no colors and motions can be given textures and motions with this approach. And even the face can be freely interchanged with the use of external face generation software.