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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.

Robot vision is a fundamental device for human–robot interaction and robot complex tasks. In this paper, the authors aim to use Kinect and propose a feature graph fusion (FGF) for robot recognition.

The feature fusion utilizes red green blue (RGB) and depth information to construct fused feature from Kinect. FGF involves multi-Jaccard similarity to compute a robust graph and word embedding method to enhance the recognition results.

The authors also collect DUT RGB-Depth (RGB-D) face data set and a benchmark data set to evaluate the effectiveness and efficiency of this method. The experimental results illustrate that FGF is robust and effective to face and object data sets in robot applications.

The authors first utilize Jaccard similarity to construct a graph of RGB and depth images, which indicates the similarity of pair-wise images. Then, fusion feature of RGB and depth images can be computed by the Extended Jaccard Graph using word embedding method. The FGF can get better performance and efficiency in RGB-D sensor for robots.

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.

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 explore the role that computer vision can play within new industrial paradigms such as Industry 4.0 and in particular to support production line improvements to achieve flexible manufacturing. As Industry 4.0 requires “big data”, it is accepted that computer vision could be one of the tools for its capture and efficient analysis. RGB-D data gathered from real-time machine vision systems such as Kinect ® can be processed using computer vision techniques.

This research exploits RGB-D cameras such as Kinect® to investigate the feasibility of using computer vision techniques to track the progress of a manual assembly task on a production line. Several techniques to track the progress of a manual assembly task are presented. The use of CAD model files to track the manufacturing tasks is also outlined.

This research has found that RGB-D cameras can be suitable for object recognition within an industrial environment if a number of constraints are considered or different devices/techniques combined. Furthermore, through the use of a HMM inspired state-based workflow, the algorithm presented in this paper is computationally tractable.

Processing of data from robust and cheap real-time machine vision systems could bring increased understanding of production line features. In addition, new techniques that enable the progress tracking of manual assembly sequences may be defined through the further analysis of such visual data. The approaches explored within this paper make a contribution to the utilisation of visual information “big data” sets for more efficient and automated production.

Building information modelling (BIM) is a digital representation of the physical and functional characteristics of a building. Its use offers a range of benefits in terms of achieving the efficient design, construction, operation and maintenance of buildings. Applying BIM at the outset of a new build project should be relatively easy. However, it is often problematic to apply BIM techniques to an existing building, for example, as part of a refurbishment project or as a tool supporting the facilities management strategy, because of inadequacies in the previous management of the dataset that characterises the facility in question. These inadequacies may include information on as built geometry and materials of construction. By the application of automated retrospective data gathering for use in BIM, such problems should be largely overcome and significant benefits in terms of efficiency gains and cost savings should be achieved.

Laser scanning can be used to collect geometrical and spatial information in the form of a 3D point cloud, and this technique is already used. However, as a point cloud representation does not contain any semantic information or geometrical context, such point cloud data must refer to external sources of data, such as building specification and construction materials, to be in used in BIM.

Hyperspectral imaging techniques can be applied to provide both spectral and spatial information of scenes as a set of high-resolution images. Integrating of a 3D point cloud into hyperspectral images would enable accurate identification and classification of surface materials and would also convert the 3D representation to BIM.

This integrated approach has been applied in other areas, for example, in crop management. The transfer of this approach to facilities management and construction would improve the efficiency and automation of the data transition from building pathology to BIM. In this study, the technological feasibility and advantages of the integration of laser scanning and hyperspectral imaging (the latter not having previously been used in the construction context in its own right) is discussed, and an example of the use of a new integration technique is presented, applied for the first time in the context of buildings.

This study aims to develop a real-time algorithm, which can detect people even in arbitrary poses. To cover poor and changing light conditions, it does not rely on color information. The developed method is expected to run on computers with low computational resources so that it can be deployed on autonomous mobile robots.

The method is designed to have a people detection pipeline with a series of operations. Efficient point cloud processing steps with a novel head extraction operation provide possible head clusters in the scene. Classification of these clusters using support vector machines results in high speed and robust people detector.

The method is implemented on an autonomous mobile robot and results show that it can detect people with a frame rate of 28 Hz and equal error rate of 92 per cent. Also, in various non-standard poses, the detector is still able to classify people effectively.

The main limitation would be for point clouds similar to head shape causing false positives and disruptive accessories (like large hats) causing false negatives. Still, these can be overcome with sufficient training samples.

The method can be used in industrial and social mobile applications because of its robustness, low resource needs and low power consumption.

The paper introduces a novel and efficient technique to detect people in arbitrary poses, with poor light conditions and low computational resources. Solving all these problems in a single and lightweight method makes the study fulfill an important need for collaborative and autonomous mobile robots.

This work presents a system based on an ensemble of Convolutional Neural Networks (CNNs) and descriptors for bioimage classification that has been validated on different datasets of color images. The proposed system represents a very simple yet effective way of boosting the performance of trained CNNs by composing multiple CNNs into an ensemble and combining scores by sum rule. Several types of ensembles are considered, with different CNN topologies along with different learning parameter sets. The proposed system not only exhibits strong discriminative power but also generalizes well over multiple datasets thanks to the combination of multiple descriptors based on different feature types, both learned and handcrafted. Separate classifiers are trained for each descriptor, and the entire set of classifiers is combined by sum rule. Results show that the proposed system obtains state-of-the-art performance across four different bioimage and medical datasets. The MATLAB code of the descriptors will be available at https://github.com/LorisNanni.

This research aims to address the issue of safe navigation for autonomous vehicles in highly challenging outdoor environments. Indeed, robust navigation of autonomous mobile robots over long distances requires advanced perception means for terrain traversability assessment.

The use of visual systems may represent an efficient solution. This paper discusses recent findings in terrain traversability analysis from RGB-D images. In this context, the concept of point as described only by its Cartesian coordinates is reinterpreted in terms of local description. As a result, a novel descriptor for inferring the traversability of a terrain through its 3D representation, referred to as the unevenness point descriptor (UPD), is conceived. This descriptor features robustness and simplicity.

The UPD-based algorithm shows robust terrain perception capabilities in both indoor and outdoor environment. The algorithm is able to detect obstacles and terrain irregularities. The system performance is validated in field experiments in both indoor and outdoor environments.

The UPD enhances the interpretation of 3D scene to improve the ambient awareness of unmanned vehicles. The larger implications of this method reside in its applicability for path planning purposes.

This paper describes a visual algorithm for traversability assessment based on normal vectors analysis. The algorithm is simple and efficient providing fast real-time implementation, since the UPD does not require any data processing or previously generated digital elevation map to classify the scene. Moreover, it defines a local descriptor, which can be of general value for segmentation purposes of 3D point clouds and allows the underlining geometric pattern associated with each single 3D point to be fully captured and difficult scenarios to be correctly handled.

This paper aims to propose an identification method based on monocular vision for cylindrical parts in cluttered scene, which solves the issue that iterative closest point (ICP) algorithm fails to obtain global optimal solution, as the deviation from scene point cloud to target CAD model is huge in nature.

The images of the parts are captured at three locations by a camera amounted on a robotic end effector to reconstruct initial scene point cloud. Color signatures of histogram of orientations (C-SHOT) local feature descriptors are extracted from the model and scene point cloud. Random sample consensus (RANSAC) algorithm is used to perform the first initial matching of point sets. Then, the second initial matching is conducted by proposed remote closest point (RCP) algorithm to make the model get close to the scene point cloud. Levenberg Marquardt (LM)-ICP is used to complete fine registration to obtain accurate pose estimation.

The experimental results in bolt-cluttered scene demonstrate that the accuracy of pose estimation obtained by the proposed method is higher than that obtained by two other methods. The position error is less than 0.92 mm and the orientation error is less than 0.86°. The average recognition rate is 96.67 per cent and the identification time of the single bolt does not exceed 3.5 s.

The presented approach can be applied or integrated into automatic sorting production lines in the factories.

The proposed method improves the efficiency and accuracy of the identification and classification of cylindrical parts using a robotic arm.