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Effective rail surface defects detection method is the basic guarantee to manufacture high-quality rail. However, the existed visual inspection methods have disadvantages such as poor ability to locate the rail surface region and high sensitivity to uneven reflection. This study aims to propose a bionic rail surface defect detection method to obtain the high detection accuracy of rail surface defects under uneven reflection environments.

Through this bionic rail surface defect detection algorithm, the positioning and correction of the rail surface region can be computed from maximum run-length smearing (MRLS) and background difference. A saliency image can be generated to simulate the human visual system through some features including local grayscale, local contrast and edge corner effect. Finally, the meanshift algorithm and adaptive threshold are developed to cluster and segment the saliency image.

On the constructed rail defect data set, the bionic rail surface defect detection algorithm shows good recognition ability on the surface defects of the rail. Pixel- and defect-level index in the experimental results demonstrate that the detection algorithm is better than three advanced rail defect detection algorithms and five saliency models.

The bionic rail surface defect detection algorithm in the production process is proposed. Particularly, a method based on MRLS is introduced to extract the rail surface region and a multifeature saliency fusion model is presented to identify rail surface defects.

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.

Accurate segmentation of artificial assembly action is the basis of autonomous industrial assembly robots. This paper aims to study the precise segmentation method of manual assembly action.

In this paper, a temporal-spatial-contact features segmentation system (TSCFSS) for manual assembly actions recognition and segmentation is proposed. The system consists of three stages: spatial features extraction, contact force features extraction and action segmentation in the temporal dimension. In the spatial features extraction stage, a vectors assembly graph (VAG) is proposed to precisely describe the motion state of the objects and relative position between objects in an RGB-D video frame. Then graph networks are used to extract the spatial features from the VAG. In the contact features extraction stage, a sliding window is used to cut contact force features between hands and tools/parts corresponding to the video frame. Finally, in the action segmentation stage, the spatial and contact features are concatenated as the input of temporal convolution networks for action recognition and segmentation. The experiments have been conducted on a new manual assembly data set containing RGB-D video and contact force.

In the experiments, the TSCFSS is used to recognize 11 kinds of assembly actions in demonstrations and outperforms the other comparative action identification methods.

A novel manual assembly actions precisely segmentation system, which fuses temporal features, spatial features and contact force features, has been proposed. The VAG, a symbolic knowledge representation for describing assembly scene state, is proposed, making action segmentation more convenient. A data set with RGB-D video and contact force is specifically tailored for researching manual assembly actions.