Efficient and effective image segmentation is an important task in computer vision and pattern recognition. Since fully automatic image segmentation is usually very hard for natural images, interactive schemes with a few simple user inputs are good solutions. In this paper, we propose a fully automatic new approach for color texture image segmentation based on neutrosophic set (NS) and multiresolution wavelet transformation. It aims to segment the natural scene images, in which the color and texture of each region does not have uniform statistical characteristics. The proposed approach combines color information with the texture information on NS and wavelet domain for segmentation. At first, it transforms each color channel and the texture information of the input image into the NS domain independently. The entropy is defined and employed to evaluate the indeterminacy of the image in NS domain. Two operations, @a-mean and @b-enhancement operations are proposed to reduce the indeterminacy. Finally, the proposed method is employed to perform image segmentation using a @c-K-means clustering. The determination of the cluster number K is carried out with cluster validity analysis. Two different segmentation evaluation criterions were used to determine the segmentations quality. Experiments are conducted on a variety of images, and the results are compared with those new existing segmentation algorithm. The experimental results demonstrate that the proposed approach can segment the color images automatically and effectively.

Color Texture Image Segmentation Based on Neutrosophic Set and Wavelet Transformation

Automated surface defect detection framework using machine vision and convolutional neural networks

Nowadays, the recycled fine aggregate sourced from construction and demolition waste is not frequently used in manufacturing of epoxy resin coatings. Therefore, the main novelty of the article is to prepare green epoxy resin coatings modified with recycled fine aggregate in a replacement ratio of natural fine aggregate ranged from 20 to 100%. The microstructural properties of the aggregates and epoxy resin were analyzed using micro-computed tomography, scanning electron microscopy and nanoindentation. The macroscopic mechanical properties were examined using pull-off strength tests. The highest improvement of the mechanical properties was observed for epoxy resin coatings modified with 20% of natural fine aggregate and 80% of recycled fine aggregate. It has been found that even 100% of natural fine aggregate can be successfully replaced using the recycled fine aggregate with consequent improvement of the pull-off strength of analyzed epoxy resin coatings. In order to confirm the assumptions resulting from the conducted research, an original analytical and numerical failure model proved the superior behavior of modified coating was developed.

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Engineering and Manufacturing Technology of Green Epoxy Resin Coatings Modified with Recycled Fine Aggregates

Industrialization has brought wealth, prosperity, and abundance to many nations. However, it has had many drawbacks on people’s health and the environment. Several paradigms have been proposed and implemented in an effort to suppress and reverse the adverse impacts of human activities and industrialization. A popular approach that serves as a solution to many of the negative implications of industry today is the circular economy (CE). With today’s top-notch technologies, the transition from the conventional linear economy to CE has been made easier. For example, the implementation of cyber-physical systems (CPSs), an Industry 4.0 (I4.0) tool, within a CE can substantially increase efficiency and reduce waste. This work presents a thorough review on the applications of cyber-physical systems within each of the CE stages, the contribution of different CPS technologies to the sustainable development goals (SDGs), and the current state of the CE assessment tools. The contribution of different CPS tools to each CE stage is demonstrated through several practical examples. In addition, this work reveals how the different CPS technologies applications contribute to the attainment of different SDGs set by the United Nations. It is also deduced from the review of the current state of the CE assessment tools that there is a need for a standardized comprehensive CE measuring or rating system to allow businesses on the micro, meso, and macro levels of the economy improve their circulatory.

Cyber-Physical Systems as an Enabler of Circular Economy to Achieve Sustainable Development Goals: A Comprehensive Review

Street view imagery (SVI) provides efficient access to data that can be used to research spatial quality at the human scale. The previous reviews have mainly focused on specific health findings and neighbourhood environments. There has not been a comprehensive review of this topic. In this paper, we systematically review the literature on the application of SVI in the built environment, following a formal innovation–decision framework. The main findings are as follows: (I) SVI remains an effective tool for automated research assessments. This offers a new research avenue to expand the built environment-measurement methods to include perceptions in addition to physical features. (II) Currently, SVI is functional and valuable for quantifying the built environment, spatial sentiment perception, and spatial semantic speculation. (III) The significant dilemmas concerning the adoption of this technology are related to image acquisition, the image quality, spatial and temporal distribution, and accuracy. (IV) This research provides a rapid assessment and provides researchers with guidance for the adoption and implementation of SVI. Data integration and management, proper image service provider selection, and spatial metrics measurements are the critical success factors. A notable trend is the application of SVI towards a focus on the perceptions of the built environment, which provides a more refined and effective way to depict urban forms in terms of physical and social spaces.

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Street View Imagery (SVI) in the Built Environment: A Theoretical and Systematic Review

Machine vision significantly improves the efficiency, quality, and reliability of defect detection. In visual inspection, excellent optical illumination platforms and suitable image acquisition hardware are the prerequisites for obtaining high-quality images. Image processing and analysis are key technologies in obtaining defect information, while deep learning is significantly impacting the field of image analysis. In this study, a brief history and the state of the art in optical illumination, image acquisition, image processing, and image analysis in the field of visual inspection are systematically discussed. The latest developments in industrial defect detection based on machine vision are introduced. In the further development of the field of visual inspection, the application of deep learning will play an increasingly important role. Thus, a detailed description of the application of deep learning in defect classification, localization and segmentation follows the discussion of traditional defect detection algorithms. Finally, future prospects for the development of visual inspection technology are explored.

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Zhonghe Ren

Papers

State of the Art in Defect Detection Based on Machine Vision