Precise Inspection of Geometric Parameters for Polyvinyl Chloride Pipe Section Based on Computer Vision

The precise inspection of geometric parameters is crucial for quality control in the context of Industry 4.0. The current technique of precise inspection depends on the operation of professional personnel, and the measuring accuracy is restricted by the proficiency of operators. To solve the defects, this paper proposes a precise inspection framework for the geometric parameters of polyvinyl chloride (PVC) pipe section (G-PVC), using low-cost visual sensors and high-precision computer vision algorithms. Firstly, a robust imaging system was built to acquire images of a PVC pipe section under irregular illumination changes. Next, an engineering semantic model was established to calculate G-PVC like inner diameter, outer diameter, wall thickness, and roundness. After that, a region-of-interest (ROI) extraction algorithm was combined with an improved edge operator to obtain the coordinates of measured points on PVC end-face image in a stable and precise manner. Finally, our framework was proved highly precise and robust through experiments.

Coal quality prediction based on multi-feature fusion of flotation foam images

Coal flotation monitoring cannot provide real-time feedback on the yield and ash of coal preparation products because it is influenced by the subjective nature of artificial judgment of coal preparation status and the lag of product quality testing of coal preparation. This paper aims to extract the texture, colour and shape features of floating foam images using various image processing methods, such as colour space, wavelet transform, greyscale co-occurrence matrix and edge operator, and to quantify the characterisation of various characteristic parameters on the basis of the indicative effect of floating foam characteristics on the quality of coal preparation products. The correlation between image features and the yield and ash of flotation products is studied, and a regression prediction model of coal preparation yield and ash was established by combining various image feature parameters using machine learning methods. Experimental results show that the proposed method can realise the real-time monitoring of coal mine flotation and effectively predict coal quality.

Coal quality prediction based on multi-feature fusion of flotation foam images

Coal flotation monitoring cannot provide real-time feedback on the yield and ash of coal preparation products because it is influenced by the subjective nature of artificial judgment of coal preparation status and the lag of product quality testing of coal preparation. This paper aims to extract the texture, colour and shape features of floating foam images using various image processing methods, such as colour space, wavelet transform, greyscale co-occurrence matrix and edge operator, and to quantify the characterisation of various characteristic parameters on the basis of the indicative effect of floating foam characteristics on the quality of coal preparation products. The correlation between image features and the yield and ash of flotation products is studied, and a regression prediction model of coal preparation yield and ash was established by combining various image feature parameters using machine learning methods. Experimental results show that the proposed method can realise the real-time monitoring of coal mine flotation and effectively predict coal quality.

Rethinking ‘residual’ Verb Second

The term “residual Verb Second ” is a misnomer for English, because V2 is, in fact, still productive in the language. Evidence for this comes from a previously undescribed negative inversion phenomenon innovated very recently in varieties of English. An analysis is proposed for how such a restrictive V2 system could, nevertheless, be productive, appealing to learner-driven models of language change in which novel structures can arise as artefacts of the acquisition procedure. Specifically, it is argued that innovative V2 arises when acquirers postulate a novel clause type characterized by a left-edge operator, which they analyse as a V2 environment by analogy with other non-declarative clause types involving such structures (e.g. interrogatives). This finds support from other cases of innovative V2 in English, Scots, and Afrikaans. Overall, we are left with a clearer picture of the status of V2 in English, and what it takes to innovate new V2 environments cross-linguistically.

Edge detection for detection of brain tumour in CT images

Brain tumour can be defined as the continuous and uncontrolled growth of the cells in the regions of brain. Analysis and detection of brain tumours from the computed tomography images can be performed by various image processing algorithms. Edge detection is special type of image processing technique, which uses operators for functioning. The Computed Tomography images are obtained from the standard data-base which undergoes pre-processing technique. Contrast adjustment is performed to enhance the region of brain tumour. Edge operators of different types are applied to the images for identification of the boundary of the brain tumour region. Appropriate edge operator for de-termination of the boundary is defined by comparing the image quality and accuracy parameters. These parameters illustrate that canny oper-ator is described to be more definite for the detection and analysis of the boundary and region of brain tumour in Computed Tomography images.

Auto-Extraction of Linear Archaeological Traces of Tuntian Irrigation Canals in Miran Site (China) from Gaofen-1 Satellite Imagery

This paper describes the use of the Chinese Gaofen-1 (GF-1) satellite imagery to automatically extract tertiary Linear Archaeological Traces of Tuntian Irrigation Canals (LATTICs) located in the Miran site. The site is adjacent to the ancient Loulan Kingdom at the eastern margin of the Taklimakan Desert in western China. GF-1 data was processed following atmospheric and geometric correction, and spectral analyses were carried out for multispectral data. The low values produced by SSI indicate that it is difficult to distinguish buried tertiary LATTICs from similar backgrounds using spectral signatures. Thus, based on the textual characteristics of high-resolutionGF-1 panchromatic data, this paper proposes an automatic approach that combines joint morphological bottom and hat transformation with a Canny edge operator. The operator was improved by adding stages of geometric filtering and gradient vector direction analysis. Finally, the detected edges of tertiary LATTICs were extracted using the GIS-based draw tool and converted into shapefiles for archaeological mapping within a GIS environment. The proposed automatic approach was verified with an average accuracy of 95.76% for 754 tertiary LATTICs in the entire Miran site and compared with previous manual interpretation results. The results indicate that GF-1 VHR PAN imagery can successfully uncover the ancient tuntian agricultural landscape. Moreover, the proposed method can be generalized and applied to extract linear archaeological traces such as soil and crop marks in other geographic locations.