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Though many segmentation methods have been published, few of them are developed especially for line scanned images. An ill‐illuminated line scanned (IILS) image tends to have a uniform intensity distribution in column direction while non‐uniform intensity distribution in the row direction. So, it is improper to segment IILS images using either a pixed threshold or threshold surface. In view of this, the purpose of this paper is to develop a segmentation method that is suitable for segmented IILS images.

To obtain satisfactory segmentation results, the illumination variation across the column of a line scanned image was taken into account and a column‐based segmentation method was developed. The method first calculates each column's standard deviation. Then a threshold value is automatically assigned to each column based on the derived values. Finally, by assembling each columns threshold value, a so‐called threshold line is formed. The method is threshold‐line segmentation method based on standard deviation (TLSTD).

The developed threshold‐line‐based segmentation method is compared with Otsu's fixed threshold segmentation method and Niblack's threshold‐surface‐based segmentation method. The results show that the threshold‐line‐based segmentation method is more suitable for segmenting IILS images.

Despite TLSTD outperforming Otsu's and Nilblack's segmentation methods, there are some limitations to it. The most obvious one is that the predetermined allowable deviation has influences on the integrality of the extracted flaws. Besides, since the proposed method is designed specifically for segmenting images captured by line scan cameras with a slant line light source, it is suitable for segmenting the kind of images only. In other words, the method shows no advantages in segment area scanned images.

Generally, the approach is useful in automated visual inspection where line scan cameras are employed.

The merit of the proposed method is that the slant of the line light source is now allowed. In other words, even if a grabbed line scanned image is unevenly illuminated, the proposed segmentation method is still able to successfully detect desired flaws.

Detecting cancer from the computed tomography (CT)images of lung nodules is very challenging for radiologists. Early detection of cancer helps to provide better treatment in advance and to enhance the recovery rate. Although a lot of research is being carried out to process clinical images, it still requires improvement to attain high reliability and accuracy. The main purpose of this paper is to achieve high accuracy in detecting and classifying the lung cancer and assisting the radiologists to detect cancer by using CT images. The CT images are collected from health-care centres and remote places through Internet of Things (IoT)-enabled platform and the image processing is carried out in the cloud servers.

IoT-based lung cancer detection is proposed to access the lung CT images from any remote place and to provide high accuracy in image processing. Here, the exact separation of lung nodule is performed by Otsu thresholding segmentation with the help of optimal characteristics and cuckoo search algorithm. The important features of the lung nodules are extracted by local binary pattern. From the extracted features, support vector machine (SVM) classifier is trained to recognize whether the lung nodule is malicious or non-malicious.

The proposed framework achieves 99.59% in accuracy, 99.31% in sensitivity and 71% in peak signal to noise ratio. The outcomes show that the proposed method has achieved high accuracy than other conventional methods in early detection of lung cancer.

The proposed algorithm is implemented and tested by using more than 500 images which are collected from public and private databases. The proposed research framework can be used to implement contextual diagnostic analysis.

The cancer nodules in CT images are precisely segmented by integrating the algorithms of cuckoo search and Otsu thresholding in order to classify malicious and non-malicious nodules.

In this paper, an optical inspection method of the ball grid array package(BGA) is proposed using a binocular machine vision system.

The height of each solder ball is calculated based on spatial geometrical size and location obtained from the two CCD cameras capturing range images of a LED illuminated BGA chip at certain orientation.

The structure of this system is simple and the accuracy is 0.02 mm, The experimental results have proved the validity of this system for BGA failure detection.

The developed machine vision system can provide some of the critical factors for BGA quality evaluation, such as the height of solder ball, diameter, pitch and coplanarity.

Compared with other systems, the structure of this system is simple and accurate, which meets the demand of off‐line and on‐line inspection. The limitation of this system is that the margin of field of view (FOV) is fuzzy. Further study could be focused on this problem.

The purpose of this paper is to provide crystal size distribution (CSD) using photogrammetric and image analysis techniques. A new algorithm is proposed to detect CSDs and a comparison is carried out with conventional watershed segmentation algorithm.

Polished granite plates were prepared to designate the metrics of CSD measurements. There are many important metrics for measurements on CSD. Some of them are orientation, size, position, area, aspect ratio, convexity, circularity, perimeter, convex hull, bounding box, eccentricity, shape, max-min length of CSD's fitted and corrected ellipse, and population density in a per unit area. Prior to image processing stage, camera calibration was performed to remove the image distortion errors. Image processing techniques were applied to corrected images for detecting the CSD parameters.

The proposed algorithm showed the improved preservation of size and shape characteristics of the crystal material when compared to the watershed segmentation. According to the experimental results, proposed algorithm revealed promising results in identifying CSDs more easily and efficiently.

This paper describes CSD of granitic rocks by using automated grain boundary detection methods in polished plate images. Some metrics of CSDs were detected by employing a new procedure. A computer-based image analysis technique was developed to measure the CSDs on the granitic rock plates. A validation is done by superimposing digitally detected CSD metrics to original samples.

This study aims to introduce a vision-based model to generate droplets with auto-tuned parameters. The model can auto-adjust the inherent uncertainties and errors involved with the fabrication and operating parameters in microfluidic platform, attaining precise size and frequency of droplet generation.

The photolithography method is utilized to prepare the microfluidic devices used in this study, and various experiments are conducted at various flow-rate and viscosity ratios. Data for droplet shape is collected to train the artificial intelligence (AI) models.

Growth phase of droplets demonstrated a unique spring back effect in droplet size. The fully developed droplet sizes in the microchannel were modeled using least absolute shrinkage and selection operators (LASSO) regression model, Gaussian support vector machine (SVM), long short term memory (LSTM) and deep neural network models. Mean absolute percentage error (MAPE) of 0.05 and R² = 0.93 were obtained with a deep neural network model on untrained flow data. The shape parameters of the droplets are affected by several uncontrolled parameters. These parameters are instinctively captured in the model.

Experimental data set is generated for varying viscosity values and flow rates. The variation of flow rate of continuous phase is observed here instead of dispersed phase. An automated computation routine is developed to read the droplet shape parameters considering the transient growth phase of droplets. The droplet size data is used to build and compare various AI models for predicting droplet sizes. A predictive model is developed, which is ready for automated closed loop control of the droplet generation.

Current practices in data classification and retrieval have experienced a surge in the use of multimedia content. Identification of desired information from the huge image databases has been facing increased complexities for designing an efficient feature extraction process. Conventional approaches of image classification with text-based image annotation have faced assorted limitations due to erroneous interpretation of vocabulary and huge time consumption involved due to manual annotation. Content-based image recognition has emerged as an alternative to combat the aforesaid limitations. However, exploring rich feature content in an image with a single technique has lesser probability of extract meaningful signatures compared to multi-technique feature extraction. Therefore, the purpose of this paper is to explore the possibilities of enhanced content-based image recognition by fusion of classification decision obtained using diverse feature extraction techniques.

Three novel techniques of feature extraction have been introduced in this paper and have been tested with four different classifiers individually. The four classifiers used for performance testing were K nearest neighbor (KNN) classifier, RIDOR classifier, artificial neural network classifier and support vector machine classifier. Thereafter, classification decisions obtained using KNN classifier for different feature extraction techniques have been integrated by Z-score normalization and feature scaling to create fusion-based framework of image recognition. It has been followed by the introduction of a fusion-based retrieval model to validate the retrieval performance with classified query. Earlier works on content-based image identification have adopted fusion-based approach. However, to the best of the authors’ knowledge, fusion-based query classification has been addressed for the first time as a precursor of retrieval in this work.

The proposed fusion techniques have successfully outclassed the state-of-the-art techniques in classification and retrieval performances. Four public data sets, namely, Wang data set, Oliva and Torralba (OT-scene) data set, Corel data set and Caltech data set comprising of 22,615 images on the whole are used for the evaluation purpose.

To the best of the authors’ knowledge, fusion-based query classification has been addressed for the first time as a precursor of retrieval in this work. The novel idea of exploring rich image features by fusion of multiple feature extraction techniques has also encouraged further research on dimensionality reduction of feature vectors for enhanced classification results.

The purpose of this paper is to develop a text extraction tool for scanned documents that would extract text and build the keywords corpus and key phrases corpus for the document without manual intervention.

For text extraction from scanned documents, a Web-based optical character recognition (OCR) tool was developed. OCR is a well-established technology, so to develop the OCR, Microsoft Office document imaging tools were used. To account for the commonly encountered problem of skew being introduced, a method to detect and correct the skew introduced in the scanned documents was developed and integrated with the tool. The OCR tool was customized to build keywords and key phrases corpus for every document.

The developed tool was evaluated using a 100 document corpus to test the various properties of OCR. The tool had above 99 per cent word read accuracy for text only image documents. The customization of the OCR was tested with samples of Microfiches, sample of Journal pages from back volumes and samples from newspaper clips and the results are discussed in the summary. The tool was found to be useful for text extraction and processing.

The scanned documents are converted to keywords and key phrases corpus. The tool could be used to build metadata for scanned documents without manual intervention.

The tool is used to convert unstructured data (in the form of image documents) to structured data (the document is converted into keywords, and key phrases database). In addition, the image document is converted to editable and searchable document.

In the process of continuous casting and rolling of steel plate, due to the influence of rolling equipment and process, there are scratches, inclusions, patches, scabs and pitted surfaces on the surface of steel plate, which will not only affect the corrosion resistance, wear resistance and fatigue strength of steel plate but also may cause production accidents. Therefore, the detection of steel plate surface defect must be strengthened to ensure the production quality of steel plate and the smooth development of industrial construction.

(1) A steel plate surface defect detection technology based on small datasets is proposed, which can detect multiple surface defects and fill in the blank of scab defect detection. (2) A detection system based on intelligent recognition technology is built. The steel plate images are collected by the front-end monitoring device, then transmitted to the back-end monitoring center and processed by the embedded intelligent algorithms. (3) In order to reduce the impact of external light on the image, an improved Multi-Scale Retinex (MSR) enhancement algorithm based on adaptive weight calculation is proposed, which lays the foundation for subsequent object segmentation and feature extraction. (4) According to the different factors such as the cause and shape, the texture and shape features are combined to classify different defects on the steel plate surface. The defect classification model is constructed and the classification results are recorded and stored, which has certain application value in the field of steel plate surface defect detection. (5) The practicability and effectiveness of the proposed method are verified by comparison with other methods, and the field running tests are conducted based on the equipment commissioning field of China Heavy Machinery Institute.

When applied to small dataset, the precision of the proposed method is 94.5% and the time is 23.7 ms. In order to compare with deep learning technology, after expanding the image dataset, the precision and detection time of this paper are 0.948 and 24.2 ms, respectively. The proposed method is superior to other traditional image processing and deep learning methods. And the field recognition precision is 91.7%.

In brief, the steel plate surface defect detection technology based on computer vision is effective, but the previous attempts and methods are not comprehensive and the accuracy and detection speed need to be improved. Therefore, a more practical and comprehensive technology is developed in this paper. The main contributions are as follows: (1) A steel plate surface defect detection technology based on small datasets is proposed, which can detect multiple surface defects and fill in the blank of scab defect detection. (2) A detection system based on intelligent recognition technology is built. The steel plate images are collected by the front-end monitoring device, then transmitted to the back-end monitoring center and processed by the embedded intelligent algorithms. (3) In order to reduce the impact of external light on the image, an improved MSR enhancement algorithm based on adaptive weight calculation is proposed, which lays the foundation for subsequent object segmentation and feature extraction. (4) According to the different factors such as the cause and shape, the texture and shape features are combined to classify different defects on the steel plate surface. The defect classification model is constructed and the classification results are recorded and stored, which has certain application value in the field of steel plate surface defect detection. (5) The practicability and effectiveness of the proposed method are verified by comparison with other methods, and the field running tests are conducted based on the equipment commissioning field of China Heavy Machinery Institute.

This study aims to develop an experimentally validated three-dimensional numerical model for predicting different flow patterns produced with a gas dynamic virtual nozzle (GDVN).

The physical model is posed in the mixture formulation and copes with the unsteady, incompressible, isothermal, Newtonian, low turbulent two-phase flow. The computational fluid dynamics numerical solution is based on the half-space finite volume discretisation. The geo-reconstruct volume-of-fluid scheme tracks the interphase boundary between the gas and the liquid. To ensure numerical stability in the transition regime and adequately account for turbulent behaviour, the k-ω shear stress transport turbulence model is used. The model is validated by comparison with the experimental measurements on a vertical, downward-positioned GDVN configuration. Three different combinations of air and water volumetric flow rates have been solved numerically in the range of Reynolds numbers for airflow 1,009–2,596 and water 61–133, respectively, at Weber numbers 1.2–6.2.

The half-space symmetry allows the numerical reconstruction of the dripping, jetting and indication of the whipping mode. The kinetic energy transfer from the gas to the liquid is analysed, and locations with locally increased gas kinetic energy are observed. The calculated jet shapes reasonably well match the experimentally obtained high-speed camera videos.

The model is used for the virtual studies of new GDVN nozzle designs and optimisation of their operation.

To the best of the authors’ knowledge, the developed model numerically reconstructs all three GDVN flow regimes for the first time.

The purpose of this paper is to provide a way to analyze satellite images using various clustering algorithms and refined bitplane methods with other supporting techniques to prove the superiority of RIFCM.

A comparative study has been carried out using RIFCM with other related algorithms from their suitability in analysis of satellite images with other supporting techniques which segments the images for further process for the benefit of societal problems. Four images were selected dealing with hills, freshwater, freshwatervally and drought satellite images.

The superiority of the proposed algorithm, RIFCM with refined bitplane towards other clustering techniques with other supporting methods clustering, has been found and as such the comparison, has been made by applying four metrics (Otsu (Max-Min), PSNR and RMSE (40%-60%-Min-Max), histogram analysis (Max-Max), DB index and D index (Max-Min)) and proved that the RIFCM algorithm with refined bitplane yielded robust results with efficient performance, reduction in the metrics and time complexity of depth computation of satellite images for further process of an image.

For better clustering of satellite images like lands, hills, freshwater, freshwatervalley, drought, etc. of satellite images is an achievement.

The existing system extends the novel framework to provide a more explicit way to analyze an image by removing distortions with refined bitplane slicing using the proposed algorithm of rough intuitionistic fuzzy c-means to show the superiority of RIFCM.