There are different applications of computer vision and digital image processing in various applied domains and automated production process. In textile industry, fabric defect detection is considered as a challenging task as the quality and the price of any textile product are dependent on the efficiency and effectiveness of the automatic defect detection. Previously, manual human efforts are applied in textile industry to detect the defects in the fabric production process. Lack of concentration, human fatigue, and time consumption are the main drawbacks associated with the manual fabric defect detection process. Applications based on computer vision and digital image processing can address the abovementioned limitations and drawbacks. Since the last two decades, various computer vision-based applications are proposed in various research articles to address these limitations. In this review article, we aim to present a detailed study about various computer vision-based approaches with application in textile industry to detect fabric defects. The proposed study presents a detailed overview of histogram-based approaches, color-based approaches, image segmentation-based approaches, frequency domain operations, texture-based defect detection, sparse feature-based operation, image morphology operations, and recent trends of deep learning. The performance evaluation criteria for automatic fabric defect detection is also presented and discussed. The drawbacks and limitations associated with the existing published research are discussed in detail, and possible future research directions are also mentioned. This research study provides comprehensive details about computer vision and digital image processing applications to detect different types of fabric defects.

<abstract><p>Content-based image analysis and computer vision techniques are used in various health-care systems to detect the diseases. The abnormalities in a human eye are detected through fundus images captured through a fundus camera. Among eye diseases, glaucoma is considered as the second leading case that can result in neurodegeneration illness. The inappropriate intraocular pressure within the human eye is reported as the main cause of this disease. There are no symptoms of glaucoma at earlier stages and if the disease remains unrectified then it can lead to complete blindness. The early diagnosis of glaucoma can prevent permanent loss of vision. Manual examination of human eye is a possible solution however it is dependant on human efforts. The automatic detection of glaucoma by using a combination of image processing, artificial intelligence and computer vision can help to prevent and detect this disease. In this review article, we aim to present a comprehensive review about the various types of glaucoma, causes of glaucoma, the details about the possible treatment, details about the publicly available image benchmarks, performance metrics, and various approaches based on digital image processing, computer vision, and deep learning. The review article presents a detailed study of various published research models that aim to detect glaucoma from low-level feature extraction to recent trends based on deep learning. The pros and cons of each approach are discussed in detail and tabular representations are used to summarize the results of each category. We report our findings and provide possible future research directions to detect glaucoma in conclusion.</p></abstract>

Glaucoma is a multifactorial ocular disease. Ophthalmologists mostly use fundus or optical coherence tomography (OCT) for diagnosis of glaucoma. In this study, a hybrid computer‐aided‐diagnosis (H‐CAD) system has been proposed that integrates both fundus and OCT imaging technologies for reliable diagnosis of glaucoma. Fundus module inspects the outer layer of eye's posterior part. It considers a variety of structural and textural features and makes a decision using support vector machine (SVM). In OCT module, the cup to disc ratio (CDR) has been computed by examining the internal layers of the retina. The cup contour has been extracted from inner‐limiting‐membrane (ILM) layer using a set of novel techniques for the calculation of cup diameter. Similarly, in the disc diameter calculation the retinal‐pigment‐epithelium (RPE) layer termination points have been identified by a number of innovative strategies to locate disc margin. Furthermore, a new criterion based on the mean value of RPE‐layer end points has been proposed for the determination of cup edges. A local‐dataset annotated by four ophthalmologists has been used for evaluation of proposed H‐CAD system. The evaluations and results have shown that the final result of H_CAD system is more trustable than its contemporary automated models.

Agile software development has large success rate due to its benefits and promising nature but natively where the size of the project is small. Requirement engineering (RE) is crucial as in each software development life cycle, “Requirements” play a vital role. Though agile provides values to customer’s business needs, changing requirement, and interaction, we also have to face impediments in agile, many of which are related to requirement challenges. This article aims to find out the challenges being faced during requirement engineering of agile projects. Many research studies have been conducted on requirement challenges which are somehow biased, no suggestions are given to improve the agile development process, and the research does not highlight large-scale agile development challenges. Hence, this article covers all the challenges discussed above and presents a comprehensive overview of agile models from requirement engineering perspective. The findings and results can be very helpful for software industry to improve development process as well as for researchers who want to work further in this direction.