Fingerprint image enhancement is a key aspect of an automated fingerprint identification system. This paper describes an effective algorithm based on a novel lighting compensation scheme. The scheme involves the use of adaptive higher-order singular value decomposition on a tensor of wavelet subbands of a fingerprint (AHTWF) image to enhance the quality of the image. The algorithm consists of three stages. The first stage is the decomposition of an input fingerprint image of size 2M × 2N into four subbands at the first level by applying a two-dimensional discrete wavelet transform. In the second stage, we construct a tensor in RM×N×4 space. The tensor contains four wavelet subbands that serve as four frontal planes. Furthermore, the tensor is decomposed through higher-order singular value decomposition to separate the fingerprint’s wavelet subbands into detailed individual components. In the third stage, a compensated image is produced by adaptively obtaining the compensation coefficient for each frontal plane of the tensor-based on the reference Gaussian template. The experimental results indicated that the quality of the AHTWF image was higher than that of the original image. The proposed algorithm not only improves the clarity and continuity of ridge structures but also removes the background and blurred regions of a fingerprint image. Therefore, this algorithm can achieve higher fingerprint classification accuracy than related methods can.

Nowadays, eyeglassesare used for vision correction as well as in the fashion industry. Eyeglasses have become more expensive and the eyewear industry has grown rapidly, thereby requiring the development of advanced coating technologies. However, defect detection by visual inspection in the manufacturing process of eyeglass coatings is difficult. To solve this problem, we propose the coated eyeglass defect detection system framework based on machine vision for real-time inspection. First, we locate and extract regions of interest (ROI) ofthe coated eyeglass by adopting cross-projection based on symmetrizedenergy analysis. Next, we propose an efficient method based on the symmetrized energy analysis of color channelsto enhance defectsin each color channel of the ROI of the coated eyeglass. Then, we adoptsymmetrized cross-projection energy analysis for locating defective areas inside the ROI of the coated eyeglass. Finally, we compare the defect detection resultsfor the coated eyeglass with the standard manufacturingquality. An experiment is conducted using real data collected froma Taiwanese eyeglass factory to validate the performance of the proposed framework. This framework achieves a 100% defect detection rate, demonstrating that it is valid and useful for inspecting coated eyeglasses in industries.

This study aims to develop a novel and robust simulation-based integration framework to identify optimal locations for wave energy projects in Australia. The process of multi-criteria evaluating and selecting locations based on feasible conditions, published numerical data, and linguistic judgments of experts. With this aim in mind, this paper proposes the first combination of Data Envelopment Analysis (DEA) approach, Fuzzy Best-Worst Method (Fuzzy BWM), and Simulation-based Fuzzy Multi-Criteria Interactive Decision-Making method (Fuzzy TODIM). Firstly, the DEA model helps to filter out potentially and highly effective locations from feasible locations. Afterward, Fuzzy BWM is a worthy and effective alternative to Analytic Hierarchy Process (AHP) in weighting criteria. Finally, the simulation-based Fuzzy TODIM method evaluates potential locations, taking into account both the consideration of the criteria interaction and the decision maker’s aversion to loss. Based on the simulation results, the study finds the thresholds of the loss attenuation coefficient that change the ranking of potential locations. Last but not least, to validate the ranking result, the comparison shows that the proposed method’s results have a high degree of similarity with actual wave energy projects in Australia. Accordingly, governments, investors, and institutions can sustainably optimize the efficiency of wave energy projects by applying the proposed model. Moreover, the integrated framework can provide robust solutions to other multiple criteria decision-making problems.

No matter your experience level or budget, there is a great ski goggle waiting to be found.Goggles are an essential part of skiing or snowboarding gear to protect your eyes from harsh environmental elements and injury. In the ski goggles manufacturing industry, defects, especially on the lens surface, are unavoidable. However, defect detection and classification by visual inspection in the manufacturing process is very difficult. To overcome this problem, a novel framework based on machine vision is presented, named as the ski goggles lens defect detection, with five high-resolution cameras and custom-made lighting field to achieve a high-quality ski goggles lens image. Next, the defects on the lens of ski goggles are detected by using parallel projection in opposite directions based on adaptive energy analysis. Before being put into the classification system, the defect images are enhanced by an adaptive method based on the high-order singular value decomposition (HOSVD). Finally, dust and five types of defect images are classified into six types, i.e., dust, spotlight (type 1, type 2, type 3), string, and watermark, by using the developed classification algorithm. The defect detection and classification results of the ski goggles lens are compared to the standard quality of the manufacturer. Experiments using 120 ski goggles lens samples collected from the largest manufacturer in Taiwan are conducted to validate the performance of the proposed framework. The accurate defect detection rate is 100% and the classification accuracy rate is 99.3%, while the total running time is short. The results demonstrate that the proposed method is sound and useful for ski goggles lens inspection in industries.

Currently, liquid crystal displays (LCDs) are the most popular type of flat panel display and are used in most applications. An LCD contains many critical optical film components that are produced in highly automated and precisely monitored facilities throughout the complex manufacturing process. However, defect detection and classification through visual inspection is very difficult during the manufacturing process. To overcome this problem, a novel framework based on machine vision known as the optical film defect detection and classification system is presented for use in the real-time inspection. First, an image acquisition system equipped with a high-resolution camera and custom-made lighting field was designed to obtain a high-quality optical film image. Second, the defects on an optical film were detected using localized cross-projection based on proposed adaptive energy analysis. Finally, the defect images were classified into four types by using the developed classification algorithm—point, scratch, foreign material, and stain. The quality of the products yielded after defect detection and classification of the optical film was compared with the standard product quality of the manufacturer. Experiments were conducted using samples collected from the largest manufacturer in Taiwan to validate the performance of the proposed framework. The accurate defect detection rate is 99.6%, the classification accuracy rate is 100%, and the total operation time is short which only 6.129s are required on average to perform the inspection for an optical film sample. The results demonstrate that the proposed method is sound and useful for optical film inspection in industries.