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The purpose of this paper is to assess the usability of two image‐based authentication methods when used in the web‐based environment. The evaluated approaches involve clicking secret points within a single image (click‐based) and remembering a set of images in the correct sequence (choice‐based).

A “one‐to‐one” usability study was conducted in which participants had to complete three main tasks; namely authentication tasks (register, confirm and login), spot the difference activity and provide feedback.

From analysing the results in terms of timing, number of attempts, user feedback, accuracy and predictability, it is found that the choice‐based approach is better in terms of usability, whereas the click‐based method performed better in terms of timing and is rated more secure against social engineering.

The majority of participants are from the academic sector (students, lecturers, etc.) and had up to seven years' IT experience. To obtain more statistically significant results, it is proposed that participants should be obtained from various sectors, having a more varied IT experience.

The results suggest that in order for image‐based authentication to be used in the web environment, more work is needed to increase the usability, while at the same time maintaining the security of both techniques.

This paper enables a direct comparison of the usability of two alternative image‐based techniques, with the studies using the same set of participants and the same set of environment settings.

The aim of this paper is to examine related studies to identify which retrieval methods are supported by current digital cultural heritage libraries. In this way it is hoped to provide a direction for future cultural heritage applications to provide more complete and/or improved retrieval functionality.

The methodology of this paper is based on introducing the general concept of image‐based retrieval systems as well as their retrieval methods. Then, users' needs are discussed to illustrate the demands of semantic‐based retrieval. After the retrieval methods have been presented, current digital cultural heritage libraries are examined in terms of their supported retrieval methods that allow users to query images.

Current digital cultural heritage libraries mostly provide only general retrieval methods based on image‐based low‐level features, i.e. query by image contents. Very few consider other retrieval methods such as browsing and semantic‐based retrieval. In addition, none of the current systems provide all possible retrieval methods for users.

This study is the first one to examine image‐based retrieval methods in digital cultural heritage libraries. This study supports the improvement of retrieval functionality for digital cultural heritage libraries in the future.

This paper aims to overview the current status of development and application of digital image processing technology used for the yarn hairiness evaluation.

Digital image processing technology is one of the new methods used for the yarn detection, which can be used for the digital characterization and objective evaluation of yarn appearance. The comparison between the traditional detection methods and this new developed method was made and analyzed.

Compared with the traditional methods, image-based methods have the advantages of being objective, fast and accurate. Therefore, it was proved that digital image processing techniques should be a new trend in terms of the yarn appearance evaluation.

Investigate the use of two imaging‐based methods – coded pattern projection and laser‐based triangulation – to generate 3D models as input to a rapid prototyping pipeline.

Discusses structured lighting technologies as suitable imaging‐based methods. Two approaches, coded‐pattern projection and laser‐based triangulation, are specifically identified and discussed in detail. Two commercial systems are used to generate experimental results. These systems include the Genex Technologies 3D FaceCam and the Integrated Vision Products Ranger System.

Presents 3D reconstructions of objects from each of the commercial systems.

Provides background in imaging‐based methods for 3D data collection and model generation. A practical limitation is that imaging‐based systems do not currently meet accuracy requirements, but continued improvements in imaging systems will minimize this limitation.

Imaging‐based approaches to 3D model generation offer potential to increase scanning time and reduce scanning complexity.

Introduces imaging‐based concepts to the rapid prototyping pipeline.

The spatial feature is important for scene saliency detection. Scene-based visual saliency detection methods fail to incorporate 3D scene spatial aspects. This paper aims to propose a cube-based method to improve saliency detection through integrating visual and spatial features in 3D scenes.

In the presented approach, a multiscale cube pyramid is used to organize the 3D image scene and mesh model. Each 3D cube in this pyramid represents a space unit similar to a pixel in the image saliency model multiscale image pyramid. In each 3D cube color, intensity and orientation features are extracted from the image and a quantitative concave–convex descriptor is extracted from the 3D space. A Gaussian filter is then used on this pyramid of cubes with an extended center-surround difference introduced to compute the cube-based 3D scene saliency.

The precision-recall rate and receiver operating characteristic curve is used to evaluate the method and other state-of-art methods. The results show that the method used is better than traditional image-based methods, especially for 3D scenes.

This paper presents a method that improves the image-based visual saliency model.

Although ground penetrating radar (GPR) technology is commonly used to assess the condition of reinforced-concrete (RC) bridge decks, the GPR data interpretation is not straightforward. Further, the thresholds that define the severity of deterioration are selected arbitrarily. This paper aims to solve a problem associated with GPR results generated by using a numerical amplitude method to assess corrosiveness of bridge decks.

Data, for more than 50 different bridge decks, were collected using a ground-coupled antenna. Depth-correction was performed for the collected data to normalize the reflected amplitude. Using k-means clustering technique, the amplitude values of each bridge deck were classified into four categories. Later, statistical analysis was performed where the threshold values of different categories of corrosion and deterioration are chosen. Monte-Carlo simulation technique was used to validate the value of these thresholds. Moreover, a sensitivity analysis was performed to realize the effect of changing the thresholds in the areas of corrosion.

The final result of this research is a four-category (good, fair, poor and critical) GPR scale with three fixed numerical thresholds (−7.71 dB, −10.04 dB and −14.63 dB) that define these categories. Besides, deterioration curves have been modeled using Weibull function and based on GPR outputs and corrosion areas.

The developed numerical GPR-based scale and deterioration models are expected to help the decision-makers in assessing the corrosiveness of bridge decks accurately and objectively. Hence, they will be able to take the right intervention decision for managing these decks.

Health monitoring of concrete is one of the important tasks in the structural health monitoring. The life of any infrastructure relies on the quality of the concrete. The computer vision-based methods are very useful to identify the structural defects. The identification of minor cracks in the noisy concrete image is complex. The purpose of this paper is to denoise the concrete crack images and also segment the cracks.

The novelty of the proposed work lies on the usage of anisotropic diffusion filter in the noisy concrete images. Initially anisotropic diffusion filter is applied to smoothen the concrete images. Adaptive threshold and gray level-based edge stopping constant are used in the diffusion process. The statistical six sigma-based method is utilized to segment the cracks from smoothened concrete images.

The proposed method is compared with five state-of-the-art-methods with the performance metrics of mean square error, peak signal to noise ratio and mean structural similarity. The experimental results highlight the advantages of the proposed method.

The novelty of the proposed work lies on the usage of anisotropic diffusion filter in the noisy concrete images. This research work gives the scope for structural damage evaluation by the automation techniques.

This paper aims to present an image-based visual servoing algorithm for a multiple pin-in-hole assembly. This paper also aims to avoid the matching and tracking of image features and the remaining robust against image defects.

The authors derive a novel model in the set space and design three image errors to control the 3 degrees of freedom (DOF) of a single-lug workpiece in the alignment task. Analytic computations of the interaction matrix that link the time variations of the image errors to the single-lug workpiece motions are performed. The authors introduce two approximate hypotheses so that the interaction matrix has a decoupled form, and an auto-adaptive algorithm is designed to estimate the interaction matrix.

Image-based visual servoing in the set space avoids the matching and tracking of image features, and these methods are not sensitive to image effects. The control law using the auto-adaptive algorithm is more efficient than that using a static interaction matrix. Simulations and real-world experiments are performed to demonstrate the effectiveness of the proposed algorithm.

This paper proposes a new visual servoing method to achieve pin-in-hole assembly tasks. The main advantage of this new approach is that it does not require tracking or matching of the image features, and its supplementary advantage is that it is not sensitive to image defects.

Decision-making in human beings is affected by emotions and sentiments. The affective computing takes this into account, intending to tailor decision support to the emotional states of people. However, the representation and classification of emotions is a very challenging task. The study used customized methods of deep learning models to aid in the accurate classification of emotions and sentiments.

The present study presents affective computing model using both text and image data. The text-based affective computing was conducted on four standard datasets using three deep learning customized models, namely LSTM, GRU and CNN. The study used four variants of deep learning including the LSTM model, LSTM model with GloVe embeddings, Bi-directional LSTM model and LSTM model with attention layer.

The result suggests that the proposed method outperforms the earlier methods. For image-based affective computing, the data was extracted from Instagram, and Facial emotion recognition was carried out using three deep learning models, namely CNN, transfer learning with VGG-19 model and transfer learning with ResNet-18 model. The results suggest that the proposed methods for both text and image can be used for affective computing and aid in decision-making.

The study used deep learning for affective computing. Earlier studies have used machine learning algorithms for affective computing. However, the present study uses deep learning for affective computing.