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Abstract Image quality assessment (IQA) has become a hot issue in the area of image processing, which aims to evaluate image quality automatically by a metric being consistent with subjective evaluation. The first stage of conventional IQA model design is the quality-aware feature selection. Taking advantages of early visual feature, Phase congruency (PC) operates in frequency domain to measure local structures such as edges, corners, lines, etc., by computing the local amplitudes and local energies in multiple scales. Conventional local PC features are calculated with log-Gabor-based filtrations in several orientations, and usually combined with other features for IQA model design. Generally, a directional filter is sensitive to the changes on specific direction, and insensitive to other directions. This leads to multi-directional calculation and much time consumption in practical applications. Recently, researchers suggested that spatially circular symmetric filters, such as gradient magnitude (GM) and Laplacian of Gaussian (LoG), are highly efficient quality features and hence have been widely used in various IQA model designs. With the odd-symmetric and even-symmetric properties of GM and LoG operators, the two features are a suitable pair for PC compositions and can be computed uniformly by a Gaussian function with one scale factor. In this regard, we propose to combine GM and LoG signals to construct a new PC model with non-directional property. With ability to catch different types of distortions, the proposed PC feature can be promoted to a full-reference IQA model simply with average pooling or standard deviation pooling, and shows state-of-the-art performance compared with existing methods. Furthermore, our proposed PC algorithm can take the place of conventional PC component in well-known FSIM metric, which achieves improved performance and spends less in computation cost. This study suggests that the proposed circular symmetric PC feature is a highly efficient quality feature and can be exclusively used in IQA model designs.

Image quality assessment (IQA) has become a hot issue in the area of image processing, which aims to evaluate image quality automatically by a metric being consistent with subjective evaluation. The first stage of conventional IQA model design is the quality-aware feature selection. Taking advantages of early visual feature, Phase congruency (PC) operates in frequency domain to measure local structures such as edges, corners, lines, etc., by computing the local amplitudes and local energies in multiple scales. Conventional local PC features are calculated with log-Gabor-based filtrations in several orientations, and usually combined with other features for IQA model design. Generally, a directional filter is sensitive to the changes on specific direction, and insensitive to other directions. This leads to multi-directional calculation and much time consumption in practical applications. Recently, researchers suggested that spatially circular symmetric filters, such as gradient magnitude (GM) and Laplacian of Gaussian (LoG), are highly efficient quality features and hence have been widely used in various IQA model designs. With the odd-symmetric and even-symmetric properties of GM and LoG operators, the two features are a suitable pair for PC compositions and can be computed uniformly by a Gaussian function with one scale factor. In this regard, we propose to combine GM and LoG signals to construct a new PC model with non-directional property. With ability to catch different types of distortions, the proposed PC feature can be promoted to a full-reference IQA model simply with average pooling or standard deviation pooling, and shows state-of-the-art performance compared with existing methods. Furthermore, our proposed PC algorithm can take the place of conventional PC component in well-known FSIM metric, which achieves improved performance and spends less in computation cost. This study suggests that the proposed circular symmetric PC feature is a highly efficient quality feature and can be exclusively used in IQA model designs. [ABSTRACT FROM AUTHOR]

An efficient video shot boundary detection is highly desirable for subsequent semantic video content analysis and retrieval applications. The major challenge of shot boundary detection problem is an appropriate choice of features to handle the illumination variation and motion artifacts of frames while finding the boundary in a shot. In this paper, to improve the efficacy of shot boundary detection in presence of the aforementioned challenges, the strength of the gradient feature is explored to develop a dual detection framework for automatic shot boundary detection. In the first phase, abrupt transition (AT) detection is addressed in presence of illumination variation and motion in the frames of a shot by generating an combined feature through joint histogram of gradient magnitude and gradient orientation features frame. In the second phase, gradual transition detection (GT) detection is applied only on the frames within two AT frames satisfying specific frame distance criteria. Handling both AT and GT by the proposed simple gradient-based framework is the uniqueness of this work. Moreover, the proposed method is fully ubiquitous, independent of the video content and free from any training process. Exhaustive simulations are carried out on different databases to validate the proposed approach. The performance of the proposed feature-based shot boundary framework, in terms of average F1 measure, is 94% for AT detection, 84% for GT detection and 90.01% for overall detection. [ABSTRACT FROM AUTHOR]

Facade structural features can represent the overall framework of buildings. However, structural features extracted by the current methods contain a quantity of trivial unstructured information. In this study, we proposed an accurate extraction method for structural features of building facades through texture fusion. By performing texture fusion on building facade images, the interference of textural elements on structural feature extraction could be eliminated. After texture fusion, the line segment detector (LSD) algorithm is used to extract the structural features from the building facade images, and random sample consensus (RANSAC) is used to improve the continuity of structural features. The accuracy and effectiveness of the proposed method was demonstrated by comparing results with the state-of-the-art methods, such as LSD, MLSD, CannyLines, and MCMLSD. Value setting of three important parameters is discussed in detail. The imagery facade features extracted through the proposed method provide valuable support for many fields, such as image feature registration and 3D reconstruction of building surfaces.

Neutrosophy theory has been widely used in solving problems involving uncertainty, especially in dealing with problems on noisy images. A new probabilistic rule to reduce the probability of noisy pixels becoming edges candidate is presented. This research proposed a new edge detection method on a digital image using Ant Colony Optimization (ACO) based on the neutrosophic set of gradient magnitude. Experimental results demonstrate the superior performance of the proposed approach on both the noise-free images and the images with different levels of noise. Based on the experimental results on the noise-free images, the proposed method has better performance than the other methods based on the visual quality and the value of the figure of merit. Evaluation of test images with different levels of noise illustrates the robustness of the proposed method on noisy images. [ABSTRACT FROM AUTHOR]

Recent research on biometrics focuses on achieving a high success rate of authentication and addressing the concern of various spoofing attacks. Although hand geometry recognition provides adequate security over unauthorized access, it is susceptible to presentation attack. This paper presents an anti-spoofing method toward hand biometrics. A presentation attack detection approach is addressed by assessing the visual quality of genuine and fake hand images. A threshold-based gradient magnitude similarity quality metric is proposed to discriminate between the real and spoofed hand samples. The visual hand images of 255 subjects from the Bogazici University hand database are considered as original samples. Correspondingly, from each genuine sample, we acquire a forged image using a Canon EOS 700D camera. Such fake hand images with natural degradation are considered for electronic screen display based spoofing attack detection. Furthermore, we create another fake hand dataset with artificial degradation by introducing additional Gaussian blur, salt and pepper, and speckle noises to original images. Ten quality metrics are measured from each sample for classification between original and fake hand image. The classification experiments are performed using the k-nearest neighbors, random forest, and support vector machine classifiers, as well as deep convolutional neural networks. The proposed gradient similarity-based quality metric achieves 1.5% average classification error using the k-nearest neighbors and random forest classifiers. An average classification error of 2.5% is obtained using the baseline evaluation with the MobileNetV2 deep network for discriminating original and different types of fake hand samples. [ABSTRACT FROM AUTHOR]

• Proposing a shift-insensitive perceptual feature based on GM and LoG signals, and theoretically selecting a ratio parameter to balance them. • Building FR-IQA models based on the proposed QGL feature maps that achieve good performance on three benchmark databases. • Achieving the best shift-insensitive performance on images with spatial changes compared with other metrics. • Combining QGL with deep neural networks and validating that QGL can help improving the accuracy in texture classification. Most existing full-reference (FR) Image quality assessment (IQA) models work in the premise of that the two images should be well registered. Shifting an image would lead to an inaccurate evaluation of image quality, because small spatial shifts are far less noticeable than structural distortion for human observers. To this regard, we propose to study an IQA feature that is shift-insensitive to the basic primitive structure of images, i.e., image edge. According to previous studies, the image gradient magnitude (GM) and the Laplacian of Gaussian (LoG) operator that depict the edge profiles of natural images are highly efficient structural features in IQA tasks. In this paper, we find that the Quadratic sum of the normalized GM and the LoG signals (QGL) has excellent shift-insensitive property in representing image edges after theoretically solving the selection problem of a ratio parameter to balance the GM and LoG signals. Based on the proposed QGL feature, two FR-IQA models can be built directly by measuring the similarity map with mean and standard deviation pooling strategies, named mQGL and sQGL, respectively. Experimental results show that the proposed sQGL and mQGL work robustly on four benchmark IQA databases, and QGL-based models show great shift-insensitive property to spatial translation and image rotation while judging the image quality. In addition, we explore the feasibility of combining QGL feature with deep neural networks, and verify that it can help to promote image pattern recognition in texture classification tasks. [ABSTRACT FROM AUTHOR]

The problem of searching a similar pattern is an exciting and challenging research field of pattern recognition. The intelligence of humans for vision to read is a crucial phenomenon for machine simulation and has been carried out for a few decades. Therefore, in this article, a recognition system of handwritten Meitei Mayek (Manipuri script) is introduced using a convolutional neural network. Generally, character recognition is performed using the gray scale of the image of characters. However, we have additionally considered the corresponding gradient direction and gradient magnitude images to create three‐channels image for every character so that supplementary information from gradient images can be obtained for efficient recognition. Experiments are conducted on 14 700 sample images collected from various individuals of different age groups and educational backgrounds. A recognition rate of 98.70% is obtained, which is compared with the existing methods, and it is found to be superior performance than other neural network methods on Meitei Mayek. [ABSTRACT FROM AUTHOR]

Abstract Image quality assessment is an important topic in the field of digital image processing. In this study, a full-reference image quality assessment method called Riesz transform and Visual contrast sensitivity-based feature SIMilarity index (RVSIM) is proposed. More precisely, a Log-Gabor filter is first used to decompose reference and distorted images, and Riesz transform is performed on the decomposed images on the basis of monogenic signal theory. Then, the monogenic signal similarity matrix is obtained by calculating the similarity of the local amplitude/phase/direction characteristics of monogenic signal. Next, we weight the summation of these characteristics with visual contrast sensitivity. Since the first-order Riesz transform cannot clearly express the corners and intersection points in the image, we calculate the gradient magnitude similarity between the reference and distorted images as a feature, which is combined with monogenic signal similarity to obtain a local quality map. Finally, we conduct the monogenic phase congruency using the Riesz transform feature matrix from the reference image and utilize it as a weighted function to derive the similarity index. Extensive experiments on five benchmark IQA databases, namely, LIVE, CSIQ, TID2008, TID2013, and Waterloo Exploration, indicate that RVSIM is a robust IQA method.

Image edge detection based on low‐level feature is usually performed on gray‐scale images. Some methods have been developed for edge detection on colour images based on low‐level feature, but they are not consistent with human colour perception. This research provides a new algorithm for edge detection based on the "HyAB" large‐colour‐difference formula. This algorithm uses Sobel operators for gradient‐magnitude calculations and Canny methods for localizing edge points. The performance of the new algorithm is qualitatively compared with Sobal and Canny methods using some challenging colour images. The results indicate that gradient magnitudes are best calculated using the HyAB colour‐difference formula, and that CIELAB and CIEDE2000 differences are not suitable for this purpose. Definition of gradient magnitudes according human perception is essential in applications such as quality control of fabric printing, calculation of disruptive colouration, and so on. The new algorithm is successful in accuracy and fine edge detection in comparison with the Sobel and Canny methods. The new method is quantitatively compared with state‐of‐the‐art methods using three datasets including BSDS500, MBDD, and BIPED. The correctness and accuracy of annotations of images in datasets have an important effect on results. The new method does not reach scores better than deep‐learning‐based methods, but it is simple and does not need training. It could probably have better results with improving noise‐suppression. [ABSTRACT FROM AUTHOR]

In this study, a novel image edge detection technique based on the combination of total variation (TV) and anisotropic diffusion (PM) models is presented. In the proposed technique, the authors first use the gradient magnitude to eliminate the noise, then utilise the adaptive weight function to detect the edges of the image. The adaptive weight function has a high ability to adapt and change according to the areas information (edges or flats areas). More specifically, TV filter is applied on the areas which suffer from double and false edges, whereas, anisotropic diffusion filter is applied on the areas which suffer from weak and discontinuous edges. Applying TV filter on the double edges areas will allow one to remove most of the false edges, and thus to obtain much sharper edges. While, applying anisotropic diffusion filter on the discontinuous edges areas will lead to obtaining robust and continuous edges. Consequently, less false edges besides high localisation accuracy were obtained. Experimental results demonstrate the superiority of the new approach in terms of removing the false edges and improving the localisation accuracy of the edges. As objective quantitative performance measures, the peak signal-to-noise ratio (PSNR) and Pratt's figure of merit (FOM) were used.

Measurement of the human body anteflexion angle is an essential method for evaluating human flexibility. In this study, a method based on edge detection is proposed for measuring the angle of human body anteflexion. In this technique, an adaptive Gaussian filter is used to filter an image while preserving as much of its edge information as possible. A 5 × 5 template is adopted to calculate the gradient amplitude and direction of the image. Moreover, the Otsu algorithm is used to compute the high and low thresholds of the gradient image, thereby rendering the algorithm adaptive and eliminating any need for manual threshold setting. After the edges are obtained, the feature points of the head, buttocks, and foot are extracted to calculate the anteflexion angle of the human body. [ABSTRACT FROM AUTHOR]

Text detection in video/images is challenging due to the presence of multiple blur caused by defocus and motion. In this paper, we present a new method for detecting texts in blurred/non-blurred images. Unlike the existing methods that use deblurring or classifiers, the proposed method estimates degree of blur in images based on contrast variations in neighbor pixels and a low pass filter, which results in candidate pixels for deblurring. We consider gradient values of each pixel as the weight for the degree of blur. The proposed method then performs K-means clustering on weighted values of candidate pixels to get text candidates irrespective of blur types. Next, Bhattacharyya distance is used to extract symmetry property of texts to remove false text candidates, which provides text components. Further, the proposed method fixes bounding box for each text component based on the nearest neighbor criteria and direction of the text component. Experimental results on defocus, motion, non-blurred images and standard datasets of curved text show that the proposed method outperforms the existing methods. [ABSTRACT FROM AUTHOR]

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A novel single image rain removal model is proposed. Based on the gradient magnitude and direction of rain streaks, the pure rain region can be extracted from the high‐frequency component of rainy image. To ensure proper rain removal, a pure rain dictionary is learned from the extracted pure rain region, and the learned rain dictionary is used to reconstruct the rainy mask from the decomposed high‐frequency component. To adaptively remove rain pixels and preserve more details of non‐rain pixels, a rainy mask is incorporated into the model. In the proposed model, an improved bilateral filter is only used to handle rain pixels. The experimental results show that the proposed model is superior to existing models in resolving the problems of over‐smoothing and rain‐streak remains in synthetic and real‐world rainy images. Consequently, a better quantitative index and visual quality can be achieved. [ABSTRACT FROM AUTHOR]

No-reference image quality assessment (NR-IQA) aims to evaluate the perceived quality of distorted images without prior knowledge of pristine version of the images. The quality score is predicted based on the features extracted from the distorted image, which needs to correlate with the mean opinion score. The prediction of an image quality score becomes a trivial task, if the noise affecting the quality of an image can be modeled. In this paper, gradient magnitude and Wiener filtered discrete wavelet coefficients are utilized for image quality assessment. In order to reconstruct an estimated noise image, Wiener filter is applied to discrete wavelet coefficients. The estimated noise image and the gradient magnitude are modeled as conditional Gaussian random variables. Joint adaptive normalization is applied to the conditional random distribution of the estimated noise image and the gradient magnitude to form a feature vector. The feature vector is used as an input to a pre-trained support vector regression model to predict the image quality score. The proposed NR-IQA is tested on five commonly used image quality assessment databases and shows better performance as compared to the existing NR-IQA techniques. The experimental results show that the proposed technique is robust and has good generalization ability. Moreover, it also shows good performance when training is performed on images from one database and testing is performed on images from another database. [ABSTRACT FROM AUTHOR]

In this study, a novel image edge detection technique based on the combination of total variation (TV) and anisotropic diffusion (PM) models is presented. In the proposed technique, the authors first use the gradient magnitude to eliminate the noise, then utilise the adaptive weight function to detect the edges of the image. The adaptive weight function has a high ability to adapt and change according to the areas information (edges or flats areas). More specifically, TV filter is applied on the areas which suffer from double and false edges, whereas, anisotropic diffusion filter is applied on the areas which suffer from weak and discontinuous edges. Applying TV filter on the double edges areas will allow one to remove most of the false edges, and thus to obtain much sharper edges. While, applying anisotropic diffusion filter on the discontinuous edges areas will lead to obtaining robust and continuous edges. Consequently, less false edges besides high localisation accuracy were obtained. Experimental results demonstrate the superiority of the new approach in terms of removing the false edges and improving the localisation accuracy of the edges. As objective quantitative performance measures, the peak signal-to-noise ratio (PSNR) and Pratt's figure of merit (FOM) were used. [ABSTRACT FROM AUTHOR]