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Extensive testing is essential to break the transmission of SARS-CoV-2, which causes the ongoing COVID-19 pandemic. Here, we present a CRISPR-based diagnostic assay that is robust to viral genome mutations and temperature, produces results fast, can be applied directly on nasopharyngeal (NP) specimens without RNA purification, and incorporates a human internal control within the same reaction. Specifically, we show that the use of an engineered AsCas12a enzyme enables detection of wildtype and mutated SARS-CoV-2 and allows us to perform the detection step with loop-mediated isothermal amplification (LAMP) at 60-65 °C. We also find that the use of hybrid DNA-RNA guides increases the rate of reaction, enabling our test to be completed within 30 minutes. Utilizing clinical samples from 72 patients with COVID-19 infection and 57 healthy individuals, we demonstrate that our test exhibits a specificity and positive predictive value of 100% with a sensitivity of 50 and 1000 copies per reaction (or 2 and 40 copies per microliter) for purified RNA samples and unpurified NP specimens respectively. As the COVID-19 pandemic continues, variants of the virus are emerging. Here the authors present a diagnostic assay that can detect wildtype and known variants using engineered Cas12a.

Polarimetric Synthetic Aperture Radar (PolSAR) is an advanced remote sensing technology that provides rich polarimetric information. Deep learning methods have been proved an effective tool for PolSAR image classification. However, relying solely on source data input makes it challenging to effectively classify all land cover targets, especially heterogeneous targets with significant scattering variations, such as urban areas and forests. Besides, multiple features can provide more complementary information, while feature selection is crucial for classification. To address these issues, we propose a novel attention mechanism-based multi-feature lightweight DeeplabV3+ network for PolSAR image classification. The proposed method integrates feature extraction, learning, selection, and classification into an end-to-end network framework. Initially, three kinds of complementary features are extracted to serve as inputs to the network, including polarimetric original data, statistical and scattering features, textural and contour features. Subsequently, a lightweight DeeplabV3+ network is designed to conduct multi-scale feature learning on the extracted multidimensional features. Finally, an attention mechanism-based feature selection module is integrated into the network model, adaptively learning weights for multi-scale features. This enhances discriminative features but suppresses redundant or confusing features. Experiments are conducted on five real PolSAR data sets, and experimental results demonstrate the proposed method can achieve more precise boundaries and smoother regions than the state-of-the-art algorithms. In this paper, we develop a novel multi-feature learning framework, achieving a fast and effective classification network for PolSAR images.

Adversarial images are able to fool the Deep Neural Network (DNN) based visual identity recognition systems, with the potential to be widely used in online social media for privacy-preserving purposes, especially in edge-cloud computing. However, most of the current techniques used for adversarial attacks focus on enhancing their ability to attack without making a deliberate, methodical, and well-researched effort to retain the perceptual quality of the resulting adversarial examples. This makes obvious distortion observed in the adversarial examples and affects users’ photo-sharing experience. In this work, we propose a method for generating images inspired by the Human Visual System (HVS) in order to maintain a high level of perceptual quality. Firstly, a novel perceptual loss function is proposed based on Just Noticeable Difference (JND), which considered the loss beyond the JND thresholds. Then, a perturbation adjustment strategy is developed to assign more perturbation to the insensitive color channel according to the sensitivity of the HVS for different colors. Experimental results indicate that our algorithm surpasses the SOTA techniques in both subjective viewing and objective assessment on the VGGFace2 dataset.

Uncrewed-Aerial Vehicles (UAVs) and hyperspectral sensors are emerging as effective alternatives for monitoring water quality on-demand. However, image mosaicking for largely featureless coastal water surfaces or open seas has shown to be challenging. Another pertinent issue observed is the systematic image misalignment between adjacent flight lines due to the time delay between the UAV-borne sensor and the GNSS system. To overcome these challenges, this study introduces a workflow that entails a GPS-based image mosaicking method for push-broom hyperspectral images, together with a correction method to address the aforementioned systematic image misalignment. An open-source toolkit, CoastalWQL, was developed to facilitate the workflow, which includes essential pre-processing procedures for improving the image mosaic’s quality, such as radiometric correction, de-striping, sun glint correction, and object masking classification. For validation, UAV-based push-broom hyperspectral imaging surveys were conducted to monitor coastal turbidity in Singapore, and the implementation of CoastalWQL’s pre-processing workflow was evaluated at each step via turbidity retrieval. Overall, the results confirm that the image mosaicking of the push-broom hyperspectral imagery over featureless water surface using CoastalWQL with time delay correction enabled better localisation of the turbidity plume. Radiometric correction and de-striping were also found to be the most important pre-processing procedures, which improved turbidity prediction by 46.5%.

<p>Visual attention is a relatively new area of study combining a number of disciplines: artificial neural networks, artificial intelligence,&#160; vision science and psychology. The aim is to build computational models similar to human vision in order to solve tough problems for many potential applications including object recognition, unmanned vehicle navigation, and image and video coding and processing. In this book, the authors provide an up to date and highly applied introduction to the topic of visual attention, aiding researchers in creating powerful computer vision systems. Areas covered include the significance of vision research, psychology and computer vision, existing computational visual attention models, and the authors' contributions on visual attention models, and applications in various image and video processing tasks.</p> <p>This book is geared for graduates students and researchers in neural networks, image processing, machine learning, computer vision, and other areas of biologically inspired model building and applications. The book can also be used by practicing engineers looking for techniques involving the application of image coding, video processing, machine vision and brain-like robots to real-world systems. Other students and researchers with interdisciplinary interests will also find this book appealing.</p> <ul> <li>Provides a key knowledge boost to developers of image processing applications</li> <li>Is unique in emphasizing the practical utility of attention mechanisms</li> <li>Includes a number of real-world examples that readers can implement in their own work:</li> <li>robot navigation and object selection</li> <li>image and video quality assessment</li> <li>image and video coding</li> <li>Provides codes for users to apply in practical attentional models and mechanisms</li> </ul>

A fast and efficient method [fast efficient blind (FEB)] for no-reference image quality assessment (IQA) is presented. Two new features, log-energy and variance, are proposed in the spatial domain, which make the IQA algorithm faster and more efficient. FEB obviates the training process of distortion images and subjective opinion scores due to the properties of the new features. The experiment shows that the proposed method outperforms conventional methods in terms of both accuracy and execution speed and is also consistent with the subjective assessment of human beings. Owing to the simplicity of the features proposed, FEB can realise real-time IQA completely.

Sun glint contamination on unmanned aerial vehicles (UAV) imagery is a ubiquitous problem and poses a significant impediment in the retrieval of water quality parameters for coastal monitoring applications. Previous studies using near-infrared (NIR) and regression-based sun glint corrections have shown overcorrection at turbid regions as water-leaving NIR radiance is non-negligible. A spatial shift in the band channels would also result in suboptimal correction in the visible spectrum. Recent total variation (TV) methods show promise in reducing spectral variation associated with glint-affected regions and achieve effective correction of sun glint while leaving non-glint regions largely unaltered. To that end, this study proposes an open-source Sun Glint-Aware Restoration (SUGAR) algorithm that bridges principles in NIR and TV methods for the effective correction of sun glint in multispectral and hyperspectral UAV imagery. The present study shows that SUGAR achieves the best sun glint correction performance among existing regression and pixel-based sun glint correction methods when applied on UAV imagery of turbid and shallow regions. Around 40–80% of the total variation at glint-affected regions have been reduced while preserving features in non-glint regions. Validation of SUGAR with in situ UAV flight surveys and turbidity measurements in the coastal region of Singapore demonstrated significant improvement in turbidity retrieval, with root-mean-squared error (RMSE) reducing from 0.464 to 0.183 FNU and 0.551 to 0.285 FNU for multispectral and hyperspectral imagery, respectively.

The concept of just noticeable difference (JND), which accounts for the visibility threshold (visual redundancy) of the human visual system, is useful in perception-oriented signal processing systems. In this work, we present a comprehensive review of JND estimation technology. First, the visual mechanism and its corresponding computational modules are illustrated. These include luminance adaptation, contrast masking, pattern masking, and the contrast sensitivity function. Next, the existing pixel domain and subband domain JND models are presented and analyzed. Finally, the challenges associated with JND estimation are discussed.

We present the first attempt in creating a binary 3D feature descriptor for fast and efficient keypoint matching on 3D point clouds. Specifically, we propose a binarization technique and apply it on the state-of-the-art 3D feature descriptor, SHOT (Salti et al., Comput Vision Image Underst 125:251–264, 2014) to create the first binary 3D feature descriptor, which we call B-SHOT. B-SHOT requires 32 times lesser memory for its representation while being six times faster in feature descriptor matching, when compared to the SHOT feature descriptor. Next, we propose a robust evaluation metric, specifically for 3D feature descriptors. A comprehensive evaluation on standard benchmarks reveals that B-SHOT offers comparable keypoint matching performance to that of the state-of-the-art real valued 3D feature descriptors, albeit at dramatically lower computational and memory costs.