Determination of Munsell Soil Colour Using Smartphones.

Soil colour is one of the most important factors in agriculture for monitoring soil health and determining its properties. For this purpose, Munsell soil colour charts are widely used by archaeologists, scientists, and farmers. The process of determining soil colour from the chart is subjective and error-prone. In this study, we used popular smartphones to capture soil colours from images in the Munsell Soil Colour Book (MSCB) to determine the colour digitally. These captured soil colours are then compared with the true colour determined using a commonly used sensor (Nix Pro-2). We have observed that there are colour reading discrepancies between smartphone and Nix Pro-provided readings. To address this issue, we investigated different colour models and finally introduced a colour-intensity relationship between the images captured by Nix Pro and smartphones by exploring different distance functions. Thus, the aim of this study is to determine the Munsell soil colour accurately from the MSCB by adjusting the pixel intensity of the smartphone-captured images. Without any adjustment when the accuracy of individual Munsell soil colour determination is only 9% for the top 5 predictions, the accuracy of the proposed method is 74%, which is significant.

Development of Debiasing Technique for Lung Nodule Chest X-ray Datasets to Generalize Deep Learning Models.

Screening programs for early lung cancer diagnosis are uncommon, primarily due to the challenge of reaching at-risk patients located in rural areas far from medical facilities. To overcome this obstacle, a comprehensive approach is needed that combines mobility, low cost, speed, accuracy, and privacy. One potential solution lies in combining the chest X-ray imaging mode with federated deep learning, ensuring that no single data source can bias the model adversely. This study presents a pre-processing pipeline designed to debias chest X-ray images, thereby enhancing internal classification and external generalization. The pipeline employs a pruning mechanism to train a deep learning model for nodule detection, utilizing the most informative images from a publicly available lung nodule X-ray dataset. Histogram equalization is used to remove systematic differences in image brightness and contrast. Model training is then performed using combinations of lung field segmentation, close cropping, and rib/bone suppression. The resulting deep learning models, generated through this pre-processing pipeline, demonstrate successful generalization on an independent lung nodule dataset. By eliminating confounding variables in chest X-ray images and suppressing signal noise from the bone structures, the proposed deep learning lung nodule detection algorithm achieves an external generalization accuracy of 89%. This approach paves the way for the development of a low-cost and accessible deep learning-based clinical system for lung cancer screening.

Soil Moisture, Organic Carbon, and Nitrogen Content Prediction with Hyperspectral Data Using Regression Models.

Soil moisture, soil organic carbon, and nitrogen content prediction are considered significant fields of study as they are directly related to plant health and food production. Direct estimation of these soil properties with traditional methods, for example, the oven-drying technique and chemical analysis, is a time and resource-consuming approach and can predict only smaller areas. With the significant development of remote sensing and hyperspectral (HS) imaging technologies, soil moisture, carbon, and nitrogen can be estimated over vast areas. This paper presents a generalized approach to predicting three different essential soil contents using a comprehensive study of various machine learning (ML) models by considering the dimensional reduction in feature spaces. In this study, we have used three popular benchmark HS datasets captured in Germany and Sweden. The efficacy of different ML algorithms is evaluated to predict soil content, and significant improvement is obtained when a specific range of bands is selected. The performance of ML models is further improved by applying principal component analysis (PCA), a dimensional reduction method that works with an unsupervised learning method. The effect of soil temperature on soil moisture prediction is evaluated in this study, and the results show that when the soil temperature is considered with the HS band, the soil moisture prediction accuracy does not improve. However, the combined effect of band selection and feature transformation using PCA significantly enhances the prediction accuracy for soil moisture, carbon, and nitrogen content. This study represents a comprehensive analysis of a wide range of established ML regression models using data preprocessing, effective band selection, and data dimension reduction and attempt to understand which feature combinations provide the best accuracy. The outcomes of several ML models are verified with validation techniques and the best- and worst-case scenarios in terms of soil content are noted. The proposed approach outperforms existing estimation techniques.

Video Rain-Streaks Removal by Combining Data-Driven and Feature-Based Models.

Video analytics and computer vision applications face challenges when using video sequences with low visibility. The visibility of a video sequence is degraded when the sequence is affected by atmospheric interference like rain. Many approaches have been proposed to remove rain streaks from video sequences. Some approaches are based on physical features, and some are based on data-driven (i.e., deep-learning) models. Although the physical features-based approaches have better rain interpretability, the challenges are extracting the appropriate features and fusing them for meaningful rain removal, as the rain streaks and moving objects have dynamic physical characteristics and are difficult to distinguish. Additionally, the outcome of the data-driven models mostly depends on variations relating to the training dataset. It is difficult to include datasets with all possible variations in model training. This paper addresses both issues and proposes a novel hybrid technique where we extract novel physical features and data-driven features and then combine them to create an effective rain-streak removal strategy. The performance of the proposed algorithm has been tested in comparison to several relevant and contemporary methods using benchmark datasets. The experimental result shows that the proposed method outperforms the other methods in terms of subjective, objective, and object detection comparisons for both synthetic and real rain scenarios by removing rain streaks and retaining the moving objects more effectively.

Deep Mining Generation of Lung Cancer Malignancy Models from Chest X-ray Images.

Lung cancer is the leading cause of cancer death and morbidity worldwide. Many studies have shown machine learning models to be effective in detecting lung nodules from chest X-ray images. However, these techniques have yet to be embraced by the medical community due to several practical, ethical, and regulatory constraints stemming from the "black-box" nature of deep learning models. Additionally, most lung nodules visible on chest X-rays are benign; therefore, the narrow task of computer vision-based lung nodule detection cannot be equated to automated lung cancer detection. Addressing both concerns, this study introduces a novel hybrid deep learning and decision tree-based computer vision model, which presents lung cancer malignancy predictions as interpretable decision trees. The deep learning component of this process is trained using a large publicly available dataset on pathological biomarkers associated with lung cancer. These models are then used to inference biomarker scores for chest X-ray images from two independent data sets, for which malignancy metadata is available. Next, multi-variate predictive models were mined by fitting shallow decision trees to the malignancy stratified datasets and interrogating a range of metrics to determine the best model. The best decision tree model achieved sensitivity and specificity of 86.7% and 80.0%, respectively, with a positive predictive value of 92.9%. Decision trees mined using this method may be considered as a starting point for refinement into clinically useful multi-variate lung cancer malignancy models for implementation as a workflow augmentation tool to improve the efficiency of human radiologists.

Hyperspectral image compression approaches: opportunities, challenges, and future directions: discussion.

This paper establishes a review of the recent study in the field of hyperspectral (HS) image compression approaches. Recently, image compression techniques have achieved significant advances from diverse types of coding standards/approaches. HS image compression requires an unconventional coding technique because of its unique, multiple-dimensional structure. The data redundancy exists in both inter-band and intra-band methods. The survey summarizes current literature in inter- and intra-band compression methods. The challenges, opportunities, and future research possibilities regarding HS image compression are further discussed. The experimental results are also provided for validity and applicability of the existing HS image compression techniques.

Adaptive fusion of human visual sensitive features for surveillance video summarization.

Surveillance video cameras capture large amounts of continuous video streams every day. To analyze or investigate any significant events, it is a laborious and boring job to identify these events from the huge video data if it is done manually. Existing approaches sometimes neglect key frames with significant visual contents and/or select some unimportant frames with low/no activity. To solve this problem, in this paper, a video summarization technique is proposed by combining three multimodal human visual sensitive features, such as foreground objects, motion information, and visual saliency. In a video stream, foreground objects are one of the most important pieces of a video as they contain more detailed information and play a major role in important events. Moreover, motion is another stimulus of a video that significantly attracts human visual attention. To obtain this, motion information is calculated in the spatial domain as well as the frequency domain. Spatial motion information can select object motion accurately; however, it is sensitive to illumination changes. On the other hand, frequency motion information is robust to illumination change, although it is easily affected by noise. Therefore, motion information in both the spatial and the frequency domains is employed. Furthermore, the visual attention cue is a sensitive feature to measure the indication of a user's attraction label for determining key frames. As these features individually cannot perform very well, they are combined to obtain better results. For this purpose, an adaptive linear weighted fusion scheme is proposed to combine the features to rank video frames for summarization. Experimental results reveal that the proposed method outperforms the state-of-the-art methods.

Correction of geometrically distorted underwater images using shift map analysis.

In underwater imaging, water waves cause severe geometric distortions and blurring of the acquired short-exposure images. Corrections for these distortions have been tackled reasonably well by previous efforts but still need improvement in the estimation of pixel shift maps to increase restoration accuracy. This paper presents a new algorithm that efficiently estimates the shift maps from geometrically distorted video sequences and uses those maps to restore the sequences. A nonrigid image registration method is employed to estimate the shift maps of the distorted frames against a reference frame. The sharpest frame of the sequence, determined using a sharpness metric, is chosen as the reference frame. A k-means clustering technique is employed to discard too-blurry frames that could result in inaccuracy in the shift maps' estimation. The estimated pixel shift maps are processed to generate the accurate shift map that is used to dewarp the input frames into their nondistorted forms. The proposed method is applied on several synthetic and real-world video sequences, and the obtained results exhibit significant improvements over the state-of-the-art methods.

A Two-Step Discrete Cosine Basis Oriented Motion Modeling Approach for Enhanced Motion Compensation.

Video coding algorithms attempt to minimize the significant commonality that exists within a video sequence. Each new video coding standard contains tools that can perform this task more efficiently compared to its predecessors. Modern video coding systems are block-based wherein commonality modeling is carried out only from the perspective of the block that need be coded next. In this work, we argue for a commonality modeling approach that can provide a seamless blending between global and local homogeneity information in terms of motion. For this purpose, at first a prediction of the current frame, the frame that need be coded, is generated by performing a two-step discrete cosine basis oriented (DCO) motion modeling. The DCO motion model is employed rather than traditional translational or affine motion model since it has the ability to efficiently model complex motion fields by providing a smooth and sparse representation. Moreover, the proposed two-step motion modeling approach can yield better motion compensation at a reduced computational complexity since an informed guess is designed for initializing the motion search procedure. After that the current frame is partitioned into rectangular regions and the conformance of these regions to the learned motion model is investigated. Depending on the non-conformance to the estimated global motion model, an additional DCO motion model is introduced to increase the local motion homogeneity. In this way, the proposed approach generates a motion compensated prediction of the current frame through the minimization of both global and local motion commonality. Experimental results show an improved rate-distortion performance of a reference high efficiency video coding (HEVC) encoder, specifically up to around 9% savings in bit rate, that employs the DCO prediction frame as a reference frame for encoding the current frame. When compared to the more recent video coding standard, the versatile video coding (VVC) encoder, a bit rate savings of 2.37% is reported.

Applications of LiDAR in Agriculture and Future Research Directions.

Light detection and ranging (LiDAR) sensors have accrued an ever-increasing presence in the agricultural sector due to their non-destructive mode of capturing data. LiDAR sensors emit pulsed light waves that return to the sensor upon bouncing off surrounding objects. The distances that the pulses travel are calculated by measuring the time for all pulses to return to the source. There are many reported applications of the data obtained from LiDAR in agricultural sectors. LiDAR sensors are widely used to measure agricultural landscaping and topography and the structural characteristics of trees such as leaf area index and canopy volume; they are also used for crop biomass estimation, phenotype characterisation, crop growth, etc. A LiDAR-based system and LiDAR data can also be used to measure spray drift and detect soil properties. It has also been proposed in the literature that crop damage detection and yield prediction can also be obtained with LiDAR data. This review focuses on different LiDAR-based system applications and data obtained from LiDAR in agricultural sectors. Comparisons of aspects of LiDAR data in different agricultural applications are also provided. Furthermore, future research directions based on this emerging technology are also presented in this review.

Advanced quantum image representation and compression using a DCT-EFRQI approach.

In recent years, quantum image computing draws a lot of attention due to storing and processing image data faster compared to classical computers. A number of approaches have been proposed to represent the quantum image inside a quantum computer. Representing and compressing medium and big-size images inside the quantum computer is still challenging. To address this issue, we have proposed a block-wise DCT-EFRQI (Direct Cosine Transform Efficient Flexible Representation of Quantum Image) approach to represent and compress the gray-scale image efficiently to save computational time and reduce the quantum bits (qubits) for the state preparation. In this work, we have demonstrated the capability of block-wise DCT and DWT transformation inside the quantum domain to investigate their relative performances. The Quirk simulation tool is used to design the corresponding quantum image circuit. In the proposed DCT-EFRQI approach, a total of 17 qubits are used to represent the coefficients, the connection between coefficients and state (i.e., auxiliary), and their position for representing and compressing grayscale images inside a quantum computer. Among those, 8 qubits are used to map the coefficient values and the rest are used to generate the corresponding coefficient XY-coordinate position including one auxiliary qubit. Theoretical analysis and experimental results show that the proposed DCT-EFRQI scheme provides better representation and compression compared to DCT-GQIR, DWT-GQIR, and DWT-EFRQI in terms of rate-distortion performance.

A hybrid approach to enhance the lifespan of WSNs in nuclear power plant monitoring system.

In recent years, the nuclear power plant has received huge attention as it generates vast amounts of power at a lower cost. However, its creation of radioactive wastes is a major environmental concern. Therefore, the nuclear power plant requires a reliable and uninterrupted monitoring system as an essential part of it. Monitoring a nuclear power plant using wireless sensor networks is a convenient and popular practice now. This paper proposes a hybrid approach for monitoring wireless sensor networks in the context of a nuclear power plant in Bangladesh. Our hybrid approach enhances the lifespan of wireless sensor networks reducing power consumption and offering better connectivity of sensors. To do so, it uses both the topology maintenance and topology construction algorithms. We found that the HGETRecRot topology maintenance algorithm enhances the network lifetime compared to other algorithms. This algorithm increases the communication and sensing coverage area but decreases the network performance. We also propose a prediction model, based on linear regression algorithm, that predicts the best combination of topology maintenance and topology construction algorithms.

Automatically Assessing Quality of Online Health Articles.

Today Information in the world wide web is overwhelmed by unprecedented quantity of data on versatile topics with varied quality. However, the quality of information disseminated in the field of medicine has been questioned as the negative health consequences of health misinformation can be life-threatening. There is currently no generic automated tool for evaluating the quality of online health information spanned over broad range. To address this gap, in this paper, we applied data mining approach to automatically assess the quality of online health articles based on 10 quality criteria. We have prepared a labelled dataset with 53012 features and applied different feature selection methods to identify the best feature subset with which our trained classifier achieved an accuracy of [Formula: see text] varied over 10 criteria. Our semantic analysis of features shows the underpinning associations between the selected features & assessment criteria and further rationalize our assessment approach. Our findings will help in identifying high quality health articles and thus aiding users in shaping their opinion to make right choice while picking health related help from online.

Factors determining generalization in deep learning models for scoring COVID-CT images.

The COVID-19 pandemic has inspired unprecedented data collection and computer vision modelling efforts worldwide, focused on the diagnosis of COVID-19 from medical images. However, these models have found limited, if any, clinical application due in part to unproven generalization to data sets beyond their source training corpus. This study investigates the generalizability of deep learning models using publicly available COVID-19 Computed Tomography data through cross dataset validation. The predictive ability of these models for COVID-19 severity is assessed using an independent dataset that is stratified for COVID-19 lung involvement. Each inter-dataset study is performed using histogram equalization, and contrast limited adaptive histogram equalization with and without a learning Gabor filter. We show that under certain conditions, deep learning models can generalize well to an external dataset with F1 scores up to 86%. The best performing model shows predictive accuracy of between 75% and 96% for lung involvement scoring against an external expertly stratified dataset. From these results we identify key factors promoting deep learning generalization, being primarily the uniform acquisition of training images, and secondly diversity in CT slice position.

COVID-19 Control by Computer Vision Approaches: A Survey.

The COVID-19 pandemic has triggered an urgent call to contribute to the fight against an immense threat to the human population. Computer Vision, as a subfield of artificial intelligence, has enjoyed recent success in solving various complex problems in health care and has the potential to contribute to the fight of controlling COVID-19. In response to this call, computer vision researchers are putting their knowledge base at test to devise effective ways to counter COVID-19 challenge and serve the global community. New contributions are being shared with every passing day. It motivated us to review the recent work, collect information about available research resources, and an indication of future research directions. We want to make it possible for computer vision researchers to find existing and future research directions. This survey article presents a preliminary review of the literature on research community efforts against COVID-19 pandemic.

COVID-19 Detection Through Transfer Learning Using Multimodal Imaging Data.

Detecting COVID-19 early may help in devising an appropriate treatment plan and disease containment decisions. In this study, we demonstrate how transfer learning from deep learning models can be used to perform COVID-19 detection using images from three most commonly used medical imaging modes X-Ray, Ultrasound, and CT scan. The aim is to provide over-stressed medical professionals a second pair of eyes through intelligent deep learning image classification models. We identify a suitable Convolutional Neural Network (CNN) model through initial comparative study of several popular CNN models. We then optimize the selected VGG19 model for the image modalities to show how the models can be used for the highly scarce and challenging COVID-19 datasets. We highlight the challenges (including dataset size and quality) in utilizing current publicly available COVID-19 datasets for developing useful deep learning models and how it adversely impacts the trainability of complex models. We also propose an image pre-processing stage to create a trustworthy image dataset for developing and testing the deep learning models. The new approach is aimed to reduce unwanted noise from the images so that deep learning models can focus on detecting diseases with specific features from them. Our results indicate that Ultrasound images provide superior detection accuracy compared to X-Ray and CT scans. The experimental results highlight that with limited data, most of the deeper networks struggle to train well and provides less consistency over the three imaging modes we are using. The selected VGG19 model, which is then extensively tuned with appropriate parameters, performs in considerable levels of COVID-19 detection against pneumonia or normal for all three lung image modes with the precision of up to 86% for X-Ray, 100% for Ultrasound and 84% for CT scans.

A novel augmented reality (AR) scheme for knee replacement surgery by considering cutting error accuracy.

BACKGROUND: Augmented reality (AR) surgery has not been successfully implemented in knee replacement surgery due to the negative effect of cutting errors. This research aims to decrease the cutting error to reduce the chronic pain after knee replacement. METHODOLOGY: The proposed system consists of a volume subtraction technique that considers the history of the area that has been cut and measures it against the target shape. RESULTS: Results minimized the cutting error by about 1 mm. Therefore, it provides a significant video accuracy improvement in alignment to 0.40 to 0.55 mm from 0.55 to 0.64 and a decrease in processing time from 12 to 13 fs/s to 9 to10 fs/s. CONCLUSION: The proposed system is focused on overlaying only the remaining areas of surgery that need to be completed. Finally, this study solves the issues of navigation with AR when cutting bones in a scheduled direction and depth.

Virtual View Synthesis for Free Viewpoint Video and Multiview Video Compression using Gaussian Mixture Modelling.

High quality virtual views need to be synthesized from adjacent available views for free viewpoint video and multiview video coding (MVC) to provide users with a more realistic 3D viewing experience of a scene. View synthesis techniques suffer from poor rendering quality due to holes created by occlusion and rounding integer error through warping. To remove the holes in the virtual view, the existing techniques use spatial and temporal correlation in intra/inter-view images and depth maps. However, they still suffer quality degradation in the boundary region of foreground and background areas due to the low spatial correlation in texture images and low correspondence in inter-view depth maps. To overcome the above-mentioned limitations, we use a number of models in the Gaussian mixture modeling (GMM) to separate background and foreground pixels in our proposed technique. Here, the missing pixels introduced from the warping process are recovered by the adaptive weighted average of the pixel intensities from the corresponding GMM model(s) and warped image. The weights vary with time to accommodate the changes due to a dynamic background and the motions of the moving objects for view synthesis. We also introduce an adaptive strategy to reset the GMM modeling if the contributions of the pixel intensities drop significantly. Our experimental results indicate that the proposed approach provides 5.40-6.60-dB PSNR improvement compared with the relevant methods. To verify the effectiveness of the proposed view synthesis technique, we use it as an extra reference frame in the motion estimation for MVC. The experimental results confirm that the proposed view synthesis is able to improve PSNR by 3.15-5.13 dB compared with the conventional three reference frames.

A novel noise filtered and occlusion removal: navigational accuracy in augmented reality-based constructive jaw surgery.

PURPOSE: Augmented reality-based constructive jaw surgery has been facing various limitations such as noise in real-time images, the navigational error of implants and jaw, image overlay error, and occlusion handling which have limited the implementation of augmented reality (AR) in corrective jaw surgery. This research aimed to improve the navigational accuracy, through noise and occlusion removal, during positioning of an implant in relation to the jaw bone to be cut or drilled. METHOD: The proposed system consists of a weighting-based de-noising filter and depth mapping-based occlusion removal for removing any occluded object such as surgical tools, the surgeon's body parts, and blood. RESULTS: The maxillary (upper jaw) and mandibular (lower jaw) jaw bone sample results show that the proposed method can achieve the image overlay error (video accuracy) of 0.23~0.35 mm and processing time of 8-12 frames per second compared to 0.35~0.45 mm and 6-11 frames per second by the existing best system. CONCLUSION: The proposed system concentrates on removing the noise from the real-time video frame and the occlusion. Thus, the acceptable range of accuracy and the processing time are provided by this study for surgeons for carrying out a smooth surgical flow.

Seizure Prediction Using Undulated Global and Local Features.

In this study, a seizure prediction method is proposed based on a patient-specific approach by extracting undulated global and local features of preictal/ictal and interictal periods of EEG signals. The proposed method consists of feature extraction, classification, and regularization. The undulated global feature is extracted using phase correlation between two consecutive epochs of EEG signals and an undulated local feature is extracted using the fluctuation and deviation of EEG signals within the epoch. These features are further used for classification of preictal/ictal and interictal EEG signals. A regularization technique is applied on the classified outputs for the reduction of false alarms and improvement of the overall prediction accuracy (PA). The experimental results confirm that the proposed method provides high PA (i.e., 95.4%) with low false positive per hour using intracranial EEG signals in different brain locations of 21 patients from a benchmark dataset. Combining global and local features enables the transition point to be determined between different types of signals with greater accuracy, resulting successful versus unsuccessful prediction of seizure. The theoretical contribution of this study may provide an opportunity for the development of a clinical device to predict forthcoming seizure in real time.