Blue Images Research Articles

Conditioned generalisation in generalised anxiety disorder: the role of concurrent perceptual and conceptual cues

ABSTRACT Previous research in extinction indicates no difference in US expectancies for aversive and non-aversive unconditioned stimuli (USs). In this study, we bridged these topics by examining how concurrent perceptual and conceptual cues influence conditioned generalisation of generalised anxiety disorder (GAD) patients by using non-aversive USs. The study included two consecutive phases: acquisition and generalisation. In the acquisition phase, we used blue and purple images as the perceptually conditioned stimuli, images of animals and household items as the conceptually conditioned stimuli, and non-aversive images as unconditioned stimuli (US). In the generalisation phase, we used images containing both conceptual and perceptual cues (e.g. blue animals) as the generalisation stimuli. Participants rated the US expectancy for all images. We found that compared with the control group, the patients exhibited generalisation in response to stimuli that included conditional conceptual cues. These results reveal novel evidence of generalisation in GAD and may have implications for considering the concept-based information in extinction treatment.

Study on the quality detection method of biochar based on red–green–blue image recognition technology

Study on the quality detection method of biochar based on red–green–blue image recognition technology

Fusion of near-infrared and RGB images on a FPGA using high level synthesis tool

Several applications utilizing a set of red green blue (RGB) and near infrared (NIR) images have been emerging over recent years. The present work proposes a technique of enhancing an image by combining color (RGB) and near infrared information (NIR). In order to fuse the two types of images, the NIR-channel is considered as a luminance counterpart to the visible image. International standard database (RGB-NIR Scene Dataset) is used in this work for image fusion. The objective of the paper is to present a simple and hardware efficient fusion method, where the original RGB image is converted into two different color spaces, namely, HSV and YCbCr. Later, the luminance channel of the RGB image is replaced with the near infrared channel, thereby obtaining a fused enhanced image. The above procedure is effectively implemented on FPGA using the Xilinx HLS tool. RGB-NIR dataset is used in the present work for testing the proposed image fusion algorithm, and the quality of the fused image is measured through peak signal to noise ratio (PSNR). The experimental results indicate that HSV color space is more efficient in image fusion compared to YCbCr color space based on the average PSNR values of approximately 29db for HSV and 25db for YCbCr for various images, respectively. Finally, this complete fusion algorithm is implemented on Xilinx Nexys4 FPGA board to be able to obtain real-time outputs in the form of vivid, contrasted images that are pleasing to the observers. The experimental results illustrate that the Xilinx FPGA utilizes only 50% of the available hardware resources and consumes approximately 5.3 Watts to implement the fusion process.

Blue Widefield Images of Scanning Laser Ophthalmoscope Can Detect Retinal Ischemic Areas in Eyes With Diabetic Retinopathy.

To determine whether the hyporeflective areas in the blue images obtained by widefield scanning laser ophthalmoscopy (SLO) correspond to the non-perfused areas (NPAs) in the fluorescein angiograms (FA) in eyes with diabetic retinopathy (DR). Retrospective observational case series. Ninety patients with diabetes mellitus (DM) were studied. All had undergone multicolor widefield SLO imaging. The NPAs in the FA images and hyporeflective areas in the blue widefield SLO images were examined. The morphology of the retina was determined by optical coherence tomography. Hyporeflective areas in the blue SLO images were found with a rate of 76.6% in eyes with proliferative DR eyes. In a comparison of the hyporeflective areas of the blue SLO images to the non-perfused areas in the FA images, the appearance and the correspondence in the locations of these two types of images were found, and the rate was highly concordant with a Cohen's kappa value of 0.675. The high concordance between the hyporeflective areas in the widefield blue SLO and the NPAs in the FA indicates that widefield blue SLO can be used to identify ischemic retinal areas in eyes with DR without the intravenous injection of any dye.

A Novel Approach for Network Intrusion Detection Using Multistage Deep Learning Image Recognition

The current rise in hacking and computer network attacks throughout the world has heightened the demand for improved intrusion detection and prevention solutions. The intrusion detection system (IDS) is critical in identifying abnormalities and assaults on the network, which have grown in size and pervasiveness. The paper proposes a novel approach for network intrusion detection using multistage deep learning image recognition. The network features are transformed into four-channel (Red, Green, Blue, and Alpha) images. The images then are used for classification to train and test the pre-trained deep learning model ResNet50. The proposed approach is evaluated using two publicly available benchmark datasets, UNSW-NB15 and BOUN Ddos. On the UNSW-NB15 dataset, the proposed approach achieves 99.8% accuracy in the detection of the generic attack. On the BOUN DDos dataset, the suggested approach achieves 99.7% accuracy in the detection of the DDos attack and 99.7% accuracy in the detection of the normal traffic.

Wheat Yield Prediction Based on Unmanned Aerial Vehicles-Collected Red–Green–Blue Imagery

Unmanned aerial vehicles-collected (UAVs) digital red–green–blue (RGB) images provided a cost-effective method for precision agriculture applications regarding yield prediction. This study aims to fully explore the potential of UAV-collected RGB images in yield prediction of winter wheat by comparing it to multi-source observations, including thermal, structure, volumetric metrics, and ground-observed leaf area index (LAI) and chlorophyll content under the same level or across different levels of nitrogen fertilization. Color indices are vegetation indices calculated by the vegetation reflectance at visible bands (i.e., red, green, and blue) derived from RGB images. The results showed that some of the color indices collected at the jointing, flowering, and early maturity stages had high correlation (R2 = 0.76–0.93) with wheat grain yield. They gave the highest prediction power (R2 = 0.92–0.93) under four levels of nitrogen fertilization at the flowering stage. In contrast, the other measurements including canopy temperature, volumetric metrics, and ground-observed chlorophyll content showed lower correlation (R2 = 0.52–0.85) to grain yield. In addition, thermal information as well as volumetric metrics generally had little contribution to the improvement of grain yield prediction when combining them with color indices derived from digital images. Especially, LAI had inferior performance to color indices in grain yield prediction within the same level of nitrogen fertilization at the flowering stage (R2 = 0.00–0.40 and R2 = 0.55–0.68), and color indices provided slightly better prediction of yield than LAI at the flowering stage (R2 = 0.93, RMSE = 32.18 g/m2 and R2 = 0.89, RMSE = 39.82 g/m2) under all levels of nitrogen fertilization. This study highlights the capabilities of color indices in wheat yield prediction across genotypes, which also indicates the potential of precision agriculture application using many other flexible, affordable, and easy-to-handle devices such as mobile phones and near surface digital cameras in the future.

Full color single pixel imaging by using multiple input single output technology.

In this paper, we propose a novel full color single pixel imaging by using multiple input single output (MISO) technology. In the scheme, the MISO technology, which is widely used in the wireless communications, is used to simultaneously produce three (red, green and blue) detection signal components corresponding to the red, green and blue components of the object with only one single pixel bucket detector respectively. Then, a full color image of object can be produced by synthesizing the reconstructed red, green and blue component images of object, where the red (green or blue) component image can be recovered by utilizing the speckle patterns and corresponding detection signal components. The experimental results demonstrate that our scheme can be robust against the interference of the intensity fluctuations of ambient light and improve the imaging quality. Moreover, our scheme requires only one single pixel bucket detector, which reduces the numbers of bucket detectors that need to be used. Our scheme provides a promising avenue to realize the full color single pixel imaging with MISO technology and has the potential to be extended to high quality multispectral single pixel imaging by using only one single pixel detector.

Role of multicolour imaging in post-fever retinitis involving posterior pole.

To describe composite multicolour (MC) imaging features along with the monocoloured fundus reflectance images in active and resolving stages of post-fever retinitis (PFR). Retrospective image analysis of cases of PFR who underwent dilated retinal clinical examination followed by optical coherence tomography and MC imaging. Twenty-five eyes of 18 patients diagnosed with PFR were included. There were 11 males and 7 females. Mean age of patients was 30.63years. The retinitis lesion appeared bright white on MC image and white mainly on blue and green reflectance images during the active stages of PFR. The lesion appeared dull-grey to greyish white during the resolving stages and as dull-green in resolved cases. The active stages showed the presence of intraretinal/subretinal fluid which appeared as green colour on MC images and less green to normal during resolving stages. Hard exudates were seen as bright yellow- or orange-coloured spots on MC image during the resolving stages of the disease. The different stages of PFR show different colour on multicolour image and different reflectance patterns on individual colour reflectance channels. Hence, multimodal fundus imaging with different wavelength can be helpful for differentiation of activity in PFR.

Applying convolutional neural networks to assess the external quality of strawberries

Deterioration in the appearance of strawberries is attributed to rapid metabolic changes, cellular damage, and softening occurring during their distribution and storage. To quickly and non-destructively monitor the external quality of strawberries, recognition models based on 750 Red Green Blue (RGB) image classifications and using convolutional neural networks (CNNs) were developed. A model using eight configurations was used to compare the discrimination accuracies according to the following variables: training–test set distributions, number of learning images, and number of epochs. Strawberry samples were classified as fresh, bruised, or moldy. According to our validation results, training the model with 90 % of the image data ensured a high learning performance. Using our test dataset, we found that the accuracy, precision, specificity, and sensitivity of the model reached 97 %. In the feature map derived from convolutional layers, the bruised and moldy areas of the strawberry were also identified. Together, these results suggest that CNNs have potential use in the non-destructive monitoring of quality changes in the food industry.

Determination of banana quality indices during the ripening process at different temperatures using smartphone images and an artificial neural network

Banana quality and ripening stages were studied at four different temperatures (20, 25, 27.5, and 30 °C) using a smartphone camera and an artificial neural network (ANN). Changes in chemical and physical properties such as firmness, total soluble solids (TSS), pH, and ratio of pulp to peel, were determined and the relationship with color features (L*a*b*, hue, saturation, value [HSV], and luminance and chrominance [YUV]) were extracted from red, green, blue (RGB) images. ANN models based on color features were used to predict the quality indices of bananas during the ripening process. In addition, the developed models were verified by the coefficient of determination (R2), root mean squared error (RMSE), and the ratio of performance to deviation (RPD). The ANN model predicted the firmness and TSS of banana efficiently, with R2 values higher than 0.96 and RMSE values less than 72.32 and 1.40 for firmness and TSS, respectively. However, the ANN model was not suitable for predicting the pH value and ratio of pulp to peel with R2 values less than 0.60 and RMSE values of over 0.20 and 0.23 for pH and the ratio of pulp to peel, respectively. Thus, we demonstrated a post-harvest methodology to determine the ripening status of bananas by predicting their quality indices during the ripening process on a smartphone camera using ANN, and suggest smartphone camera as a potential tool to estimate product quality.

Temporal action detection based on two-stream You Only Look Once network for elderly care service robot

Human action segmentation and recognition from the continuous untrimmed sensor data stream is a challenging issue known as temporal action detection. This article provides a two-stream You Only Look Once-based network method, which fuses video and skeleton streams captured by a Kinect sensor, and our data encoding method is used to turn the spatiotemporal temporal action detection into a one-dimensional object detection problem in constantly augmented feature space. The proposed approach extracts spatial–temporal three-dimensional convolutional neural network features from video stream and view-invariant features from skeleton stream, respectively. Furthermore, these two streams are encoded into three-dimensional feature spaces, which are represented as red, green, and blue images for subsequent network input. We proposed the two-stream You Only Look Once-based networks which are capable of fusing video and skeleton information by using the processing pipeline to provide two fusion strategies, boxes-fusion or layers-fusion. We test the temporal action detection performance of two-stream You Only Look Once network based on our data set High-Speed Interplanetary Tug/Cocoon Vehicles-v1, which contains seven activities in the home environment and achieve a particularly high mean average precision. We also test our model on the public data set PKU-MMD that contains 51 activities, and our method also has a good performance on this data set. To prove that our method can work efficiently on robots, we transplanted it to the robotic platform and an online fall down detection experiment.

Wheat Spike Blast Image Classification Using Deep Convolutional Neural Networks.

Wheat blast is a threat to global wheat production, and limited blast-resistant cultivars are available. The current estimations of wheat spike blast severity rely on human assessments, but this technique could have limitations. Reliable visual disease estimations paired with Red Green Blue (RGB) images of wheat spike blast can be used to train deep convolutional neural networks (CNN) for disease severity (DS) classification. Inter-rater agreement analysis was used to measure the reliability of who collected and classified data obtained under controlled conditions. We then trained CNN models to classify wheat spike blast severity. Inter-rater agreement analysis showed high accuracy and low bias before model training. Results showed that the CNN models trained provide a promising approach to classify images in the three wheat blast severity categories. However, the models trained on non-matured and matured spikes images showing the highest precision, recall, and F1 score when classifying the images. The high classification accuracy could serve as a basis to facilitate wheat spike blast phenotyping in the future.

Classification of the sperm chromatin compaction alterations in bulls (Bos taurus) and its correlation with the efficiency of the in vitro production of embryos

This paper proposes to classify the sperm chromatin compaction alterations in bulls, according to the affected area location and its objective is evaluating the correlation of the intensity, the heterogeneity and these kinds of chromatin decompaction with the rates of cleavage and the formation of blastocysts of in vitro production of embryos (IVPE). It was used several subfertile animals sperm samples, which were evaluated using the toluidine blue staining and computer image analysis, making possible the categorization of the chromatin decompaction according to their location. The percentages of chromatin decompaction and heterogeneity were also evaluated. IVPEs were done and the rates of cleavage and of blastocysts were correlated with the chromatin characteristics. It made possible the classification of the chromatin decompaction according to the head affected part in at least four types: base decompaction, basal half decompaction, central axis decompaction, total decompaction. Based on the correlation, it can be implicated that each type of classification has different influences on the bull fertility. It made possible understanding that sperms amount with 5% or more of chromatin decompaction intensity interferes in the bull fertility and this condition can be featured as an uncompensable defect, while the heterogeneity of chromatin is not an important factor in the IVPE results.

Imaging pyrometry for most color cameras using a triple pass filter.

A simple combination of the Planck blackbody emission law, optical filters, and digital image processing is demonstrated to enable most commercial color cameras (still and video) to be used as an imaging pyrometer for flames and explosions. The hardware and data processing described take advantage of the color filter array (CFA) that is deposited on the surface of the light sensor array present in most digital color cameras. In this work, a triple-pass optical filter incorporated into the camera lens allows light in three 10-nm wide bandpass regions to reach the CFA/light sensor array. These bandpass regions are centered over the maxima in the blue, green, and red transmission regions of the CFA, minimizing the spectral overlap of these regions normally present. A computer algorithm is used to retrieve the blue, green, and red image matrices from camera memory and correct for remaining spectral overlap. A second algorithm calibrates the corrected intensities to a gray body emitter of known temperature, producing a color intensity correction factor for the camera/filter system. The Wien approximation to the Planck blackbody emission law is used to construct temperature images from the three color (blue, green, red) matrices. A short pass filter set eliminates light of wavelengths longer than 750nm, providing reasonable accuracy (±10%) for temperatures between 1200 and 6000K. The effectiveness of this system is demonstrated by measuring the temperature of several systems for which the temperature is known.

Securing audio transmission based on encoding and steganography

One of the things that must be considered when establishing a data exchange connection is to make that communication confidential and hide the file’s features when the snoopers intercept it. In this work, transformation (encoding) and steganography techniques are invested to produce an efficient system to secure communication for an audio signal by producing an efficient method to transform the signal into a red–green–blue (RGB) image. Subsequently, this image is hidden in a cover audio file by using the least significant bit (LSB) method in the spatial and transform domains using discrete wavelet transform. The audio files of the message and the cover are in *.wav format. The experimental results showed the success of the transformation in concealing audio secret messages, as well the remarkability of the stego signal quality in both techniques. A peak signal-to-noise ratio peak signal-to-noise ratio (PSNR) scored (20-26) dB with wavelet and (81-112) dB with LSB for cover file size 4.96 MB and structural similarity index metric structural similarity index metric (SSIM) has been used to measure the signal quality which gave 1 with LSB while wavelet was (0.9-1), which is satisfactory in all experimented signals with low time consumption. This work also used these metrics to compare the implementation of LSB and WAV.

A novel semi-supervised framework for UAV based crop/weed classification.

Excessive use of agrochemicals for weed controlling infestation has serious agronomic and environmental repercussions associated. An appropriate amount of pesticide/ chemicals is essential for achieving the desired smart farming and precision agriculture (PA). In this regard, targeted weed control will be a critical component significantly helping in achieving the goal. A prerequisite for such control is a robust classification system that could accurately identify weed crops in a field. In this regard, Unmanned Aerial Vehicles (UAVs) can acquire high-resolution images providing detailed information for the distribution of weeds and offers a cost-efficient solution. Most of the established classification systems deploying UAV imagery are supervised, relying on image labels. However, this is a time-consuming and tedious task. In this study, the development of an optimized semi-supervised learning approach is proposed, offering a semi-supervised generative adversarial network for crops and weeds classification at early growth stage. The proposed algorithm consists of a generator that provides extra training data for the discriminator, which distinguishes weeds and crops using a small number of image labels. The proposed system was evaluated extensively on the Red Green Blue (RGB) images obtained by a quadcopter in two different croplands (pea and strawberry). The method achieved an average accuracy of 90% when 80% of training data was unlabeled. The proposed system was compared with several standards supervised learning classifiers and the results demonstrated that this technique could be applied for challenging tasks of crops and weeds classification, mainly when the labeled samples are small at less training time.

Industrial objects recognition in intelligent manufacturing for computer vision

PurposeThe overall goal of this research is to develop algorithms for feature-based recognition of 2D parts from intensity images. Most present industrial vision systems are custom-designed systems, which can only handle a specific application. This is not surprising, since different applications have different geometry, different reflectance properties of the parts.Design/methodology/approachComputer vision recognition has attracted the attention of researchers in many application areas and has been used to solve many ranges of problems. Object recognition is a type of pattern recognition. Object recognition is widely used in the manufacturing industry for the purpose of inspection. Machine vision techniques are being applied in areas ranging from medical imaging to remote sensing, industrial inspection to document processing and nanotechnology to multimedia databases. In this work, recognition of objects manufactured in mechanical industry is considered. Mechanically manufactured parts have recognition difficulties due to manufacturing process including machine malfunctioning, tool wear and variations in raw material. This paper considers the problem of recognizing and classifying the objects of such mechanical part. Red, green and blue RGB images of five objects are used as an input. The Fourier descriptor technique is used for recognition of objects. Artificial neural network (ANN) is used for classification of five different objects. These objects are kept in different orientations for invariant rotation, translation and scaling. The feed forward neural network with back-propagation learning algorithm is used to train the network. This paper shows the effect of different network architecture and numbers of hidden nodes on the classification accuracy of objects as well as the effect of learning rate and momentum.FindingsOne important finding is that there is not any considerable change in the network performances after 500 iterations. It has been found that for data smaller network structure, smaller learning rate and momentum are required. The relative sample size also has a considerable effect on the performance of the classifier. Further studies suggest that classification accuracy is achieved with the confusion matrix of the data used. Hence, with these results the proposed system can be used efficiently for more objects. Depending upon the manufacturing product and process used, the dimension verification and surface roughness may be integrated with proposed technique to develop a comprehensive vision system. The proposed technique is also highly suitable for web inspections, which do not require dimension and roughness measurement and where desired accuracy is to be achieved at a given speed. In general, most recognition problems provide identity of object with pose estimation. Therefore, the proposed recognition (pose estimation) approach may be integrated with inspection stage.Originality/valueThis paper considers the problem of recognizing and classifying the objects of such mechanical part. RGB images of five objects are used as an input. The Fourier descriptor technique is used for recognition of objects. ANN is used for classification of five different objects. These objects are kept in different orientations for invariant rotation, translation and scaling. The feed forward neural network with back-propagation learning algorithm is used to train the network. This paper shows the effect of different network architecture and numbers of hidden nodes on the classification accuracy of objects as well as the effect of learning rate and momentum.

Image-Based Plant Disease Detects using Machine Learning

Computational algorithms are used for the processing of numerical images. It includes a wide variety of algorithms that can prevent problems during processing, such as noise buildup and signal distortion, and that can be applied to input data. In the context of multivariate systems, digital image processing can be modeled and images are defined in two dimensions (perhaps more). To automatically identify and classify plant leaf diseases, we need to plan and develop a software solution using image processing. The image processing method has primarily four steps. The first step is the structure of the color transformation of the Red Green Blue (RGB) leaf images and it will be translated into a grayscale image after enrichment using Otsu. Using the K-means clustering technique, the images are at hand in the second step. At step 3, Gray Level Co-Occurrence Matrix computes and extracts segmented contaminated objects. To categorize the disease according to the characteristics computed and obtained from the target image, we use the Multi-Class Support Vector Machine (MCSVM). The proposed approach was tested using a baseline data set and showed greater accuracy than the use of Artificial Neural Networks (ANN). In conclusion, the proposed classification model, the MCSVM, is highly effective in identifying leaf diseases and can work well for different types of diseases

Convolutional neural networks for water segmentation using sentinel-2 red, green, blue (RGB) composites and derived spectral indices

ABSTRACT Near-real time water segmentation with medium resolution satellite imagery plays a critical role in water management. Automated water segmentation of satellite imagery has traditionally been achieved using spectral indices. Spectral water segmentation is limited by environmental factors and requires human expertise to be applied effectively. In recent years, the use of convolutional neural networks (CNN’s) for water segmentation has been successful when used on high-resolution satellite imagery, but to a lesser extent for medium resolution imagery. Existing studies have been limited to geographically localized datasets and reported metrics have been benchmarked against a limited range of spectral indices. This study seeks to determine if a single CNN based on Red, Green, Blue (RGB) image classification can effectively segment water on a global scale and outperform traditional spectral methods. Additionally, this study evaluates the extent to which smaller datasets (of very complex pattern, e.g harbour megacities) can be used to improve globally applicable CNNs within a specific region. Multispectral imagery from the European Space Agency, Sentinel-2 satellite (10 m spatial resolution) was sourced. Test sites were selected in Florida, New York, and Shanghai to represent a globally diverse range of waterbody typologies. Region-specific spectral water segmentation algorithms were developed on each test site, to represent benchmarks of spectral index performance. DeepLabV3-ResNet101 was trained on 33,311 semantically labelled true-colour samples. The resulting model was retrained on three smaller subsets of the data, specific to New York, Shanghai and Florida. CNN predictions reached a maximum mean intersection over union result of 0.986 and F1-Score of 0.983. At the Shanghai test site, the CNN’s predictions outperformed the spectral benchmark, primarily due to the CNN’s ability to process contextual features at multiple scales. In all test cases, retraining the networks to localized subsets of the dataset improved the localized region’s segmentation predictions. The CNN’s presented are suitable for cloud-based deployment and could contribute to the wider use of satellite imagery for water management.

Multistimuli-Responsive Fluorescent Organometallic Assemblies Based on Mesoionic Carbene-Decorated Tetraphenylethene Ligands and Their Applications in Cell Imaging

Multistimuli-Responsive Fluorescent Organometallic Assemblies Based on Mesoionic Carbene-Decorated Tetraphenylethene Ligands and Their Applications in Cell Imaging