Blue Images Research Articles

Extraction method for single Zanthoxylum bungeanum in karst mountain area based on unmanned aerial vehicle visible-light images

The efficient and rapid extraction of information about the growth period of the Zanthoxylum bungeanum maxim is one of the prerequisites for analyzing and mastering its growth trend. The 5-cm high-resolution visible-light red, green, blue (RGB) image of the Zanthoxylum bungeanum growing period is obtained based on the low-altitude aerial photography of a quad-rotor unmanned aerial vehicle (UAV) platform. The color space of the R-, G-, and B-channels is converted into the H-, S-, L-, and V-components, and the sensitivity of seven feature vectors to target features is analyzed. The combination of the R-band, H-component, and S-component (which distinguish ground objects very differently) is obtained as the identification feature. Motivated by this, the color space index (CSI) is built by three sensitive component features, which are segmented by the maximum class variance method (OTSU), filtered by majority filtering analysis, and processed by clump to obtain spots containing only the plants. The number of single plants completely separated was calculated by ArcGIS10.2 software, and the average area of a single plant was obtained. The number of conjoined plants was obtained by dividing the cluster by the average area of a single plant. The number of separated complete plants and cluster divisions is counted to get the final total amount of plants and compared with the excess green mainstream index (EXG), visible-light band difference vegetation index (VDVI), and normalized green red difference index (NGRDI). The results indicate the following: (1) the reflectance overlapping phenomenon between ground objects is usually encountered during the construction of the index using only the RGB channel features of the visible-light band. However, the H-, S-, L-, and V-components are merged when the original image features are changed by the color space, in which the images containing only the RGB band are enriched and more conducive to finding sensitive features to distinguish between features; (2) in the CSI extraction results of three experimental research areas and one test verification area, the overall accuracy exceeded 90% (kappa index over 0.90), the false extraction rate and leakage extraction rate were <10 % , and the results showed that the CSI had an effect on the identification and extraction of Zanthoxylum bungeanum; and (3) when the growth environment of Zanthoxylum bungeanum is disturbed by weeds, the CSI has more advantages than the EXG and VDVI.

Advanced Color Edge Detection Using Clifford Algebra in Satellite Images

Edge detection is widely used for image processing to improve the detection and classification of objects, segmentation, and extraction of other features. Satellite images are rich in information about objects with different color intensity and have a large amount of noise, so it is difficult to achieve recognition, classification, and feature extraction of small objects through traditional edge detection algorithms. The colors in satellite images suffer from a large amount of overlap due to areas or weather conditions that generate a lot of noise. Edge detection provides detailed information about objects in an image by reducing unnecessary feature information. Edge detection in color images is more challenging than edge detection in gray-level images. This paper proposes a method for the edge detection of color images using Clifford algebra and its sub-algebra, quaternions. Quaternion-based Fourier transform is used to process red, green and blue (RGB) images separately in the vector field. A 3×3 quaternion mask is developed to filter out frequencies of the image in multiple directions and only provides details about the edges. The algorithm works on three channels individually; the output is then processed through quaternion Fourier transform (QFT) and inverse QFT with a 3×3 mask to filter high frequencies. The proposed algorithm is compared with traditional edge detection algorithms using a satellite image dataset that has different types of objects and detailed information. Results are validated through entropy, structure similarity, and noise error to prove that our proposed algorithm provides satisfactory performance on different remote sensed images.

Potential- and Color-Resolved Electrochemiluminescence of Polymer Dots for Array Imaging of Multiplex MicroRNAs.

The development of electrochemiluminescence (ECL) emitters remains a great research interest in ECL analysis. Herein, luminol-doped polymer dots (L-Pdots) and diethylamine-coupled Pdots (N-Pdots) were synthesized to design a both potential- and color-resolved ECL strategy. L-Pdots showed the maximum ECL emission at 450 nm in the presence of hydrogen peroxide at +0.6 V, while the maximum emission of N-Pdots was at 675 nm under +1.0 V. This strategy was conveniently used to construct a novel ECL array imaging method for high-throughput detection of two microRNAs (miRNAs). The array was prepared with the mixture of L-Pdots and N-Pdots that were covalently modified with quencher-labeled DNAs, respectively, to recognize the corresponding miRNAs. Upon the addition of duplex-specific nuclease, the DNAs hybridized with miRNAs were digested to release the quenchers and miRNAs, which led to the ECL recovery of Pdots and target-cyclic signal amplification. By imaging the array at +0.6 and +1.0 V and using miRNA-21 and miRNA-205 as the analytes, the blue and red channel images could be extracted to quantify these miRNAs with detection limits of 2.5 and 3.1 pM, respectively. This work provides a new family member of potential- or color-resolved ECL emitters and successfully realizes the simultaneous and high-throughput sensing of multiplex miRNAs.

Performance evaluation of SVM-based hand gesture detection and recognition system using distance transform on different data sets for autonomous vehicle moving applications

PurposeThis paper aims to propose a novel methodology for classifying the gestures using support vector machine (SVM) classification method. Initially, the Red Green Blue color hand gesture image is converted into YCbCr image in preprocessing stage and then palm with finger region is segmented by threshold process. Then, distance transformation method is applied on the palm with finger segmented image. Further, the center point (centroid) of palm region is detected and the fingertips are detected using SVM classification algorithm based on the detected centroids of the detected palm region.Design/methodology/approachGesture is a physical indication of the body to convey information. Though any bodily movement can be considered a gesture, generally it originates from the movement of hand or face or combination of both. Combined gestures are quiet complex and difficult for a machine to classify. This paper proposes a novel methodology for classifying the gestures using SVM classification method. Initially, the color hand gesture image is converted into YCbCr image in preprocessing stage and then palm with finger region is segmented by threshold process. Then, distance transformation method is applied on the palm with finger segmented image. Further, the center point of the palm region is detected and the fingertips are detected using SVM classification algorithm. The proposed hand gesture image classification system is applied and tested on “Jochen Triesch,” “Sebastien Marcel” and “11Khands” data set hand gesture images to evaluate the efficiency of the proposed system. The performance of the proposed system is analyzed with respect to sensitivity, specificity, accuracy and recognition rate. The simulation results of the proposed method on these different data sets are compared with the conventional methods.FindingsThis paper proposes a novel methodology for classifying the gestures using SVM classification method. Distance transform method is used to detect the center point of the segmented palm region. The proposed hand gesture detection methodology achieves 96.5% of sensitivity, 97.1% of specificity, 96.9% of accuracy and 99.3% of recognition rate on “Jochen Triesch” data set. The proposed hand gesture detection methodology achieves 94.6% of sensitivity, 95.4% of specificity, 95.3% of accuracy and 97.8% of recognition rate on “Sebastien Marcel” data set. The proposed hand gesture detection methodology achieves 97% of sensitivity, 98% of specificity, 98.1% of accuracy and 98.8% of recognition rate on “11Khands” data set. The proposed hand gesture detection methodology consumes 0.52 s as recognition time on “Jochen Triesch” data set images, 0.71 s as recognition time on “Sebastien Marcel” data set images and 0.22 s as recognition time on “11Khands” data set images. It is very clear that the proposed hand gesture detection methodology consumes less recognition rate on “11Khands” data set when compared with other data set images. Hence, this data set is very suitable for real-time hand gesture applications with multi background environments.Originality/valueThe modern world requires more numbers of automated systems for improving our daily routine activities in an efficient manner. This present day technology emerges touch screen methodology for operating or functioning many devices or machines with or without wire connections. This also makes impact on automated vehicles where the vehicles can be operated without any interfacing with the driver. This is possible through hand gesture recognition system. This hand gesture recognition system captures the real-time hand gestures, a physical movement of human hand, as a digital image and recognizes them with the pre stored set of hand gestures.

Yolk color measurement using image processing and deep learning

A high yellow yolk color of laying hens is required by customer. As yolk color measurement is determined by visual perception, color score may be expressed differently. The objective of this study was to develop the recognition of yolk color using red green blue (RGB) image and deep learning. The three hundred and fifty-three RGB images were obtained. The rectified linear unit (ReLU) and softmax were used as the activation function. An optimizer was configured with Adam, and categorical crossentropy was used as a loss function. The results showed that the loss had decreased to 0.45 and 0.63, whereas the accuracy had increased and reached 0.80 and 0.76 for training dataset and testing dataset, respectively. For evaluation, the loss value was 0.27 and 0.63, whereas the accuracy value was 0.90 and 0.76 for training dataset and testing dataset, respectively. The average f1-score was 0.76, whereas the highest precision (1.00) was observed in color score 5, 6 and 8. In conclusion, RGB image can be used as an alternative method to classify yolk color score with lower cost of analysis for egg producers in the near future.

Implementation of Deep Learning Using Convolutional Neural Network Algorithm for Classification Rose Flower

Flora in Indonesia has about 25% of the species of flowering plant species present in the world. Roses are one type of flowering plants and are usually used as an ornamental plant that has a thorny stem. Roses have more than 150 species. In Indonesia there are several flower gardens that is larger than the others. One of the famous flower garden in Indonesia is located on Malang city, East Java. The flower garden in Malang has several varieties of many roses and has a large production of roses. To help the sales system of roses there, the researchers want to create a program that can classify the type of roses in order to help simplify the system of automatic sales of roses without through manual sorting. So that will accelerate the sale of roses with an automated system. Ordinary people with limited botanical knowledge usually don’t know how to classify the flowers just by looking at them. To classify the flowers properly, it is important to provide enough information, and one of them is the name of it. Convolutional Neural Network (CNN) is one method of deep learning that can be used for image classification process. The CNN design is motivated by the discovery of the visual mechanism, the visual cortex present in the brain. CNN has been widely used in many real-world applications, such as Face Recognition, Image Classification and Recognition, and Object Detection, because this is one of the most efficient method for extracting important features. In this research, the classification accuracy value obtained from the test data is 96.33% using 2-dimension Red Green Blue (RGB) input image, and the size of each image is 32 × 32 pixels that are trained with CNN algorithm and the network structure of four convolution layers and four layers pooling supported by dropout technique.

Analysis of Non-Destructive Testing Ultrasonic Signal for Detection of Defective Materials Based on the Simulink Matlab Mathematica Computation Method

In this paper, an Ultrasonic Non-Destructive Test (NDT-UT) has been carried out on steel using the Simulink-Matlab Mathematica computation method. This study aims to analyze the NDT-UT output signal for material defect detection using secondary data as the first sample. The sample is then analyzed using the Fast Fourier Transform (FFT) method to produce a spectrum waveform and image thermography. It can be seen that there is a decrease in signal height from 1 a.u to 0.55 a.u. The first sample waveforms were used to analyze the second sample, third sample, and fourth sample, and all the samples had different defects. The results of the sample analysis are in the form of a thermographic image that shows the temperature level based on the distribution of red (R), green (G), and blue (B) images on the sample surface. The NDT-UT output signal produces a sinusoidal wave similar to the results of the Simulink-Matlab modeling analysis on the initial input echoes and the back wall, with a percentage inequality of 10%. Then validated the sinusoidal signal from the NDT-UT which gave a percentage of inequality between 0% – 42%. More complex or irregular defects result in a larger percentage or vice versa.

Red-Green-Blue and Multispectral Imaging as Potential Tools for Estimating Growth and Nutritional Performance of Cassava under Deficit Irrigation and Potassium Fertigation

Cassava has high energy value and rich nutritional content, yet its productivity in the tropics is seriously constrained by abiotic stresses such as water deficit and low potassium (K) nutrition. Systems that allow evaluation of genotypes in the field and greenhouse for nondestructive estimation of plant performance would be useful means for monitoring the health of plants for crop-management decisions. We investigated whether the red–green–blue (RGB) and multispectral images could be used to detect the previsual effects of water deficit and low K in cassava, and whether the crop quality changes due to low moisture and low K could be observed from the images. Pot experiments were conducted with cassava cuttings. The experimental design was a split-plot arranged in a completely randomized design. Treatments were three irrigation doses split into various K rates. Plant images were captured beginning 30 days after planting (DAP) and ended at 90 DAP when plants were harvested. Results show that biomass, chlorophyll, and net photosynthesis were estimated with the highest accuracy (R2 = 0.90), followed by leaf area (R2 = 0.76). Starch, energy, carotenoid, and cyanide were also estimated satisfactorily (R2 &gt; 0.80), although cyanide showed negative regression coefficients. All mineral elements showed lower estimation accuracy (R2 = 0.14–0.48) and exhibited weak associations with the spectral indices. Use of the normalized difference vegetation index (NDVI), green area (GA), and simple ratio (SR) indices allowed better estimation of growth and key nutritional traits. Irrigation dose 30% of pot capacity enriched with 0.01 mM K reduced most index values but increased the crop senescence index (CSI). Increasing K to 16 mM over the irrigation doses resulted in high index values, but low CSI. The findings indicate that RGB and multispectral imaging can provide indirect measurements of growth and key nutritional traits in cassava. Hence, they can be used as a tool in various breeding programs to facilitate cultivar evaluation and support management decisions to avert stress, such as the decision to irrigate or apply fertilizers.

FACEMASK RECOGNITION IN PANDEMIC CAUSES MULTIPLE HEAD CRUCIAL PROBLEM CLASSIFICATION

—A chronicle respiratory system condition like Breathing issues to Asthma patients after covering his/her mouth and nose make a tough challenge. Mainly the physical barrier are to take breath in oxygen, it also pack CO2 which the person exhale. A mask can feel high suffocation and add a compromised Head crucial problem stratification is in general used for the fore handling ahead of the facial acknowledgement &amp; facial several inclination issues and only for this reason an algorithm like recognition of front facial expressions as a input images. However pretentious by Corona virus epidemic, public put on face masks to safe ourselves safety, for that face will protected by mask. However this research paper set up a proposed method in this research paper of combining the face portrait with the High speed - channel of the Hue Saturation Value color channel and grayscale image, and train the Convolution Neural Networks to enhance applications that is HGL method. In line portrait we have to generate the image insert it into Convolution Neural Networks for training. Without any processing insert the original picture of the Red Green Blue color space into the Convolution Neural Networks. Fine grained Net Structured Aggregation: FSA – NET stand for Fine grained Structured Aggregation. It is the method to use to remember the structure of a single Red Green Blue color space image. Since the output of this network is the Euler angle of the head pose, we have chosen a set of thresholds that are most effective for pose classification. The way to solve this issue is to provide help for the study of multi-angle problems. In practically, we can practice for a face detection algorithm that differentiate between wearing a mask or demask. If the facial image with masks, we can recommend this method proposed in this paper, and if it is a normal facial image, we can recommend Fine grained Structured Aggregation or Line Portrait algorithm, etc. The very first step of this algorithm to res

Comparison between Three Registration Methods in the Case of Non-Georeferenced Close Range of Multispectral Images

Cucumber powdery mildew, which is caused by Podosphaera xanthii, is a major disease that has a significant economic impact in cucumber greenhouse production. It is necessary to develop a non-invasive fast detection system for that disease. Such a system will use multispectral imagery acquired at a close range with a camera attached to a mobile cart’s mechanic extension. This study evaluated three image registration methods applied to non-georeferenced multispectral images acquired at close range over greenhouse cucumber plants with a MicaSense® RedEdge camera. The detection of matching points was performed using Speeded-Up Robust Features (SURF), and outliers matching points were removed using the M-estimator Sample Consensus (MSAC) algorithm. Three geometric transformations (affine, similarity, and projective) were considered in the registration process. For each transformation, we mapped the matching points of the blue, green, red, and NIR band images into the red-edge band space and computed the root mean square error (RMSE in pixel) to estimate the accuracy of each image registration. Our results achieved an RMSE of less than 1 pixel with the similarity and affine transformations and of less than 2 pixels with the projective transformation, whatever the band image. We determined that the best image registration method corresponded to the affine transformation because the RMSE is less than 1 pixel and the RMSEs have a Gaussian distribution for all of the bands, but the blue band.

Explainable identification and mapping of trees using UAV RGB image and deep learning

The identification and mapping of trees via remotely sensed data for application in forest management is an active area of research. Previously proposed methods using airborne and hyperspectral sensors can identify tree species with high accuracy but are costly and are thus unsuitable for small-scale forest managers. In this work, we constructed a machine vision system for tree identification and mapping using Red–Green–Blue (RGB) image taken by an unmanned aerial vehicle (UAV) and a convolutional neural network (CNN). In this system, we first calculated the slope from the three-dimensional model obtained by the UAV, and segmented the UAV RGB photograph of the forest into several tree crown objects automatically using colour and three-dimensional information and the slope model, and lastly applied object-based CNN classification for each crown image. This system succeeded in classifying seven tree classes, including several tree species with more than 90% accuracy. The guided gradient-weighted class activation mapping (Guided Grad-CAM) showed that the CNN classified trees according to their shapes and leaf contrasts, which enhances the potential of the system for classifying individual trees with similar colours in a cost-effective manner—a useful feature for forest management.

Vision is protected against blue defocus

Due to chromatic aberration, blue images are defocused when the eye is focused to the middle of the visible spectrum, yet we normally are not aware of chromatic blur. The eye suffers from monochromatic aberrations which degrade the optical quality of all images projected on the retina. The combination of monochromatic and chromatic aberrations is not additive and these aberrations may interact to improve image quality. Using Adaptive Optics, we investigated the optical and visual effects of correcting monochromatic aberrations when viewing polychromatic grayscale, green, and blue images. Correcting the eye’s monochromatic aberrations improved optical quality of the focused green images and degraded the optical quality of defocused blue images, particularly in eyes with higher amounts of monochromatic aberrations. Perceptual judgments of image quality tracked the optical findings, but the perceptual impact of the monochromatic aberrations correction was smaller than the optical predictions. The visual system appears to be adapted to the blur produced by the native monochromatic aberrations, and possibly to defocus in blue.

Suction-based Grasp Point Estimation in Cluttered Environment for Robotic Manipulator Using Deep Learning-based Affordance Map

Perception and manipulation tasks for robotic manipulators involving highly-cluttered objects have become increasingly indemand for achieving a more efficient problem solving method in modern industrial environments. But, most of the available methods for performing such cluttered tasks failed in terms of performance, mainly due to inability to adapt to the change of the environment and the handled objects. Here, we propose a new, near real-time approach to suction-based grasp point estimation in a highly cluttered environment by employing an affordance-based approach. Compared to the state-of-the-art, our proposed method offers two distinctive contributions. First, we use a modified deep neural network backbone for the input of the semantic segmentation, to classify pixel elements of the input red, green, blue and depth (RGBD) channel image which is then used to produce an affordance map, a pixel-wise probability map representing the probability of a successful grasping action in those particular pixel regions. Later, we incorporate a high speed semantic segmentation to the system, which makes our solution have a lower computational time. This approach does not need to have any prior knowledge or models of the objects since it removes the step of pose estimation and object recognition entirely compared to most of the current approaches and uses an assumption to grasp first then recognize later, which makes it possible to have an object-agnostic property. The system was designed to be used for household objects, but it can be easily extended to any kind of objects provided that the right dataset is used for training the models. Experimental results show the benefit of our approach which achieves a precision of 88.83%, compared to the 83.4% precision of the current state-of-the-art.

Automatic Detection of Genetics and Genomics of Eye Disease Using Deep Assimilation Learning Algorithm.

Diabetic retinopathy (DR) is one of the most prevalent genetic diseases in human and it is caused by damage to the blood vessels in the eye retina. If it is undetected and untreated at right time, it can lead to vision loss. There are many medical imaging and processing technologies to improve the diagnostic process of DR to overcome the lack of human experts. In the existing image processing methods, there are issues such as lack of noise removal, improper clustering segmentation and less classification accuracy. This can be accomplished by automatic diagnosis of DR using advanced image processing method. The cotton wool spot (CWS), hard exudates (HE) contains a common manifestation of many diseases in retina including DR and acquired immunodeficiency syndrome. In the present work, super iterative clustering algorithm (SICA) is proposed to identify the CWS, HE on retinal image. Feature-based medical image retrieval (FBMIR) datasets are utilized for this purpose. Noises present on the images and histogram-filtering technique is used to convert red, green, and blue (RGB) images into a perfect greyscale image without noise. After pre-processing, SICA is used to identify the CWS, HE detection on retinal images and eliminates unnecessary areas of interest. In the third stage, after detecting CWS and HE, various statistical features are extracted for further classification using deep assimilation learning algorithm (DALA). The performance of DALA technique is examined with various classification parameters like recall, precision, and F-measure. Finally, the false classification ratios are computed to compare the performance of the trained networks. The proposed method produces accurate detection of affected regions with an accuracy ratio of 98.5% and it is higher than the other conventional methods. This method may improve the accuracy of automatic detection and classification of eye diseases.

Resin infiltration and remineralization interventions in management of moderate dental fluorosis: A quantitative light-induced fluorescence-based randomized controlled trial.

Aesthetic concerns due to dental fluorosis (DF) considerably impact pyschosocial health in young children and requires aesthetic microinvasive treatment. The aim of this study was to use quantitative light-induced fluorescence to evaluate two intervention protocols: microabrasion with resin infiltration and microabrasion with remineralization and assess which brings about more fluorescence gain; better colour masking and greater patient satisfaction among subjects with dental fluorosis. 120 maxillary central incisors with Grade 3 DF (Dean's Fluorosis Index) were randomly distributed into four groups: I-microabrasion + resin infiltration (fluorosis with stains); II microabrasion + remineralization (fluorosis with stains); III-microabrasion + resin infiltration (fluorosis without stains); IV microabrasion + remineralization (fluorosis without stains). At baseline, post-microabrasion and post-intervention Quantitative light-induced fluorescent images were captured. Fluorescence gain (ΔF) was analysed from the blue light images. The white light images were used to evaluate post intervention colour difference (ΔE = [(ΔL*) 2+(Δa*) 2+(Δb*) 2]1/2) and child satisfaction was evaluated. Collected data was analysed using repeated measures ANOVA and Independent t-test. Intra-group comparison of ΔF values showed statistically significant improvement in ΔF value in all the four groups (P < 0.001, 0.002). Inter-group comparison of ΔF values based on intervention showed statistically significant fluorescence gain (P < 0.004) indicating resin infiltration intervention being better than remineralization in unstained grade III fluorosis. The colour difference was statistically significantly better with resin infiltration in both stained and unstained Grade III DF (P < 0.001). Resin infiltration intervention showed better fluorescence gain and colour masking compared to remineralization intervention.

Open-Source Automatic Segmentation of Ocular Structures and Biomarkers of Microbial Keratitis on Slit-Lamp Photography Images Using Deep Learning.

We propose a fully-automatic deep learning-based algorithm for segmentation of ocular structures and microbial keratitis (MK) biomarkers on slit-lamp photography (SLP) images. The dataset consisted of SLP images from 133 eyes with manual annotations by a physician, P1. A modified region-based convolutional neural network, SLIT-Net, was developed and trained using P1's annotations to identify and segment four pathological regions of interest (ROIs) on diffuse white light images (stromal infiltrate (SI), hypopyon, white blood cell (WBC) border, corneal edema border), one pathological ROI on diffuse blue light images (epithelial defect (ED)), and two non-pathological ROIs on all images (corneal limbus, light reflexes). To assess inter-reader variability, 75 eyes were manually annotated for pathological ROIs by a second physician, P2. Performance was evaluated using the Dice similarity coefficient (DSC) and Hausdorff distance (HD). Using seven-fold cross-validation, the DSC of the algorithm (as compared to P1) for all ROIs was good (range: 0.62-0.95) on all 133 eyes. For the subset of 75 eyes with manual annotations by P2, the DSC for pathological ROIs ranged from 0.69-0.85 (SLIT-Net) vs. 0.37-0.92 (P2). DSCs for SLIT-Net were not significantly different than P2 for segmenting hypopyons (p > 0.05) and higher than P2 for WBCs (p < 0.001) and edema (p < 0.001). DSCs were higher for P2 for segmenting SIs (p < 0.001) and EDs (p < 0.001). HDs were lower for P2 for segmenting SIs (p = 0.005) and EDs (p < 0.001) and not significantly different for hypopyons (p > 0.05), WBCs (p > 0.05), and edema (p > 0.05). This prototype fully-automatic algorithm to segment MK biomarkers on SLP images performed to expectations on an exploratory dataset and holds promise for quantification of corneal physiology and pathology.

The Pharmacokinetics and Skin Changes in Rabbit on Selamectin Solution

Background: It found that many antiparasitic drugs have a good treatment effect on the pets skin diseases. whether selamectin solutions have a bad effect on skin tissues needs more research. Objective: In order to investigate the effect of selamectin solution on skin changes in rabbit. Method: the skin of rabbit was given different doses of selamectin solution, then the quantity of per unit area of hair follicles and sebaceous glands and the morphology structure of them and the changes of skin thickness, epidermis thickness and dermis thickness were researched through routine paraffin sections and H. E, Evg, Masson’s, Pas, Toluidine blue staining and Image Pro Plus6.0. The concentration of blood and urine were measured by High Performance Liquid Chromatography after administration. Results: The thickness of skin, per unit of hair follicles and sebaceous glands all had an obviously increasing trend with the increasing of doses. 1 day later, the blood and urine concentration of the selamectin and the metabolite avermectin all were in highest. Many selamectin solution were excluded in the form of prototypes and many were in avermectin. There were a higher blood concentration and metabolite avermectin until 72h. Conclusion: The skin appearance changes were insignificant while the skin thickness and accessory organs increased obviously in microscope after given more selamectin. In 24h, the blood and urine concentration all were in highest. This study, which discussed the influences by selamectin solution in rabbit skin and the pharmacokinetics, was to provide the basis for the deeper study of Antiparasitic drugs effects on pet skin diseases.

Character of images development on Gaussian copula model using distribution of cumulative distribution function

This paper is a preliminary study that aims to build the character of color image object through the Gaussian copula model. The development of the Gaussian copula model is carried out with the distribution of Cumulative Distribution Function (CDF) of the pixels as input. In contrast to other studies that use image pixel values. The random variables used are the CDF value at the point of differentiation determined by the Kulback-Leibler Divergence (KLD). The case discussed in this paper is the image modeling of red apples and green apples. The model is built on each Red, Green, and Blue (RGB) image with three distinguishing points. The result obtained is a Gaussian copula model with different parameters for red apples and green apples. The character of joint distribution is also different. The joint distribution value of green apples is greater than that of red apples in R and G images. The difference in the models obtained shows that the character of this image is different. Future research will use the acquired models to identify new images, whether they are recognized as red and green apples or not. Further development in the field of healthy vision. This model can be used to identify fruit images in an application.

Forecasting dynamic body weight of nonrestrained pigs from images using an RGB-D sensor camera

Average daily gain is an indicator of the growth rate, feed efficiency, and current health status of livestock species including pigs. Continuous monitoring of daily gain in pigs aids producers to optimize their growth performance while ensuring animal welfare and sustainability, such as reducing stress reactions and feed waste. Computer vision has been used to predict live body weight from video images without direct handling of the pig. In most studies, videos were taken while pigs were immobilized at a weighing station or feeding area to facilitate data collection. An alternative approach is to capture videos while pigs are allowed to move freely within their own housing environment, which can be easily applied to the production system as no special imaging station needs to be established. The objective of this study was to establish a computer vision system by collecting RGB-D videos to capture top-view red, green, and blue (RGB) and depth images of nonrestrained, growing pigs to predict their body weight over time. Over a period of 38 d, eight growers were video recorded for approximately 3 min/d, at the rate of six frames per second, and manually weighed using an electronic scale. An image-processing pipeline in Python using OpenCV was developed to process the images. Specifically, each pig within the RGB frame was segmented by a thresholding algorithm, and the contour of the pig was identified to extract its length and width. The height of a pig was estimated from the depth images captured by the infrared depth sensor. Quality control included removing pigs that were touching the fence and sitting, as well as those showing extremely distorted shape or motion blur owing to their frequent movement. Fitting all of the morphological image descriptors simultaneously in linear mixed models yielded prediction coefficients of determination of 0.72–0.98, 0.65–0.95, 0.51–0.94, and 0.49–0.93 for 1-, 2-, 3-, and 4-d ahead forecasting, respectively, of body weight in time series cross-validation. Based on the results, we conclude that our RGB-D sensor-based imaging system coupled with the Python image-processing pipeline could potentially provide an effective approach to predict the live body weight of nonrestrained pigs from images.

Time-Frequency-Analysis-Based Blind Modulation Classification for Multiple-Antenna Systems.

Blind modulation classification is an important step in implementing cognitive radio networks. The multiple-input multiple-output (MIMO) technique is widely used in military and civil communication systems. Due to the lack of prior information about channel parameters and the overlapping of signals in MIMO systems, the traditional likelihood-based and feature-based approaches cannot be applied in these scenarios directly. Hence, in this paper, to resolve the problem of blind modulation classification in MIMO systems, the time–frequency analysis method based on the windowed short-time Fourier transform was used to analyze the time–frequency characteristics of time-domain modulated signals. Then, the extracted time–frequency characteristics are converted into red–green–blue (RGB) spectrogram images, and the convolutional neural network based on transfer learning was applied to classify the modulation types according to the RGB spectrogram images. Finally, a decision fusion module was used to fuse the classification results of all the receiving antennas. Through simulations, we analyzed the classification performance at different signal-to-noise ratios (SNRs); the results indicate that, for the single-input single-output (SISO) network, our proposed scheme can achieve 92.37% and 99.12% average classification accuracy at SNRs of −4 and 10 dB, respectively. For the MIMO network, our scheme achieves 80.42% and 87.92% average classification accuracy at −4 and 10 dB, respectively. The proposed method greatly improves the accuracy of modulation classification in MIMO networks.