Hue Saturation Research Articles

Crop identification and disease classification using traditional machine learning and deep learning approaches

Crop and disease classification is one of the important problems in automation of agricultural processes with multi-cropping method where the field is cultivated with more than one crop. In order to solve this classification problem, a study has been carried out in the field cultivating eggplant (Solanum melongena) and tomato (Solanum lycopersicum) using the images obtained from a mobile phone camera. Textural descriptors namely contrast, correlation, energy and homogeneity were extracted from the gray-scale converted RGB image for crop identification, i.e., (tomato or eggplant) and the same descriptors were extracted from the gray-scale converted image from Hue Saturation Value (HSV) for disease classification (due to Cercospora leaf spot disease or two-spotted spider infestation). Discriminant analysis, Naive Bayes algorithm, support vector machine and neural network were the classification algorithms used with a resulting best accuracy of 97.61%, 95.62%, 98.01% and 98.94% for crop identification, 86.09%, 76.52%, 86.96% and 86.04% for disease classification respectively. Similarly, application of algorithm with 6 histogram-based descriptors for health status detection resulted in an accuracy of 66.67%, 37.04%, 50% and 72.9% respectively. Deep learning algorithm namely AlexNet was also evaluated which resulted in an accuracy of 100% for crop identification, 89.36% for health status detection and 81.51% for disease classification. Among the algorithms, AlexNet resulted in the best average accuracy of 90.29% for the above classification tasks.

Identification of Ripe Levels in Plantain Fruits Using Hue Saturation Intensity (HSI) Color Space Transformation Method

The study aimed to determine the maturity level of plantain fruit using the Hue Saturation Intensity (HSI) color space transformation method. This study uses the Hue Saturation Intensity (HSI) color space transformation method to classify the level of ripeness of plantain fruit. The results showed that the Hue Saturation Intensity (HSI) color space transformation method was quite good at identifying the ripeness level of bananas based on the high accuracy of the test. Of the 20 fruit samples where ten unripe and ten ripe plantains were calculated, the maximum and minimum average values of H and S obtained conformity accuracy of 85%.

Underwater image processing method based on red channel prior and Retinex algorithm

Due to light being absorbed by water and scattered by suspended particles, color distortion, low image contrast, and detail loss are the common problems found in underwater images. We present an underwater image processing method, which based on an underwater imaging model, makes initial image restoration with the improved red channel prior combining transmission reduction, background light correction, and color compensation to acquire clearer underwater images, and then combines multiscale retinex (MSR) enhancement and hue saturation value to enhance image color contrast and saturation. Results indicate that, compared with the original images, the method proposed in this paper is capable of significantly enhancing image definition, saturation, contrast, and luminance. In comparison with dark channel prior, single-scale retinex (SSR), MSR, and multiscale retinex with color restoration (MSRCR), this method greatly improved the average gradient of images while keeping the information entropy of images unchanged. The average gradient calculated with the proposed method increased by 239%, 199%, 200%, and 220% for the three given images, respectively, compared with the other methods above.

Construction and application of quality evaluation index system for remote-sensing image fusion

To objectively and justly compare the results of remote-sensing image fusion, evaluate the fusion algorithm, and optimize the fusion process, a quality evaluation index system is constructed by considering both qualitative and quantitative evaluations. The quantitative evaluation indexes are divided into three types based on the analysis of evaluation methods of the fusion quality of primary remote-sensing images. The applicability and effectiveness of six pixel-level fusion methods of optical remote-sensing image fusion were verified based on the constructed quality evaluation index system used to evaluate those methods in offshore and archipelagic areas. The experimental results indicate that the constructed quality evaluation index system evaluates the fusion algorithm objectively, comprehensively, and accurately. The intensity–hue–saturation transformation-based method distorts spectral features of the original image and easily causes spectrum degradation, whereas the wavelet transformation-based method has an advantage in the preservation of spectral features but efficiently produces blockiness and image penumbrae. The principal component analysis transformation-based method preserves more detailed textures and structural characteristics of original images but loses some physical properties. The Brovey transformation-based method results in spectral distortion, the high pass filter-based method shows a clear boundary of surface features after image fusion, and the Gram–Schmidt-based method shows a high ability of spectral information retention, but the latter performs poorly in the preservation of spatial information. The proposed quality evaluation index system of remote-sensing image fusion can be used as the objective effect evaluation criteria for remote-sensing image fusion and offers a reference for future applications of fusion results.

Estimating the aquatic-plant area on a pond surface using a hue-saturation-component combination and an improved Otsu method

Accurately estimating the aquatic-plant area on a pond surface is of great significance for regulating nutrients, such as nitrogen and phosphorus, increasing the dissolved-oxygen content, and ensuring the healthy growth of aquaculture organisms. Currently, remote-sensing methods for monitoring pond-surface aquatic plants have a high cost, long cycle, and poor real-time performance, which limits the practicability of these methods in aquaculture. To address the above problems, a pond-surface aquatic-plant area estimation method based on a hue-saturation (H-S)-component combination and an improved Otsu method, was proposed, which utilizes the advantages of fast and non-destructive computer-vision technology. First, the pond-surface images collected by the camera were preprocessed using a median filter. Second, a visual-saliency map of the aquatic plants was generated by combining the H and S components of the HSV color space. Then, the appropriate Otsu optimal threshold-selection criterion formula was selected to determine the optimal segmentation thresholds of the aquatic plants and background water. Finally, the aquatic plants in the image were segmented using the binary method, and the aquatic-plant area on the pond surface was estimated, based on the proportion of aquatic-plant pixels. In this study, an improved Otsu method was proposed to adaptively select the Otsu optimal threshold-selection criterion formula by judging the position of the highest and second highest peaks of the gray histogram of the visual-saliency map, which solved the problem of finding the appropriate segmentation threshold for different proportions of aquatic plants and background water. The proposed method was tested on real pond-surface images with a mean misclassification error of 0.0284, mean F1 score of 0.8396, mean precision of 0.8428, and mean recall of 0.9146. The experimental results indicated that the proposed method could accurately segment aquatic plants to accurately estimate the aquatic-plant area on the pond surface.

Unsupervised Cosegmentation Model based on Saliency Detection and Optimized Hue Saturation Value Features of Superpixels

Unsupervised Cosegmentation Model based on Saliency Detection and Optimized Hue Saturation Value Features of Superpixels

Portable paper-micro well device composed of agglomerated nano-hematite clusters in enzyme-hydrogel composite for beta glucan detection using smartphone

Abstract In present study, we have harnessed the irreplaceable merits of nano-biocomposite biosensing for microfluidic devices preparation. A microfluidic device-prototype (μPADs) with coroneted immobilized enzymes on hematite nanoparticle (αFe2O3NPs) was designed for direct detection of cancer marker β-glucan. Reconfigurable open-smartphone software applications (App) with visual detection and smartphone application can further ease the collection, calculation and display of sensor data on the screen for the biomarker analyte. The bienzymatic-microfluidic analytical devices (Enz-μPADs) with micro-well test-zone were coated with β-glucanase, glucose oxidase, and fenugreek hydrogel stabilized-αFe2O3NPs. Distinct blue color change of as-prepared Enz-μPADs can also be observed by naked eyes and quantified by smartphone App with high accuracy. The smartphone images represented by the saturation parameter of hue–saturation–value (HSV) color space, enables an easy readout of the enzymatic-TMB reaction for β-glucan detection. The linear calibration graph was obtained in the range of 1.0–15 ng mL−1 for standard β-glucan with 0.5 ng mL−1 limit of detection (3 Sb). The practical applicability of Enz-μPADs was tested successfully for anti-cancer dietary-supplements with β-glucan claims. Thus, as-prepared Enz-μPADs with Smartphone-App readout has notable features such as fast colorimetric visual response, low-cost, feasible device bulk production and multiplexity with high specificity and sensitivity.

A multi-feature fusion method for image recognition of gastrointestinal metaplasia (GIM)

Gastrointestinal metaplasia (GIM) is a disease that is closely related to early gastric cancer. The early diagnosis of GIM can effectively avoid gastric cancer. Traditionally, GIM diagnosis is done through human analysis of endoscopy imaging, which is time-consuming and exhausting. Computer aided diagnosis of GIM is urgently needed but currently there is no such computer system in commercial market. Considering the complex features of gastroscopic images, and different pixels contain different weight information of color and texture features, a novel multi feature fusion method composed of new feature module (FM) and attention feature module (AFM) is proposed. First, a residual deep network is used as the base framework to build FM combined with high and low-level features, which can make up for the deficiency of single high-level features. Then, the RGB image, HSV (Hue Saturation Value) image, and LBP (Local Binary Pattern) features are considered as 3-way inputs of the proposed model. In other words, the deep features of the endoscopy image are extracted respectively from image pixels, colors, and texture. Finally, these deep features are sent into a novel AFM to generate the final features for GIM recognition. AFM first adaptively learns feature weights through attention mechanism, and then fuses the above three types of features. Experimental results show that the proposed method achieves a high recognition accuracy of 90.28% under a dataset of 1050 images collected from a local hospital. In addition, the proposed method is superior to single-featured networks and existing method in term of recognition accuracy.

KLARIFIKASI KEMATANGAN BUAH NANAS DENGAN RUANG WARNA HUE SATURATION INTENSITY (HSI)

Pineapple fruit is included in the type of tropical fruit, which is quite popular because it contains a lot of Vitamin C, which is quite high. Pineapple is a local fruit in the Kampar area, this fruit can be consumed directly and become other local processed products. Therefore, the quality of pineapple ripeness must be maintained. The problem that occurs at this time is that the pineapple fruit selection process is still done manually, by looking at it visually, so mistakes can occur in the process of clarifying pineapple fruit identification according to standards. Therefore, it is necessary to research the ripeness of pineapples using the Color Space Algorithm Hue Saturation Intensity (HIS). The variables to be input are based on photos of ripe, half ripe, and raw pineapples using a smartphone camera or DSLR camera with a minimum resolution of 8 MP. Clarifying the results with image processing and Hue Saturation Intensity (HIS) transformation has an accuracy rate of 80% for the 20 image test data. So that the expected results can help pineapple farmers in detecting the level of maturity of pineapple fruit, which is difficult, can minimize errors in determining the ripeness of pineapple fruit

Multimodal Image Fusion based on Hybrid of Hilbert Transform and Intensity Hue Saturation using Fuzzy System

Multimodal Image Fusion based on Hybrid of Hilbert Transform and Intensity Hue Saturation using Fuzzy System

Genotypic and Environmental Variations in Kernel Color Indices in the Main Almond (Prunus dulcis (Mill.) D.A. Webb) Cultivars Grown in North-Eastern Morocco.

Color indices are important quality traits that define the consumer visual acceptance and agroindustrial preferences. Little is known regarding these properties in the commercial almond cultivars grown in Morocco. This work aimed at investigating kernel color indices in five cultivars, namely, “Fournat de Brézenaud,” “Tuono,” “Ferragnès,” “Ferraduel,” and “Marcona.” Color indices consisted in the following: brightness (L∗), redness index (a∗), yellowness index (b∗), chroma (C∗), hue (H∗), and metric saturation (S∗). Measurements were performed over three consecutive growing seasons (2016–2018) across five different sites from northern (Aknoul, Bni Hadifa, and Tahar Souk) and eastern (Rislane and Sidi Bouhria) Morocco. All factors (cultivar, growing season, and site) affected significantly studied color properties; however, genotype was the main variability source. Wide variabilities were found among cultivars. “Marcona” showed the highest L∗, while “Ferragnès” and “Ferraduel” displayed greater scores of a∗, b∗, C∗, and S∗. Sidi Bouhria presented the lowest L∗ but higher a∗, H∗, and S∗. Moreover, Bni Hadifa displayed higher L∗, b∗, and C∗. 2016 (drier growing season) had the highest values of most indices. Principal component analyses (PCA) discriminate all factors through the first three components: PC1 (61%, genetic component) and PC2 (30%) and PC3 (7%) which were of environmental nature since they separate sites and growing seasons, respectively. Despite environmental effects, we suggested a possible discrimination among the studied cultivars based on their kernel color indices. Drought conditions during fruit development seemed to improve kernel quality via synthesis of pigments resulting in higher a∗ and b∗.

Emotions on pictures in motion

Movies are stories that convey the viewer to intensely experiment deep-rooted emotional messages. Film contents are predominantly delivered through vision and audition perceptive channel senses, with a wide gamma of colour sets and sound atmospheres managed to create moods, able as well to generate equilibrium awareness effects, as motion pictures glide through our attention. They can trigger emotions, thoughts and memories, derived of personal ecosystem attention structures. Distinct behaviours at the message interpretation are predisposed by cultural antecedents, validation associations and a reflection of individual emotions. Communication is a process of interaction, that leads the individual to establish perceptual and cognitive relationships with the transmitted message, such as: sensing, selecting and signifying. Therefore, meanings generated by the movie scene unravelling can be enhanced by detailed scenic cinema photography composition, as well through electing specific hue palette, saturation and brightness on image implementation, features that drive to conform an involving narrative, modulating the communication space mediation on movie creation. The semiotic field understanding, at cinema photography aesthetics and color-grading, bring about new insights of how to achieve meaningful messages, that can produce enjoyment and connection between people, within the cinematic masterpiece.

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 & 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

Identification of Freshness of Marine Fish Based on Image of Hue Saturation Value and Morphology

Euthynus is one of the fish that is widely consumed for the enjoyment of the people of Indonesia or abroad, because of its very soft quality, easy to obtain, and contains a lot of essential protein amino acids that are good for the body. This research aims to identify the freshness of the fish purchased based on the eyes and fish gills. The initial process of identifying the freshness of fish uses several methods. Image input process through image object taking using a cell phone camera. The image object is used to determine the value of the RGB image object. RGB color extraction clarifies the value obtained from the image object before proceeding to the next process. Image resize is the process of cutting the image on the desired object part. Image conversion using the HSV method was used to determine the freshness of fish in the gills. The Local Binary Pattern method is used to determine the freshness of the fisheye. The next step is to refine the RGB image into Morphology. The KNN (K-Nearest Neighbor Method) method is used to group objects based on learning data closest to the object. The journal analysis results on the comparison of methods, after 45 trials for each method, found that the Hue Saturation Value method obtained the highest success by 90% and for the texture value obtained 85% success.

Algorithm Configuration K-Nearest To Clarification Medicine Tree Based On Extraction, Variation Of Color, Texture And Shape Of Leaf

At this time to overcome difficulties in identifying medicinal plants that have an impact on the frequent errors in the use of medicinal plants. The formulation of the problem to be discussed in this study is how to identify medicinal plants based on feature extraction of color, texture, and leaf shape. Steps to resolve this problem by collecting image data of medicinal plants, then the image data extracted leaf color features using Red Green Blue (RGB) and Hue Saturation Value (HSV), based on leaf texture using the Gray Level Co-occurrence Matrix (GLCM), based on the shape leaves use eccentricity and metrics. and then classified by the K-Nearest Neighbor (KNN) method. The results in this study the accuracy of Chinese Petai leaves is superior to other types of leaves, which is 98%, which occurs at each K value. Other types of leaves have various values. Saga leaves range between 94% - 97%, Green Betel leaves between 92.8% - 97%, and Red Betel leaves between 91.7% - 95%, Optimal K values ​​indicated by K = 3 have an average accuracy rate of 96.7% also have sensitivity value of 93.3%. The addition of K = 5, K = 7, K = 9, and K = 11 tends to decrease the average value of accuracy and sensitivity.

DETEKSI DINI KEBAKARAN HUTAN DAN LAHAN MEMANFAATKAN EKSTRAKSI EXIF PADA INFORMASI GAMBAR BERBASIS PENGOLAHAN CITRA

Forest fire detection system is one of important tools in preventing and mitigating forest and land fires. In Indonesia, the detection of forest and land fires relies on hotspot information captured from satellites. However, the location obtained by the satellite has a horizontal error of 2 km from the ground check data. Therefore, these information are less relevant to the actual location. In this research, an android app is proposed to extract Exchangeable Image Format (EXIF) photo metadata. The metadata has image information such as latitude and longitude, to obtain the location of forest fires reported by the application user. In addition, this research implemented one of the image processing methods to classify fire and smoke in images of fires. Color filtering method is used based on the color space of Red Green Blue (RGB), Hue Saturation Value (HSV) and YCbCr. This classification process aims to ease the burden on the admin in confirming user reports. The results of the fire and smoke classification process are described using a confusion matrix. This matrix produces an accuracy rate of 75%, a precision of 80% and a recall of 80% for a fire classification and an accuracy of 70%, a precision of 92% and a recall of 87% for smoke classification. There are 25% and 30% of misclassified data of fire and smoke. This is because the color filtering method classifies each color pixel from the image, therefore many pixels that are not classified as fire or smoke images are classified because there are other objects that have a range of colors to classify fire and smoke

Applying RGB- and Thermal-Based Vegetation Indices from UAVs for High-Throughput Field Phenotyping of Drought Tolerance in Forage Grasses

The persistence and productivity of forage grasses, important sources for feed production, are threatened by climate change-induced drought. Breeding programs are in search of new drought tolerant forage grass varieties, but those programs still rely on time-consuming and less consistent visual scoring by breeders. In this study, we evaluate whether Unmanned Aerial Vehicle (UAV) based remote sensing can complement or replace this visual breeder score. A field experiment was set up to test the drought tolerance of genotypes from three common forage types of two different species: Festuca arundinacea, diploid Lolium perenne and tetraploid Lolium perenne. Drought stress was imposed by using mobile rainout shelters. UAV flights with RGB and thermal sensors were conducted at five time points during the experiment. Visual-based indices from different colour spaces were selected that were closely correlated to the breeder score. Furthermore, several indices, in particular H and NDLab, from the HSV (Hue Saturation Value) and CIELab (Commission Internationale de l’éclairage) colour space, respectively, displayed a broad-sense heritability that was as high or higher than the visual breeder score, making these indices highly suited for high-throughput field phenotyping applications that can complement or even replace the breeder score. The thermal-based Crop Water Stress Index CWSI provided complementary information to visual-based indices, enabling the analysis of differences in ecophysiological mechanisms for coping with reduced water availability between species and ploidy levels. All species/types displayed variation in drought stress tolerance, which confirms that there is sufficient variation for selection within these groups of grasses. Our results confirmed the better drought tolerance potential of Festuca arundinacea, but also showed which Lolium perenne genotypes are more tolerant.

Dual Color Space Guided Sketch Colorization.

Automatic sketch colorization is a challenging task in both computer graphics and computer vision since all the color, texture, shading generation have to be created based on the abstract sketch. Besides, it is a subjective task in painting process, which needs illustrators to comprehend drawing priori (DP), such as hue variation, saturation contrast and gray contrast and utilize them in the HSV color space which is closer to human visual cognition system. As such, incorporating supplementary supervision in the HSV color space may be beneficial to sketch colorization. However, previous methods improve the colorization quality only in the RGB color space without considering the HSV color space, often causing results with dull color, inappropriate saturation contrast, and artifacts. To address this issue, we propose a novel sketch colorization method, dual color space guided generative adversarial network (DCSGAN), that considers the complementary information contained in both the RGB and HSV color space. Specifically, we incorporate the HSV color space to construct dual color spaces for supervising our method with a color space transformation (CST) network that learns transformation from the RGB to HSV color space. Then, we propose a DP loss that enables the DCSGAN to generate vivid color images with pixel level supervision. Additionally, a novel dual color space adversarial (DCSA) loss is designed to guide the generator at global level to reduce the artifacts to meet audiences' aesthetic expectations. Extensive experiments and ablation studies demonstrate the superiority of the proposed method over previous state-of-the-art (SOTA) methods.

A Fast Single Image Fog Removal Method Using Geometric Mean Histogram Equalization

Fog is a natural phenomenon that affects scene visibility, it reduces the contrast of the image and causes color-fade. While various works in the literature have addressed this issue, a fast effective model is still lacking. In this paper, a single image fog removal based on Geometric Mean Histogram Equalization (GMHE) is proposed. In particular, the proposed method is composed of three steps. The primary step is to adaptively tune the performance of GMHE according to the properties of the color histogram of the foggy image. The obtained result then enters two levels of chromaticity enhancement using the Hue Saturation Value (HSV) and rotors color transformations, respectively. Extensive experiments demonstrate that the proposed method attains high performance compared to the state-of-the-art methods in terms of quality and execution time. The evaluation is performed qualitatively by visual assessment, and quantitatively using a set of full reference and no-reference-based measures. As well, we suggest an assessment criterion to combine the results of the standard measures in a single score to facilitate the comparisons between the different fog removal methods.

Image Contrast Optimization using Local Color Correction and Fuzzy Intensification

Global image enhancement techniques are used to enhance contrast in images but these techniques are found to be under-enhanced or over-enhanced in differently illuminated regions of the image. Local color correction methods work on local pixel regions to optimize the color contrast enhancement but they also have been found to show a lag while covering pixel regions which are overexposed, compared to those which are underexposed causing local artifacts. In this work, we overcome the shortcomings of both the local color correction and global color correction. This method uses local color correction in the Hue Saturation Luminance (HSL) domain, and fuzzy intensification operators are used to control the color fidelity of the local color corrected images. Thus, is able to sort out the problem of overexposed and underexposed regions and provide optimized contrast enhancement in colored images. Several experiments have been performed to analyze the performance of the proposed method and feasibility as compared to existing techniques. Performance parameters such as Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Structural Similarity Index Measurement (SSIM) and Naturalness Image Quality Evaluator (NIQE) is evaluated and the comparison with some existing techniques of contrast enhancement of color images is performed. The obtained result have good contrast and approve the better performance of the proposed method in support of the quantitative measure of perceptual appearance of the processed images and low computational time.