Hue Saturation Intensity Research Articles

An integrated enhancement method to improve image visibility and remove color cast for sand-dust image

AbstractSand-dust color images suffer from poor image visibility and serious color cast that significantly affect the performance of outdoor computer vision systems. Therefore, this paper proposes an integrated enhancement method for the sand-dust image. The proposed method improves the image visibility and removes the sand-dust color cast. It integrates two main processes in two different color models. The adaptive gray world-blue channel (AGW-B) is utilized in the Red-Green-Blue (RGB) color model to remove the sand-dust color cast. Then, the contrast limited adaptive histogram equalization with normalized intensity and saturation correction (CLAHE-NISC) is conducted in a Hue-Saturation-Intensity (HSI) color model to enhance the image visibility. Sand-dust images with weak, medium, and extreme sand-dust color casts were utilized in the subjective and objective evaluations. Results show that the proposed method produced better and clearer enhanced images than the other four current sand-dust image enhancement methods.

Application of digital image processing method for roasted coffee bean quality identification: a systematic literature review

In coffee processing, there are several important stages, one of which is roasting. The roasting process is an important determinant of coffee quality. Determination of coffee quality can be done using digital image processing methods to produce parameters and quality classifications precisely, make images of better quality so that photos and moving images can be easily understood. This analysis uses a Systematic Literature Review (SLR) for the identification, evaluation, and interpretation of all available research results on the topics discussed. The purpose of this study was to identify and analyze the main quality parameters and the best digital image processing methods used in classifying the quality of roasted coffee beans. From the results of the analysis of 31 journals, it is known that the parameters for evaluating the quality of roasted coffee are color parameters, texture parameters, and shape parameters. The color parameters consist of Red Green Blue (RGB), Grayscale, Hue Saturation Intensity (HSI), and L*a*b* features. The texture parameters consist of energy, entropy, homogeneity, and contrast. As for the feature shape parameters, they are area, circumference, diameter, and percentage of roundness. Results of the analysis show that the main parameter that plays an important role in assessing the quality of roasting coffee is the color parameter. This can be seen from the function of the color parameter in quality identification based on the image of the roasted coffee beans. The quality parameters used are image capture, image resolution, training data, testing data, iterations, and accuracy values. In addition, the resulting image processing methods used for quality classification include Backpropagation (BP), Learning Vector Quantization (LVQ), and K-Nearest Neighbor (KNN). Based on results of the analysis, the best method for classifying the quality of roasting results is Backpropagation, and it is known that the accuracy value of this method has a high range of values. Key words: Backpropagation, K-Nearest Neighbour, Learning Vector Quantization, Coffee Bean Roasting, Image Processing

Real-time three-dimensional measurement for color object based on HSI multi-level Gray code demodulation temporal phase unwrapping

A real-time three-dimensional(3D) measuring method for color object based on hue-saturation-intensity (HSI) multi-level Gray code demodulation temporal phase unwrapping is proposed. In order to reduce the number of auxiliary fringe patterns to be more adopted to real-time measurement, the multi-level Gray code is used to assist temporal phase unwrapping. While measuring color object, the fringe order may be disorder by color-to-gray algorithm(C2G). In order to improve the characteristic of anti-color interference in real-time measurement, a multi-level Gray code demodulation method using HSI color space is proposed. Intensity in HSI color space instead of the gray in RGB space is used in multi-level Gray code decoding, at the same time, the color texture of the object surface can also be reconstructed without projecting additional auxiliary color texture patterns. Experimental results show that this method can effectively correct the fringe order confusion caused by color object chroma, and reduce errors in fringe order.

Predictive analysis of effects of water stress on strawberry seedlings using fluorescent image channel components.

To investigate the effect of water stress on strawberry seedlings, a chlorophyll-fluorescence-image-acquisition system was developed. Strawberry seedlings of uniform growth were selected for grouped water-stress incubation experiments; the collected chlorophyll-fluorescence images of leaves were converted to red-green-blue (RGB), hue-saturation-value (HSV), and hue-saturation-intensity (HSI) color spaces and analyzed for water and chlorophyll contents measured at the same time for 14 consecutive days. The results indicate that the analysis and prediction of plant stress conditions can be effectively conducted using the channel components of the color-space model and the channel component ratios, which provide a reference for promoting agricultural development.

A new steganography technique based on genetic algorithm

As technology grows, it affect our daily lives becomes more penetrating. Our private information may be violated when communications media are used, to achieve information security this paper was based on a new technique of steganography using cryptography, color model and genetic algorithm. First, the secret text was encrypted with the advanced encryption standard (AES) algorithm. Second, the hiding was applied in two stages. First, the cover-image was converted in to the hue saturation intensity (HSI) color model and select one of the models, then divide it into a group of blocks and hiding the text in them by using least significant bit (LSB) method on specified bytes randomly, then using the genetic algorithm that calculates peak signal-to-noise-ratio (PSNR) for all blocks after the hiding process and thus obtain the best value for PSNR for the optimal block. Second, includes the final hiding of all blocks based on the results of the first stage of the best random distribution of bytes according to the results of the genetic algorithm. The PSNR and mean square error (MSE) measures were adopted to prove the accuracy and efficiency of the results.

Anthocyanins estimation in homogeneous bean landrace (<em>Phaseolus vulgaris</em> L.) using probabilistic representation and convolutional neural networks

Studying chemical components in food of natural origin allows us to understand their nutritional contents. However, nowadays, this analysis is performed using invasive methods that destroy the sample under study. These methods are also expensive and time-consuming. Computer vision is a non-invasive alternative to determine the nutritional contents through digital image processing to obtain the colour properties. This work employed a probability mass function (PMF) in colour spaces HSI (hue, saturation, intensity) and CIE L*a*b* (International Commission on Illumination) as inputs for a convolutional neural network (CNN) to estimate the anthocyanin contents in landraces of homogeneous colour. This proposal is called AnthEstNet (Anthocyanins Estimation Net). Before applying the CNN, a methodology was used to take digital images of the bean samples and extract their colourimetric properties represented by PMF. AnthEstNet was compared against regression methods and artificial neural networks (ANN) with different characterisation in the same colour spaces. The performance was measured using precision metrics. Results suggest that AnthEstNet presented a behaviour statistically equivalent to the invasive method results (pH differential method). For probabilistic representation in channels H and S, AnthEstNet obtained a precision value of 87.68% with a standard deviation of 10.95 in the test set of samples. As to root mean square error (RMSE) and R2, this configuration was 0.49 and 0.94, respectively. On the other hand, AnthEstNet, with probabilistic representations on channels a* and b* of the CIE L*a*b* colour model, reached a precision value of 87.49% with a standard deviation of 11.84, an RMSE value of 0.51, and an R2 value of 0.93.

Nanosecond laser coloration of Ti-based metallic glass under various laser parameters and irradiation atmospheres

Laser coloration is an emerging technique for surface functionalization of metallic glasses (MGs). Understanding the influence of laser parameters on surface color of MGs will accelerate their commercialization. Herein, surface coloration was conducted on a Ti-based MG, and the hue–saturation–intensity (HSI) color space was employed to evaluate color variation. Various colors were achieved by varying the average power and scanning speed in an ambient atmosphere. The results indicated that the overlap rate and number of repetitive scans had a negligible effect on the color and significantly changed the microscopic morphology. The irradiated surfaces were yellowish and colorless in nitrogen and argon atmospheres. Furthermore, the effect of morphology on the surface color and the mechanism of surface coloration were examined. This study demonstrates that nanosecond laser irradiation is an effective method for achieving surface coloration of MGs, which is expected to enrich the surface functionality of MG products.

A Hybrid Method for Enhancement of Both Contrast Distorted and Low-Light Images

Many different histogram equalization (HE)-based image enhancement methods have been developed to overcome the problems of low or high image brightness, contrast sensitivity, and difficulty in revealing details of dark areas under low-light environments. In this paper, a novel image enhancement method based on HE and adaptive gamma correction with weight distribution (AGCWD) is proposed for natural and effective image enhancement. In this method, histogram stretching is performed on Red–Green–Blue (RGB) color components of image, and then the color space of RGB image is converted to Hue–Saturation–Intensity (HSI) color space. The histograms of S and I components are divided into sub-histograms according to the exposure threshold. The underexposure regions are enhanced with a new AGCWD. Then, the color space of HSI image is converted to RGB color space. Finally, the HE is applied to the input image with the obtained image histogram map. Thus, the method has not only effectively prevented the over-enhancement of the contrast but also obtained the quality and natural enhanced image. The proposed method is compared with the most known contrast enhancement methods and low-light enhancement methods. Experimental results have supported that the proposed method outperforms other methods in terms of both visual perception and objective evaluation.

A dichotomy color quantization algorithm for the HSI color space

Color quantization is used to obtain an image with the same number of pixels as the original but represented using fewer colors. Most existing color quantization algorithms are based on the Red Green Blue (RGB) color space, and there are few color quantization algorithms for the Hue Saturation Intensity (HSI) color space with a simple uniform quantization algorithm. In this paper, we propose a dichotomy color quantization algorithm for the HSI color space. The proposed color quantization algorithm can display images with a smaller number of colors than other quantization methods of RGB color space. The proposed algorithm has three main steps as follows: first, a single-valued monotonic function of the Hue (H) component in the from RGB color space to HSI color space (RGB-HSI) color space conversion is constructed, which can avoid the partition calculation of the H component in the RGB-HSI color space; second, an iterative quantization algorithm based on the single-valued monotonic function is proposed; and third, a dichotomy quantization algorithm is proposed to improve the iterative quantization algorithm. Both visual and numerical evaluations reveal that the proposed method presents promising quantization results.

Synchronized dewatering measured-value fuzzification improves the partial least squares regression method for the hyperspectral analysis of soil organic matter

Visible and near-infrared (vis-NIR) reflectance spectroscopy has been confirmed to be a convenient, rapid, and effective method for monitoring soil organic matter (SOM) content. However, its estimation accuracy is always affected by a number of factors, including soil moisture. The synchronized dewatering measured-value fuzzification (SDMF) method was investigated, with the aim being to reduce the effect of soil moisture during vis-NIR modeling. To magnify the effects of soil moisture, mixed samples (air-dried and moist samples) were used for SOM content estimation. Vis-NIR spectra were collected using an analytica spectra devices (ASD) FieldSpec 3 spectroradiometer. The soil moisture content values used were derived using the hue–saturation–intensity color-change values acquired from the soil spectra. Both the partial least squares regression (PLSR) method and the SDMF-PLSR method were then incorporated to build the SOM models. The optimal model (Group I-change; j = 1) of the SDMF-PLSR method was considered as the final model, which performed much better (root-mean-square error of validation 6.99 g / kg, mean relative error of validation 28.97%) than the original PLSR model. The SDMF method when combined with PLSR shows great potential for improving SOM content estimation accuracy.

Support Vector Machine Based Classification of Leaf Diseases

Myanmar is well known for agricultural country; wherein about 65% of the labor force depends on agriculture. Since the leaf diseases are microscopic organism, cannot be detected normal human eyes. Leaves are special indicator to distinguish the diseases because the image information of the leaf are changed when the leaf surf the diseases. So, the image processing techniques can be used in agricultural sector. The research work presents a support vector machine classifier algorithm by using MATLAB R2017a for the classification of leaf diseases such as Alternaria Alternata, Cercospora leaf spot, Bacterial Blight and so on. In this research work, RGB color space is converted into HSI (Hue Saturation Intensity) color space. In segmentation step, k-means clustering is used to select the defected area, and it is extracted the features by using GLCM (Gray Level Co-occurrence Matrix). Prior to the features extraction, the median filter is used for getting noise free feature results. Finally, the leaf disease is classified by using support vector machine (SVM) and computes the accuracy. From the obtained results, the maximum accuracy of the system is 83%.

CLASSIFICATION OF RICE USING CONVOLUTIONAL NEURAL NETWORK (CNN)

This paper describes the technique for automatic recognition and classification of different rice grain samples using neural network classifier. The Red Green Blue (RGB), Hue Saturation Intensity (HSI) and Hue Saturation Value (HSV) color models of the image were considered for extracting 18 color features. The classification was carried out using color and texture features separately. The color image was converted to Gray scale image and the Gray Level Co-occurrence Matrixes (GLCM) for four different directions was calculated. A total of eight texture features were calculated from the Co-occurrence matrices. Convolutional Neural Network (CNN) is used for the classification process. The classification accuracy with color features and texture features were compared. Result shows that the classification base on texture features outperform the color feature-based classification even with lesser number of features. It is found that Convolutional Neural network was able to classify two varieties of rice with 100% accuracy using texture features and the edge detection with Sobel and Canny edge detection of the fiber features in the food grain.

Quantum Geometric Transformation Based on QIRHSI Quantum Color images

The negative effects caused by geometric distortion can be removed to the maximum extent possible through appropriate geometric transformations, allowing us to focus on the image content itself in subsequent processing and recognition. Therefore, geometric transformations are often used as a pre-processing step for other image processing applications. In this paper, quantum algorithms are designed to implement geometric transformations, including two-point swapping, circular translation, flipping transformations and right-angle rotation, across a quantum image representation QIRHSI (Quantum Image Representation based on HSI color space) which is built on HSI (Hue-Saturation-Intensity) color space. The above geometric transformations are realized by quantum circuits composed of elementary quantum gates. By analyzing the complexity of the fundamental quantum gates needed for the above geometric transformations, it is found that the global transformations (circular translation, flip transformation and right-angle rotation) are lower than the local transformation (two-point swapping). The proposed geometric transforms are used to facilitate the applications of quantum images with low complexity and high efficiency.

Early warning of Diaporthe infection in kiwifruit soft rot by plasmonic dimer-enhanced Raman spectroscopy.

Diaporthe genus is the dominant pathogens of kiwifruit soft rot with long incubation period and rapid onset and very hard to detect in advance. It is of great significance to develop point-of-care tests for disease prevention and field management. Here we screen a pair of genus-level primers for constructing plasmonic dimer Rayleigh/Raman spectroscopy, which enables rapid, specific, and sensitive detection of Diaporthe genus in real kiwifruits. Dark-filed Rayleigh scattering clearly visualizes the target-induced dimer assembly of nanoprobes. Plasmonic dimer-enhanced Raman spectroscopy has high specificity for 12 common isolates of Diaporthe genus in one batch samples with highly accurate results and much higher sensitivity than polymerase chain reaction (PCR). It realizes the recognition of Diaporthe infection at an early stage of 24h when the kiwifruits do not have any noticeable symptoms. It demonstrates a bright prospect of point-of-care tests for early warning of kiwifruit soft rot infection and quality control.

Application of Multimedia Semantic Extraction Method in Fast Image Enhancement Control

In order to solve the problem that it is difficult to effectively enhance the details of the compressed domain and maintain the overall brightness and clarity of the image when improving the image contrast in the current image enhancement method in the compressed domain, a multimedia semantic extraction method is applied in fast image enhancement control. It has been proposed that thealgorithm that synthesizes training samples according to the Retinex model converts the original low-light image from RGB (red-green-blue) space to HSI (hue saturation intensity) color space, keeps the chrominance and saturation components unchanged, and uses DCNN to enhance the luminance component; finally, it converts the HSI color space to RGB space to get the final enhanced image. The experimental results show that the performance of the model will increase with the increase of the number of convolution kernels, but the increase of the number of convolution kernels will undoubtedly increase the amount of calculation; it can also be found that when the number of network layers is 7, the PSNR of the image output by the model increases. The highest value, increasing the number of network layers, does not necessarily improve the performance of the model; with or without BN, his training method converges more easily than direct RGB image enhancement, with higher average PSNR and SSIM values. The experimental results show that, compared with the traditional Retinex enhancement algorithm and the DCT compression domain enhancement algorithm, the algorithm has better detail enhancement and color preservation effects and can better suppress the block effect.

Ripeness Identification of Chayote Fruits using HSI and LBP Feature Extraction with KNN Classification

This study aims to build a system to identify the ripeness level of chayote that can be done easily and without damaging the quality of the chayote. This study employs digital image processing technology using Hue Saturation Intensity color feature extraction and texture feature extraction of Local Binary Pattern with K-Nearest Neighbor classification so that the process of identifying the ripeness level of chayote will be easier and more effective. This study uses 100 image datasets and is carried out by taking photos of chayote. The stages in this study include the input of chayote images followed by the image pre-processing stage. Next is feature extraction which is divided into three scenarios, namely HSI feature extraction, LBP feature extraction and a combination of the two feature extractions. The final stage is to classify objects that are closest to the object being tested using the KNN method. By determining the value of K in the KNN classification method, the results show that the use of the Chebyshev distance calculation model in LBP feature extraction with K = 5 is a test that has the best accuracy of 90%.

An Algorithm for Image Segmentation in HSI Color Space

This paper presents a new segmentation algorithm that uses priority components in HSI (Hue, Saturation, Intensity) color space. The algorithm (named SegPC) is designed for HSI color space to support image understanding in different areas of human activities because this color space is modeled to be closer to humans’ color perception. The innovative idea for the usage of all color components with respect to their importance is exploited in algorithm development. Two parameters are used for controlling the segmentation process, the first parameter is the number of colors used in the segmentation process and the second is the order of applying calculated thresholds for color components. The main goal of the research is the analysis of the proposed new segmentation algorithm. A set of images with different types of histograms is used for practical examination of the developed algorithm. The peak signal-to-noise ratio (PSNR) is used as a measure of the effect of image segmentation. The highest PSNR values (≈22 for images with histogram near to bi-modal type and ≈23 for images with histogram with several peak values) are calculated for the priority of color components in which the first analyzed component is color intensity and the number of colors in the segmented image is a quarter of the number of colors in the input image. This result could be explained by the meaning of the intensity component which describes the brightness of the color and because of this, it carries a significant part of color information. The time complexity of SegPC is evaluated through theoretical analysis and experimentally. It is observed that performance time depends on three variables: first, the size of the image strongly influences time complexity (when the size of the image increases then the time for processing increases too), second, the number of colors also is related to time for processing, and third, the selected color components chosen as per the priority has a weak influence on time complexity because it defines the number of pixels which have to be additionally analyzed. The developed algorithm presents promising results. Thus, it could be applied in image analysis and natural sciences.

Classification of Banana Maturity Levels Based on Skin Image with HSI Color Space Transformation Features Using the K-NN Method

Banana or Musa Paradisiaca is one type of fruit that is often found in Southeast Asia. The most popular is the Raja banana (Musa paradisiaca L.). The advantage of the plantain is that it has a fragrant aroma and is of medium size and has a very sweet taste that is appetizing when it is fully ripe. While the drawback of plantains is that they ripen quickly, if not handled properly, it can change the nutritional value and nutrients contained in plantains. In this study, the author focuses on identifying the level of ripeness of bananas using the image of a plantain fruit that is still intact and its skin. Processing of the image of the plantain fruit using HSI (Hue Saturation Intensity) color space transformation feature extraction. The tool used to extract the HSI (Hue Saturation Intensity) color space transformation feature is Matlab. The attribute values obtained from the extraction are the Red, Green, Blue values obtained from the RGB values. Hue, saturation and intensity attributes were obtained from HSI extraction. Classification of the level of ripeness of plantain fruit is done with the help of the rapidminer tool. The method used is K-NN. The results obtained from this test are the accuracy value of 91.33% with a standard deviation value of+/- 4.52% with a value of k=4. The RMSE value obtained is 0.276.

Goat milk authentication by one-class classification of digital image-based fingerprint signatures: Detection of adulteration with cow milk

This work used a chemometrics-assisted color histogram-based analytical system (CACHAS) as a low-cost alternative analytical tool for the authentication of goat milk in terms of its adulteration with cow milk. For this fingerprint signatures using Grayscale RGB (red–greenblue) and HSI (hue-saturation-intensity) histograms were explored as analytical information. Then One-Class Partial Least Squares (OC-PLS) and Data-Driven Soft Independent Modeling of Class Analogy (DD-SIMCA) were employed as one-class classifiers. The best result was obtained by the RGB/DD-SIMCA model correctly classifying all pure goat milk and adulterated samples in the test set. This differentiation is favored by the presence of carotenoids in cow milk having a slightly yellowish color while goats convert β-carotene to retinol. Thus considering the complexity of the raw materials and the accessibility of the developed methodology the proposed CACHAS method stands advantageously out by finding the principles of green food analysis for screening the authenticity of goat milk.

Soft sensor of iron tailings grade based on froth image features for reverse flotation

In the iron reverse flotation production process, the amount of flotation agent and the quality of flotation products are usually judged according to the grade of tailings, so it is essential to measure the grade of tailings froth. This research applies computer vision and image feature extraction technology to the soft sensor of tailings froth grade. An adaptive selection method for the image target region is proposed. The relationship between RGB (Red, Green, Blue), HSI (Hue, Saturation, Intensity), and Lab color space and tailings grade of reverse flotation in iron mine has been analyzed. A new image feature is proposed to characterize the degree of froth mineralization. The RGB and HSI dual color space feature values and froth mineralization degree values are determined as input, and the tailing grade soft sensor model is established by the multilayer feedforward perceptrons and VGG-19 neural network. A tailings grade soft sensor system has been developed and applied in a flotation workshop. The results of industrial tests show that this method is efficient and reliable.