Hue , Saturation And Intensity Research Articles

Gamma Correction for Brightness Preservation in Natural Images

Due to improper acquisition settings and other noise artifacts, the image degraded to yield poor mean preservation in brightness. The simplest way to improve the preservation is the implementation of histogram equalization. Because of over-enhancement, it failed to preserve the mean brightness and produce the poor quality of the image. This paper proposes a multi-scale decomposition for brightness preservation using gamma correction. After transformation to hue, saturation and intensity (HSI) channel, the 2D- discrete wavelet transform decomposed the intensity component into low and high-pass coefficients. At the next phase, gamma correction is used by auto-tuning the scale value. The scale is the modified constant value used in the logarithmic function. Further, the scale value is optimized to obtain better visual quality in the image. The optimized value is the weighted distribution of standard deviation-mean of low pass coefficients. Finally, the experimental result is estimated in terms of quality assessment measures used as absolute mean brightness error, the measure of information detail, signal to noise ratio and patch-based contrast quality in the image. By comparison, the proposed method proved to be suitably remarkable in retaining the mean brightness and better visual quality of the image.

Natural scene text detection and recognition based on saturation-incorporated multi-channel MSER

The detection and recognition of natural scene text is a crucial computer task for the automatic identification and classification of industrial resources with words or labels, and it has gained much attention recently because of its potential to boost production efficiency. Maximally Stable Extremal Region (MSER) is an effective detection method for the natural scene text. However, MSER and its variants have not adequately considered the color information like Red, Green, and Blue (RGB). To address this problem, we present a combined detection method by Canny edge detector focusing on the edge and the improved multi-channel MSER concentrating on the area, where the channels incorporated into MSER consist of R, G, and B channels of the RGB color space and the S channel of the Hue, Saturation and Intensity (HSI) color space. Such a multi-channel MSER method takes full consideration of the color information into the text detection without causing a significant increase in the computational cost. In addition, a text recognition system is presented on the basis of the proposed text detection method. The proposed method for text detection and recognition is evaluated on three datasets including the ICDAR2013, the ICDAR2015, and the ICDAR2017-MLT. Experimental results show that our method outperforms the state-of-the-art methods with the highest Precision of 89.4%, Recall of 93.7%, F-score of 91.5%, and about the shortest runtime of 0.76s on an image of 512 × 512 pixels.

Developing an Automatic Color Determination Procedure for the Quality Assessment of Mangos (Mangifera indica) Using a CCD Camera and Color Standards

Color is one of the key sensory characteristics in the evaluation of the quality of mangos (Mangifera indica) especially with regard to determining the optimal level of ripeness. However, an objective color determination of entire fruits can be a challenging task. Conventional evaluation methods such as colorimetric or spectrophotometric procedures are primarily limited to a homogenous distribution of the color. Accordingly, a direct assessment of the mango quality with regard to color requires more pronounced color determination procedures. In this study, the color of the peel and the pulp of the mango cultivars “Nam Dokmai”, “Mahachanok”, and “Kent” was evaluated and categorized into various levels of ripeness using a charge-coupled device (CCD) camera in combination with a computer vision system and color standards. The color evaluation process is based on a transformation of the RGB (red, green, and blue) color space values into the HSI (hue, saturation, and intensity) color system and the Natural Color Standard (NCS). The results showed that for pulp color codes, 0560-Y20R and 0560-Y40R can be used as appropriate indicators for the ripeness of the cultivars “Nam Dokmai” and “Mahachanok”. The peels of these two mango cultivars present two distinct colors (1050-Y40R and 1060-Y40R), which can be used to determine the fruit maturity during the post-ripening process. However, in the case of the cultivar “Kent”, peel color detection was not an applicable approach for determining ripeness; thus, the determination of the pulp color with the color code 0550-Y20R gave promising results.

Small water bodies mapped from Sentinel-2 MSI (MultiSpectral Imager) imagery with higher accuracy

ABSTRACT Small water bodies have always been an important part of water ecology systems. In the past, due to the limitations of satellite spatial resolution and recognition method precision, there have been few satisfactory remote sensing small water bodies extraction methods. In this article, a method based on index composition and HSI (hue, saturation, and intensity) colour space transformation is proposed to precisely extract small water bodies. An easy-to-deploy, fast, universal, and effective algorithm is used to accurately identify paddy fields and exclude shadows. This method is tested and verified with Sentinel-2 MSI (MultiSpectral Imager) images in seven cities in the Guangdong-Hong Kong-Macao Greater Bay Area. Compared with the traditional modified normalized difference water index (MNDWI) and enhanced water index (EWI) water extraction methods, the proposed HSI method has shown a better performance in small water bodies mapping with a kappa coefficient of 0.94, overall accuracy of 97%, producer’s accuracy of 96%, and user’s accuracy of 98% in test regions, which is significantly higher than the benchmarking water extraction methods. It provides a powerful supplement for the remote sensing monitoring of water resources in surface water bodies. The method proposed in this study exhibits extendibility, it also has the potential to extract other small features with minor modifications of the method.

Single Remote-Sensing Image Dehazing in HSI Color Space

In order to improve the visibility of a single hazy remote sensing multi-spectral image, we develop a novel and effective dehazing algorithm in HSI (hue, saturation and intensity) color space. First of all, the hazy image is transformed into HSI color space; then a linear regression model is built for saturation component image processing. In addition, we implement the improved dark channel prior method in the intensity channel processing. Compared with the traditional haze removal methods in remote-sensing images, the experimental results demonstrate that the developed algorithm can achieve better visual effect and better color fidelity. Both qualitative evaluations and quantitative assessments indicate that the proposed method achieves a better performance than the state-of-the-art methods.

Smoke vehicle detection based on multi-feature fusion and hidden Markov model

Existing smoke vehicle detection methods and vision-based smoke detection methods are vulnerable to false alarms. This paper presents an automatic smoke vehicle detection method based on multi-feature fusion and hidden Markov model (HMM). In this method, we first detect moving objects using an improved visual background extractor (ViBe) algorithm and obtain smoke-colored blocks using color histogram features in the HSI (hue, saturation, and intensity) color space. The adaptive scale local binary pattern (AS-LBP) and the discriminative edge orientation histogram (disEOH) are proposed and combined to characterize the smoke-colored blocks. More specifically, the proposed AS-LBP, a texture feature descriptor, is based on the quadratic fitting of our labelled data to obtain the best scale. The proposed disEOH, a gradient-based feature descriptor, is robust to noise by extracting discriminative edge information using Gaussian filters and principal component analysis (PCA). The discrete cosine transform (DCT) is employed to extract frequency domain information from the region fused by smoke blocks. To utilize the dynamic features, the HMMs are employed to analyze and classify the smoke-colored block sequences and region sequences in continuous frames. The experimental results show that the proposed method achieves better performances than existing smoke detection methods, especially achieves lower false alarms.

A novel color space of fundus images for automatic exudates detection

Abstract This paper has compared the performance of different color spaces of fundus images for automatic detection of exudates. A convolutional neural network was employed to assess the performances of different color spaces generated by orthogonal transformation of the original colors in red/green/blue (RGB) space. Experiments were conducted on two publicly available databases: (1) DIARETDB1 and (2) e-Ophtha. Based on the experimental results, this study has proposed a new color space of fundus images with three channels: (i) second eigenchannel of the RGB space, (ii) hue and (iii) saturation channels of Hue/Saturation and Intensity (HSI) space. This achieved an accuracy, sensitivity and specificity of 98.2%, 0.99 and 0.98, respectively. Twenty times 20-fold cross validation technique confirmed that proposed color space obtained higher replicability compared with conventional color spaces.

Tongue image segmentation via color decomposition and thresholding

SummaryTongue image segmentation is a crucial step in developing an automatic tongue diagnosis system. After exploring characteristics of image thresholding in different color spaces, we propose a simple and effective tongue image segmentation method. Specifically, the proposed method first extracts an initial tongue body region by performing image thresholding on the transformed hue component in HSI (Hue, Saturation, and Intensity) color space. Then, one of two image thresholding results on the red component of an original tongue image is adaptively selected to find the gap region between the tongue body root and the upper lip. Finally, the initial tongue body region is refined by removing fake object regions such as the upper lip to obtain final tongue image segmentation result. Experimental results on a tongue image dataset containing 200 subjects demonstrate the effectiveness of the proposed method.

Assessment of spinach seedling health status and chlorophyll content by multivariate data analysis and multiple linear regression of leaf image features

Plant health and physiological status significantly influence chlorophyll content and photosynthetic capacity. Analysis of leaf reflectance information from digitized leaf images allows high-throughput, non-invasive and real-time estimation of chlorophyll content in a cost-effective manner. In the present study the application of multivariate data analysis tools, viz. principal component analysis (PCA) and agglomerative hierarchical clustering analysis (AHCA), has been discussed for distinguishing between spinach seedlings having high and low chlorophyll contents by simultaneously using the information provided by various image features. Further, leaf color information contained within different color spaces, viz. RGB (red, green and blue), rgb (normalized red, green and blue), HSI (hue, saturation and intensity), CIE (Commission Internationale de l’Eclairage) L∗a∗b∗, CIE-XYZ, and CIE-xyY color spaces, has been used to predict chlorophyll content in terms of SPAD (Soil Plant Analysis Development) chlorophyll meter values by multiple linear regression. It was observed that the color indices R, G, R + G, R−B, G−B, R + G−B, Y (luminance) and DGCI (dark-green color index) exhibited high correlation (R2 > 0.8) with the SPAD values. Further, subjecting the leaf reflectance information provided by these color indices to PCA and AHCA enabled a clear segregation of seedlings with high and low chlorophyll contents. SPAD values predicted by the L∗a∗b∗ color space information yielded the lowest RMSE (root mean square error) and the highest R2 (coefficient of determination) amongst the six color space features assessed. The findings of the present study indicate that concatenation of leaf reflectance information provided by different color indices may be more useful than individual color indices for assessing plant health status and predicting chlorophyll content using machine vision.

Nusselt number-friction characteristic for a twisted rib roughened rectangular duct using liquid crystal thermography

Liquid crystal thermography technique is applied over a wide wall of a rectangular channel having twisted rib roughness to evaluate heat transfer distribution. The temperature distribution can be easily visualized easily as color pattern obtained by a CCD camera. The captured color pattern is digitized and converted to HIS (Hue, Saturation and Intensity) from RGB (red, Green and Blue). In this study the effect of different roughness parameters such as relative roughness pitch, twist ratio and rib inclination angle on heat transfer coefficient and friction factor are investigated. The range of Reynolds number and aspect ratio of the duct is selected those are most suitable for solar air heater. It is observed that relative roughness pitch of 8 shows the maximum heat transfer while friction factor decreases with increase in relative roughness pitch. The effect of twist ratio is to create the jet formation on the downstream side of the ribs, which creates vigorous mixing to increase in heat transfer. The increase in twist ratio decreases the number of jets formed and the Nusselt number, while friction factor increases. The rib inclination to the main flow generates secondary flow, increases turbulence level as well as Nusselt number. The secondary flow increases with increase in inclination angle to create maxima in Nusselt number at an angle of 60°. The friction factor increases with increase in rib inclination angle.

Near-set Based Mucin Segmentation in Histopathology Images for Detecting Mucinous Carcinoma.

This paper introducesnear-set based segmentation method for extraction and quantification of mucin regions for detecting mucinouscarcinoma (MC which is a sub type of Invasive ductal carcinoma (IDC)). From histology point of view, the presence of mucin is one of the indicators for detection of this carcinoma. In order to detect MC, the proposed method majorly includes pre-processing by colour correction, colour transformation followed by near-set based segmentation and post-processing for delineating only mucin regions from the histological images at 40×. The segmentation step works in two phases such as Learn and Run.In pre-processing step, white balance method is used for colour correction of microscopic images (RGB format). These images are transformed into HSI (Hue, Saturation, and Intensity) colour space and H-plane is extracted in order to get better visual separation of the different histological regions (background, mucin and tissue regions). Thereafter, histogram in H-plane is optimally partitioned to find set representation for each of the regions. In Learn phase, features of typical mucin pixel and unlabeled pixels are learnt in terms of coverage of observed sets in the sample space surrounding the pixel under consideration. On the other hand, in Run phase the unlabeled pixels are clustered as mucin and non-mucin based on its indiscernibilty with ideal mucin, i.e. their feature values differ within a tolerance limit. This experiment is performed for grade-I and grade-II of MC and hence percentage of average segmentation accuracy is achieved within confidence interval of [97.36 97.70] for extracting mucin areas. In addition, computation of percentage of mucin present in a histological image is provided for understanding the alteration of such diagnostic indicator in MC detection.

Machine vision based soybean quality evaluation

A novel proof of concept was developed targeted at the detection of Materials Other than Grain (MOGs) in soybean harvesting. Front lit and back lit images were acquired, and image processing algorithms were applied to detect various forms of MOG, also known as dockage fractions, such as split beans, contaminated beans, defect beans, and stem/pods. The HSI (hue, saturation and intensity) colour model was used to segment the image background and subsequently, dockage fractions were detected using median blurring, morphological operators, watershed transformation, and component labelling based on projected area and circularity. The algorithms successfully identified the dockage fractions with an accuracy of 96% for split beans, 75% for contaminated beans, and 98% for both defect beans and stem/pods.

Apple leaf disease identification using genetic algorithm and correlation based feature selection method

Apple leaf disease is one of the main factors to constrain the apple production and quality. It takes a long time to detect the diseases by using the traditional diagnostic approach, thus farmers often miss the best time to prevent and treat the diseases. Apple leaf disease recognition based on leaf image is an essential research topic in the field of computer vision, where the key task is to find an effective way to represent the diseased leaf images. In this research, based on image processing techniques and pattern recognition methods, an apple leaf disease recognition method was proposed. A color transformation structure for the input RGB (Red, Green and Blue) image was designed firstly and then RGB model was converted to HSI (Hue, Saturation and Intensity), YUV and gray models. The background was removed based on a specific threshold value, and then the disease spot image was segmented with region growing algorithm (RGA). Thirty-eight classifying features of color, texture and shape were extracted from each spot image. To reduce the dimensionality of the feature space and improve the accuracy of the apple leaf disease identification, the most valuable features were selected by combining genetic algorithm (GA) and correlation based feature selection (CFS). Finally, the diseases were recognized by SVM classifier. In the proposed method, the selected feature subset was globally optimum. The experimental results of more than 90% correct identification rate on the apple diseased leaf image database which contains 90 disease images for there kinds of apple leaf diseases, powdery mildew, mosaic and rust, demonstrate that the proposed method is feasible and effective. Keywords: apple leaf disease, diseased leaf recognition, region growing algorithm (RGA), genetic algorithm and correlation based feature selection (GA-CFS) DOI: 10.3965/j.ijabe.20171002.2166

Development of machine vision-based ore classification model using support vector machine (SVM) algorithm

The product of the mining industry (ore) is considered to be the raw material for the metal industry. The destination policy of the raw materials of iron mine is highly dependent on the class of iron ores. Thus, regular monitoring of iron ore class is the urgent need at the mine for accurately assigning the destination policy of raw materials. In most of the iron ore mines, decisions on ore class are made based on either visual inspection by the geologist or laboratory analyses of the ores. This process of ore class estimation is time consuming and also challenging for continuous monitoring. Thus, the present study attempts to develop an online vision-based technology for classification of iron ores. A laboratory-scale transportation system is designed using conveyor belt for online image acquisition. A multiclass support vector machine (SVM) model was developed to classify the iron ores. A total of 2200 images were captured for developing the ore classification model. A set of 18 features (9-histogram-based colour features in red, green and blue (RGB) colour space and 9-texture features based on intensity (I) component of hue, saturation and intensity (HSI) colour space) were extracted from each image. The performance of the SVM model was evaluated using four confusion matrix parameters (sensitivity, accuracy, misclassification and specificity). The SVM model performance was also compared with the other methods like K-nearest neighbour, classification discriminant, Naive Bayes, classification tree and probabilistic neural network. It was observed that the SVM classification model performs better than the other classification methods.

Fuzzy Logic Technique Based Image Contrast Enhancement

Image enhancement plays a vital role in image processing technique. This paper presents a Novel method for image enhancement by using Fuzzy. In this proposed algorithm, the RGB image converted into HSI (Hue, Saturation and Intensity) model, where as Intensity and Hue color considered for Image enhancement after conversion. The Hue component decomposed into directional co-efficient by Fuzzy. The higher directional coefficients are eliminated as it causes artifact and unnatural efforts in a image and the intensity components of image is contrast enhanced by using Fuzzy. The performance of the proposed image enhancement method is compared with existing histogram equalization and contrast limited adaptive histogram equalization based image enhancement. The result of the proposed method achieves satisfactory performance in visualization.

Beef and horse meat discrimination and storage time classification using a portable device based on DSP and PCA method

Food authenticity is an issue of major concern. The adulteration of meat products with horse meat drew attention to the development of robust techniques for meat species classification. This work presents an instrument and a method to discriminate among horse and beef meat and to classify their degree of spoilage based on meat colour. The proposed device employs charge-coupled device (CCD) imaging techniques, digital image processing, digital signal processor (DSP), processing techniques and liquid crystal display (LCD) screen. Samples were placed under cold storage at 4°C for two weeks. Two colour models are used to define beef and horse meat: red, green, and blue (RGB) and hue, saturation and intensity (HSI). Principal component analysis (PCA) was employed to optimise the data matrix. Results show that the device was able to distinguish between beef and horse meat and to classify them according to the number of days spent in cold storage.

Development of an Improved Algorithm for Image Processing: A Proposed Algorithm for Optimal Reduction of Shadow from the Image

Shadow detection is the most important aspect in the field of image processing. It has become essential to develop such algorithms that are capable of processing the images with the maximum efficiency. Therefore, the research has aimed to propose an algorithm that effectively processes the image on the basis of shadow reduction. An algorithm has been proposed, which was based on RGB (red, green, and blue) and HIS (hue, saturation and intensity) model. Steps for Shadow detection have been defined. Median filter and colour saturation have been widely used to process the outcomes. Algorithm has proved efficient for the detection of shadow from the images. It was found efficient when compared with two previously developed algorithms. 87% efficiency has been observed, implementing the proposed algorithm as compared to the algorithms implemented previously by other researchers. The study proved to make a supportive effort in the development of optimized algorithm. It has been suggested that the market requires such practices that can be used to improve the working conditions of the image processing paradigm.

Prediction of Pork Color Grade using Image Two-tone Color Ratio Features and Support Vector Machine

The objective of this study was to investigate the usefulness of pork loin color image features in predicting pork two-tone color grade according to objective L* value. Nine image color features (specifically, the means for two-tone ratios of R, G, B, L*, a*, b*, H, S and I) were extracted from 3 different color spaces (RGB (Red, Green and Blue), CIE LAB (L*: luminance; a*: green to red; b*: blue to yellow) and HIS (Hue, saturation and Intensity)). Color features were extracted from a laboratory-based high-quality camera imaging system. Objective color (CIE L*, a* and b*) was measured using a Minolta Colorimeter, calibrated using both white and black tiles. Boneless, 2.54-cm thick sirloin chops (enhanced, n = 541; non-enhanced, n = 232) were collected. K-means clustering technique was used for grouping pork into two color grades based on Minolta L* value. The image color features were used as predictors for multivariate classification of the samples using machine learning method (Support Vector Machine, SVM). For establishing the model, each data set was separated into training (70%) and testing (30%) sets. Ten-fold cross validation was used to set up the model and test for the best model parameters. The results showed that, for both enhanced and non-enhanced chops, the SVM machine method predicted 100% correct for both grades. Therefore, color image features can be used to correctly classify pork chops by SVM model according to the Minolta L* value.

A study on the oil palm fresh fruit bunch (FFB) ripeness detection by using Hue, Saturation and Intensity (HSI) approach

To increase the quality of palm oil means to accurately grade the oil palm fresh fruit bunches (FFB) for processing. In this paper, HSI color model was used to determine the relationship between FFB ’ s color with the underipe and ripe category so that the grading system could be accurately done. From the analysis manipulation, a formula was generated and applied to the data obtained. The by linear regression in the data shows an average success rate at 45% accuracy for oil palm ripeness detection. Artificial Neural Network (ANN) however return a better accuracy result for both underipe and ripe categories which are 60% and 80% respectively. This yield an overall accuracy of 70%. This can be increased more by improving the grading system.

A Method for Determining the Severity of Sudden Death Syndrome in Soybeans

Abstract. Sudden death syndrome (SDS), caused by the fungus Fusarium virguliforme, is a widespread mid- to late-season soybean disease with distinctive foliar symptoms that in some extreme cases may cause nearly 100% yield loss. This article reports on the development of an image analysis method to quantify SDS severity using an RGB camera under natural light in a laboratory environment. An HSI (hue, saturation, and intensity) color space based image processing approach was developed. A lesion index (LI) was defined as the ratio of affected leaf area to healthy area for evaluating SDS severity levels on leaves of infected plants. A method of using the frequency distribution of hue values of whole leaf area to measure the severity of SDS was also investigated. Preliminary results obtained from laboratory-scale validation demonstrated that the developed method could provide a feasible means of recording SDS severity in situ that could potentially be used for high-throughput greenhouse evaluation of breeding lines or commercial soybean cultivars.