Hue Saturation Intensity Color Space Research Articles

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.

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.

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%.

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.

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.

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.

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.

An Environmental-Adaptability-Improved RatSLAM Method Based on a Biological Vision Model

Inspired by rodents’ free navigation through a specific space, RatSLAM mimics the function of the rat hippocampus to establish an environmental model within which the agent localizes itself. However, RatSLAM suffers from the deficiencies of erroneous loop-closure detection, low reliability on the experience map, and weak adaptability to environmental changes, such as lighting variation. To enhance environmental adaptability, this paper proposes an improved algorithm based on the HSI (hue, saturation, intensity) color space, which is superior in handling the characteristics of image brightness and saturation from the perspective of a biological visual model. The proposed algorithm first converts the raw image data from the RGB (red, green, blue) space into the HSI color space using a geometry derivation method. Then, a homomorphic filter is adopted to act on the I (intensity) channel and weaken the influence of the light intensity. Finally, guided filtering is used to process the S (saturation) channel and improve the significance of image details. The experimental results reveal that the improved RatSLAM model is superior to the original method in terms of the accuracy of visual template matching and robustness.

Low and non-uniform illumination color image enhancement using weighted guided image filtering

In the state of the art, grayscale image enhancement algorithms are typically adopted for enhancement of RGB color images captured with low or non-uniform illumination. As these methods are applied to each RGB channel independently, imbalanced inter-channel enhancements (color distortion) can often be observed in the resulting images. On the other hand, images with non-uniform illumination enhanced by the retinex algorithm are prone to artifacts such as local blurring, halos, and over-enhancement. To address these problems, an improved RGB color image enhancement method is proposed for images captured under non-uniform illumination or in poor visibility, based on weighted guided image filtering (WGIF). Unlike the conventional retinex algorithm and its variants, WGIF uses a surround function instead of a Gaussian filter to estimate the illumination component; it avoids local blurring and halo artifacts due to its anisotropy and adaptive local regularization. To limit color distortion, RGB images are first converted to HSI (hue, saturation, intensity) color space, where only the intensity channel is enhanced, before being converted back to RGB space by a linear color restoration algorithm. Experimental results show that the proposed method is effective for both RGB color and grayscale images captured under low exposure and non-uniform illumination, with better visual quality and objective evaluation scores than from comparator algorithms. It is also efficient due to use of a linear color restoration algorithm.

Color Conversion Formula with Saturation Correction from HSI Color Space to RGB Color Space

In color image processing, preservation of hue is required. Therefore, perceptual color models such as HSI and HSV have been used. Hue-Saturation-Intensity (HSI) is a public color model, and many color applications have been made based on this model. However, the transformation from the conventional HSI (C-HSI) color space to the RGB color space after processing intensity/saturation in the C-HSI color space often generates the gamut problem, because the shape of C-HSI color space is a triangular pyramid which includes the RGB color space. When the output of intensity/saturation processing result is located in the outside of the common region of RGB color space and C-HSI color space, it is necessary to move to the RGB color space. The effective way of hue and intensity preserving saturation correction algorithm is proposed. According to the proposed saturation correction algorithm, the corrected saturation value is same as the processing result in the ideal HSI color space whose gamut same as the RGB gamut.

Perceptual Image Quality Assessment Technique for Color Images based on HSI Colour Space

A new full-reference Perceptual Image Quality Assessment (PIQA) technique based on Hue-Saturation-Intensity (HSI) transformation method for colour images is offered in this work. Fundamentally, it is combined with three approaches (colour transformation, histogram processing and Human Visual System (HVS) based weighting) and it uses the Discrete Cosine Transform (DCT) domain. The PIQA is composed of four phases. At the first phase, a colour image is transformed using the HSI because an important characteristic of eyes reaction to intensity of light and colour knowledge is used for quality assessment processes. All channels' DCT coefficients have been calculated at the second phase, because many specifications cannot be assessed in the spatial domain. At the third phase, histogram based quality assessment results are obtained by using histogram of each channel (Hue, Saturation and Intensity). These results are weighted for obtaining perceptual quality result taking into account the HVS specifications at the last phase because the human eye has different sensitivities to the intensity and the others (i.e., hue and saturation). Experimental outcomes about feasibility of the PIQA on test images under different deteriorations, both sensible by the HVS and with the same Peak Signal to Noise Ratio (PSNR) result are offered. The PIQA shows better performance in comparison to state-of-the-art techniques

Perceptual Image Quality Assessment Technique for Color Images based on HSI Colour Space

A new full-reference Perceptual Image Quality Assessment (PIQA) technique based on Hue-Saturation-Intensity (HSI) transformation method for colour images is offered in this work. Fundamentally, it is combined with three approaches (colour transformation, histogram processing and Human Visual System (HVS) based weighting) and it uses the Discrete Cosine Transform (DCT) domain. The PIQA is composed of four phases. At the first phase, a colour image is transformed using the HSI because an important characteristic of eyes reaction to intensity of light and colour knowledge is used for quality assessment processes. All channels' DCT coefficients have been calculated at the second phase, because many specifications cannot be assessed in the spatial domain. At the third phase, histogram based quality assessment results are obtained by using histogram of each channel (Hue, Saturation and Intensity). These results are weighted for obtaining perceptual quality result taking into account the HVS specifications at the last phase because the human eye has different sensitivities to the intensity and the others (i.e., hue and saturation). Experimental outcomes about feasibility of the PIQA on test images under different deteriorations, both sensible by the HVS and with the same Peak Signal to Noise Ratio (PSNR) result are offered. The PIQA shows better performance in comparison to state-of-the-art techniques

Using HSI Color Space to Improve the Multispectral Lidar Classification Error Caused by Measurement Geometry

Multispectral lidar has become a promising technology with the rise in capability of 3-D spectral imaging. However, the precise acquisition of spectral information is interfered by measurement geometry, namely, incidence angle and detection distance. These issues may cause discrepancy within the spectral information, thus limiting the classification capabilities of multispectral lidar. To fill this gap, a hue-saturation-intensity (HSI) color space-based method for multispectral lidar classification is proposed in this study. The proposed scheme does not require radiometric calibration, as the HSI color space is robust to spectral intensity variations within a single target. In this method, spectral data are transformed from red-green-blue (RGB) color space to HSI color space. The three components of the HSI color space are inputted for the classification. Then, a reference target-based radiometric calibration is conducted for comparison. The complex indoor scene and the random forest classifier are used for the validation. The classification results of using raw RGB data, raw HSI data, calibrated RGB data, and calibrated HSI data are compared. Results show that the raw HSI data outperform the raw RGB data in terms of classification accuracy. In particular, the raw HSI data can correct the classification error caused by the measurement geometry more effectively than the calibrated RGB data. The improvement resulting from using the HSI color space is demonstrated by both the three-wavelength multispectral lidar and the 32-channel multispectral lidar. That indicates that HSI color space is a promising tool for enhancing the classification capability of multispectral lidar.

A Computer Vision System Based on Majority-Voting Ensemble Neural Network for the Automatic Classification of Three Chickpea Varieties.

Since different varieties of crops have specific applications, it is therefore important to properly identify each cultivar, in order to avoid fake varieties being sold as genuine, i.e., fraud. Despite that properly trained human experts might accurately identify and classify crop varieties, computer vision systems are needed since conditions such as fatigue, reproducibility, and so on, can influence the expert’s judgment and assessment. Chickpea (Cicer arietinum L.) is an important legume at the world-level and has several varieties. Three chickpea varieties with a rather similar visual appearance were studied here: Adel, Arman, and Azad chickpeas. The purpose of this paper is to present a computer vision system for the automatic classification of those chickpea varieties. First, segmentation was performed using an Hue Saturation Intensity (HSI) color space threshold. Next, color and textural (from the gray level co-occurrence matrix, GLCM) properties (features) were extracted from the chickpea sample images. Then, using the hybrid artificial neural network-cultural algorithm (ANN-CA), the sub-optimal combination of the five most effective properties (mean of the RGB color space components, mean of the HSI color space components, entropy of GLCM matrix at 90°, standard deviation of GLCM matrix at 0°, and mean third component in YCbCr color space) were selected as discriminant features. Finally, an ANN-PSO/ACO/HS majority voting (MV) ensemble methodology merging three different classifier outputs, namely the hybrid artificial neural network-particle swarm optimization (ANN-PSO), hybrid artificial neural network-ant colony optimization (ANN-ACO), and hybrid artificial neural network-harmonic search (ANN-HS), was used. Results showed that the ensemble ANN-PSO/ACO/HS-MV classifier approach reached an average classification accuracy of 99.10 ± 0.75% over the test set, after averaging 1000 random iterations.

An FPGA-Based Simple RGB-HSI Space Conversion Algorithm for Hardware Image Processing

In this paper, a new, low-complexity, easy-to-implement hardware method for color space conversion between the red-green-blue (RGB) and the hue-saturation-intensity (HSI) color spaces called the simple RGB-HSI space conversion (S-SC) algorithm is proposed, which aims to provide more rapid computing due to the need for fewer operations. In the S-SC algorithm, we reconstruct the model of space conversion between the RGB color space and the HSI color space (RGB-HSI) by inverting the conversion from the HSI color space to the RGB color space of the traditional geometric derivation algorithm. As a result, the nonlinear model-realized RGB-HSI color space conversion by the geometric derivation algorithm is transformed into a linear conversion model, which can avoid complicated calculations such as trigonometric and inverse trigonometric functions in the color space conversion process. The model can effectively reduce the computational complexity of the algorithm and facilitate hardware implementation at the same time. To evaluate the performance of the S-SC algorithm, we first compare the S-SC algorithm with the geometric derivation algorithm from the computational complexity perspective. On this basis, we compare the S-SC algorithm with five other RGB-HSI color space conversion algorithms from the perspectives of error and conversion effect. Finally, we use the field programmable gate array (FPGA) hardware platform to analyze and verify the timing sequence and logical resource consumption and verify the effectiveness of the proposed algorithm with experimental results. We show that the S-SC algorithm achieves good performance in terms of conversion accuracy, logical unit resource occupancy, and output timing.

An Improved LWD Azimuth Gamma Imaging Model Based on HSI Space

In recent years, in the process of oil and gas exploration and development, the situation of drilling tools drilling out of the reservoir often occurs. Due to the lack of directional indication, the traditional LWD(Logging While Drilling) natural gamma measurement technology cannot guide drilling tools to return to the reservoir efficiently. In order to solve this problem, this paper improves the traditional radiometric gamma measurement technology and establishes a set of azimuth gamma imaging model. Firstly, the basic imaging model is established based on HSI (Hue, Saturation, Intensity) color space. Then, the study focuses on the design of the gamma data quality control algorithm, which can effectively suppress the original gamma data noise. In addition, the cubic spline interpolation algorithm is selected to support the model to obtain 360° omni-directional gamma data. Finally, the image processing technology based on fast grayscale grouping is applied in this study, which greatly improves the imaging effect. Field application shows that the model runs stably on site, and the azimuth gamma image is well indicative. It can monitor the relative position relationship between the borehole trajectory and the formation, which effectively improves the reservoir encounter rate, and has high application and promotion value.

Diurnal and nocturnal cloud segmentation of all-sky imager (ASI) images using enhancement fully convolutional networks

Abstract. Cloud segmentation plays a very important role in astronomical observatory site selection. At present, few researchers segment cloud in nocturnal all-sky imager (ASI) images. This paper proposes a new automatic cloud segmentation algorithm that utilizes the advantages of deep-learning fully convolutional networks (FCNs) to segment cloud pixels from diurnal and nocturnal ASI images; it is called the enhancement fully convolutional network (EFCN). Firstly, all the ASI images in the data set from the Key Laboratory of Optical Astronomy at the National Astronomical Observatories of Chinese Academy of Sciences (CAS) are converted from the red–green–blue (RGB) color space to hue saturation intensity (HSI) color space. Secondly, the I channel of the HSI color space is enhanced by histogram equalization. Thirdly, all the ASI images are converted from the HSI color space to RGB color space. Then after 100 000 iterative trainings based on the ASI images in the training set, the optimum associated parameters of the EFCN-8s model are obtained. Finally, we use the trained EFCN-8s to segment the cloud pixels of the ASI image in the test set. In the experiments our proposed EFCN-8s was compared with four other algorithms (OTSU, FCN-8s, EFCN-32s, and EFCN-16s) using four evaluation metrics. Experiments show that the EFCN-8s is much more accurate in cloud segmentation for diurnal and nocturnal ASI images than the other four algorithms.

Image Analysis in Neuropathology: Hue-Saturation-Intensity vs. Colour Deconvolution

As image analysis expands into clinical and basic applications it is important that users be aware of opportunities and limitations. A common image analysis workflow involves the digitization of stained tissue sections into a red-green-blue (RGB) colour model for quantitative interpretation. Upstream of the digital image, quality and variability can be degraded at each step (tissue handling, fixation, sectioning, staining, image acquisition). Digital image analysis presents additional steps where variables can affect data quality. Image analysis platforms are not uniform. Aside from interface preferences, some introduce unintended variability due to their processing architecture that may not be obvious to the end-user. One important component of this is colour space representation: hue-saturation-intensity (HSI) vs. colour deconvolution (CD). A potential weakness of analyses within the HSI colour space is the mis-identification of darkly stained pixels, particularly when more than one stain is present. We were interested to discover whether HSI or CD provided greater fidelity in a typical immunoperoxidase/hematoxylin dataset.Fifty-nine samples were processed using HSI- and CD-based analyses. Processed image pairs were compared with the original sample to determine which processed image provided a more accurate representation. CD proved superior to HSI in 94.9% of the analyzed image pairs. Where the option exists, CD-based image analysis is strongly recommended.LEARNING OBJECTIVESThis presentation will enable the learner to:1.To describe differences between HSI and CD colour spaces2.To explain limitations in the use of HSI-based analyses3.To be aware of recent developments in CD-based platforms

Sinkage definition and visual detection for planetary rovers wheels on rough terrain based on wheel–soil interaction boundary

Abstract Wheel sinkage detection is of tremendous significance for planetary rover mobility optimization control and prevention of serious wheel sinking. Wheel sinkage is regarded as the distance between the lowest point of the wheel in the soil and the horizontal flat terrain on relative research. For rovers moving on rough terrains, the above definition is not reasonable because the horizontal flat terrain cannot be found. New wheel sinkage definition and detection method are proposed, based on vision. The sinkage definition rationality is analyzed for various terrain conditions. The discrete mathematical wheel sinkage calculation model, for which the input is acquired through a visual method, is built. Saturation of wheel–soil interaction image is adjusted by a dynamic piecewise nonlinear adjustment method The image is processed into a binary image based on HSI (Hue, Saturation, Intensity) color space. The wheel–soil boundary is extracted from the corrected wheel region outline according to its morphological features. The wheel sinkage and equivalent terrain interface angles are calculated through the discrete mathematical wheel sinkage calculation model. Sinkage definition rationality and detection method applicability are experimentally validated in the four typical terrain conditions (flat, bulgy, sunken, and uneven terrains). Particularly, the experimental results prove the sinkage detection method has high accuracy for flat terrain, for which, the deviation of the sinkage modulus is less than 2% of the wheel radius. The sinkage detection method is proved to have fairly reasonable adaptability to complex illumination conditions, based on the experimental results for different illumination conditions.