HSV Space Research Articles

Diagnosing Fungal Infection in Wheat Kernels by Integrating Spectroscopic Technology and Digital Color Imaging System: Artificial Neural Network, Principal Component Analysis and Correlation Feature Selection Techniques

ABSTRACTContamination of cereal grain, especially wheat, with fungal infections can cause significant economic impacts and it endangers the health of humans and livestock. This study aims to appraise the UV/VIS–NIR and digital color (RGB) imaging systems and spectroscopic methodology to detect wheat kernels infected by fungi such as Penicillium expansum and Fusarium graminearum. NIR spectra of 190–1100 nm at 10 nm intervals, visible color reflectance images and non‐visible reflectance images of wheat kernels in the ultraviolet and near‐infrared ranges were applied to develop the multi‐layer perceptron (MLP) artificial neural network model. The optimum wavelengths were selected by application of the principal component analysis (PCA) after preprocessing the raw spectra. A confusion matrix was used in the correlation feature selection method (CFS) for the decision tree classifier of selected features. The results showed that the four UV wavelengths of 310, 330, 400, and 410 nm were the best wavelengths using PCA to distinguish healthy and unhealthy wheat kernels. Considering the intensity of the wavelengths as the neural network inputs, samples were classified into healthy and unhealthy categories with an accuracy of 90.9%. Also, 18 features of color images in RGB, LAB, HSV, HSI, YCbCr, and YIQ spaces provided the highest average accuracy of 44.4% in classifying healthy and infected wheat kernels by using a CCD Proline camera in the ultraviolet range. In contrast, other cameras in the visible and invisible range showed low accuracy. Furthermore, the best classification accuracy of the healthy and infected samples by the use of the CFS method was obtained at 88.1%. Based on the findings, spectroscopic methodology proved to be highly effective for detecting, classifying and automatic cleaning of various agricultural seeds, with a particular emphasis on wheat kernals.

Rectifying inhomogeneous illumination in digital images using a latent light manifestation algorithm

ABSTRACT Rectification of illumination is vital in enhancing the visibility of images acquired in suboptimal lighting conditions. Still, it remains a challenging yet intriguing process as many existing algorithms fail to meet expectations. Hence, a latent light manifestation (LLM) algorithm is introduced, beginning with a conversion to the HSV space, processing the V channel using the Naka-Rushton and exponential equations. Next, the two output images are blended using a two-step range-expansion blending approach. After that, a statistics-based tonality adjustment method and a dynamic range extension process are applied. Finally, a transfer to the RGB space is performed to produce the output image. The LLM is tested using three datasets, compared with eight contemporary algorithms, and the quality evaluations are carried out via five performance measures. The results denote that the developed LLM consistently outperforms existing algorithms, scoring the best according to the used measures and yielding visually pleasing results.

Tone mapping algorithm based on BL-Hilbert-L 2 decomposition model for HDR image

Abstract Tone mapping algorithms are mainly used to produce standard dynamic range (SDR) images from high dynamic range (HDR) images. To address the issues of halo artifacts and over-enhancement in current tone mapping algorithms based on layer decomposition model, we propose a tone mapping algorithm based on the BL-Hilbert-L2 decomposition model. In our proposed algorithm, we first convert HDR image from RGB space to HSV space. Subsequently, utilizing the BL-Hilbert-L2 decomposition model, we decompose the V channel of the HDR image into cartoon, texture and high-frequency detail components. Next, we separately compress the cartoon component and stretch the texture and high-frequency detail components. The processed cartoon, texture and high-frequency detail components are then recombined to form a new V channel. Finally, by converting the H and S channels of the HDR image, along with the newly formed V channel, back into the RGB color space, we obtain a tone mapped SDR image. Experimental results demonstrate that our proposed algorithm can effectively eliminate the halo artifacts and avoid the issue of over-enhancement while retaining the edge details.

Low-light image enhancement based on cell vibration energy model and lightness difference

Low-light image enhancement algorithms play a crucial role in revealing details obscured by darkness in images and substantially improving overall image quality. However, existing methods often suffer from issues like color or lightness distortion and possess limited scalability. In response to these challenges, we introduce a novel low-light image enhancement algorithm leveraging a cell vibration energy model and lightness difference. Initially, a new low-light image enhancement framework is proposed, building upon a comprehensive understanding and analysis of the cell vibration energy model and its statistical properties. Subsequently, to achieve pixel-level multi-lightness difference adjustment and exert control over the lightness level of each pixel independently, a lightness difference adjustment strategy is introduced utilizing Weibull distribution and linear mapping. Furthermore, to expand the adaptive range of the algorithm, we consider the disparities between HSV space and RGB space. Two enhanced image output modes are designed, accompanied by a thorough analysis and deduction of the relevant image layer mapping formulas. Finally, to enhance the reliability of experimental results, certain image faults in the SICE database are rectified using the feature matching method. Experimental results showcase the superiority of the proposed algorithm over twelve state-of-the-art algorithms. The resource code of this article will be released at https://github.com/leixiaozhou/CDEGmethod.

Smart urban management identification by integrating optical flow characteristics and HSV space

n smart cities, sanitation workers are the key to urban construction. Accurate targeting of sanitation workers can help managers better monitor and manage them. Pedestrian detection is the core and key component of object detection technology. The difficulty of pedestrian detection in the actual feature recognition is still how to quickly and accurately identify the identity in the complex video scene. To realize the effective detection of sanitation workers, the study designs an identity identification scheme conducive to the friendly management of smart urban management. Since the optical flow feature extraction method and HSV color space extraction method can not meet the actual detection efficiency, this study innovatively integrates the two methods to improve the detection accuracy of the mode. Meanwhile, the study also introduces PCA algorithm to identify the specific identity of sanitation workers. In the actual detection of sanitation workers, the identification rate of two sanitation workers is high, and the similarity is 98.61%. This technology greatly reduces the false detection rate of actual detection and improves the detection accuracy.

Highlight Removal Emphasizing Detail Restoration

In existing highlight removal methods, research on highlights on metal surfaces is relatively limited. Therefore, this paper proposes a new, simple, effective method for removing highlights from metal surfaces, which can better restore image details. Additionally, the approach presented in this paper is highly effective for highlight removal in everyday real-world highlight scenarios. Specifically, we first separate the image’s illumination space based on the Retinex model and generate a highlight mask using the mean plus standard deviation method. Then, based on the mask, we transform the original image and the image at the corresponding mask position to the V channel of the HSV space, achieving the effective elimination of highlights. To enhance the details of the restored image, this paper introduces a method involving adaptive Laplacian sharpening operators and gradient fusion for detail enhancement at highlight removal positions. Finally, a highlight-free image with well-preserved details is obtained. In the experimental phase, we validate the proposed method using real welding seam highlight datasets and real-world highlight datasets. Compared with the existing methods, the proposed method achieves high-quality qualitative and quantitative evaluation.

No‐reference image quality assessment via a dual‐branch residual network

AbstractThe No‐Reference Image Quality Assessment (NR‐IQA) method can predict quality scores of distorted images without the reference image. However, due to the variability in both color and structure of images, existing NR‐IQA methods struggle to accurately predict quality scores of distorted images. Therefore, an NR‐IQA method based on a Dual‐Branch Residual Network (DBRIQA) for evaluating the quality scores of color‐distorted images is proposed. First, guided filtering is applied to the hue images in the HSV space to extract distortion information from the image's color. Then, due to significant differences in distorted images, enhancements are made to the traditional residual blocks, forming a feature extraction module that captures multi‐scale features from the image. In order to capture the global relationships in the distorted image, a Global‐Level Attention Block (GLAB) is introduced, facilitating the interaction of information among the extracted features. Experiments were conducted across four publicly available IQA datasets, including LIVE, CSIQ, TID2008, and TID2013. The experimental results demonstrate that the proposed method exhibits strong performance and generalization capabilities in predicting image quality compared to peer methods.

Global vision object detection using an improved Gaussian Mixture model based on contour.

Object detection plays an important role in the field of computer vision. The purpose of object detection is to identify the objects of interest in the image and determine their categories and positions. Object detection has many important applications in various fields. This article addresses the problems of unclear foreground contour in moving object detection and excessive noise points in the global vision, proposing an improved Gaussian mixture model for feature fusion. First, the RGB image was converted into the HSV space, and a mixed Gaussian background model was established. Next, the object area was obtained through background subtraction, residual interference in the foreground was removed using the median filtering method, and morphological processing was performed. Then, an improved Canny algorithm using an automatic threshold from the Otsu method was used to extract the overall object contour. Finally, feature fusion of edge contours and the foreground area was performed to obtain the final object contour. The experimental results show that this method improves the accuracy of the object contour and reduces noise in the object.

Adaptive enhancement method of irregular low-pixel architectural design image based on lightness component

ABSTRACT This study explores adaptive enhancement for irregular, low-pixel architectural design images, focusing on lightness components. Utilizing a median filter and wavelet threshold method removes image noise, followed by grayscale conversion in HSV space. Applying Retinex theory generates the initial enhanced image, further improved through wavelet transform image fusion technology, resulting in the final enhanced architectural design image. Experimental results demonstrate the method's effectiveness in adapting and enhancing irregular, low-pixel architectural design images, achieving notably improved outcomes. The proposed approach successfully extracts and utilizes valuable image information, enhancing overall image quality. This study found that the proposed method can effectively extract and better utilize useful information in images, achieving more ideal image enhancement effects.

Multi-target tracking algorithm in aquaculture monitoring based on deep learning

In order to analyze fish behavior, real-time underwater fish monitoring is essential. This work presents an underwater multi-target tracking algorithm for aquaculture monitoring, utilizing deep learning techniques. Underwater images are acquired using an underwater robot, and the images are defogged using the multi-scale Retinex algorithm based on HSV space. To enhance object detection performance, GhostNetv2 was integrated into the You Only Look Once version 5 (YOLOv5) object detection algorithm, supplemented by the addition of the Coordinate Attention (CA) module, resulting in the development of GN-YOLOv5. For the tracking algorithm, the more accurate Generalized Intersection over Union (GIoU) method was incorporated into the StrongSORT tracking algorithm. Moreover, to achieve more precise target tracking, a fish re-identification model was established. The proposed algorithms were evaluated through experiments conducted on various datasets, including VOC 2012, MOT16, and self-built datasets. The results demonstrate notable improvements: the GN-YOLOv5 model showed a 32.91% reduction in parameters and a 3.22% increase in precision. Furthermore, the enhanced StrongSORT algorithm exhibited a 3.84% increase in MOTA, a 28.00% reduction in IDS, and a speed boost of 7 FPS, leading to stable and accurate multi-target fish tracking.

A low-light image enhancement method based on HSV space

ABSTRACT To enhance the visual performance of low-illumination images, many low-illumination images are analyzed. Based on this, a low-light image enhancement method based on HSV space and semi-implicit ROF model is proposed. First, the low-illumination image is decomposed into HSV space for saturation denoising and brightness enhancement. Then, the Bayesian rules are applied to fuse the saturation and value. The three components in HSV space are converted to the RGB space and obtain a rough enhanced image. Finally, the semi-implicit ROF model is introduced to denoise the global noise and obtain the enhanced image. Such a comprehensive method can improve the low illumination image more clearly. The experimental results show that the algorithm has a PSNR score of 26.48, 6.29, 0.8947, and 28.4124, and the PSNR score is the highest in the comparison algorithm. The experiments on the Low-Light image data set also show that the proposed method can effectively improve the visibility of low-light images, and can provide a simple and effective method for low-light image enhancement.

Non-Uniform-Illumination Image Enhancement Algorithm Based on Retinex Theory

To address the issues of fuzzy scene details, reduced definition, and poor visibility in images captured under non-uniform lighting conditions, this paper presents an algorithm for effectively enhancing such images. Firstly, an adaptive color balance method is employed to address the color differences in low-light images, ensuring a more uniform color distribution and yielding a low-light image with improved color consistency. Subsequently, the image obtained is transformed from the RGB space to the HSV space, wherein the multi-scale Gaussian function is utilized in conjunction with the Retinex theory to accurately extract the lighting components and reflection components. To further enhance the image quality, the lighting components are categorized into high-light areas and low-light areas based on their pixel mean values. The low-light areas undergo improvement through an enhanced adaptive gamma correction algorithm, while the high-light areas are enhanced using the Weber–Fechner law for optimal results. Then, each block area of the image is weighted and fused, leading to its conversion back to the RGB space. And a multi-scale detail enhancement algorithm is utilized to further enhance image details. Through comprehensive experiments comparing various methods based on subjective visual perception and objective quality metrics, the algorithm proposed in this paper convincingly demonstrates its ability to effectively enhance the brightness of non-uniformly illuminated areas. Moreover, the algorithm successfully retains details in high-light regions while minimizing the impact of non-uniform illumination on the overall image quality.

Zanthoxylum bungeanum Fruit Detection by Adaptive Thresholds in HSV Space for an Automatic Picking System

Zanthoxylum bungeanum Fruit Detection by Adaptive Thresholds in HSV Space for an Automatic Picking System

Rail Surface Defect Detection Based on Image Enhancement and Improved YOLOX

During the long and high-intensity railway use, all kinds of defects emerge, which often produce light to moderate damage on the surface, which adversely affects the stable operation of trains and even endangers the safety of travel. Currently, models for detecting rail surface defects are ineffective, and self-collected rail surface images have poor illumination and insufficient defect data. In light of the aforementioned problems, this article suggests an improved YOLOX and image enhancement method for detecting rail surface defects. First, a fusion image enhancement algorithm is used in the HSV space to process the surface image of the steel rail, highlighting defects and enhancing background contrast. Then, this paper uses a more efficient and faster BiFPN for feature fusion in the neck structure of YOLOX. In addition, it introduces the NAM attention mechanism to increase image feature expression capability. The experimental results show that the detection of rail surface defects using the algorithm improves the mAP of the YOLOX network by 2.42%. The computational volume of the improved network increases, but the detection speed can still reach 71.33 fps. In conclusion, the upgraded YOLOX model can detect rail surface flaws with accuracy and speed, fulfilling the demands of real-time detection. The lightweight deployment of rail surface defect detection terminals also has some benefits.

Low-light image enhancement based on Retinex theory for beam-splitting prism system

The existing color image enhancement methods are utilized for the most part to directly improve the color image that is obtained through the use of a Bayer array camera, whereas the color image that is obtained through the use of a prism camera is the result of the fusion of three primary color images that have been collected independently. As a result, a method for enhancing color photographs based on three images of primary colors that have a low level of light has been proposed. In this paper, we first transfer RGB to HSV space for image enhancement. In the V channel, we use guided filtering combined with improved SSR method to better preserve edge information. Then the S channel is adjusted adaptively to make the image look more natural. Finally, the image is transferred from HSV space back to RGB space. Compared with other traditional methods, our method achieves better results in PSNR, SSIM and NIQE, and has less computational complexity.

Gesture Recognition Algorithm Based on Computer Vision

With the advent of the era of artificial intelligence, computer vision and machine learning are developing rapidly, and human-computer interaction technology has become one of the important research directions. This paper mainly combines computer vision and other related technologies to analyze and judge the gestures, and predict the corresponding models according to the different matching of gestures, so as to recognize the corresponding gesture information. By converting the image into HSV space, HSV channel separation is realized, and then filtering and skin color segmentation are carried out to carry out morphological operation. Finally, shape matching is carried out according to the contour information of the image to judge the information of gesture. The experimental results show that the gesture recognition algorithm proposed in this paper can effectively judge the gesture information contained in the image, has good operation effect and strong practicability.

Progressive feature-aware recurrent net for low-light image enhancement

Mixed mapping across illumination and color domains directly from low-light images is prone to cause domain ambiguity and mislead the holistic enhancement process. The results appear color distorted and unbalanced between overexposure and underexposure. To deal with this issue, we propose a progressive feature-aware recurrent network decoupling the enhancement task into domain-specific subtasks, which gradually restores the illumination and color information of the low-light image under different attention patterns. Specifically, the illumination is adaptively enhanced by the recurrent sub-network with pixel-wise attention. Then, under the guidance of normal illumination, the color enhancement sub-network uses channel attention to balance the restoration of color channels in HSV space. Finally, the preliminary restoration image is further enhanced by the recurrent refined sub-network with dual attention. This progressive learning pattern enables each sub-network to focus on the learning of the specific feature, and the whole network can realize the cumulative learning of multiple features. Experiments on public low-light image datasets demonstrate that our method outperforms the SOTA methods.

Monitoring Postharvest Color Changes and Damage Progression of Cucumbers Using Machine Vision

To monitor cucumbers' external quality, such as color changes or the presence of any damage during storage, a machine vision system was used. Red, Green, Blue (RGB) images were acquired in a "soft box," which provided a highly diffused lighting scene for observing visual changes such as color and appearance in the skin of cucumber. The RGB images were transformed into L*, a*,b*, and HSV spaces. Histograms for each channel in each color space were evaluated for image segmentation, and the blue (B) channel in the RGB color space was found superior in terms of measuring damage progression. Damage progression plots (DPP) were made from accumulated grayscale images in each of the color channels and to observe variation over time, absolute differential damage progression (ADDP) plots were generated. Overall, the order of channel utility was [B], [R, G, V], and [H, S, L*, a*, b*]. To assess which channel, in which colorspace, was most sensitive, i.e., could capture most of the information regarding day-to-day color changes, a principal component analysis (PCA) was performed. The PCA showed that all individual components in the RGB color space were suitable for obtaining information about the external changes of cucumber. Based on the results, the machine vision approach is recommended as a non-destructive technique for monitoring the external quality of stored fresh produce.

A Time Series Analysis of Buddhist Painting Using Digital Image Data

Damage to the painting cultural heritage is recognized through visual observation, photographic data, and conservation diagnosis records, and technology that accurately understands the degree of change over time can prevent damage and be effectively used for conservation treatment. In this study, a time series analysis was conducted to diagnose and compare the conservation status of the past and the present using the digital image of Hanging Painting of Bongjeongsa Temple, Andong (The Vulture Peak Assembly) with different filming times. Color information was analyzed using a digital image analysis program. The shape information and area of crease, exfoliation of painting layer, loss of background material and contamination were calculated using the Relief, Threshold Switching, Color Mapping in HSV space function. Based on the calculated data, the change pattern was confirmed by comparing the damage that occurred in the same area. Quantitative data such as the area of damage and the length of the object were used together to comprehensively compare and analyze the degree of improvement and change in damage that occurred throughout the work to evaluate the change over time.

A Robust Real-Time Ellipse Detection Method for Robot Applications

Over the years, many ellipse detection algorithms have been studied broadly, while the critical problem of accurately and effectively detecting ellipses in the real-world using robots remains a challenge. In this paper, we proposed a valuable real-time robot-oriented detector and simple tracking algorithm for ellipses. This method uses low-cost RGB cameras for conversion into HSV space to obtain reddish regions of interest (RROIs) contours, effective arc selection and grouping strategies, and the candidate ellipses selection procedures that eliminate invalid edges and clustering functions. Extensive experiments are conducted to adjust and verify the method’s parameters for achieving the best performance. The method combined with a simple tracking algorithm executes only approximately 30 ms on a video frame in most cases. The results show that the proposed method had high-quality performance (precision, recall, F-Measure scores) and the least execution time compared with the existing nine most advanced methods on three public actual application datasets. Our method could detect elliptical markers in real-time in practical applications, detect ellipses adaptively under natural light, well detect severely blocked and specular reflection ellipses when the elliptical object was far from or close to the robot. The average detection frequency can meet the real-time requirements (>10 Hz).