Image Histogram Research Articles

Leaf Area Prediction of Pennywort Plants Grown in a Plant Factory Using Image Processing and an Artificial Neural Network

The leaf is a primary part of a plant, and examining the leaf area is crucial in understanding growth and plant physiology. Accurately estimating leaf area is key to this understanding. This study proposed a methodology for the non-destructive estimation of leaf area in pennywort plants using image processing and an artificial neural network (ANN) model. The image processing method involved a series of steps, including grayscale conversion, histogram equalization, binary masking, and region filling, achieving an accuracy of around 96.6%. The ANN model, trained with 70% of a dataset, exhibited high correlations of 97.1% in training and 96.6% in testing phases, with leaf length and width significantly impacting the model output. A comparative analysis revealed the superior performance of the ANN model over the image processing method, demonstrating higher R2 values (>0.99) and lower errors. Furthermore, it showed the impact of diverse LED light combinations and nutrient levels (electrical conductivity, EC) on pennywort plant growth, indicating that the R70:B30 LED light ratio with nutrient level 2 (2.0 dS·m−1) fostered the most favorable growth for pennywort plants. The non-destructive nature, simplicity, and speed of the ANN model in estimating leaf area based on easily obtainable measurements of length and width render it an accessible and accurate tool for plant growth assessment in controlled environments. This approach offers opportunities for future studies, tracking changes in leaf areas under varied growth conditions without harming the plant, thus enhancing precision in research.

Microcosmic characteristics of particulate matter emitted by GDI gasoline engine in plateau environment

The particulate matter samples of gasoline direction injection (GDI) gasoline engine obtained from road experiments are photographed to obtain the emission particulate matter images based on the transmission electron microscope under high altitude environment. The results show that the micro-morphology characteristics of the particulate matter emitted by GDI gasoline engine in plateau area are similar to those in low altitude area, and the main morphology is cloud-like, filamentous, flocculent, chain-like, cluster-like, like altitude contour line, etc. The images are processed by MATLAB and Python software, and the fractal dimension, average gray value and gray histogram are obtained. The fractal dimensions of the emission particulate data obtained are between 1.81 and 1.89, which has some deviation compared with the fractal dimension of the emission particulate data at low altitude of 1.58 and 1.80. With the increase of altitude, its fractal dimension increases more obviously. It can be seem from the characteristics of gray value and gray histogram of particle images that the average gray value of GDI gasoline engine particles is higher. The gray histogram distribution of 5–50 nm images is uniform, and the individual particles in the images are clear. The gray histogram of 100–500 nm images is scattered, so it is easy to extract and separate the image edge contour. 5–50 nm images are used to study the details and characteristics of individual particles, and 100–500 nm transmission electron microscope images are used to study the overall morphology of particles.

Automatic segmentation by the method of interval fusion with preference aggregation when recognizing weld defects

Quality control of welding is usually carried out during the visual inspection process and is highly dependent on an operator experience. In the paper, it is proposed an approach to automatic detection and classification of a defective region, where segmentation of the analyzed photographic image of a weld (i.e., its division into defective and defect-free regions) is performed using the region growing procedure. The starting points for this procedure are selected by the authors' robust method of interval fusion with preference aggregation (IF&PA) on the base of image histogram analysis. Testing of the proposed approach for real life photographic images showed its ability to detect different types of weld defects with higher accuracy compared to traditional methods such as Otsu method and k-means.

A Modified KLEIN Encryption-based Knight Tour for Image Encryption

The security considerations should be balanced with the specific use case and potential risks associated with using lightweight encryption. The security offered by lighter encryption algorithms could not be as high as that offered by heavier encryption techniques. In this paper, a Modified KLEIN Algorithm is proposed for image encryption based on the Knight Tour movement in Chessboard. The required key generation is represented by inputting an image as a key image and then applying a specific operation based on knight tour movement to produce a key scheduled in the proposed encryption algorithm. The movement of Knight Tour applied in modifying the proposed algorithm for increasing security. The experimental results explain the efficiency of a modified algorithm when comparing the histogram of the input image with the encrypted image also the correlation is tested before and after encryption in three directions horizontal, vertical, and diagonal which explains there are very low values of them in all directions. The similarity test also explains there are high differences between the plain and encrypted images. The chessboard movement might be useful when used with another encryption algorithm which increases the confidentiality of transferring data. The contribution of this work is the use of an image as a key for encryption with a specific planning method which helps in key management.

A Multivariate Analysis-Driven Workflow to Tackle Uncertainties in Miniaturized NIR Data.

This study focuses on exploring and understanding measurement errors in analytical procedures involving miniaturized near-infrared instruments. Despite recent spreading in different application fields, there remains a lack of emphasis on the accuracy and reliability of these devices, which is a critical concern for accurate scientific outcomes. The study investigates multivariate measurement errors, revealing their complex nature and the influence that preprocessing techniques can have. The research introduces a possible workflow for practical error analysis in experiments involving diverse samples and instruments. Notably, it investigates how sample characteristics impact errors in the case of solid pills and tablets, typical pharmaceutical samples. ASCA was used for understanding critical instrumental factors and the potential and limitations of the method in the current application were discussed. The joint interpretation of multivariate error matrices and their resume through image histograms and K index are discussed in order to evaluate the impact of common preprocessing methods and to assess their influence on signals.

An efficient adaptive Masi entropy multilevel thresholding algorithm based on dynamic programming

Masi entropy multilevel thresholding can utilize additive and non-extensive information in images to effectively segment a complex image. However, the entropy index of Masi entropy cannot be selected automatically, and the time complexity of the multilevel algorithm by exhaustive searching grows exponentially with the increase of the threshold numbers. To address these two problems, an adaptive entropy index selection strategy based on image histogram information is proposed first. To improve the computation efficiency, an efficient solution for the adaptive Masi entropy multilevel thresholding algorithm based on dynamic programming (DP + AMasi) is also proposed. The DP + AMasi algorithm is compared with the Masi entropy multilevel thresholding algorithm by exhaustive search and state-of-the-art metaheuristic algorithms on three benchmark datasets. The effectiveness of the DP + AMasi is verified by fitness function values, Uniformity Measure, Davies Bouldin index, and CPU run time. In addition, the Wilcoxon test is used to analyze the differences between algorithms.

Enhancement of Old Historical Document by Image Processing from Gray scale to RGB Scale Conversion

The enhancement of historical documents through image processing stands as a pivotal bridge between the past and the present. In this project, we embark on a journey to rejuvenate these treasured artifacts by utilizing cutting-edge techniques. Central to this endeavor is the conversion of grayscale documents into the vibrant realm of RGB, breathing new life into their visual presentation. The Nonlocal Mean Denoising algorithm takes center stage, diligently removing noise while preserving the document’s authenticity. Accompanied by image division code, we break down these historical scrolls and manuscripts into comprehensible segments, ensuring efficient processing without compromising their essence. At the heart of our methodology lies the profound significance of image histograms. Through these histograms, we delve into the intricacies of pixel intensities, unearthing valuable insights into the document’s characteristics. The outcome of our rigorous efforts is a transformation of these historical gems, making them not only aesthetically pleasing but also accessible to a broader audience, fostering a deeper appreciation of our cultural heritage. As technology and history intersect in this project, we highlight the transformative potential of image processing in preserving and reinvigorating the stories of the past. In an age where the digital realm converges with the analog, this endeavor reiterates the importance of ensuring that history remains alive and tangible for generations to come

Research on the Auto-Exposure Method of an Aerial TDI Camera Based on Scene Prediction

Aerial TDI cameras can obtain images with a high sensitivity, high resolution, and wide dynamic range under low-illumination conditions. Under the condition of low illumination, exposure is an important parameter that affects the image quality of the camera. Auto-exposure aims to obtain the information of target scene in advance and uses it as the basis for determining exposure parameters, which can avoid the loss of image information caused by overexposure or underexposure. The auto-exposure of TDI CCD is usually difficult, as the shooting mode of TDI CCD is a push and sweep mode, which can only take one image of the target scene, and the output image is not repetitive, so it is difficult to obtain an image of the target scene in advance. At present, the common method is to add an additional sensor to collect the feature information of the target scene in advance; however, this increases the complexity of the system. Therefore, this paper proposes that the camera uses TDI CCD to collect the first N rows of data of the target scene in advance as the basis, and then adjusts and determines the exposure parameters with the median gray value as the target; thus, without adding additional sensors, the auto-exposure of TDI CCD can be realized. To evaluate the effect of auto-exposure, three methods of image power spectrum variance, image histogram, and image information entropy are used. The test results show that, after auto-exposure, the variance in the image power spectrum increased by 0.4362, the entropy of image information increased by 1.7064, and the distribution of the image histogram was more uniform than that before auto-exposure. This shows that the effect of auto-exposure is good, and it has better scene adaptation, allowing for it to meet the requirements of auto-exposure imaging under low-illumination conditions in aerial TDI cameras.

Мethod for determining the level of similarity of images by color component based on correlation analysis and color space quantization

The color component of an image is the basic information about the image. Color determines the mood of an image, its concept, and can also serve as a semantic accent. Obtaining information about the color component of an image is necessary when solving various kinds of tasks. For example, the task of finding similar images according to the content, as information about the color component can be combined with other metadata of the image and more fully describe its concept. In this paper authors describe the physical representation of color, the most popular digital representations of color, ways of extracting color components of an image, such as color histograms, quantization, coherent vectors and K -means, and ways of comparing color components of images, such as Euclidean distance, cosine distance, Manhattan distance, Chebyshev distance, Jaccard index, correlation analysis, Pearson's agreement test and Bhattacharya distance. It is suggested that the use of histogram comparison and vector comparison methods is acceptable for comparing the color components of images, since color histograms can be written in vector form. The authors conduct an experiment in which a human expert reviews combinations of color component acquisition and comparison methods and completes a test protocol based on the criteria highlighted by the authors. The criteria were based on the human interpretability of the results of the methods of determining and comparing the color component. On the basis of the experiment it has been determined that the best of the methods of comparison of color components of the definition images considered in the article is the correlation analysis of color histograms of images, which have been extracted in a quantized color space, because this approach meets all the criteria for testing and keeps the greatest amount of information about the color component.

Abstract 351: Novel Quantitative Imaging Biomarkers From Acute Brain CT Using Artificial Intelligence for Predicting Neurological Prognosis in Cardiac Arrest Patients: A Pilot Study

Background: Effective use of information extracted from medical images has attracted attention for decision support in primary care of patients with cardiac arrest (CA). Hypothesis and Aims: This study aimed to predict the neurological prognosis of patients with cardiac arrest by quantitative imaging biomarkers (QIBs) extracted from acute CT brain images before admission using artificial intelligence. Methods: Eighty-six CA patients (good prognosis; Glasgow-Pittsburgh cerebral performance categories [CPC]=1 or 2 at 90 days after the CA : 32 patients, poor prognosis; CPC=3, 4, or 5 at 90 days after the CA : 54 patients) treated at three hospitals between 2017 and 2019 were retrospectively analyzed. None of the CA patients caused by trauma or brain damage were included in the database. The data were divided into training and test data, and the training data were used to select informative QIBs for classification between the good and poor neurological prognosis in this analysis. Results: Seven CA patients’ data were excluded from the study due to severe metal and bone artifacts broadly spreading on the brain image, and 1170 QIBs were extracted from whole brain and regional brain interest volumes on CT images of each patient. The QIBs were composed of first-order histogram and texture features and informative QIBs for the classification were selected by feature selection algorithms using the training dataset. The feature corresponding to the gray level having a maximum gradient in the image histogram from the brain region of interest on CT images showed the strongest significant difference between the good and poor neurological prognosis of the test data set with p=0.009 and AUC=0.775 (95% CI; 0.590-0.960). Conclusions: We have shown that QIB can be used to predict neurological prognosis in patients with CA. Because this is a pilot study for methodological development and there are limitations in sample size and outcomes, new studies on a larger number of patients are required.

Tracking of moving human in different overlapping cameras using Kalman filter optimized

Tracking objects is a crucial problem in image processing and machine vision, involving the representation of position changes of an object and following it in a sequence of video images. Though it has a history in military applications, tracking has become increasingly important since the 1980s due to its wide-ranging applications in different areas. This study focuses on tracking moving objects with human identity and identifying individuals through their appearance, using an Artificial Neural Network (ANN) classification algorithm. The Kalman filter is an important tool in this process, as it can predict the movement trajectory and estimate the position of moving objects. The tracking error is reduced by weighting the filter using a fuzzy logic algorithm for each moving human. After tracking people, they are identified using the features extracted from the histogram of images by ANN. However, there are various challenges in implementing this method, which can be addressed by using Genetic Algorithm (GA) for feature selection. The simulations in this study aim to evaluate the convergence rate and estimation error of the filter. The results show that the proposed method achieves better results than other similar methods in tracking position in three different datasets. Moreover, the proposed method performs 8% better on average than other similar algorithms in night vision, cloud vision, and daylight vision situations.

Enhanced CT combined with texture analysis for differential diagnosis of pleomorphic adenoma and adenolymphoma

ObjectiveThis study sought to evaluate the worth of the general characteristics of enhanced CT images and the histogram parameters of each stage in distinguishing pleomorphic adenoma (PA) and adenolymphoma (AL).MethodsThe imaging features and histogram parameters of preoperative enhanced CT images in 20 patients with PA and 29 patients with AL were analyzed. Tumor morphology and histogram parameters of PA and AL were compared. Area under the curve (AUC), sensitivity, and subject operational feature specificity (ROC) analysis were used to determine the differential diagnostic effect of single-stage or multi-stage parameter combinations.ResultsThe difference in CT value and net enhancement value of arterial phase (AP) were significant (p < 0.05); Flat sweep phase (FSP), AP mean, percentiles, 10th, 50th, 90th, 99th and arterial period variance and venous phase (VP) kurtosis in the nine histogram parameters of each period (p < 0.05). An analysis of the ROC curve revealed a maximum area beneath the curve (AUC) in the 90th percentile of FSP for a single-parameter differential diagnosis to be 0.870. The diagnostic efficacy of the mean value of FSP + The 90th percentile of AP + Kurtosis of VP was the best in multi-parameter combination diagnosis, with an AUC of 0.925, and the sensitivity and specificity of 0.900 and 0.850, respectively.ConclusionThe histogram analysis of enhanced CT images is valuable for the differentiation of PA and AL. Moreover, the combination of single-stage parameters or multi-stage parameters can improve the differential diagnosis efficiency.

Skeleting of Low-Contrast Noisy Halftone Images

The problem of forming the skeletons of halftone images with two-mode brightness histograms under conditions of changing contrast and noise is considered. On such histograms, one mode corresponds to the objects, and the other to the background. Thanks to this feature, images are relatively easy to binarize and then skeletonize. The skeleton of a region uniform in brightness is a set of thin (limited by one-pixel) connected lines enclosed within this region and compactly describing its structure. Under conditions of high contrast and low noise on the original halftone image, binary skeletonization algorithms are widely used. They are relatively simple and can be resistant to multiplicative noise that appears at the boundaries of the regions after binarization. However, when the contrast is reduced and the noise of the original halftone image is increased, the skeletons formed by such algorithms are destroyed under the influence of additive noise, which manifests itself in the depth of the regions of the skeletonized binary image. To reduce skeletonization errors in such cases, algorithms based on preliminary low-pass filtering of the original grayscale image are used. To increase the stability of the skeletons of halftone images with a two-mode brightness histogram to noise, the article proposes a skeletonization model that takes into account the presence of multiplicative and additive noise components in a binary skeletonized image. Taking this model into account, a skeletonization algorithm has been developed, which takes into account the distortions in the shapes of the areas of the skeletonized binary image as a result of low-frequency filtering of the original halftone image and allows to reduce errors in the skeletonization of halftone images.

Evaluation of histogram image defogging methods based on histogram equalization, dark channel prior, and convolutional neural network

Air pollution condition is getting worse with the advancement of society development, environmental pollution has gradually intensified, and smog frequently occurs in more and more cities. On foggy day, the saturation and contrast of an image could be low, and colors tend to drift and distortion. As a result, seeking a simple and effective image de-fogging technique is important for the subsequent research. In this study, three existing classical de-fogging algorithms are reproduced: histogram equalization, dark channel prior method, and convolutional neural network. The three de-fogging algorithms were compared respectively under the conditions of thin fog, thick fog, high brightness, and low brightness, so as to analyze their advantages and disadvantages. It is concluded that there is no obvious difference among the three algorithms in the de-fogging effect under the conditions of thick fog and high brightness, but relatively speaking, the de-fogging image generated by the dark channel prior is more real. When the fog is thin, the dark channel prior and convolutional neural network work better. Under the condition of low brightness, the histogram equalization has a better de-fogging effect.

A CRNN‐based method for Chinese ship license plate recognition

AbstractExisting deep learning methods cannot achieve satisfactory ship license plate (SLP) recognition due to the harsh marine environment, such as foggy weather, unstable ship state and small targets. Therefore, a convolutional recurrent neural network (CRNN)‐based method is proposed for accurate SLP image recognition. Overall, the suggested method improves a CRNN recognition model by SLP image enhancement and data augmentation. The SLP image enhancement employs dark channel prior and Hough transform line detector to address the fog/blurriness and tilt issues existing in SLP images. As separate and joint operations, the two enhancements contribute to data augmentation for CRNN recognition. Preprocessing algorithms, including adaptive histogram equalization and image edge padding, are used to improve and unify the enlarged dataset for augmenting the CRNN model. As a final step, correction of the CRNN recognition results is made according to the character rule of SLPs, using an edit‐distance algorithm to match against a pre‐established SLP dictionary. A variety of real SLP images were collected to build an SLP image dataset for verification. The experimental results indicate that our method can reach an SLP recognition accuracy of , which is significantly superior to other text‐based deep learning methods.

Classification Algorithm of 3D Pattern Film Using the Optimal Widths of a Histogram

A recent development in marketing tools aimed at boosting sales involves the use of three-dimensional pattern films. These films are designed to captivate customers’ interest by applying 3D patterns to the surface of products. However, ensuring the quality of these produced 3D films can be quite challenging due to factors such as low contrast and unclear layout. Furthermore, there has been a shortage of research on methods for evaluating the quality of 3D pattern films, and existing approaches have often failed to yield satisfactory results. To address this pressing issue, we propose an algorithm for classifying 3D pattern films into either ‘good’ or ‘bad’ categories. Unlike conventional segmentation algorithm or edge detection methods, our proposed algorithm leverages the width information at specific heights of the image histograms. The experimental results demonstrate a significant disparity in histogram shapes between good and bad patterns. Specifically, by comparing the widths of all images at the quintile of the histogram height, we show that it is possible to achieve a 100% accuracy in classifying patterns as either ‘good’ or ‘bad’. In comparative experiments, our proposed algorithm consistently outperformed other methods, achieving the highest classification accuracy.

Research and application of Big data encryption technology based on quantum lightweight image encryption

Given the limitations of current classic image encryption algorithms and the unconditional security characteristics of quantum key distribution protocols, the author proposes an image encryption method based on quantum key distribution and pixel scrambling. The author first analyzed the security issues of quantum image encryption algorithms based on image scrambling methods, namely that image scrambling methods have periodicity, which can ensure that the image can be restored after scrambling. If an image cannot be guaranteed to return to its original state after performing scrambling operations, legitimate users cannot obtain the secret information in the original image, and image scrambling loses its original meaning; However, the periodicity of image scrambling methods not only ensures that legitimate users can decrypt images, but also provides convenience for attackers to steal original image information. Secondly, it is pointed out that when evaluating and analyzing quantum image encryption algorithms, the evaluation indicators should include two parts: One is performance analysis, which is the analysis of the network complexity of quantum circuits. In quantum computing, the network complexity of quantum circuits is usually represented by the number of quantum basic logic gates. The second is security analysis, which mainly includes key analysis (including key space size and key sensitivity analysis), statistical analysis (including pixel correlation, histogram analysis, information fusion, and scatter plot distribution density analysis). Finally, experimental simulations have shown that the NPCR values of the plaintext and ciphertext images of the three images tend to 100%. The encrypted image histogram is evenly distributed, with very weak pixel correlation and high pixel change rate and information entropy.

Automated Disease Severity Measurement Using Image Processing

Apple orchards are vital to the agricultural industry, but they face constant threats from diseases like apple scab, powdery mildew, rust, and rot. Traditional methods for measuring disease severity in apple leaves are labour-intensive, subjective, and prone to errors, limiting their practicality for large-scale agriculture. This paper introduces an innovative approach leveraging image processing techniques to automate disease severity measurement in apple leaves, offering a transformative solution for orchard management. The research aims to provide an efficient and accurate method for assessing disease severity, addressing the critical need for precise disease management and improved crop yields. Current methods relying on visual inspections by human experts are inefficient and unreliable. In contrast, the proposed method automates the measurement process, saving time and resources, enabling frequent monitoring, and enhancing early disease detection. This automation, in turn, leads to more effective disease management and increased crop yield and quality. The methodology involves image preprocessing, grayscale conversion, histogram equalization, thresholding, and defect area extraction, ultimately resulting in a quantitative disease severity index. The study utilizes a diverse dataset of apple leaf images, allowing for robust model development capable of accurate disease detection under varying conditions. This research holds significant implications for disease management and crop yield optimization, with broader applicability across agriculture. It paves the way for precision agriculture by enabling data-driven decisions on disease control measures while reducing the environmental impact of chemical inputs. Furthermore, the image processing techniques developed here can be adapted to other crops and plant diseases, promising efficient and accurate disease severity measurement methods across agricultural domains.

Expression of Endothelin-1, Endothelin Receptor-A, and Endothelin Receptor-B in facial melasma compared to adjacent skin.

Although melasma is highly prevalent, its pathogenesis is not yet fully understood. In the skin, endothelin-1 (ET-1) is primarily produced by keratinocytes in response to UVB exposure, which is mediated by an increase in IL-1α or reactive oxygen species. ET-1 plays a role in melanogenesis by binding to specific receptor B (ERB) or receptor A (ERA). However, the expression of ET-1, ERA, and ERB in melasma has not been systematically investigated. The objective of this study was to evaluate the expression of ET-1, ERA, and ERB in facial melasma compared to the adjacent unaffected skin. Cross-sectional study, with 40 skin samples (20: facial melasma; 20: adjacent unaffected skin) from women with facial melasma without treatment for 30 days except for sunscreen. A triple staining immunofluorescence technique was performed for anti-vimentin, DAPI, plus one of the following antibodies: (a) anti-ET1, (b) anti-ERA; (c) anti-ERB. Interfollicular areas on the slides of each topography (melasma; unaffected skin) were photographed in triplicate under confocal laser microscopy. The mean staining intensities of the image histograms (0-255 pixels intensity) were estimated for different types of cells (suprabasal keratinocytes, basal layer, and upper dermis) and were blindly compared between topographies. The mean (SD) age of the participants was 44.9 (9.2). The expression of ET-1 was increased in the whole epidermis with melasma when compared to the adjacent skin, being 32.8% (CI95% 14.7%-52.6%) higher in the spinous layer (p=0.013), 30.4% (CI95% 13.7%-47.9%) higher in the basal layer (p=0.014), and 29.7% (CI95% 11.4%-49.7%) higher in the melanocytes (p=0.006). There was no noticeable expression of ET-1 within the cells on the upper dermis. Neither ERA nor ERB resulted in differential epidermal expression between melasma and unaffected skin (p≥0.1). ET-1 is expressed more intensely on the epidermis from the skin with facial melasma compared to the unaffected adjacent skin.

Gumbel (EVI)-Based Minimum Cross-Entropy Thresholding for the Segmentation of Images with Skewed Histograms

In this study, we delve into the realm of image segmentation, a field characterized by a multitude of approaches; one frequently used technique is thresholding-based image segmentation. This process divides intensity levels into different regions based on a specified threshold value. Minimum Cross-Entropy Thresholding (MCET) stands out as an independent objective function that can be applied with any distribution and is regarded as a mean-based thresholding method. In certain cases, images exhibit diverse structures that result in different histogram distributions. Some images possess symmetric histograms, while others feature asymmetric ones. Traditional mean-based thresholding methods are well-suited for symmetric image histograms, relying on Gaussian distribution definitions for mean estimations. However, in situations involving asymmetric distributions, such as left and right-skewed histograms, a different approach is required. In this paper, we propose the utilization of a Maximum Likelihood Estimation (MLE) of Gumbel’s distribution or Extreme Value Type I (EVI) distribution for the objective function of an MCET. Our goal is to introduce a dedicated image-thresholding model designed to enhance the accuracy and efficiency of image-segmentation tasks. This model determines optimal thresholds for image segmentation, facilitating precise data analysis for specific image types and yielding improved segmentation results by considering the impact of mean values on thresholding objective functions. We compare our proposed model with original methods and related studies in the literature. Our model demonstrates better performance in terms of segmentation accuracy, as assessed through both unsupervised and supervised evaluations for image segmentation.