Binarization Method Research Articles

Increased choroidal stromal area in patients with active Graves’ ophthalmopathy based on binarisation method of optical coherence tomographic images

ObjectiveTo investigate the change in choroidal components of patients with Graves’ ophthalmopathy (GO) with different degrees of disease activity and severity by using the image binarisation method of optical coherence...

K-means 알고리즘을 기반으로 한 야오족 자수 색상 추출 및 가상 캐릭터 의상디자인 응용

To promote the digital preservation and transmission of traditional cultural elements, this study focuses on Yao embroidery as a case study, combining digital technologies and algorithms to extract and analyze its color characteristics. These extracted color features were applied to the design of virtual character clothing. First, a bilateral filtering algorithm was used to preprocess the images of Yao embroidery, reducing noise while preserving intricate details. Subsequently, Euclidean distance was calculated in the Lab color space, and the OSTU adaptive binarization method was employed to separate the image from its background. Additionally, K-means clustering was applied multiple times to the color data, and the optimal result was achieved when the number of clusters was set to eight. Through this process, eight major colors of Yao embroidery were extracted. Based on these extracted colors, 3D point cloud analysis was conducted to study the three-dimensional spatial distribution of color points, and a color combination model based on hue, brightness, and saturation was constructed. The results revealed that the color composition of Yao embroidery predominantly consists of high-saturation colors, with strong color contrasts and distinct ethnic characteristics. Ultimately, this study demonstrated the applicability of integrating the color model and design patterns of Yao embroidery into virtual character clothing design, showcasing the potential for incorporating traditional embroidery into digital design. This contributes to the preservation and promotion of intangible cultural heritage and serves as a valuable reference for further research on the color systems of traditional ethnic costumes.

Evaluation of Binarization Methods for Hyperspectral Samples of 16th and 17th Century Family Trees

Evaluation of Binarization Methods for Hyperspectral Samples of 16th and 17th Century Family Trees

MDD brain network analysis based on EEG functional connectivity and graph theory

BackgroundExisting studies have shown that the brain network of major depression disorder (MDD) has abnormal topologies. However, constructing reliable MDD brain networks is still an open problem. New methodThis paper proposed a reliable MDD brain network construction method. First, seven connectivity methods are used to calculate the correlation between channels and obtain the functional connectivity matrix. Then, the matrix is binarized using four binarization methods to obtain the EEG brain network. Besides, we proposed an improved binarization method based on the criterion of maximizing differences between groups: the adaptive threshold (AT) method. The AT can automatically set the optimal binarization threshold and overcome the artificial influence of traditional methods. After that, several network metrics are extracted from the brain network to analyze inter-group differences. Finally, we used statistical analysis and Fscore values to compare the performance of different methods and establish the most reliable method for brain network construction. ResultsIn theta, alpha, and total frequency bands, the clustering coefficient, global efficiency, local efficiency, and degree of the MDD brain network decrease, and the path length of the MDD brain network increases. Comparison with existing methodsThe results show that AT outperforms the existing binarization methods. Compared with other methods, the brain network construction method based on phase-locked value (PLV) and AT has better reliability. ConclusionsMDD has brain dysfunction, particularly in the frontal and temporal lobes.

Binary Graph Convolutional Network With Capacity Exploration.

The current success of Graph Neural Networks (GNNs) usually relies on loading the entire attributed graph for processing, which may not be satisfied with limited memory resources, especially when the attributed graph is large. This paper pioneers to propose a Binary Graph Convolutional Network (Bi-GCN), which binarizes both the network parameters and input node attributes and exploits binary operations instead of floating-point matrix multiplications for network compression and acceleration. Meanwhile, we also propose a new gradient approximation based back-propagation method to properly train our Bi-GCN. According to the theoretical analysis, our Bi-GCN can reduce the memory consumption by an average of ∼31x for both the network parameters and input data, and accelerate the inference speed by an average of ∼51x, on three citation networks, i.e., Cora, PubMed, and CiteSeer. Besides, we introduce a general approach to generalize our binarization method to other variants of GNNs, and achieve similar efficiencies. Although the proposed Bi-GCN and Bi-GNNs are simple yet efficient, these compressed networks may also possess a potential capacity problem, i.e., they may not have enough storage capacity to learn adequate representations for specific tasks. To tackle this capacity problem, an Entropy Cover Hypothesis is proposed to predict the lower bound of the width of Bi-GNN hidden layers. Extensive experiments have demonstrated that our Bi-GCN and Bi-GNNs can give comparable performances to the corresponding full-precision baselines on seven node classification datasets and verified the effectiveness of our Entropy Cover Hypothesis for solving the capacity problem.

A Novel Multi-Stage Recognition System in Handwritten Chinese Characters

Due to the various individual handwriting styles and diverse structures of Chinese characters, it is rather intricate to recognize handwritten Chinese characters precisely. In this paper, a muti-stage system that combines a Differentiable Binarization Network (DBNet) for text detection and Scene Text Recognition with a Single Visual Model (SVTR) for effective character recognition is developed, aiming to offer a potential solution for correct identification of handwritten Chinese characters in real-world scenarios. More specifically, the proposed system utilized the CASIA-HWDB2.x dataset from the Institute of Automation of the Chinese Academy of Sciences to train text detection and recognition models. For the detection, the Differentiable Binarization method in the Head Network applies a dynamic threshold map for text-background differentiation. On top of the SVTR employed as a recognition module, this research proposed a performance-optimized PP-LCNet architecture and the Guided Training of Continuous Time-Series Classifiers (GTC) with TextConAug, which is a data augmentation strategy to accommodate real-world scenarios. Briefly, DBNet and SVTR are integrated to form an end-to-end system. The output of DBNet after undergoing cropping and direction classifier, guides SVTR for a comprehensive system, ensuring seamless detection and recognition of handwritten Chinese characters. Experimental results indicate that this system achieves a promising performance and can provide accurate information on handwritten Chinese characters.

Histogram-based global thresholding method for image binarization

One of the most fundamental issues in image processing is the thresholding (binarization) method. This method is generally used for segmenting regions with different homogeneity in grayscale images. In other words, it performs clustering based on the intensity levels of pixels in an image histogram. This paper presents a new and effective approach to the global thresholding method of grayscale images. In the proposed method, alpha and beta regions are determined using the mean and standard deviation values of an image histogram. The optimum threshold value is obtained by calculating the average of gray-scale values of the alpha and beta regions. The experiments were carried out on three different image sets to demonstrate the effectiveness of the thresholding method. The result of experimental studies show that the proposed method achieves promising performance compared to many traditional, state-of-the-art thresholding and document binarization methods performed in H-DIBCO'14 (Document Image Binarization Competition), Human HT29 colon-cancer cells (BBBC008) and C. elegans live/dead assay (BBBC010) datasets based on various evaluation criteria.

Intelligent Identification and Quantitative Characterization of Pores in Shale SEM Images Based on Pore-Net Deep-Learning Network Model

Among the various shale reservoir evaluation methods, the scanning electron microscope (SEM) image method is widely used. Its image can intuitively reflect the development stage of a shale reservoir and is often used for the qualitative characterization of shale pores. However, manual image processing is inefficient and cannot quantitatively characterize pores. The semantic segmentation method of deep learning greatly improves the efficiency of image analysis and can calculate the face rate of shale SEM images to achieve quantitative characterization. In this paper, the high-maturity shale of the Longmaxi Formation in the Changning area of Yibin City, Sichuan Province, and the low-maturity shale of Beibu Gulf Basin in China are studied. Based on the Pore-net network model, the intelligent identification and quantitative characterization of pores in shale SEM images are realized. The pore-net model is improved from the U-net deep-learning network model, which improves the ability of the network model to identify pores. The results show that the pore-net model performs better than the U-net model, FCN model, DeepLab V3 + model, and traditional binarization method. The problem of low accuracy of the traditional pore identification method is solved. The porosity of SEM images of high-maturity shale calculated by the pore-net network model is between 12 and 19% deviation from the experimental data. The calculated porosity of the SEM image of the low-maturity shale has a large deviation from the experimental data, which is between 14 and 47%. Compared with the porosity results calculated by other methods, the results calculated by pore-net are closer to the true value, which proves that the porosity calculated by the pore-net network model is reliable. The deep-learning semantic image segmentation method is suitable for pore recognition of shale SEM images. The fully convolutional neural network model is used to train the manually labeled shale SEM images, which can realize the intelligent recognition and quantitative characterization of the pores in the shale SEM images. It provides a certain reference value for the evaluation of shale oil and gas reservoirs and the study of other porous materials.

A binarization method for logos

In the field of digital image processing, the binarization of logo images presents a significant challenge due to the diversity in their color distribution and graphical structures. Logos, as distinctive visual symbols, are integral to brand recognition and commercial advertising. Traditional binarization methods often struggle with this diversity, leading to difficulties in preserving the original color and details of the images. To address this issue, this paper introduces a novel binarization approach specifically designed for logo images. The method comprises three stages: preprocessing, binarization, and noise removal. In the binarization stage, the Otsu method is adapted from a global to a local thresholding technique, combining both global and local thresholds for each pixel. The efficacy of this method is demonstrated through comparative experiments with logos of varying colors and structures, showing superior performance in maintaining image color and detail restoration over traditional methods. Additionally, the preprocessing and noise removal steps further enhance the method's effectiveness in handling logos with rich background colors and complex details. Overall, this approach proves to be highly adaptable across a wide range of logo types.

BiPFT: Binary Pre-trained Foundation Transformer with Low-Rank Estimation of Binarization Residual Polynomials

Pretrained foundation models offer substantial benefits for a wide range of downstream tasks, which can be one of the most potential techniques to access artificial general intelligence. However, scaling up foundation transformers for maximal task-agnostic knowledge has brought about computational challenges, especially on resource-limited devices such as mobiles. This work proposes the first Binary Pretrained Foundation Transformer (BiPFT) for natural language understanding (NLU) tasks, which remarkably saves 56 times operations and 28 times memory. In contrast to previous task-specific binary transformers, BiPFT exhibits a substantial enhancement in the learning capabilities of binary neural networks (BNNs), promoting BNNs into the era of pre-training. Benefiting from extensive pretraining data, we further propose a data-driven binarization method. Specifically, we first analyze the binarization error in self-attention operations and derive the polynomials of binarization error. To simulate full-precision self-attention, we define binarization error as binarization residual polynomials, and then introduce low-rank estimators to model these polynomials. Extensive experiments validate the effectiveness of BiPFTs, surpassing task-specific baseline by 15.4% average performance on the GLUE benchmark. BiPFT also demonstrates improved robustness to hyperparameter changes, improved optimization efficiency, and reduced reliance on downstream distillation, which consequently generalize on various NLU tasks and simplify the downstream pipeline of BNNs. Our code and pretrained models are publicly available at https://github.com/Xingrun-Xing/BiPFT.

Automatic Detection of Cast Billet Dendrite Based on Improved Hough Transform

Primary dendrite information is one of the most important metrics to measure the quality of continuous cast slabs. The contrast of low magnification images is very low under the influence of illumination and sampling devices, so the traditional dendrite detection method has the problem of missed detections. We propose an automatic dendrite detection method based on an improved Hough transform, which effectively improves the accuracy and efficiency of primary dendrite detection. By using the local grayscale features of the image, a genetic algorithm-based local contrast enhancement algorithm is proposed. Compared with the traditional contrast enhancement algorithm, it can retain all the information of the dendrites. Combined with the image binarization method based on Hessian matrix, we can obtain more detailed information about the dendrites. According to the continuity and solidification characteristics of dendrites, the Hough transform is improved to extract dendrite information, which effectively reduces the computational cost of the Hough transform. The experimental results show that the method of this paper has versatility, and the error is four pixels compared with the manual method, which can provide a reliable basis for the subsequent judgement of the quality of cast billets.

Pore characterization was achieved based on the improved U-net deep learning network model and scanning electron microscope images

The SEM image method is commonly used in the qualitative characterization of shale pores. The development of shale micro-reservoir pores can be visually observed through SEM images, but the efficiency of manual image processing is low and subjective. The introduction of deep learning greatly improves the efficiency of pore analysis. In this paper, the argon ion polishing SEM image of Longmaxi Formation shale in southern Sichuan is taken as an example. Intelligent identification and quantitative characterization of pores in shale SEM images are realized by Pore-net network model. Pore-net is based on the U-net network model. The way the model reads the data is changed so that the model does not focus on the region of interest. The number of convolutional layers of the model is increased. The Canny edge extraction algorithm is added. It not only reduces the workload of data set production, but also enhances the ability of network model to identify pores. The results show that the deep learning semantic image segmentation method is suitable for pore recognition of shale SEM images. The full convolutional neural network model is used to train the manually labeled shale SEM images, which can realize the intelligent recognition and quantitative characterization of shale SEM images. Compared with FCN and DeepLab V3+ network model, Pore-net performs better. Only 170 data sets are used to train the model. The Pore-net network model still has a good recognition effect on pores, which solves the problem of low accuracy of traditional pore recognition methods. The deviation between the porosity calculated by the Pore-net network model and the experimental data is between 12% and 19%. Compared with the porosity results calculated by the binarization method and other network models, the results calculated by Pore-net are closer to the real values, which proves that the porosity calculated by the Pore-net network model is reliable.

Image Segmentation Solutions for Improved Non-Cooperative Target Recognition

The number of failed satellites in space is increasing, and they need to be fueled through space docking to extend their life or clean them out of orbit. Visual measurement is widely used in the docking session because of its real-time performance and economy, but the collected images will have background interference, which directly leads to the degradation of the accuracy of visual measurement. Reduce the difficulty of subsequent image processing by solving these problems. For the non-cooperative target of the star-arrow docking ring and the solar sail panel, which are two significant targets, this paper is based on target detection, according to the image difference, binarization, morphological operations and other methods to complete the accurate segmentation of the two targets, which can be more accurately complete the target detection, and the image segmentation effect is obvious. The target image obtained by image segmentation does not contain background interference, which is convenient for feature extraction and matching of the image.

A novel CNN framework for skin disease classification using adaptive percentage filter for image binarization and fast-marching inpainting method

A novel CNN framework for skin disease classification using adaptive percentage filter for image binarization and fast-marching inpainting method

Numerical studies on a proposed stepwise binarization method for the topology optimization analysis of the sloshing control problem

Numerical studies on a proposed stepwise binarization method for the topology optimization analysis of the sloshing control problem

Spatial–Temporal Binarization Method via Jointly Optimizing Diffusion Kernel and Quantization Threshold for 3-D Surface Imaging

Spatial–Temporal Binarization Method via Jointly Optimizing Diffusion Kernel and Quantization Threshold for 3-D Surface Imaging

Analysis and improvement of methods and means for eliminating distortions in image and video signals

The main purpose of this article was to study existing image processing methods, binarization and noise reduction, and to develop an improved adaptive thresholding algorithm. The analysis of methods and tools for eliminating distortions in image and video signals is an urgent task in numerous fields, including medicine, computer vision, and document science. The article discusses in detail the existing binarization and noise reduction methods, highlighting their advantages and limitations. However, the main achievement is the development and implementation of an improved adaptive thresholding algorithm. This algorithm considers the specific features of the image and automatically adapts the binarization threshold for better processing quality. It is a significant contribution to the field of image processing and can be used in various fields, including medical diagnostics and visual object detection in images.

The Influence of Seasonal Allergic Conjunctivitis and Its Treatment on Choroidal Vascular Index

Purpose: To evaluate the the effects of seasonal allergic counjunctivitis (AC) and its treatment upon choroidal structure such as choroidal thickness (ChT) and choroidal vascular index (CVI) through the use of topical antihistamine agents. Methods. The 60 eyes of 30 patients were included in the AC group. Another 30 patients were included in the control group. The choroid was imaged by using enhanced depth imaging optical coherence tomography (EDI-OCT) instrument without pupillary dilation. ChT was defined as the area between the outer hyperreflective border of the RPE and the sclerochoroidal border at the fovea, 750 µm temporal to the fovea and 750 µm nasal to the fovea. Image J was used to measure CVI. With the images obtained from EDI-OCT, the total choroidal area (TCA), luminal area (LA), stromal area (SA), and CVI were calculated using the binarization method. Results. The mean ChT value in the AC group was 358.5 ± 93.8 µm at baseline and 356.8 ± 86.6 µm following 1 month of treatment. Meanwhile, the mean ChT in the control group was 316.6 ± 60.7 µm. The mean CVI value was 66.65 ± 2.98 in the control group, 70.75 ± 3.26 in the AC group at baseline, and 69.50 ± 3.17 following 1 month of treatment. Statistically significant difference was tracked between control and AC group (p = 0.028) and control group and posttreatment values (p=0,031). There were no statistically significant difference between initial AC treatment values and posttreatment values for all of the measurements. Conclusion. ChT and CVI can increase in patients with AC due to inflammation and increased vascular permeability. Although symptoms and signs related to AC may disappear after the treatment, effects in the choroid do not immediately normalize.

Quantification of lithium deposition under mechano-electrochemical coupling effect

The non-uniform growth of lithium dendrites is the main factor causing internal short-circuits and thermal runaway in lithium-ion batteries. Due to the internal environment of the battery being a complex multi-field coupling, the mechanisms of triggering lithium deposition are still unclear. Here, a cross-sectional in-situ optical microscope device was fabricated to observe lithium dendrite evolution. The amount of irreversible and reversible lithium was quantified by the image binarization method and titration gas chromatography, which is used to analyze the relationship between separator deformation, current density, and lithium deposition. The greater the separator deformation and current density, the more irreversible lithium increases. The pore defect mapping algorithm was developed to reconstruct a high-precision three-dimensional finite element model of the separator, which was used to quantify the relationship between displacement loading and the microstructure evolution. A three-dimensional lithium deposition model was established on the actual microstructural parameters of the separator, predicting the concentration and electrochemical field of lithium deposition processes. A phase diagram of lithium precipitated failure in lithium-ion batteries with mechano-electrochemical coupling was developed with silicon-graphite electrode. This work will provide a new direction for understanding the mechanism of lithium deposition and guidance for commercial battery design.

Filtering of Complex Signals Based on a Two-Level Fuzzy-Logic Model

Purpose of research. Development of a method and algorithm of complex analog radio signals filtering and binarization, such as the signal of Automatic dependent surveillance-broadcast (ADS-B), which allows to increase the sensitivity of the receiver of the AZN-B signal and increase the number of correctly detected received messages.Methods. To solve this problem, the basics of the theory of signal filtering and the theory of fuzzy sets were applied in the work. The proposed method is based on combining signal filtering by known filters and a two-level fuzzy model. The first and second levels of the fuzzy model contain three operations: automatic formation of membership functions, compositional output and defuzzification. Input variables of both levels are given by trapezoidal membership functions. At the first level, they are formed automatically depending on the characteristics of the complex signal. The output function at the first level is given by a singleton function, and defuzzification is carried out using a simplified center of gravity model.Results. The proposed algorithm was implemented in the developed device based on a programmable logic integrated circuit (FPGA). In addition to filtering, the developed device implements all signal processing functions, such as: receiving input data, decoding, checking the correctness of decoded data, storing them, transmitting ADS-B messages for further processing. A distinctive feature of the device is its small size and low power consumption, which allows use it in small spacecraft and unmanned aerial vehicles.Conclusion. A method of filtering complex signals based on a fuzzy logic model is considered, which can be used to filter complex signals, such as ADS-B messages in small spacecraft modules. The proposed implementation of the filtering method makes it possible to increase the sensitivity of the AZN-B signal receiver by 20% and correctly decode the received signal. The method was implemented by an FPGA-based device, which made it possible to reduce the size and power consumption compared to analogues.