Binary Image Research Articles

High-Precision Automated Soybean Phenotypic Feature Extraction Based on Deep Learning and Computer Vision.

The automated collection of plant phenotypic information has become a trend in breeding and smart agriculture. Four YOLOv8-based models were used to segment mature soybean plants placed in a simple background in a laboratory environment, identify pods, distinguish the number of soybeans in each pod, and obtain soybean phenotypes. The YOLOv8-Repvit model yielded the most optimal recognition results, with an R2 coefficient value of 0.96 for both pods and beans, and the RMSE values were 2.89 and 6.90, respectively. Moreover, a novel algorithm was devised to efficiently differentiate between the main stem and branches of soybean plants, called the midpoint coordinate algorithm (MCA). This was accomplished by linking the white pixels representing the stems in each column of the binary image to draw curves that represent the plant structure. The proposed method reduces computational time and spatial complexity in comparison to the A* algorithm, thereby providing an efficient and accurate approach for measuring the phenotypic characteristics of soybean plants. This research lays a technical foundation for obtaining the phenotypic data of densely overlapped and partitioned mature soybean plants under field conditions at harvest.

Development of an image binarization software tool for net occlusion estimations

Marine biofouling poses a set of challenges to the salmon aquaculture industry, where an accumulation of biota on pens can lead to significant net mesh occlusion. This can reduce flow rates, risking oxygen depletion. The industry currently manages this challenge through regular cleaning of nets, with sporadic manual visual estimations of net occlusion an important but time-consuming task. This study developed a simple automated desktop application to more regularly and more robustly quantify pen net occlusion caused by biofouling. This software application pre-processes and binarizes images collected from cameras currently used by the industry into water and non-water pixels. The percentage of net occlusion is then calculated from the binary image. Accurate binarization of representative images was achieved by training a deep learning network on images collected in situ. The resulting network attained a validation accuracy of 96.4 % and a mean test accuracy of 93.5 %. From the test images, 98.3 % of pixels annotated as non-water and 88.8 % of pixels annotated as water were correctly classified by the network. This automated tool has the capacity to better inform industry and create a more efficient cleaning framework based on the needs of individual pens, based on data that can be more readily obtained as compared to manual net inspections.

Lung cancer classification model using convolutional neural network with feature ranking process

Abstract Lung cancer is the leading cause of cancer-related deaths worldwide, highlighting the importance of early detection to improve patient outcomes. The goal of this study is to create a computer-aided diagnosis (CAD) system that detects and classifies lung cancer based on medical images using a Convolutional Neural Network (CNN) and feature extraction techniques. The study employs the LIDC-IDRI dataset, a comprehensive collection of lung cancer-related medical images, to achieve this goal. Contrast Limited Adaptive Histogram Equalization (CLAHE) is used to improve contrast and detail while reducing noise. The Sobel operator is used to refine the segmentation process by enhancing edges and contours in binary images. Morphological operations such as dilation and filling are used to refine the segmented regions even further. Feature extraction is used to extract statistical data and texture characteristics from segmented regions. Mean and variance calculations reveal information about brightness and variability within regions, whereas co-occurrence matrices and Gray-Level Co-occurrence Matrix (GLCM) properties quantify texture features. The correlation between different regions is also evaluated to assess their relationships. Using the pre-processed and ranked features as inputs, a CNN model with five hidden layers is trained. To classify the segmented regions into cancerous and non-cancerous classes, the model learns patterns and relationships in the data. A confusion matrix is used to assess the accuracy, specificity, and sensitivity of the model's predictions, with an emphasis on correctly identifying lung cancer-affected regions. The results show promising results, with the proposed CAD system identifying lung cancer-affected regions with an accuracy of 99.4375%. The system also outperforms other existing methods with a specificity of 99.12% and a sensitivity of 99.26%. These findings highlight the developed system's potential as a valuable tool for early lung cancer detection, assisting doctors in making accurate diagnoses and improving patient outcomes.

Fourier coded aperture transform rapid spectral imaging

The Fourier coded aperture transform hyperspectral imaging system (FCTS), which we proposed in our previous work, has excellent physical interpretability compared to other encoding methods. Due to the excellent energy concentration characteristic of Fourier encoding, it can achieve high-quality spectral imaging even under undersampling conditions. However, since the grayscale encoding method limits the system's sampling speed, we propose a binary sampling Fourier-encoded spectral imaging method. Besides, to address the significant errors of reconstructed spectral image at low sampling rates, we construct a spectral image enhancement network, which introduces spatial and channel attention modules to successively enhance the spatial and spectral information of the reconstructed spectral images. During the spectral image refinement and enhancement stage, band grouping is performed to alleviate the difficulty in feature extraction and make the training process more stable. The effectiveness of our proposed method has been validated on both public datasets and our own datasets. When the sampling times are reduced by an order of magnitude, on the three public datasets, the PSNR improved by 10.79 dB, the SSIM increased by 0.35, and the SAM decreased by 12.1° on average. On our own datasets, the PSNR improved by 12 dB, the SSIM increased by 0.24, and the SAM decreased by 6.5°.

The crack propagation behaviors, microstructure and mechanical properties of T-welded joints for TIGW with crystal plasticity model and XFEM

In view of the significant impact of welding defects on the fracture behaviors involving the strength, stress concentration and bearing capacity of welded joint, etc., the propagation behaviors and mechanical properties of welded joints with porosity and micro crack are deeply investigated using a multiscale method. In this work, a crack nucleation and propagation model based on crystal plasticity theory, combined with the extended finite element method (XFEM), is established for the T-welded joint. The maximum slip on the predominant slip system method is applied to predict the crack propagation path of pores and micro cracks in the weld zone (WZ), and the effect of crystal orientation on crack growth is explored. Then, a continuous model is used to analyze the micro and macro fracture behaviors near the weld under tensile load, combined with the maximum principal stress method. The WZ and heat affected zone (HAZ) are observed using electron backscatter diffraction (EBSD) to study the microstructure evolution. Considering grain boundaries, the image information of crystal morphology is processed through binary image analysis for FE modeling. The local mechanical properties testing is carried out using micro-specimens of HAZ and WZ to calibrate the crystal plastic parameters. The results show that the resolved shear stress of the predominant slip system of crack initiation and propagation elements is increased by pore and crack defects. The fracture positions of tensile specimens through numerical simulation are in good agreement with the macroscopic experimental results. It will provide an analysis basis for preventing fracture failure and improving the service performance of thin-walled structures in future.

The Application of Tsallis Entropy Based Self-Adaptive Algorithm for Multi-Threshold Image Segmentation.

Tsallis entropy has been widely used in image thresholding because of its non-extensive properties. The non-extensive parameter q contained in this entropy plays an important role in various adaptive algorithms and has been successfully applied in bi-level image thresholding. In this paper, the relationships between parameter q and pixels' long-range correlations have been further studied within multi-threshold image segmentation. It is found that the pixels' correlations are remarkable and stable for images generated by a known physical principle, such as infrared images, medical CT images, and color satellite remote sensing images. The corresponding non-extensive parameter q can be evaluated by using the self-adaptive Tsallis entropy algorithm. The results of this algorithm are compared with those of the Shannon entropy algorithm and the original Tsallis entropy algorithm in terms of quantitative image quality evaluation metrics PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity). Furthermore, we observed that for image series with the same background, the q values determined by the adaptive algorithm are consistently kept in a narrow range. Therefore, similar or identical scenes during imaging would produce similar strength of long-range correlations, which provides potential applications for unsupervised image processing.

Efficient compressed storage and fast reconstruction of large binary images using chain codes

AbstractLarge binary images are used in many modern applications of image processing. For instance, they serve as inputs or target masks for training machine learning (ML) models in computer vision and image segmentation. Storing large binary images in limited memory and loading them repeatedly on demand, which is common in ML, calls for efficient image encoding and decoding mechanisms. In the paper, we propose an encoding scheme for efficient compressed storage of large binary images based on chain codes, and introduce a new single-pass algorithm for fast parallel reconstruction of raster images from the encoded representation. We use three large real-life binary masks to test the efficiency of the proposed method, which were derived from vector layers of single-class objects – a building cadaster, a woody vegetation landscape feature map, and a road network map. We show that the masks encoded by the proposed method require significantly less storage space than standard lossless compression formats. We further compared the proposed method for mask reconstruction from chain codes with a recent state-of-the-art algorithm, and achieved between $$12\%$$ 12 % and $$33\%$$ 33 % faster reconstruction on test data.

A Mumford-Shah Model-based Method for Inpainting Markers in Ultrasound Images

Abstract During ultrasound diagnosis in clinics, clinicians often mark the lesion area, resulting in the presence of unnecessary objects in the collected ultrasound images. These markers potentially affect subsequent image analysis. To address this problem, we proposed an image inpainting method that combines image processing with Mumford-Shah algorithm to remove markers from ultrasound images. The proposed method consists of two parts. First, the input ultrasound images were processed by several image processing algorithms, including contrast enhancement, image binarization, edge detection, hole filling, and connected domain marking. These algorithms were combined to extract the mask of the manual marker in the ultrasound images. Second, the original ultrasound images and mask images were put into the Mumford-Shah model to implement ultrasound image inpainting by minimizing the following energy functional. The continuous iteration of Euler-Lagrange equation was employed to update the iteration item. If the difference between the two output results was less than the pre-set threshold, the output result could be regarded as the repaired ultrasound image. Experimental results show that the designed image processing method accurately extracted the binary mask, whether they are colourful or binary, number or line in ultrasound images. Meanwhile, the improved Mumford-Shah model effectively implemented the ultrasound image inpainting, with the accuracy of 96.55%. Therefore, the proposed method can be used to remove unnecessary objects in ultrasound images, which is beneficial to improve the accuracy of the artificial intelligence model.

Morphology for hexagonal image processing: a comprehensive simulation analysis

<p>Morphological operators for binary and grayscale images are commonly used to eliminate noise, recognize contours or specific structures, and arrange shapes in image processing for physiological modeling and biomechanics applications. Even though morphology has been substantially developed in square-pixelbased-image-processing (SIP), no effort has been made to construct morphological operators in hexagonal-pixel-based-image-processing (HIP) yet. In this paper, we transform basic SIP-domain-morphological operators such as dilation, erosion, closing, and opening into HIP-domain and compare their performance with their SIP counterparts. It is the first time to give the fundamental morphological operators in the HIP domain. The operators developed in this paper initiate the research about morphology in the HIP domain by successfully filling a significant gap by eliminating HIP’s lack of basic operators, thus capable of producing enhanced images for better analysis in anatomical models related to biology and medicine research fields.</p>

Effect of raw material fineness on the properties of inorganic foam materials from solid waste

Solid waste inorganic lightweight materials not only compensate for the flammability of organic materials but also have a lower carbon footprint. They are environmentally safe and cost-effective compared with cement-based foaming materials, and they have good development prospects. This study aimed to improve the chemical foaming reaction kinetics by reducing the particle size of aluminum powder and alkaline solid waste, thereby increasing the expansion degree of solid waste slurry, to further reduce the absolute dry density of inorganic lightweight materials for solid waste and optimize their performance. The influence of the particle size of raw material on the absolute dry density, water absorption, and compressive strength of lightweight materials was investigated. The influence of the particle size of raw material on the macroscopic pore structure of foamed materials was analyzed through binarization of cross-sectional images of specimens. Furthermore, the hydration products and microstructure of materials were characterized using XRD, FTIR, TG-DTG, and SEM to provide a theoretical basis for related research. The experimental results showed that the smaller the particle size of the raw material, the easier it was to carbonize and the lower the flowability of the corresponding cementitious material slurry. Reducing the particle size of raw materials and aluminum powder increased the degree of slurry expansion, thereby reducing the absolute dry density of foaming materials. In addition, reducing the particle size of raw materials and using carbide slag wastewater instead of tap water increased the degree of foaming reaction and hydration reaction, which helped C-(A)-S-H gel formation and promoted the development of sample strength. Finally, the absolute dry density of inorganic lightweight materials was reduced to 0.35 g/cm3 at the minimum, and the corresponding 28-day compressive strength was 0.28 MPa, which met the requirements of the JG/T 266–2011 “foam concrete” specification.

Radar Based Secure Contactless Fall Detection Using Hybrid Optimizer with Convolution Neural Network

Introduction: senior citizens can lead to severe injuries. Existing wearable fall-alert sensors are often ineffective as seniors tend to avoid using them, highlighting the need for non-contact sensor applications in smart homes. This study proposes a CNN-based fall detection system using time-frequency analyses. A unique hybrid optimizer, GWO-ABC, combining Artificial Bee Colony (ABC) and Grey Wolf Optimizer (GWO), is employed to optimize CNN architectures. Radar return signals are transformed into spectrograms and binary images for training the HOCNN with fall and non-fall data.Methods: Radar signals are processed using short-time Fourier transformation to create time-frequency spectrograms, converted into binary images. These images are fed into a CNN optimized by the GWO-ABC algorithm. The CNN is trained on labelled fall and non-fall instances, focusing on high-level feature extraction.Results: The HOCNN showed superior accuracy in fall detection, successfully extracting critical high-level features from radar signals. Performance metrics, including precision, recall, and F1-score, demonstrated significant improvements over traditional methods.Conclusion: This study introduces a non-contact, automatic fall detection system for smart homes using GWO-ABC optimized CNNs, offering a promising solution for enhancing geriatric care and ensuring senior citizen safety. Index Terms—Grey Wolf Optimizer, Artificial Bee Colony algorithm, Convolutional neural network, fall detection, time-frequency analysis, ultra-wideband (UWB) radar

Automatic recognition system for concrete cracks with support vector machine based on crack features

Cracks are a common problem in concrete surfaces. With the continuous optimization of machine vision-based inspection systems, effective crack detection and recognition is the core of the entire system. In this study, support vector machine (SVM) was used to distinguish cracks from other regions. To complete the recognition system of the SVM, a framework consisting of an image processing and recognition model was proposed. An algorithm combining the Prewitt operator with the Otsu threshold was proposed for image segmentation. The binary image processed by the new algorithm combined with mathematical morphology can result in a more complete crack zone and fewer interference regions. After the initial parameter extraction, most of the impurity areas were screened by preliminary discrimination, removing 99% of the impurities. This processing step ensured the balance and effectiveness of the samples. To establish an automatic identification model based on SVM with a radial basis function, compactness, occupancy rate, and length–width ratio were selected as input parameters after comparing these three features with all six features of the crack. The recognition accuracy of this system reaches 97.14%, demonstrating that the proposed method is effective and satisfies practical requirements.

A Learning Dendritic Neuron-Based Motion Direction Detective System and Its Application to Grayscale Images.

In recent research, dendritic neuron-based models have shown promise in effectively learning and recognizing object motion direction within binary images. Leveraging the dendritic neuron structure and On-Off Response mechanism within the primary cortex, this approach has notably reduced learning time and costs compared to traditional neural networks. This paper advances the existing model by integrating bio-inspired components into a learnable dendritic neuron-based artificial visual system (AVS), specifically incorporating mechanisms from horizontal and bipolar cells. This enhancement enables the model to proficiently identify object motion directions in grayscale images, aligning its threshold with human-like perception. The enhanced model demonstrates superior efficiency in motion direction recognition, requiring less data (90% less than other deep models) and less time for training. Experimental findings highlight the model's remarkable robustness, indicating significant potential for real-world applications. The integration of bio-inspired features not only enhances performance but also opens avenues for further exploration in neural network research. Notably, the application of this model to realistic object recognition yields convincing accuracy at nearly 100%, underscoring its practical utility.

Unbalanced Data-Based Fault Diagnosis Method of Bearing Utilizing Time-Frequency DCGAN Processing

Aiming at the unbalanced datasets of bearing fault samples, a fault diagnosis method based on time-frequency conversion and processing of bearing vibration signals based on deep convolutional generative adversarial network (DCGAN) is proposed in this paper. Firstly, through short-time Fourier transform(STFT), the vibration signals are converted into time-frequency images, and then time-frequency images are input into DCGAN to expand the fault samples.Secondly, the expanded fault samples are evaluated for image quality through the comprehensive method of peak signal-to-noise ratio(PSNR) and structural similarity(SSIM). Thirdly, using the Canny edge detection algorithm to extract features of the time-frequency image, and using the obtained binary image as the feature.Finally, k-nearest neighbor algorithm is used for classification to testify the superiority of time-frequency DCGAN processing. The experimental results show that the expanded samples can effectively improve the imbalance of the samples and improve the accuracy of fault diagnosis of bearing.

Image analysis to evaluate removal of particles from fabric surface

We propose an objective method to quantify the solid-particle removal rate from fabric. The method extracts the ratio ( K/S) λ of light absorption K to scattering S at wavelength λ from fused digital images captured under a stereo photometric system in which illumination is from four directions. Three different white polyester fabrics were contaminated with iron oxide particles. Digital images of the fabrics were obtained before contamination, then before and after cleaning. The ( K/S) λ ratios extracted from images were used in a fabric-detergency formula to determine the solid-particle removal rate. Digital image acquisition conditions were optimized to minimize the effects of fabric structural factors. Our method was faster, more accurate, and cheaper than existing methods. Moreover, it is nondestructive and does not require a tracer. The average accuracy of the proposed method was improved by 44.77% compared with the existing surface reflectance method and by 42.51% compared with the binary image method. Moreover, the accuracy was further increased by calculating ( K/S) λ for a signal that corresponds to the color of the contaminant particles. This method can be used to quantify the effectiveness of self-cleaning textiles and garment-care machines.

An experimental analysis of outdoor UAV localisation through diverse estimators and crowd-sensed data fusion

Motivated by the challenge of achieving precise 3D outdoor localisation for unmanned aerial vehicles (UAVs) in global navigation satellite system (GNSS)-denied environments, this paper introduces an innovative technique. Integrating crowd-sensed data fusion to counter inertial navigation system (INS) drift during GNSS signal outages, the proposed method exploits diverse estimators to enhance its efficacy. A micro lightweight frequency modulated continuous wave (FMCW) radar mounted on the UAV captures ground scatterer reflections, processed via fast Fourier transform (FFT) to generate a range-Doppler map. This map facilitates forward velocity estimation during GNSS signal loss. This approach employs adaptive thresholding, image binarisation, and connected components-based techniques for target detection from a computer vision standpoint. The derived radar-based velocity fuses with magnetometer, barometer, and inertial measurement unit (IMU) data using diverse estimators like extended Kalman filter (EKF) and particle filter (PF). Real-time flight data evaluation and simulated outage periods using EKF and PF validate the outdoor localisation system. Experimental analyses demonstrate substantial improvements, enhancing 3D positioning accuracy by 99.89% and 99.83% for the initial and subsequent flights, respectively, leveraging PF to fortify the standalone INS mode during GNSS signal loss. This approach significantly enhances UAV localisation precision, particularly in challenging GNSS-denied scenarios, showcasing the potential for real-world applications.

A model is worth tens of thousands of examples for estimation and thousands for classification

Traditional signal processing methods relying on mathematical data generation models have been cast aside in favour of deep neural networks, which require vast amounts of data. Since the theoretical sample complexity is nearly impossible to evaluate, these amounts of examples are usually estimated with crude rules of thumb. However, these rules only suggest when the networks should work, but do not relate to the traditional methods. In particular, an interesting question is: how much data is required for neural networks to be on par or outperform, if possible, the traditional model-based methods? In this work, we empirically investigate this question in three simple examples covering estimation and classification, where the data is generated according to precisely defined mathematical models, and where well-understood optimal or state-of-the-art mathematical data-agnostic solutions are known. A first problem is deconvolving one-dimensional Gaussian signals, a second one is estimating a circle’s radius and location in random grayscale images of disks, and a third one both classifies the presence of a line and locates it when present in a binary random dot image. By training various networks, either naive custom designed or well-established ones, with various amounts of training data, we find that networks require tens of thousands of examples for estimation in comparison to the traditional methods and thousands for classification, whether the networks are trained from scratch or even with transfer-learning or finetuning.

H-QNN: A Hybrid Quantum–Classical Neural Network for Improved Binary Image Classification

Image classification is an important application for deep learning. With the advent of quantum technology, quantum neural networks (QNNs) have become the focus of research. Traditional deep learning-based image classification involves using a convolutional neural network (CNN) to extract features from the image and a multi-layer perceptron (MLP) network to create the decision boundaries. However, quantum circuits with parameters can extract rich features from images and also create complex decision boundaries. This paper proposes a hybrid QNN (H-QNN) model designed for binary image classification that capitalizes on the strengths of quantum computing and classical neural networks. Our H-QNN model uses a compact, two-qubit quantum circuit integrated with a classical convolutional architecture, making it highly efficient for computation on noisy intermediate-scale quantum (NISQ) devices that are currently leading the way in practical quantum computing applications. Our H-QNN model significantly enhances classification accuracy, achieving a 90.1% accuracy rate on binary image datasets. In addition, we have extensively evaluated baseline CNN and our proposed H-QNN models for image retrieval tasks. The obtained quantitative results exhibit the generalization of our H-QNN for downstream image retrieval tasks. Furthermore, our model addresses the issue of overfitting for small datasets, making it a valuable tool for practical applications.

Resnet50 and logistic Gaussian map-based zero-watermarking algorithm for medical color images

Medical image copyright protection is becoming increasingly relevant as medical images are used more frequently in medical networks and institutions. The traditional embedded watermarking system is inappropriate for medical images since it degrades the original images’ quality. Furthermore, medical-colored image watermarking options are constrained since most medical watermarking systems are built for gray-scale images. This paper proposes a zero-watermarking scheme for medical color image copyright protection based on a chaotic system and Resnet50, which is a convolutional neural network method. The network Resnet50 is used to extract features from the color medical image, and then a logistic Gaussian map is used to scramble these features and scramble the binary image. Finally, an exclusive OR operation is performed (scrambled binary image, scrambled features for the medical color image) to form a zero watermarking. The experimental result proves that our scheme is effective and robust to geometric and common image processing attacks. The BER values of the extracted watermarks are below 0.0039, and the NCC values are above 0.9942, while the average PSNR values of the attacked images are 29.0056 dB. Also, it is superior to other zero-watermark schemes for medical images in terms of robustness to conventional image processing and geometric attacks. Furthermore, the experimental results show that the Resnet50 model outperforms other models in terms of reducing the mean squared errors of the features between the attacked and original image.

An Enhanced Deep Learning-Based Pharmaceutical Crystal Detection with Regional Filtering

In the process of pharmaceutical crystallization, the automatic detection of crystal shapes in images is important since controlling the morphology of the crystals improves the quality of pharmaceutical crystals. In this paper, a novel image detection method called RECDet is proposed. It leverages an automatically adapted binary image to bypass background regions, thereby reducing the detection field. In addition, the method greatly reduces the training time while improving the detection accuracy by using a specially designed detection box for the crystal shape. The performance of our model is evaluated through experimental analysis on a publicly available glutamate crystal dataset and a self-made entecavir pharmaceutical crystal dataset. Experimental results show that RECDet improves the accuracy of prediction bounding boxes by more than 2% compared to other popular models and achieves a classification accuracy of 98%. It can be used as a promising tool in the application of pharmaceutical crystallization control.