Grayscale Image Processing Research Articles

Head tracking using an optical soft tactile sensing surface.

This research proposes a sensor for tracking the motion of a human head via optical tactile sensing. It implements the use of a fibrescope a non-metal alternative to a webcam. Previous works have included robotics grippers to mimic the sensory features of human skin, that used monochrome cameras and depth cameras. Tactile sensing has shown advantages in feedback-based interactions between robots and their environment. The methodology in this paper is utilised to track motion of objects in physical contact with these sensors to replace external camera based motion capture systems. Our immediate application is related to detection of human head motion during radiotherapy procedures. The motion was analysed in two degrees of freedom, respective to the tactile sensor (translational in z-axis, and rotational around y-axis), to produce repeatable and accurate results. The movements were stimulated by a robot arm, which also provided ground truth values from its end-effector. The fibrescope was implemented to ensure the device's compatibility with electromagnetic waves. The cameras and the ground truth values were time synchronised using robotics operating systems tools. Image processing methods were compared between grayscale and binary image sequences, followed by motion tracking estimation using deterministic approaches. These included Lukas-Kanade Optical Flow and Simple Blob Detection, by OpenCV. The results showed that the grayscale image processing along with the Lukas-Kanade algorithm for motion tracking can produce better tracking abilities, although further exploration to improve the accuracy is still required.

Creating terracotta panels through grayscale image processing and robotic hotwire cut molds

This research presents a method for fabricating relief terracotta panels, utilizing polystyrene molds shaped by robotic hotwire cutting. A crucial aspect of this work is the use of grayscale images as input for generating toolpaths for hotwire cutting, bridging the gap between digital image processing in computer graphics and the physical creation of architectural elements. The process integrates computational tools with fabrication constraints and material properties to generate an integrated design approach. Utilizing computational tools, we generate toolpaths that translate grayscale images into tangible tile patterns. These patterns are formed by ridges, designed to accurately depict the grayscale images while ensuring functional tiling. The methodology synergizes traditional clay handling techniques with digital modeling, providing crucial data for material handling during drying and baking stages. The research presents a significant contribution to the realm of architectural design and computer graphics. It demonstrates the potential of image processing techniques in conjunction with robotic fabrication for creating built environments. The final tiles’ appearance, evaluated using the peak signal-to-noise ratio (PSNR), exhibits values between 20 dB and 30 dB, aligning within the realms of acceptable image resemblance according to established image compression standards.

High-Noise Grayscale Image Denoising Using an Improved Median Filter for the Adaptive Selection of a Threshold

Grayscale image processing is a key research area in the field of computer vision and image analysis, where image quality and visualization effects may be seriously damaged by high-density salt and pepper noise. A traditional median filter for noise removal may result in poor detail reservation performance under strong noise and the judgment performance of different noise characteristics has strong dependence and rather weak robustness. In order to reduce the effects of high-density salt and pepper noise on image quality when processing high-noise grayscale images, an improved two-dimensional maximum Shannon entropy median filter (TSETMF) is proposed for the adaptive selection of a threshold to enhance the filter performance while stably and effectively retaining the details of the images. The framework of the proposed improved TSETMF algorithm is designed in detail. The noise in images is filtered by means of automatically partitioning a window size, the threshold value of which is adaptively calculated using two-dimensional maximum Shannon entropy. The theoretical model is verified and analyzed through comparative experiments using three kinds of classical grayscale images. The experimental results demonstrate that the proposed improved TSETMF algorithm exhibits better processing performance than that of the traditional filter, with a higher suppression of high-density noise and denoising stability. This stronger ability while processing high-density noise is demonstrated by a higher peak signal-to-noise ratio (PSNR) of 24.97 dB with a 95% noise density located in the classical Lena grayscale image. The better denoising stability, with a noise density from 5% to 95%, is demonstrated by the minor decline in the PSNR of approximately 10.78% relative to a PSNR of 23.10 dB located in the classical Cameraman grayscale image. Furthermore, it can be advanced to promote higher noise filtering and stability for processing high-density salt and pepper noise in grayscale images.

Evaluation of Fissures and Cracks in Bridges by Applying Digital Image Capture Techniques Using an Unmanned Aerial Vehicle

The evaluation of cracks and fissures in bridge structures is essential to ensure the long-term safety, durability, and functionality of these infrastructures. In this sense, processing grayscale images and adjusting brightness and contrast levels can improve the visibility of cracks and fissures in bridge structures. These techniques, complemented by professional expertise and efficient inspection tools such as Unmanned Aerial Vehicles (UAVs), allow for a comprehensive and accurate structural integrity assessment. This study used the edge detection technique to analyze photographs obtained with a low-cost UAV as a means of image capture. This tool was used to reach hard-to-reach areas where there could be damage, thus making it easier to detect fissures or cracks. To capture the failures, two case studies, a small bridge and a large bridge, were selected, both located in Concepción City in southern Chile. During both inspections, cracks were detected that could affect the structure of the bridges in the future. To analyze these findings, ImageJ software 1.54h was used, which allowed the length and thickness of the cracks to be measured and evaluated. In addition, to validate the procedure proposed, real values manually measured on-site were compared with those delivered by the software analyses, where no statistically significant differences were found. With the method presented in this study, it was possible to quantify the damage, following the bridge maintenance standards established by the Ministry of Public Works of Chile, whose inspection criteria can be applied to other projects worldwide.

Deep and Low-Rank Quaternion Priors for Color Image Processing

Due to the physical nature of color images, color image processing such as denoising and inpainting has shown extensive and versatile possibilities over grayscale image processing. The monochromatic and the concatenation model have been widely used to process color images by processing each color channel independently or concatenating three color channels as one unified one and then used existing grayscale image processing methods directly without specific operations. These above schemes, however, have some limitations: (1) they would destroy the inherent correlation among three color channels since they cannot represent color images holistically; (2) they usually focus on one specific handcrafted prior such as smoothness, low-rankness, or even deep prior and thus failing to fuse deep and handcrafted priors of color images flexibly. To conquer these limitations, we propose one unified model to integrate deep prior and low-rank quaternion prior (DLRQP) for color image processing under the plug-and-play (PnP) framework. Specifically, the quaternion representation with low-rank constraint is introduced to denote the color image in a holistic way and one advanced denoiser is adopted to explore the deep prior in an iterative process. To tightly approximate the quaternion rank, one nonconvex penalty function is further utilized. We derive an alternate iterative approach to tackle the proposed model. We empirically demonstrate that our model can achieve superior performance over existing methods on both color image denoising and inpainting tasks.

A novel robust edge detection method for full-field modal parameter estimation using an image-based tracking continuously scanning laser Doppler vibrometer system

A novel robust edge detection method is developed for an image-based tracking continuously scanning laser Doppler vibrometer (CSLDV) system to track a rotating structure without attaching any encoder or mark to it and scan its whole surface. The robust edge detection method can extract edges of the rotating structure from its complex background by processing grayscale images of the rotating structure. Once edges of the rotating structure are clearly shown in the processed image, their positions can be easily determined. To track a rotating structure like a rotating blade of a wind turbine with multiple blades, a distance condition is used to determine the position of an edge of the structure that needs to be tracked and estimate its real-time rotation speed. Once the position of the edge of the rotating structure is determined, the image-based tracking CSLDV system can scan the whole surface of the rotating structure via a two-dimensional scan scheme. An improved demodulation method is used to process measured data of response of the rotating structure subject to random excitation and estimate its modal parameters including damped natural frequencies and full-field undamped mode shapes. The robust edge detection method is investigated by tracking and scanning a rotating fan blade subject to random excitation with a complex background using the image-based tracking CSLDV system, and estimating modal parameters of the rotating fan blade with different constant speeds.

Defect corrections for coherent optical information processing of grayscale images in a DMD-based 4f-system using a collimated light source.

Digital micromirror device (DMD)-based 4f-systems, a type of coherent optical information processing system, have become a powerful tool for optical convolutional neural networks taking advantage of their fast modulation speed and high-resolution capability. However, proper high bit-depth image information processing remains challenging due to the optical diffractions that arise from the binary nature of DMD operation. In this paper, we first characterize the diffraction phenomena that cause irradiance defects, namely the nonlinear grayscale and unintended dark lines. Then to resolve the issues, we propose a DMD operation method and a modified structure of the 4f-system based on blazed diffraction grating theory and numerical calculation of the Rayleigh-Sommerfeld propagation model. As a demonstration, we implement high bit-depth image information processing with an optimized optical 4f-system using DMDs and a collimated coherent light source.

Image Processing Based on Fuzzy Mathematics Theory

In order to further improve the problems of poor rationality and weak antinoise ability of existing image processing algorithms and technical algorithms, an image processing research method based on fuzzy mathematical theory is proposed. First, aiming at the ill-posed problem of the PFCM algorithm, the neutrality and rejection degree are used to construct a regular term and embed the algorithm objective function to enhance the correlation between the attribute parameters of the fuzzy set of the sample graph, so as to solve the ill-posed problem of the PFCM algorithm. Secondly, in view of the same noise sensitivity problem of PFCM algorithm as a traditional fuzzy clustering algorithm, combined with the robust ideas of FCM_S1 and FCM_S2 algorithms, the objective function of robust segmentation algorithm for graph fuzzy clustering (RPFCM_s) is constructed. The misclassification rate of the clustering algorithm proposed in this study in image segmentation is reduced by 38%–76%, and the misclassification rate of the corresponding segmentation result of the ATPFCA algorithm is reduced by 5%–77%. Therefore, the algorithm not only improves the effective segmentation efficiency of the fuzzy mathematical theory algorithm for the processing of uneven grayscale images but also enhances the anti-noise robustness of the algorithm.

Research on docking ring pose estimation method based on point cloud grayscale image

The star-arrow docking ring is a ubiquitous structure on space vehicles and is often used as a feature component for pose measurement and capture in non-cooperative space capture missions. When the capture task is performed at a close distance of 0.5 m, due to the high radiation and high light intensity of the space and the coating of the satellite surface with thermal control multilayer materials, the lighting environment is complex, and the grayscale image processing is difficult. The point cloud grayscale image contour feature localization detection method converts the point cloud image into a grayscale image and then estimates the relative pose of the docking ring. Aiming at the problem of fast pose estimation in the case of relative motion, the algorithm adds a fast positioning module of frame image feature area based on the motion model on the original basis, which effectively reduces the feature retrieval area and improves the real-time performance of the algorithm. It is verified by physical simulation experiments. The results show that the method can effectively reduce the influence of stray light and eliminate noise contours. The pose estimation results of the docking ring are accurate and stable, the relative position calculation error is less than 1.5 mm, and the relative pose error is less than 0.45°. The frequency is better than 2 Hz, which meets the needs of real-time capture by space robotic arms.

K-Means Segmentation Based-on Lab Color Space for Embryo Detection in Incubated Egg

The quality of the hatching process influences the success of the hatch rate besides the inherent egg factors. Eliminating infertile or dead eggs and monitoring embryonic growth are very important factors in efficient hatchery practices. This process aims to sort eggs that only have embryos to remain in the incubator until the end of the hatching process. This process aims to sort eggs with embryos to remain hatched until the end. Maximum checking is done the first week in the hatching period. This study aims to detect the presence of embryos in eggs. Detection of the existence of embryos is processed using segmentation. Egg images are segmented using the K-means algorithm based on Lab color images. The results of the image acquisition are converted into Lab color space images. The results of Lab color space images are processed using K-means for each color. The K-means process uses cluster k=3, where this cluster divides the image into three parts: background, eggs, and yolk. Egg yolks are part of eggs that have embryonic characteristics. This study applies the concept of color in the initial segmentation and grayscale in the final stages. The initial phase results show that the image segmentation results using k-means clustering based on Lab color space provide a grouping of three parts. At the grayscale image processing stage, the results of color image segmentation are processed with grayscaling, image enhancement, and morphology. Thus, it seems clear that the yolk segmented shows the presence of egg embryos. Based on this process and results, the initial stages of the embryo detection process used K-means segmentation based on Lab color space. The evaluation uses MSE and MSSIM, with values of 0.0486 and 0.9979; this can be used as a reference that the results obtained can detect embryos in egg yolk. This protocol could be used in a non-destructive quantitative study on embryos and their morphology in a precision poultry production system in the future.

Quaternion-based weighted nuclear norm minimization for color image restoration

Color image restoration is one of the basic tasks in pattern recognition. Unlike grayscale image, each color image has three channels in the RGB color space. Due to the inner-relationship within the three channels, color image restoration is usually much more difficult than its grayscale counterpart. Indeed, new problems such as color artifacts could emerge when the grayscale image processing methods are extended to color images directly. Note that one of the most effective gray image restoration methods is the weighted nuclear norm minimization (WNNM) approach. However, when applied to color images, the results of WNNM are usually not as promising as that of grayscale images. In order to solve this problem, in this paper, we propose to restore color images with the quaternion-based WNNM method (QWNNM) since the structure of color channels can be well preserved with quaternion representation. The proposed model can be solved efficiently by the alternating direction method of multipliers (ADMM). The theoretical analysis of the optimal solution is also presented. Numerical experiments are carefully conducted with different kinds of degradation to illustrate the superior performance of our proposed QWNNM over the state-of-the-art methods, including a celebrated deep learning approach, in both visual quality and numerical results.

Towards All-optical Stochastic Computing Using Photonic Crystal Nanocavities

Stochastic computing allows a drastic reduction in hardware complexity using serial processing of bit streams. While the induced high computing latency can be overcome using integrated optics technology, the design of realistic optical stochastic computing architectures calls for energy efficient switching devices. Photonics Crystal (PhC) nanocavities are μm 2 scale devices offering 100fJ switching operation under picoseconds-scale switching speed. Fabrication process allows controlling the Quality factor of each nanocavity resonance, leading to opportunities to implement architectures involving cascaded gates and multi-wavelength signaling. In this paper, we investigate the design of cascaded gates architecture using nanocavities in the context of stochastic computing. We propose a transmission model considering key nanocavity device parameters, such as Quality factors, resonance wavelength, and switching efficiency. The model is calibrated with experimental measurements. We propose the design of XOR gate and multiplexer. We illustrate the use of the gates to design an edge detection filter. System-level exploration of laser power, bit-stream length and bit-error rate is carried out for the processing of gray-scale images. The results show that the proposed architecture leads to 8.5nJ/pixel energy consumption and 512ns/pixel processing time.

Non-Contact Heart Rate Detection Based on Hand Vein Transillumination Imaging

Vein recognition technology identifies human vein characteristics under near-infrared light and compares it with stored vein information for personal identification. Although this has high anti-counterfeiting performance, it is possible to fabricate artificial hands that simulate vein characteristics to deceive the identity authentication system. In view of this potential deficiency, we introduced heart rate information to vein authentication, a means of living body detection, which can further improve the anti-counterfeiting effect of vein authentication. A hand vein transillumination imaging experiment was designed to prove its effectiveness. In the proposed method, a near-infrared light source is used to transilluminate the hand, and the transillumination images are collected by a common camera. Then, the region of interest is selected for gray-scale image processing, the feature value of each frame is extracted by superimposing and averaging the images, and then the one-dimensional pulse wave is drawn. Furthermore, the baseline drift phenomenon is filtered by morphological methods, and the maximum percentage frequency is determined by Fast Fourier Transform, that is, the pulse wave frequency. The heart rate value is then calculated, and finally, the stability of the heart rate detection result is evaluated. The experiment shows that the method produces accurate and stable results, demonstrating that it can provide living information (heart rate value) for vein authentication, which has great application prospects and development opportunities in security systems.

A Quadratic Traversal Algorithm of Shortest Weeding Path Planning for Agricultural Mobile Robots in Cornfield

In order to improve the weeding efficiency and protect farm crops, accurate and fast weeds removal guidance to agricultural mobile robots is an utmost important topic. Based on this motivation, we propose a time-efficient quadratic traversal algorithm for the removal guidance of weeds around the recognized corn in the field. To recognize the weeds and corns, a Faster R-CNN neural network is implemented in real-time recognition. Then, an ultra-green characterization (EXG) hyperparameter is used for grayscale image processing. An improved OTSU (IOTSU) algorithm is proposed to accurately generate and optimize the binary image. Compared to the traditional OTSU algorithm, the improved OTSU algorithm effectively shortens the search speed of the algorithm and reduces the calculation processing time by compressing the range of the search grayscale range. Finally, based on the contour of the target plants and the Canny edge detection operator, the shortest weeding path guidance can be calculated by the proposed quadratic traversal algorithm. The experimental results proved that our search success rate can reach 90.0% on the testing date. This result ensured the accurate selection of the target 2D coordinates in the pixel coordinate system. Transforming the target 2D coordinate point in the pixel coordinate system into the 3D coordinate point in the camera coordinate system as well as using a depth camera can achieve multitarget depth ranging and path planning for an optimized weeding path.

Research on Improved YOLOv3 Fire Detection Based on Enlarged Feature Map Resolution and Cluster Analysis

Fire detection can effectively prevent the occurrence of fire. For the current fire detection methods, traditional image processing techniques such as grayscale image processing and feature extraction processing have poor anti-interference ability, weak generalization ability, and the detection results are more sensitive to data fluctuations. At present, based on the concept of deep learning, the proposed convolutional neural network processing of extracted image features has been widely used. On this basis, an improved YOLOv3 fire detection algorithm is proposed in this paper: The K-Means++ algorithm is used for clustering analysis to obtain the corresponding anchor boxes dimension, which reduces the error detection rate caused by the bounding boxes not matching the label; secondly, the resolution of the feature image is improved and the receptive field is enlarged; the image data are sharpened and the contrast is enhanced to make the data features more prominent. The experimental results show that the detection precision of the method is 97.7%, the recall rate is 98.5%, and the fps is 19, effectively solving the problem of high error detection rate and high missing rate of traditional image processing and general CNN networks for suspected flame objects.

Design and evaluation of high–luminance porous asphalt mixtures based on wasted glass for sponge city

In this paper, based on the concept of water glass recovery and sponge city, the waste glass was applied as part of aggregate to prepare a kind of porous glasphalt mixtures. The glass cullet was crushed to a certain size as glass aggregates to partially replace the mineral aggregates. The performances of glasphalt mixtures were tested and evaluated. In the experiment, four groups are designed, which are ordinary asphalt mixtures, cement-modified asphalt mixtures, glasphalt mixtures with no surface-treated glass, and glasphalt mixtures with surface-treated glass. Through the tests of road performance, the No. 4 group of glasphalt mixtures with 84% mineral aggregate + 1% Portland cement + 15% glass aggregates treated by Fe2O3 suspension has good technical properties in high temperature stability, low temperature stability, and water stability, although slightly poorer than that of asphalt mixtures. As the glasphalt mixtures are for the porous asphalt pavement, the volume of connected air voids and permeability coefficient was calculated. In addition, the method of MATLAB grayscale image processing is used to evaluate the reflective property of glasphalt mixtures, and it is found that the application of glass can significantly improve the surface luminance. This environmentally friendly glasphalt mixtures not only solve the problem of environmental pollution caused by waste glass but also saves the cost of pavement materials, improves the road traffic environment visibility. Therefore, the new type of glass asphalt pavement has good economic and social benefits, and it is worth to be promoted.

Design and implementation of power efficient image compressor for WSN systems

In the digital electronics era, video and image processing, storage, data transmitting, data streaming, data retrieval, video playback features increasing exponentially. These requirements data processing and storage improvement results to necessity of hybrid compression, efficient encoding and decoding, compression ratio techniques in demand for modern real time applications. For that spatial domine technology of compression is prefer to satisfy the computational constraint for real time gadgets. Discrete wavelet transformation (DWT) based Multi-Level Block Truncation Coding (MLBTC) methodology used for special domine. Discrete Wavelet Transform procedure is utilized to constant and motion images and implemented to all mean pixel estimation of the image. Loss type compression technology BTC is implemented for gray scale image processing. It designed that original image divided as square blocks with help of quantize for decrease the gray intensity images every portion of square box having equal standard deviation and Mean values. In this proposed methodology is analysis of BWT-MLBTC image compression technologies for both binary and color images. The methodology has been implemented in Xilinx ISE software, the simulation results shows that the proposed method consumes less hardware resources, delay generations and power utilizations compared to the state of art approaches.

Application of binocular disparity and receptive field dynamics: A biologically-inspired model for contour detection

Neurophysiological evidence demonstrates that classical receptive field responses in the primary visual cortex can be modulated by the non-classical receptive field. Although models based on the non-classical receptive field have been proposed, which has not employed the two following characteristics: dynamic regulation of the receptive field under external stimuli and depth determination by binocular cells. We propose a model that includes processing of gray-scale images and depth images. Luminance of grayscale image response was preprocessed using weighted least squares filtering; luminance of grayscale image and disparity information of depth image responses were obtained by V1 neuron responses. The receptive field size of each pixel used to calculate the luminance and disparity response is determined by the depth information. Luminance and disparity information responses were combined based on the optimal orientation, and robust contour maps were developed. Experimental results show that the proposed contour detection model outperforms biologically inspired models.

Automated Brain Tumor Detection and Segmentation from MRI Images using Adaptive Connected Component Pixel Segmentation

Magnetic resource imaging (MRI) imagesare used in examining the soft tissues which include brain tumors, ligament and tendon injury, spinal cord injury. Gray scale image processing is good for basic segmentation application.The exact location of brain tumor and its length is hard to find.This paper proposes an efficient method to segment the brain tumor. The result shows good segmentation accuracy.

Low-rank quaternion approximation for color image processing.

Low-rank matrix approximation (LRMA)-based methods have made a great success for grayscale image processing. When handling color images, LRMA either restores each color channel independently using the monochromatic model or processes the concatenation of three color channels using the concatenation model. However, these two schemes may not make full use of the high correlation among RGB channels. To address this issue, we propose a novel low-rank quaternion approximation (LRQA) model. It contains two major components: first, instead of modeling a color image pixel as a scalar in conventional sparse representation and LRMA-based methods, the color image is encoded as a pure quaternion matrix, such that the cross-channel correlation of color channels can be well exploited; second, LRQA imposes the low-rank constraint on the constructed quaternion matrix. To better estimate the singular values of the underlying low-rank quaternion matrix from its noisy observation, a general model for LRQA is proposed based on several nonconvex functions. Extensive evaluations for color image denoising and inpainting tasks verify that LRQA achieves better performance over several state-of-the-art sparse representation and LRMA-based methods in terms of both quantitative metrics and visual quality.