Edge Detection Research Articles

Research on an SICM Scanning Image Resolution Enhancement Algorithm.

Scanning ion conductance microscopy (SICM) enables the non-invasive three-dimensional imaging of live cells and other structures in physiological environments. However, when imaging complex samples, SICM faces challenges such as having a low temporal resolution during slow scanning and a reduced signal-to-noise ratio during fast scanning, making it difficult to simultaneously improve both temporal and spatial resolution. To address these issues, this paper proposes an algorithm for enhancing image resolution under high-speed scanning. Firstly, scanning images are preprocessed using a median filtering algorithm to remove the salt-and-pepper noise generated during high-speed scanning. Next, the Canny edge detection algorithm is employed to extract the edges of the image targets. To avoid blurring the edges, the new edge-directed interpolation (NEDI) algorithm is then used to fill the edges, while non-edge areas are filled using bilinear interpolation, thereby enhancing the image resolution. Finally, the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) are used to analyze the imaging of articular chondrocytes. The results show that under a scanning speed of 480 nm/ms, the proposed algorithm improves the temporal resolution of imaging by 60% compared to traditional 2× resolution imaging, increases the peak signal-to-noise ratio of the scanning images by 7 dB, and achieves a structural similarity of 0.97. Therefore, the proposed algorithm effectively removes noise during high-speed scanning and improves the SICM scanning imaging resolution, thereby avoiding the reduction in temporal resolution when scanning larger resolution samples and effectively enhancing the performance of SICM scanning imaging.

Convolutional neural networks combined with conventional filtering to semantically segment plant roots in rapidly scanned X-ray computed tomography volumes with high noise levels

BackgroundX-ray computed tomography (CT) is a powerful tool for measuring plant root growth in soil. However, a rapid scan with larger pots, which is required for throughput-prioritized crop breeding, results in high noise levels, low resolution, and blurred root segments in the CT volumes. Moreover, while plant root segmentation is essential for root quantification, detailed conditional studies on segmenting noisy root segments are scarce. The present study aimed to investigate the effects of scanning time and deep learning-based restoration of image quality on semantic segmentation of blurry rice (Oryza sativa) root segments in CT volumes.ResultsVoxResNet, a convolutional neural network-based voxel-wise residual network, was used as the segmentation model. The training efficiency of the model was compared using CT volumes obtained at scan times of 33, 66, 150, 300, and 600 s. The learning efficiencies of the samples were similar, except for scan times of 33 and 66 s. In addition, The noise levels of predicted volumes differd among scanning conditions, indicating that the noise level of a scan time ≥ 150 s does not affect the model training efficiency. Conventional filtering methods, such as median filtering and edge detection, increased the training efficiency by approximately 10% under any conditions. However, the training efficiency of 33 and 66 s-scanned samples remained relatively low. We concluded that scan time must be at least 150 s to not affect segmentation. Finally, we constructed a semantic segmentation model for 150 s-scanned CT volumes, for which the Dice loss reached 0.093. This model could not predict the lateral roots, which were not included in the training data. This limitation will be addressed by preparing appropriate training data.ConclusionsA semantic segmentation model can be constructed even with rapidly scanned CT volumes with high noise levels. Given that scanning times ≥ 150 s did not affect the segmentation results, this technique holds promise for rapid and low-dose scanning. This study offers insights into images other than CT volumes with high noise levels that are challenging to determine when annotating.

RFLSE: Joint radiomics feature‐enhanced level‐set segmentation for low‐contrast SPECT/CT tumour images

AbstractDoctors typically use non‐contrast‐enhanced computed tomography (NCECT) in the treatment of kidney cancer to map kidney and tumour structural information to functional imaging single‐photon emission computed tomography, which is then used to assess patient kidney function and predict postoperative recovery. However, the assessment of kidney function and formulation of surgical plans is constrained by the low contrast of tumours in NCECT, which hinders the acquisition of accurate tumour boundaries. Therefore, this study designed a radiomics feature‐enhanced level‐set evolution (RFLSE) to precisely segment small‐sample low‐contrast kidney tumours. Integration of high‐dimensional radiomics features into the level‐set energy function enhances the edge detection capability of low‐contrast kidney tumours. The use of sensitive radiomics features to control the regional term parameters achieves adaptive adjustment of the curve evolution amplitude, improving the level‐set segmentation process. The experimental data used low‐contrast, limited‐sample tumours provided by hospitals, as well as the public datasets BUSI18 and KiTS19. Comparative results with advanced energy functionals and deep learning models demonstrate the precision and robustness of RFLSE segmentation. Additionally, the application value of RFLSE in assisting doctors with accurately marking tumours and generating high‐quality pseudo‐labels for deep learning datasets is demonstrated.

Quantitative assessment of the troCarWash™ system for automated laparoscopic camera cleaning.

Soilage of the surgical endoscope occurs frequently during minimally invasive surgery. The resultant impairment of visualization of the surgical field compromises patient safety, prolongs operative times, and frustrates surgeons. The standard practice for cleaning the surgical camera involves a disruption in the conduct of surgery by completely removing the endoscope from the field, manually cleaning its lens, treating it with a surfactant, and reinserting it into the patient; after which the surgeon resumes the procedure. We developed an automated solution for in vivo endoscope cleaning in minimally invasive surgery- a port that detects the position of the endoscope in its distal lumen, and precisely and automatically delivers a pressurized mist of cleaning solution to the lens of the camera. No additions to the scope and minimal user interaction with the port are required. We tested the efficacy of this troCarWash™ device in a porcine model of laparoscopy. Four board-certified general surgeons were instructed to soil and then clean the laparoscope using the device. Representative pre- and post-clean images were exported from the surgical video and clarity was graded (1) digitally by a canny edge detection algorithm, and (2) subjectively by 3 blinded, unbiased observers using a semi-quantitative scale. We observed statistically significant improvements in clarity by each method and for each surgeon, and we noted significant correlation between digital and subjective scores. Based on these data, we conclude that the troCarWash™ effectively restored impaired visualization in a large animal model of laparoscopy.

Electron beam response corrections for an ultra-high-dose-rate capable diode dosimeter.

Ultra-high-dose-rate (UHDR) electron beams have been commonly utilized in FLASH studies and the translation of FLASH Radiotherapy (RT) to the clinic. The EDGE diode detector has potential use for UHDR dosimetry albeit with a beam energy dependency observed. The purpose is to present the electron beam response for an EDGE detector in dependence on beam energy, to characterize the EDGE detector's response under UHDR conditions, and to validate correction factors derived from the first detailed Monte Carlo model of the EDGE diode against measurements, particularly under UHDR conditions. Percentage depth doses (PDDs) for the UHDR Mobetron were measured with both EDGE detectors and films. A detailed Monte Carlo (MC) model of the EDGE detector has been configured according to the blueprint provided by the manufacturer under an NDA agreement. Water/silicon dose ratios of EDGE detector for a series of mono-energetic electron beams have been calculated. The dependence of the water/silicon dose ratio on depth for a FLASH relevant electron beam was also studied. An analytical approach for the correction of PDD measured with EDGE detectors was established. Water/silicon dose ratio decreased with decreasing electron beam energy. For the Mobetron 9 MeV UHDR electron beam, the ratio decreased from 1.09 to 1.03 in the build-up region, maintained in range of 0.98-1.02 at the fall-off region and raised to a plateau in value of 1.08 at the tail. By applying the corrections, good agreement between the PDDs measured by the EDGE detector and those measured with film was achieved. Electron beam response of an UHDR capable EDGE detector was derived from first principles utilizing a sophisticated MC model. An analytical approach was validated for the PDDs of UHDR electron beams. The results demonstrated the capability of EDGE detector in measuring PDDs of UHDR electron beams.

Flow control method in concrete particles pneumatic sweeping process based on image recognition and OPC protocol

Based on image recognition and OPC(Object linking and embedding for Process Control) protocol, an intelligent pneumatic sweeping scheme is proposed to solve the problem of pavement aggregates cleaning. Firstly, the effects of four different auxiliary light sources and six different image segmentation methods on the recognition of single particle images were analyzed, and the maximum entropy segmentation method was the best under white light. Secondly, the effect of four different filtering methods on discrete multi-particle image recognition was studied. Among them, the adaptive median filter can better solve the problem of interference points misidentified as particles. The recognition effect of four different edge detection methods on aggregate particle pile was analyzed. And Canny operator with better effect was finally used to recognize particle pile from the overhead and side view. Finally, the communication mode between the host computer and the controller established based on OPC protocol was verified by experiments.

LVP-net: A deep network of learning visual pathway for edge detection

Deep learning-based edge detectors typically consist of the encoder and the decoder. To integrate multi-scale features into a global edge map effectively, researchers utilize classification networks such as VGG16 as the encoder and focus on the decoder architecture. In contrast to existing approaches, we propose a novel deep network for edge detection called learning-visual-pathway network (LVP-Net), in which an enhancer-encoder-decoder architecture is designed inspired by the biological visual pathway: the retina/lateral geniculate nucleus→the primary visual cortex (V1) → V2 → V4 → the inferior temporal cortex (IT). To simulate the visual mechanisms along this pathway, we design a feature enhancer network (FENet) that boosts the feature representation capability of the encoder. FENet is combined with VGG16 based on the hierarchical structure of the pathway. Furthermore, inspired by the integration ability of multiple features in IT, we introduce a feedforward fusion module (FFM). Finally, we evaluate LVP-Net on three benchmark datasets, i.e., BSDS500, NYUDv2, and Multicue. Experimental results demonstrate that our method achieves competitive performance compared with most state-of-the-art approaches.

Interval type-2 fuzzy approach for retinopathy detection in fundus images

In the manuscript, an automatic approach for analysis and detection of various stages of retinopathy defects in human eyes has been proposed. The approach consists of a robust preprocessing technique of the retina fundus image to mitigate the effects of noise and poor lightening in the image. To realize a compressive analysis of the defects, methods for extracting blood vessels and optic disc in the fundus image has also been developed. Adaptive Histogram Equalization (AHE), median filtering and Connected Component Analysis techniques were used in separating blood vessels and optic disc from each fundus image. The pre-processing utilizes canny edge detection and Morphological Closing on the fundus image. An interval type-2 fuzzy (IT2F) clustering is applied to segments an input image into four clusters. These four clusters from the fuzzy segmentation are further analyzed to extract various stages of retinopathy abnormalities (e.g., Hemorrhage, hard exudates etc.). The extracted blood vessels and optic disc are removed from the analysis to enhance the defects detection process. Experiments were conducted on DIARETDB1 database. The experimental results obtained are validated using the ground-truth images contained in DIARETDB1 database. Impressive results are recorded throughout the experiment. Hard-Exudates and Hemorrhage were detected from the fundus images and results from similarity indexes such as, accuracy (94.11%) sensitivity (93.03%) and specificity (98.45%) were recorded.

Automated Foreign Object Detection for Carbon Fiber Laminates Using High-Resolution Ultrasound Testing.

Carbon fiber laminates have become popular in the manufacturing industry for their many desirable properties, like good vibration damping, high strength-to-weight ratio, toughness, high dimensional stability, and low coefficient of thermal expansion. During the manufacturing process, undesirable foreign objects, such as peel-ply strips, gloving material, and Kapton film, can be introduced into the part which can lead to a localized weakness. These manufacturing defects can function as stress concentration points and oftentimes cause a premature catastrophic failure. In this study, a method using high-resolution pulse-echo ultrasound testing is employed for the detection and quantification of the dimensions of foreign object debris (FOD) embedded within carbon fiber laminates. This research presents a method to create high-resolution C-scans using an out of immersion tank portable housing ultrasound scanning system, with similar capabilities to that of a full immersion system. From the full-waveform dataset, we extract the FOD depth and planar dimensions with an automatic edge detection technique. Results from several carbon fiber laminates are investigated with embedded foreign objects that are often considered undetectable. Results are presented for FOD identification for two different shapes: circles with diameters ranging from 7.62 mm to 12.7 mm, and 3-4-5 triangles with hypotenuses ranging from 7.6 mm to 12.7 mm. CT imaging is used to confirm proper FOD placement and that the FOD was not damaged or altered during manufacturing. Of importance for the ultrasound inspection results, in every single case studied, the FOD is detected, the layer depth is properly identified, and the typical error is less than 1.5 mm for the primary dimension.

Research on High Performance Apple Recognition Model Construction Strategy Based on Deep Learning

In order to improve the working efficiency and environmental adaptability of apple picking robots, which can realize accurate recognition and localization, ripening assessment, and quality assessment under complex situations such as "leaf occlusion", "branch occlusion", "fruit occlusion", "mixed occlusion", etc., this paper proposes a Faster R-CNN-based apple recognition method. "mixed occlusion" and other complex situations to achieve accurate recognition and localization, maturity assessment, quality assessment, the paper proposes an apple recognition method based on Faster R-CNN. The method combines the cv2.inRange and cv2.fitEllipse functions in OpenCV to optimize the model, and performs color segmentation and shape fitting by setting up color and shape thresholds to recognize apples. The trained model was tested to have a recall of 88.44% under the validation set, an accuracy of 90.35%, an F1 value of 89.38%, and a recognition time of about 0.3 s per image.The experimental results showed that the number of apples in the dataset showed a normal distribution, with the number distributed more in the range of 0 to 10. Candidate frames are screened by RPN, and apple coordinates are derived based on the coordinate transformation formula, and the experimental results show that the apples are more densely distributed near the range of x=100, y=100. The image color distribution was extracted through color histogram, and the entropy value of GLCM, contrast as the texture feature of apples, and the classification prediction of apple ripeness was carried out by using Random Forest Model, and the accuracy of the model prediction was 83.2%, the precision rate was 84.1%, and the recall rate was 83.5%, and the experimental results showed that 88.7% of the apples were ripe. Image edges were extracted by the Canny edge detection algorithm and the area values of pixel points within the edges were calculated. The ratio of pixel area converted to apple mass was estimated to be 0.007 based on the image resolution and apple density, and the test results indicated that the mass of apples was mostly concentrated in the range of 0~100g.

Optimization of Digital Inheritance Pathway of Folk Art in Information Age

How to make full use of digital information technology to realize the digital heritage of folk art has become a key concern in the field of non-legacy. Based on digital information technology, this paper proposes image restoration technology to assist folk art digital heritage research. Folk art images are acquired by double mirror reflection method, and pre-processing operations such as defogging, denoising and edge detection are carried out on the images to reduce the influence of interference information in the original pictures on the image restoration effect. The Criminisi algorithm is improved and optimized by combining the structure tensor theory, the improved priority calculation function, the matching criterion and the confidence updating method, and then the research design of image restoration technology assisting folk art digitization is completed and analyzed by examples. The results show that the repair effect of the improved Criminisi algorithm is better than that of the classical Criminisi algorithm, but the repair time of the improved Criminisi algorithm is longer than that of the traditional algorithm by 64.25%, but it is still less than 0.500 seconds, which is within the acceptable range, and it has certain practicability for the digitization and inheritance of folk art in the information age. algorithm has some practicality. Compared with other digital inheritance paths, the method proposed in this study can restore the original appearance of folk art to the maximum extent, and it has theoretical guidance value for the study of optimizing the digital inheritance of traditional folk art.

Lane detection using hough transformation and Yolov8

Autonomous vehicles necessitate the integration of advanced technologies such as computer vision and deep learning to comprehend and navigate their surroundings. A crucial yet challenging component of this integration is the accurate detection of lanes, which can be influenced by a multitude of varying lane characteristics and conditions. This research undertakes a comparative analysis of lane detection methodologies, explicitly focusing on traditional image processing techniques and Convolutional Neural Networks (CNNs). The evaluation utilized a sample of 500 images from the CULane dataset, which encompasses a diverse range of traffic scenarios. Initially, a method incorporating Gaussian blurring, Canny edge detection, and Hough line transformation was examined. Despite its efficiency, operating at 30 frames per second, this approach exhibited a high error rate (average Mean Squared Error (MSE) of 0.537), which is attributable to the loss of critical image details during the preprocessing stage. Subsequently, the performance of a fine-tuned YOLOv8 model, trained on a reformatted version of the CULane dataset was assessed. The combination of object detection and subsequent Hough transformation yielded high accuracy, demonstrating the model’s ability to learn and identify relevant lane features. The deep CNNs demonstrated superior performance over classical image processing techniques in terms of lane detection accuracy, thereby underscoring their potential applicability within the realm of autonomous vehicle technology

Fiber Bragg grating sensing system for temperature measurements based on optically injected DFB-LD with an OEO loop

A novel fiber Bragg grating (FBG) sensing system, based on an optically injected distributed feedback laser diode (DFB-LD) with an optoelectronic oscillating (OEO) loop, is proposed and experimentally demonstrated for temperature measurements with high and tunable sensitivity. The FBG sensor device works as an edge filter to adjust the optical power of the injected beam in response to temperature variations. The optically injected DFB-LD works at Period-one (P1) oscillating state, and the central wavelength of the oscillating mode of the DFB-LD can be tuned by the variable power of the injected beam. Furthermore, an OEO loop is implemented to improve the signal quality of the generated P1 microwave signal. Hence, the sensing parameter of temperature is converted to the frequency variation of the generated P1 microwave signal in the proposed sensing system. In the proof-of-concept experiment, a series of P1 microwave signals are generated while different temperatures are applied to the FBG sensor. The sensitivity of the proposed FBG sensing system for temperature measurements can be tuned from 0.44322 GHz/°C to 1.25952 GHz/°C. The stability and repeatability experiments are also performed, demonstrating the high measurement accuracy (0.0629°C) and low error of the system. The proposed FBG-based sensing and interrogation system exhibits high sensitivity, large tunability, good linearity, and flexible sensing generality.

AN EFFICIENT VLSI IMPLEMENTATION OF EDGE DETECTION OF IMAGES

Edge detection is a fundamental process in image processing with significant implications across multiple domains. Within the realm of object detection, the accurate delineation of image perimeters forms the bedrock for subsequent analysis. In this study, we present an optimized Laplacian Edge Detection algorithm tailored for efficient implementation in hardware.Leveraging approximation methodologies and strategic pipelining, we streamline complex operations, thereby reducing latency and resource consumption. Our implementation, realized through the Verilog Hardware Description Language (VHDL), stands as a testament to the efficacy of our approach. Through rigorous optimization and meticulous comparison with conventional Sobel edge detection, we demonstrate superior performance metrics, including enhanced Area utilization, reduced Time Delay, and improved Power Efficiency. This work underscores the importance of methodological refinement and technological innovation in advancing edge detection techniques for practical applications. Keywords: Laplacian, Sobel, Kernel, Pixel range, Area, Power

A Novel Lightweight Model for Underwater Image Enhancement.

Underwater images suffer from low contrast and color distortion. In order to improve the quality of underwater images and reduce storage and computational resources, this paper proposes a lightweight model Rep-UWnet to enhance underwater images. The model consists of a fully connected convolutional network and three densely connected RepConv blocks in series, with the input images connected to the output of each block with a Skip connection. First, the original underwater image is subjected to feature extraction by the SimSPPF module and is processed through feature summation with the original one to be produced as the input image. Then, the first convolutional layer with a kernel size of 3 × 3, generates 64 feature maps, and the multi-scale hybrid convolutional attention module enhances the useful features by reweighting the features of different channels. Second, three RepConv blocks are connected to reduce the number of parameters in extracting features and increase the test speed. Finally, a convolutional layer with 3 kernels generates enhanced underwater images. Our method reduces the number of parameters from 2.7 M to 0.45 M (around 83% reduction) but outperforms state-of-the-art algorithms by extensive experiments. Furthermore, we demonstrate our Rep-UWnet effectively improves high-level vision tasks like edge detection and single image depth estimation. This method not only surpasses the contrast method in objective quality, but also significantly improves the contrast, colorimetry, and clarity of underwater images in subjective quality.

Improved 3D human face reconstruction from 2D images using blended hard edges

This study reports an effective and robust edge-based scheme for the reconstruction of 3D human faces from input of single images, addressing drawbacks of existing methods in case of large face pose angles or noisy input images. Accurate 3D face reconstruction from 2D images is important, as it can enable a wide range of applications, such as face recognition, animations, games and AR/VR systems. Edge features extracted from 2D images contain wealthy and robust 3D geometric information, which were used together with landmarks for face reconstruction purpose. However, the accurate reconstruction of 3D faces from contour features is a challenging task, since traditional edge or contour detection algorithms introduce a great deal of noise, which would adversely affect the reconstruction. This paper reports on the use of a hard-blended face contour feature from a neural network and a Canny edge extractor for face reconstruction. The quantitative results indicate that our method achieves a notable improvement in face reconstruction with a Euclidean distance error of 1.64 mm and a normal vector distance error of 1.27 mm when compared to the ground truth, outperforming both traditional and other deep learning-based methods. These metrics show particularly significant advancements, especially in face shape reconstruction under large pose angles. The method also achieved higher accuracy and robustness on in-the-wild images under conditions of blurring, makeup, occlusion and poor illumination.

Investigating the Influence of Varied Particle Sizes on the Load-Bearing Properties of Arrester Bed Aggregates.

This study employs the discrete element method to investigate the influence of particle size on the load-bearing characteristics of aggregates, with a specific emphasis on the aggregates used in escape ramp arrester beds. This study utilises the log edge detection algorithm to introduce an innovative approach for modelling irregularly shaped pebbles, integrating their physical properties into a comprehensive discrete element model to enhance the accuracy and applicability of simulations involving such pebbles. Meticulous validation and parameter calibration (friction coefficient: 0.37, maximum RMSE: 3.43) confirm the accuracy of the simulations and facilitate an in-depth examination of the mechanical interactions between aggregate particles at macroscopic and microscopic scales. The findings reveal a significant relationship between the particle size and load-bearing capacity of aggregates. Smaller pebbles, which are more flexible under pressure, can be packed more densely, thereby improving the distribution of vertical forces and increasing the concentration of local stress. This enhancement substantially increases the overall load-bearing capacity of aggregates. These discoveries hold significant implications for engineering practices, particularly in the optimisation of safety for truck escape ramps and in identifying the ideal sizes of pebbles with irregular shapes.

HANDVIBE: HAND GESTURE VOLUME AND BRIGHTNESS CONTROL

Gesture Recognition has become increasingly relevant in the field of human-computer interaction, as it is a natural way to convey information. We aim to create a system that can identify specific human gestures and utilize them to transmit information for device control. This enables users to operate a computer by performing a specific gesture in front of the camera. The below approach can detect multiple hands simultaneously, that is left and right, each having its own purpose. It is detected by a standard webcam, and requires no extra equipment. The left hand is responsible in controlling the brightness and the right hand will be responsible for controlling the volume. Here we have used different computer vision techniques which includes border detection, and convex-hull detection. The system was able to detect the distance between two points present in the hand, namely fingertip of thumb as well as index finger. Then it calculates the distance between them which is used to apply for volume and brightness, i.e. If the fingers are pinched, the distance between them becomes “zero”, thereby setting the volume or brightness to zero. If it has the maximum distance ,then the volume or brightness is set to maximum that is “100”.The Volume or brightness is decided based upon the hand. The primary aim here is to enable users to adjust the volume as well as the brightness of their system with ease, either by increasing or decreasing it. This offers a promising alternative to touch-based controls as well as voice-based controls. Key Words: Hand Gesture, Volume Control , Brightness Control, Human Control Interaction, Edge Detection, OpenCV, Computer Vision

Optimized Wide-Angle Metamaterial Edge Filters: Enhanced Performance with Multi-Layer Designs and Anti-Reflection Coatings

Optimized Wide-Angle Metamaterial Edge Filters: Enhanced Performance with Multi-Layer Designs and Anti-Reflection Coatings

Three-Stage Interpolation Method for Demosaicking Monochrome Polarization DoFP Images.

The emergence of polarization image sensors presents both opportunities and challenges for real-time full-polarization reconstruction in scene imaging. This paper presents an innovative three-stage interpolation method specifically tailored for monochrome polarization image demosaicking, emphasizing both precision and processing speed. The method introduces a novel linear interpolation model based on polarization channel difference priors in the initial two stages. To enhance results through bidirectional interpolation, a continuous adaptive edge detection method based on variance differences is employed for weighted averaging. In the third stage, a total intensity map, derived from the previous two stages, is integrated into a residual interpolation process, thereby further elevating estimation precision. The proposed method undergoes validation using publicly available advanced datasets, showcasing superior performance in both global parameter evaluations and local visual details when compared with existing state-of-the-art techniques.