Adaptive Edge Detection Research Articles

A framework of insole blanking robot based on adaptive edge detection and FSPS-BIT* path planning

Insole blanking production technology plays a vital role in contemporary machining and manufacturing industries. Existing insole blanking production models have limitations because most robots are required to accurately position the workpiece to a predetermined location, and special auxiliary equipment is usually required to ensure the precise positioning of the robot. In this paper, we present an adaptive blanking robotic system for different lighting environments, which consists of an industrial robot arm, an RGB-D camera configuration, and a customized insole blanking table and mold. We introduce an innovative edge detection framework that utilizes color features and morphological parameters optimized through particle swarm optimization (PSO) techniques to Adaptive recognition of insole edge contours. A path planning framework based on FSPS-BIT* is also introduced, which integrates the BIT* algorithm with the FSPS algorithm for efficient path planning of the robotic arm.

Optically enhanced organic phototransistors for adaptive image processing under complex light conditions

Effectively handling massive information in complex lighting conditions is crucial for security-focused machine vision, as it relies on precise image feature detection and encryption/decryption to ensure integrity and confidentiality. Traditional hardware, primarily based on complementary metal-oxide-semiconductor (CMOS) technology, struggles with the complexity of processing tasks around the clock. To address this challenge, a ternary organic heterostructure visuomorphic phototransistor was tailored for adaptive critical image processing. The light coupling induced by the porous heterostructural layer enhances the light absorption efficiency by 2.5-fold. Our phototransistor exhibits bidirectional photoresponse across the visible and near-infrared (NIR) spectral regions, enabling adaptive edge detection in varying lighting conditions with precision above 85 %. The phototransistor array facilitates image encryption/decryption with a superior accuracy of 12 %/90 % compared to existing counterparts. This ternary organic hetero-integration combines multiple response modes, enabling compact and efficient critical feature processing.

Layer Contour Geometric Characterization in MEX/P through CIS-Based Adaptive Edge Detection

The industrial adoption of material extrusion of polymers (MEX/P) is hindered by the geometric quality of manufactured parts. Contact image sensors (CISs), commonly used in flatbed scanners, have been proposed as a suitable technology for layer-wise characterization of contour deviations, paving the way for the application of corrective measures. Nevertheless, despite the high resolution of CIS digital images, the accurate characterization of layer contours in MEX/P is affected by contrast patterns between the layer and the background. Conventional edge-recognition algorithms struggle to comprehensively characterize layer contours, thereby diminishing the reliability of deviation measurements. In this work, we introduce a novel approach to precisely locate contour points in the context of MEX/P based on evaluating the similarity between the grayscale pattern near a particular tentative contour point and a previously defined gradient reference pattern. Initially, contrast patterns corresponding to various contour orientations and layer-to-background distances are captured. Subsequently, contour points are identified and located in the images, with coordinate measuring machine (CMM) verification serving as a ground truth. This information is then utilized by an adaptive edge-detection algorithm (AEDA) designed to identify boundaries in manufactured layers. The proposed method has been evaluated on test targets produced through MEX/P. The results indicate that the average deviation of point position compared to that achievable with a CMM in a metrology laboratory ranges from 8.02 µm to 13.11 µm within the experimental limits. This is a substantial improvement in the reliability of contour reconstruction when compared to previous research, and it could be crucial for implementing routines for the automated detection and correction of geometric deviations in AM parts.

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.

Adaptive edge detection of rebar thread head image based on improved Canny operator

AbstractThe quality of the rebar thread head is crucial for the connection between the rebars, thereby affecting the public safety of the building. The edge extraction of the thread image is the most important step in obtaining the accurate size parameters, which helps to complete online inspection of quality. Here, an edge detection method based on improved Canny operator is proposed to solve the problems on sensitivity to noise, existence of false edges, and lack of self‐adaptability. Firstly, an improved adaptive median filtering algorithm is used to denoise the image. Then replace Sobel with Scharr to enhance the gap between pixel values, add 45° and 135° directional templates to prevent edge information loss as well. Besides, introduce a scale factor to improve the interpolation method for non‐maximum suppression and reduce false edges. Finally, use Ostu operator to achieve adaptive extraction of high and low thresholds to complete adaptive edge detection. The qualitative analysis and quantitative results show that the improved algorithm can not only have a good visual effect, have good robustness and feasibility, but also be faster than other algorithms, which provide a basic guarantee for real‐time online quality detection of the thread head.

Ore Rock Fragmentation Calculation Based on Multi-Modal Fusion of Point Clouds and Images

The accurate calculation of ore rock fragmentation is important for achieving the autonomous mining operation of mine excavators. However, a single mode cannot accurately calculate the ore rock fragmentation due to the low resolution of the point cloud mode and the lack of spatial position information of the image mode. To solve this problem, we propose an ore rock fragmentation calculation method (ORFCM) based on the multi-modal fusion of point clouds and images. The ORFCM makes full use of the advantages of multi-modal data, including the fine-grained object segmentation of images and spatial location information of point clouds. To solve the problem of image under-segmentation, we propose a multiscale adaptive edge-detection method based on an innovative standard deviation map to enhance the weak edges. Furthermore, an improved marked watershed segmentation algorithm is proposed to solve the problem of low segmentation accuracy caused by excessive noise of the gradient map and weak edges submerged. Experiments demonstrate that ORFCM can accurately calculate ore rock fragmentation in the local excavation area without relying on external markers for pixel calibration. The average error of the equivalent diameter of ore rock blocks is 0.66 cm, the average error of the elliptical long diameter is 1.42 cm, and the average error of the elliptical short diameter is 1.06 cm, which can effectively meet practical engineering needs.

Automated and Adaptive Ridge Extraction for Rotating Machinery Fault Detection

A ridge in a time-frequency graph (TFG) describes the relationship of a signal component's instantaneous frequencies over time. Accurate ridge extraction from TFGs is beneficial for assessing machine health conditions without rotational speed measurement. This article proposes a new automated and adaptive ridge extraction (AARE) method. The AARE develops an adaptive edge detection strategy to avoid excessive interferences when searching for a ridge. Besides, the AARE creates a balance between exploring peak amplitudes and guaranteeing a continuous curve through an adaptive core function, which is constructed entirely based on the instantaneous characteristics of the analyzed signal. The unique advantage of the proposed method is that it dispenses with the tuning of parameters and runs automatically. Thus, human intervention is minimized. Gear and bearing vibration signals collected under variable speed conditions are applied to investigate and demonstrate the performance of AARE. In addition, some challenging cases are analyzed and discussed. Results show that AARE has a superior performance in ridge extraction compared with the reported approaches.

Corn Row Navigation Line Extraction Method Based on the Adaptive Edge Detection Algorithm

Aiming at the problems of long detection time and large detection error when agricultural machinery extracts corn row navigation lines, a method of corn row navigation line extraction based on the adaptive edge detection algorithm is proposed. First, the improved super-green feature algorithm and the maximum inter-class variance method are used to automatically obtain the green feature binary image, and the morphological processing is used to improve the image quality, determining dynamic regions of interest by constraining pixel thresholds, extraction of corn edge contour using adaptive edge detection algorithm, finally, the feature points are fitted by the Theil-Sen estimation method. Experimental results show: the super-green feature algorithm reflects the green content in the image more realistically, using the adaptive edge detection algorithm to extract corn row features, the accuracy rate is 94%, and the processing time of a single frame image is 104ms. Compared with the Hough algorithm extraction and the vertical projection algorithm, the navigation line extraction accuracy is increased by 15% and 8% respectively, and the time-consuming is reduced by 258ms and 150ms respectively. In addition, the stability of the algorithm is analyzed in different environments, all with good timeliness.

An adaptive sub-pixel edge detection method based on improved Zernike moment

An adaptive sub-pixel edge detection method based on improved Zernike moment

An adaptive sub-pixel edge detection method based on improved Zernike moment

An adaptive sub-pixel edge detection method based on improved Zernike moment

Combining convex hull and directed graph for fast and accurate ellipse detection

Detecting ellipses from images is a fundamental task in many computer vision applications. However, due to the complexity of real-world scenarios, it is still a challenge to detect ellipses accurately and efficiently. In this paper, we propose a novel method to tackle this problem based on the fast computation of convex hull and directed graph, which achieves promising results on both accuracy and efficiency. We use Depth-First-Search to extract branch-free curves after adaptive edge detection. Line segments are used to represent the curvature characteristic of the curves, followed by splitting at sharp corners and inflection points to attain smooth arcs. Then the convex hull is constructed, together with the distance, length, and direction constraints, to find co-elliptic arc pairs. Arcs and their connectivity are encoded into a sparse directed graph, and then ellipses are generated via a fast access of the adjacency list. Finally, salient ellipses are selected subject to strict verification and weighted clustering. Extensive experiments are conducted on eight real-world datasets (six publicly available and two built by ourselves), as well as five synthetic datasets. Our method achieves the overall highest F-measure with competitive speed compared to representative state-of-the-art methods.

An APF-ACO algorithm for automatic defect detection on vehicle paint

As a popular technology in the field of artificial intelligence, computer vision is gradually adapting to the needs of convenience for human beings, improving production efficiency and reducing production costs. Therefore, this study proposes a computer vision algorithm to locate and identify the location of defects. For the traditional edge detection algorithm Sobel, LoG, Canny, the decisive factor for the detection effect of paint defect image is the adjustment of parameters, which can’t achieve an adaptive edge detection algorithm for paint defects, so it is thought that the evolution idea of ant colony algorithm can be used to achieve accurate detection of defects. This paper proposes an automatic detection method for vehicle body paint film defects based on computer vision. An ant colony optimization edge detection algorithm based on automotive paint features (APF-ACO) is proposed. By combining global update and local update, the convergence speed of ant colony algorithm is improved and a new pheromone calculation and update method is proposed to effectively preserve the edge details of the detected image. A reflection area detection algorithm based on HSV color space is designed to detect the reflective area and eliminate interference. Establish defect classification identification rules, identify and mark five types of defects, and determine defect categories. Experiments show that the method can effectively detect the defect area and the recognition accuracy is 97.76%.

Adaptive Edge Detection Algorithm Based on Improved Grey Prediction Model

Existing edge detection algorithms suffer from inefficient edge localization, noise sensitivity, and/or relatively poor automatic detection capability. Contemporary edge detection algorithms can be improved by targeting these problems to help bolster their performance. Grey system theory can be used to resolve the small data and poor information issues in the local information of uncertain systems. An automatic edge detection algorithm was developed in this study based on a grey prediction model to remedy these problems. Noise characteristics in grey images are used to deploy a noise-filtering algorithm based on local features. A mask with twenty-four edge direction information points (345°) was established based on edge line texture features. By compressing the amplitude of the sequence, the randomly oscillated grey prediction sequence can be converted into a smooth, new sequence. The discrete grey model (1,1) (DGM(1,1)) was established based on this new grey prediction sequence to obtain the grey prediction maximum value. A grey prediction image with enhanced edges was obtained by replacing the pixel value in the original image with the maximum grey prediction value. A grey prediction subtraction image with edges separated from non-edge points was also obtained by subtracting the original image from the grey prediction image. The optimal separation threshold in the grey prediction subtraction image can be determined via the global adaptive threshold selection method. The neighborhood search method was then deployed to remove stray points and burrs from the image after the target was separated from the background, creating the final edge image. Experiments were performed on a computer-simulated phantom to find that both the subjective visual effects and objective evaluation criteria are better under the proposed method than several other competitive methods. The proposed edge detection algorithm shows excellent edge detection ability and is highly robust to noise, though the grey prediction model needs further improvement to optimize the run time.

Dynamic Granularity Matrix Space Based Adaptive Edge Detection Method for Structured Light Stripes

Structured light has been widely applied to 3D shape measurement with the capabilities of rapidness, high-accuracy, and noncontact. Because of uneven illumination and noise, it is difficult to distinguish the light stripes and background of the image, which reduces the measurement accuracy. In this paper, an adaptive Canny edge detection method with two phases is proposed for structured light stripes. Firstly, the idea of dynamic granularity is introduced and the dynamic granularity matrix space is established, in which image segmentation problems are described as the transformations and jumping of image at different granularity layers. The hierarchical structure and optimal granularity layer of image are obtained as the basis of adaptive edge detection. Secondly, a quantum-inspired group leader hybrid algorithm is adopted to calculate the optimal threshold from the optimal granularity layer, which is taken as the adaptive threshold for Canny operator. Finally, experimental tests and comparisons have been conducted to verify the effectiveness of the adaptive method proposed. The experiments show that the proposed method achieves high segmentation accuracy, improves the segmentation efficiency, and has strong robustness to noise.

Texture Construction Edge Detection Algorithm

The edge detection algorithm is the cornerstone of image processing; a good edge detection result can further extract the required information through rich texture information and achieve object detection, segmentation, and identification. To obtain a rich texture edge detection technology, this paper proposes using edge texture change for edge construction and constructs the edge contour through constructing an edge texture extension between the blocks to reduce the missing edge problem caused by the threshold setting. Finally, through verification of the experimental results, the proposed method can effectively overcome the problem caused by unsuitable threshold setting and detect rich object edge information compared to the adaptive edge detection method.

Adaptive Edge Detection Algorithm Based on Grey Entropy Theory and Textural Features

The traditional edge detection method is altogether inaccurate, nonadaptive, and particularly ineffective on noisy images. This paper proposes a novel edge detection algorithm based on gray entropy theory and local texture features. In the 3×3 neighborhood window, 28 comparison sequences are constructed according to local texture features. The reference sequence is composed of the median of all elements in the 3×3 neighborhood window. A total of 28 gray relation degrees as obtained by gray relation analysis between the 28 comparison sequences and reference sequences, as well as 28 gray relation degrees, are analyzed by gray entropy theory to initially filter the image. Gray entropy analysis is then performed on the comparison sequences composed of 28 texture features and reference sequences composed of the central pixel points of the filtered image to determine the maximum gray entropy difference. A comparative threshold adaptive acquisition method is designed to separate gray entropy difference sequence elements and identify all edge points accordingly. The simulation results show that the proposed algorithm effectively achieves adaptive edge detection and has strong anti-noise capability. The results of this study may provide a workable reference for edge information detection in the field of artificial intelligence (e.g., image recognition, pattern recognition applications).

An adaptive edge-detection method based on histogram

In order to solve the problems of poor adaptability when setting threshold and the high probability of detecting pseudo-edges in the existing methods of edge detection, the paper proposes an adaptive edge-detection method based on histogram. Multi-scale wavelet transform is used to preprocess the image, the image details are highlighted obviously, and it also can avoid the effect of manual setting filter coefficients. Difference of gray values between the pixels of local area are used to calculate the gradients comprehensively, it extends the gradient direction to four directions. When calculating the gradient of edge pixel, the four directions make the expression of the gradients of edge points more perfect and avoid the edge points missing. The adaptive method is used to compute the threshold of edge-detection, the image is represented by histogram. Then use the ratio of the number of pixels in the bar and the total numbers of pixels to set the initial threshold. The regions on both sides of the initial threshold are used to calculate the high threshold and low threshold until the reasonable error between the current threshold and the previous threshold is very small iteratively. The acquired threshold makes the self-adaptability more reasonable and stronger, it also avoids the detection errors, the connection errors and the pseudo-edges which are caused by setting threshold artificially. The experimental results show that the proposed algorithm of edge detection has a good effect of preserving edge detail and filtering noise of image.

A Fast Learning Method for Accurate and Robust Lane Detection Using Two-Stage Feature Extraction with YOLO v3.

To improve the accuracy of lane detection in complex scenarios, an adaptive lane feature learning algorithm which can automatically learn the features of a lane in various scenarios is proposed. First, a two-stage learning network based on the YOLO v3 (You Only Look Once, v3) is constructed. The structural parameters of the YOLO v3 algorithm are modified to make it more suitable for lane detection. To improve the training efficiency, a method for automatic generation of the lane label images in a simple scenario, which provides label data for the training of the first-stage network, is proposed. Then, an adaptive edge detection algorithm based on the Canny operator is used to relocate the lane detected by the first-stage model. Furthermore, the unrecognized lanes are shielded to avoid interference in subsequent model training. Then, the images processed by the above method are used as label data for the training of the second-stage model. The experiment was carried out on the KITTI and Caltech datasets, and the results showed that the accuracy and speed of the second-stage model reached a high level.

Covert information sharing with novel fuzzy adaptive edge detection

The secure communication systems should provide covert, stealth, or unauthorized detection of critical information. Covert communication systems not only shield the secure information enclosed in the cover data from being decoded but also avoid the opponent from perceiving the transmission. In this paper, we propose adaptive Fuzzy Edge detection‐based covert communication. The proposed system is capable of locating the accurate edge zones of a cover picture to shield the secure information. Experimental outcomes show that the proposed strategy has accomplished good imperceptibility compared with the other analogous strategies.

Automatic Registration Between Low-Altitude LiDAR Point Clouds and Aerial Images Using Road Features

Among the many means of acquiring surface information, low-altitude light detection and ranging (LiDAR) systems (e.g., unmanned aerial vehicle LiDAR, UAV-LiDAR) have become an important approach to accessing geospatial information. Considering the lower level of hardware technology in low-altitude LiDAR systems compared to that in airborne LiDAR, and the greater flexibility in-flight, registration procedures must be first performed to facilitate the fusion of laser point data and aerial images. The corner points and edges of buildings are frequently used for the automatic registration of aerial imagery with LiDAR data. Although aerial images and LiDAR data provide powerful support for building detection, adaptive edge detection for all types of building shapes is difficult. To deal with the weakness of building edge detection and reduce matching-related computation, the study presents a novel automatic registration method for aerial images, with LiDAR data, on the basis of main-road information in urban areas. Firstly, vector road centerlines are extracted from raw LiDAR data and then projected onto related aerial images with the use of coarse exterior orientation parameters (EOPs). Secondly, the corresponding image road features of each LiDAR vector road are determined using an improved total rectangle-matching approach. Finally, the endpoints of the conjugate road features obtained from the LiDAR data and aerial images are used as ground control points in space resection adjustment to refine the EOPs; an iterative strategy is used to obtain optimal matching results. Experimental results using road features verify the feasibility, robustness and accuracy of the proposed approach.