Connected Component Labeling Algorithm Research Articles

Human Object Counter Tracking Using Connected Component Labelling On Digital Image Processing

Object tracking is usually used to determine the position of objects and environmental conditions around these objects. Objects that require supervision in order to run properly require a system to monitor the object. Every movement of the object will be known directly through the camera image. So in this study a system was created to detect and track objects. Implementing an object detection system based on image processing using the connected component labeling method. Then displaying the detection results on the divided image frame. Moving object detection and tracking The algorithm used to detect and track moving objects is connected component labeling. Based on the analysis of the workings of the connected component labeling algorithm for detecting and tracking moving objects using video, it can be concluded that the connected component labeling algorithm has succeeded in tracking and labeling each object based on the position and number of objects, even though they look erratic, in human movement.

Automatic Monitoring of Maize Seedling Growth Using Unmanned Aerial Vehicle-Based RGB Imagery

Accurate and rapid monitoring of maize seedling growth is critical in early breeding decision making, field management, and yield improvement. However, the number and uniformity of seedlings are conventionally determined by manual evaluation, which is inefficient and unreliable. In this study, we proposed an automatic assessment method of maize seedling growth using unmanned aerial vehicle (UAV) RGB imagery. Firstly, high-resolution images of maize at the early and late seedling stages (before and after the third leaf) were acquired using the UAV RGB system. Secondly, the maize seedling center detection index (MCDI) was constructed, resulting in a significant enhancement of the color contrast between young and old leaves, facilitating the segmentation of maize seedling centers. Furthermore, the weed noise was removed by morphological processing and a dual-threshold method. Then, maize seedlings were extracted using the connected component labeling algorithm. Finally, the emergence rate, canopy coverage, and seedling uniformity in the field at the seedling stage were calculated and analyzed in combination with the number of seedlings. The results revealed that our approach showed good performance for maize seedling count with an average R2 greater than 0.99 and an accuracy of F1 greater than 98.5%. The estimation accuracies at the third leaf stage (V3) for the mean emergence rate and the mean seedling uniformity were 66.98% and 15.89%, respectively. The estimation accuracies at the sixth leaf stage (V6) for the mean seedling canopy coverage and the mean seedling uniformity were 32.21% and 8.20%, respectively. Our approach provided the automatic monitoring of maize growth per plot during early growth stages and demonstrated promising performance for precision agriculture in seedling management.

Multi-objective optimization of elastic metaplates for lightweight and ultrawide bandgaps

There is inevitably a conflict between multiple objectives when designing elastic metaplates (EMPs) with desirable functionalities from the perspective of practical applications. The non-dominated sorting genetic algorithm-II (NSGA-II) and the improved fast plane wave expansion method (IFPWEM) are combined to develop a multi-objective topological optimization method for EMPs with ideal efficiency and accuracy regarding lightweight and bandgap characteristics. The results indicate that initial designs featuring concentrated scatterers can yield Pareto front solutions with greater structural diversity. Additionally, the appropriate mesh resolution and the number of iterations are determined based on convergence and computational costs. Note that the post-processing method is proposed to improve the manufacturability by utilizing the connected component labeling algorithm while achieving convergence at least 15 generations earlier than the results without post-processing. The bandgap characteristics can be effectively improved by 2.34 and 2.19 at the same relative density compared to the conventional unit cell embedded with square or circular scatterers, demonstrating the superiority of the method. Additionally, the optimization objectives are further strengthened by reducing the symmetries of the unit cells. The wave propagation within finite periodic lattices is also investigated numerically and experimentally. The results of this study would provide useful guidance for developing and optimizing EMPs in the future.

A connected component labeling algorithm for implicitly defined domains

A connected component labeling algorithm for implicitly defined domains

Correlation of fragmented pattern of tumor mass captured by artificial intelligence (AI)-powered whole-slide image (WSI) analysis with biased fibroblast expansion over tumor growth and distinct mutational signatures.

e14657 Background: Little is known about the underlying mechanism of how fragmented tumor mass is observed in pathology images. Here, we present the tumor fragmentation index (TFI) defined by the number of separated tumor fragments per total area occupied by tumor, and investigate its association with distinct growth patterns of a variety of cell types in tumor microenvironment (TME) and mutational signature. Methods: Tumor and stromal areas of TCGA H&E whole-slide images (WSI, n = 7472) across the 23 carcinoma cancer types were segmented using Lunit SCOPE, an AI-powered WSI analyzer. Independent tumor masses were isolated by the connected component labeling algorithm. Tumor fragments which are too small in size ( < 968.2 μm2) or which are not in contact with the stromal area were filtered out, then TFI (count / mm2) was calculated. We evaluated the association of the TFI with the proportions of various cell types including tumor cells (TC), fibroblasts (FB), and other cells with nuclei (OT), as well as immune phenotype (IP), and single-base substitution (SBS) mutational signatures. IPs were defined as follows: inflamed IP (IIP) as high intratumoral tumor-infiltrating lymphocyte (iTIL) and stromal TIL (sTIL); immune-excluded IP (IEP) as low iTIL and high sTIL; immune-desert IP (IDP) as low TIL overall. Results: In TCGA pan-carcinoma dataset, median TFI was 12 (interquartile range, IQR 4-34 /mm2), and pancreatic adenocarcinoma (median, [IQR]; 77, [48-112]), prostate adenocarcinoma (58, [28-91]), and breast cancer (57, [22-109]) had top-ranked TFI, whereas renal cell carcinoma (1, [0-3]), and melanoma (1, [0-3]) had the lowest TFI. Considering No. of OT is relatively constant over carcinogenesis process, TFI was negatively correlated with TC-to-OT ratio (rho = -0.338, p < 0.001), but positively correlated with FB-to-OT ratio (rho = 0.312, p < 0.001), reflecting biased fibroblast expansion over tumor growth in the samples with high TFI. TFI was significantly higher in IEP (14, [6-33]) compared to that in the IIP (11, [3-30]) or IDP (9, [1-39], p < 0.001). TFI was positively correlated with SBS30, SBS1, SBS23, and SBS10B, which are dominant for C > T mutation, but negatively correlated with SBS37, SBS26, which are dominant for T > C mutation. Gene set enrichment analysis showed epithelial mesenchymal transition (normalized enrichment score, NES 2.28, adjusted p = 0.019) was up-regulated, but hypoxia (NES -1.49, adjusted p = 0.011) was down-regulated in samples with high TFI (upper 25%). Conclusions: Tumors with highly fragmented patterns in the TME had a distinct patho-genomic profile of faster expansion of fibroblasts, relatively slow-growing tumor cells, and were prone to immune-excluded phenotype. Differential mutational signatures were captured based on TFI score, warranting further investigation.

Picking point recognition for ripe tomatoes using semantic segmentation and morphological processing

Identifying picking points is the key to the intelligent picking of ripe tomatoes and an important guarantee of efficient and lossless picking. A semantic segmentation model with the improved Swin Transformer V2 and a picking point recognition algorithm based on the connection of tomato fruit, calyx and stem are proposed for the problem of picking point recognition of ripe tomatoes in complex environments. Firstly, the tomato fruit, calyx and stem are accurately identified by an improved semantic segmentation model. On the one hand, the SeMask module is integrated into the traditional Swin Transformer V2 architecture for incorporating semantic information of the image into the encoder to improve the segmentation performance. On the other hand, UPerNet is used as the decoder of the model to improve the prediction results by integrating the feature selection module and feature alignment module into the decoder. Secondly, the calyx regions and stem regions of ripe tomatoes are extracted by a connected component labeling algorithm and morphological processing. Finally, the picking points located on the tomato lateral stems are extracted by the image refinement algorithm, deburring algorithm and position constraints of the tomato calyxes. The results of the ablation and comparison experiments show that the improved semantic segmentation model proposed in this paper has the best performance on Mean Intersection over Union (MIoU) and Mean Pixel Accuracy (MPA) with 82.5% and 89.79%, respectively. After accurately identifying tomato fruits, calyxes and stems, the picking point recognition algorithm proposed in this paper has 97.43% and 86.04% on precision and recall, respectively. This research provides an effective picking point recognition method for tomato harvesting robots.

Pavement Crack Detection in Infrared Images Using a DCNN and CCL Algorithm

In-pavement crack detection, image segmentation techniques can accurately identify and extract cracks in pavement images with complex backgrounds, however, due to the interference of noise and non-crack damage, the resulting irrelevant non-crack regions reduce the extraction accuracy. To address the above problems, this research presents a road crack segmentation model consisting of a skip connection and decoder of U-Net, residual blocks in the encoding part, and the connected components labeling algorithm (CCL) in the extremity. The proposed method, depth-wise separable convolution replaces the standard convolution to reduce the number of parameters and operation cost. Post-processing using the connected components labeling algorithm solves the interference of irrelevant non-cracked regions. 4710 infrared images are used for the training and testing of the model. The testing results show that the new method for the pavement crack detection in different shadowed images is satisfactory, the detection accuracy can be up to 92.23%, and the algorithm comparison proves that the proposed algorithm is much better than that by the widely used traditional algorithms. In terms of similarity, the Dice Similarity Coefficient (DSC) is improved by 5.97%, 5.22%, and 1.49% compared to FCN, U-Net, and DeeplabV3+ networks, respectively. Therefore, the method is effective in detecting cracks in different pavement IR images, and can significantly improve the accuracy of segmentation results, reduce the error rate, improve the segmentation accuracy and robustness, and can extract cracks in pavement IR images well.

Bas-relief modeling from RGB monocular images with regional division characteristics

Traditional Bas-relief modeling methods are often limited to inefficient and difficult to be altered after the product is formed. This paper presents a novel method for bas-relief modeling from RGB monocular images with regional division characteristics. The problem discussed in this paper involves edge detection, region division, height value recovery and three-dimensional reconstruction of image. In our framework, we can automatically obtain the pixel height of each area in the image and can adjust the concave–convex relationship of each image area to obtain a bas-relief modeling which can be printed directly in 3D. The edge detection algorithm used Gaussian difference pyramid to combine the luminance information and chrominance information of digital image; the regions of the RGB monocular image are divided by the improved connected-component labeling algorithm;and the 3D pixel point cloud of each region is calculated by the shape-from-shading algorithm. Different from previous work, our method can fully obtain the image height field data and completely restored the depth information,which makes it possible to use any RGB monocular image for bas-relief modeling. Experiments with groups of images show that our method can effectively generate bas-relief modeling.

Assessing the patchiness of early life stage of a fish stock (Gadus morhua) and its contribution to the stock recruitment

Patchiness, defined as spatial heterogeneity in distribution of organisms, is a common phenomenon in zooplankton including ichtyoplankton. In heterogeneous landscapes, depending on the scale of prey and predatory distributions, individuals in patches may experience distinct differences in the survival rate compared to individuals distributed more homogeneously outside patches. In this study, we focused on drifting eggs and larvae of Northeast Arctic (NEA) cod, one of the largest exploited fish stock in the world. The eggs and larvae are largely distributed along the north-western coast of Norway and northern Russia. We ask to what degree individuals are located in patches contribute to the species recruitment. For this purpose, we developed a patch recognition method to detect the existence of patches in particle tracking simulations using a connected-component labeling algorithm. We then assessed the contribution of individuals in detected patches to the total recruitment. Our results showed that depending on year, day of year, and resolution scale for detection of patches, recruits present in patches can vary between 0.6% and 38.7% with an average of 20.4% of total recruitment. The percentage decreased with increasing day of year in the drifting season but increased with decreasing patch resolution scale, down to the finest investigated scale of 8 km. On the basis of these results, we advise field recruitment studies of NEA cod to at least resolve an 8-km spatial scale to capture effects of spatial heterogeneity in the survival rate on the species recruitment.

A parallel pipeline connected-component labeling method for on-orbit space target monitoring

The paper designs a peripheral maximum gray difference (PMGD) image segmentation method, a connected-component labeling (CCL) algorithm based on dynamic run length (DRL), and a real-time implementation streaming processor for DRL-CCL. And it verifies the function and performance in space target monitoring scene by the carrying experiment of Tianzhou-3 cargo spacecraft (TZ-3). The PMGD image segmentation method can segment the image into highly discrete and simple point targets quickly, which reduces the generation of equivalences greatly and improves the real-time performance for DRL-CCL. Through parallel pipeline design, the storage of the streaming processor is optimized by 55% with no need for external memory, the logic is optimized by 60%, and the energy efficiency ratio is 12 times than that of the graphics processing unit, 62 times than that of the digital signal proccessing, and 147 times than that of personal computers. Analyzing the results of 8756 images completed on-orbit, the speed is up to 5.88 FPS and the target detection rate is 100%. Our algorithm and implementation method meet the requirements of lightweight, high real-time, strong robustness, full-time, and stable operation in space irradiation environment.

An FPGA Accelerator for High-Speed Moving Objects Detection and Tracking With a Spike Camera.

Ultra-high-speed object detection and tracking are crucial in fields such as fault detection and scientific observation. Existing solutions to this task have deficiencies in processing speeds. To deal with this difficulty, we propose a neural-inspired ultra-high-speed moving object filtering, detection, and tracking scheme, as well as a corresponding accelerator based on a high-speed spike camera. We parallelize the filtering module and divide the detection module to accelerate the algorithm and balance latency among modules for the benefit of the task-level pipeline. To be specific, a block-based parallel computation model is proposed to accelerate the filtering module, and the detection module is accelerated by a parallel connected component labeling algorithm modeling spike sparsity and spatial connectivity of moving objects with a searching tree. The hardware optimizations include processing the LIF layer with a group of multiplexers to reduce ADD operations and replacing expensive exponential operations with multiplications of preprocessed fixed-point values to increase processing speed and minimize resource consumption. We design an accelerator with the above techniques, achieving 19 times acceleration over the serial version after 25-way parallelization. A processing system for the accelerator is also implemented on the Xilinx ZCU-102 board to validate its functionality and performance. Our accelerator can process more than 20,000 spike images with 250 × 400 resolution per second with 1.618 W dynamic power consumption.

A Provisional Labels-Reduced, Real-Time Connected Component Labeling Algorithm for Edge Hardware

Connected component labeling (CCL) is an important operation in computer vision tasks, widely used for real-time image processing on edge hardware. The existing connected component labeling algorithms always generate plenty of provisional labels. Solving the equivalence of provisional labels needs much memory access and processing time. The greater difficulty is that the processing time varies with the number and shape of the connected components. To solve the problems, this paper proposes a new two-scan CCL algorithm. It consists of novel provisional label predicting and provisional label assignment methods based on predicting table, linked lists, and equivalent table. The provisional label predicting method reduces the number of provisional labels effectively. The provisional label assignment method can implement quickly the assignment operation of CCL by combination of the first scan and the second scan with fixed processing time. We have implemented the proposed algorithm on Field Programmable Gate Arrays (FPGA) for raster scan images. The results show that its processing speed can achieve 726fps@225MHz for 640×480 image resolution, and its resource utilization is less than 15% of compared implementation. It is suitable for real-time edge applications. Compared with other algorithms, the proposed algorithm reduces provisional labels efficiently and completes the CCL operation with a fixed number of iterations. Therefore, it shortens processing time and makes the processing time constant.

Libyan Vehicle License Plate Recognition with Support Vector Machine

Computer vision has become widely used in many aspects of our daily lives. There are a great number of applications that consider computer vision as a core part of them, such as those associated with law enforcement. This paper presents the design and implementation of a model for a vehicle building entrance, using a license plate recognition algorithm for Libyan vehicles. In addition, a small dataset of Libyan vehicles was created for research purposes. As with most recognition systems, there are mainly three stages to be distinguished: plate detection using vertical and horizontal histograms, character segmentation is performed through a connected-component labeling algorithm, and finally, optical character recognition (OCR) by using support vector machines (SVMs). An Arduino board was used to control the gate opening and closing processes according to the authorized vehicles stored in the database. Ultrasonic sensors were used to detect a vehicle stop at the gate. The system was programmed with MATLAB executed on a 2.20GHz Core i7 CPU, 8 GB RAM, Windows 10. Despite the limited size of the vehicle images dataset, the experiments showed promising performance in terms of average accuracy estimated at 83.3%, and the computation time was 5 seconds.

High-Precision Measurement of 3-D Rock Morphology on Mars Using Stereo Rover Imagery

Fine-scale 3-D morphological features of rocks on the Martian surface provide important clues to Mars exploration missions and scientific discoveries. To obtain such information, an automatic approach for high-precision measurement of 3-D morphological features of Martian rocks is proposed in this letter. The approach directly detects 3-D rocks from dense point cloud that is generated by interpolating triangulated irregular network (TIN) model, through a coarse-to-fine process combining cloth simulation filtering (CSF) and connected-component labeling algorithms. Multiple 3-D morphological features are then precisely extracted by modeling rock points and fitting local ground plane. Experimental results demonstrate that the proposed approach provides an effective way for the extraction of complete 3-D rocks and comprehensive morphological features with over 90% accuracy.

Algorithmic Scheme for Concurrent Detection and Classification of Printed Circuit Board Defects

An ideal printed circuit board (PCB) defect inspection system can detect defects and classify PCB defect types. Existing defect inspection technologies can identify defects but fail to classify all PCB defect types. This research thus proposes an algorithmic scheme that can detect and categorize all 14-known PCB defect types. In the proposed algorithmic scheme, fuzzy c-means clustering is used for image segmentation via image subtraction prior to defect detection. Arithmetic and logic operations, the circle hough transform (CHT), morphological reconstruction (MR), and connected component labeling (CCL) are used in defect classification. The algorithmic scheme achieves 100% defect detection and 99.05% defect classification accuracies. The novelty of this research lies in the concurrent use of CHT, MR, and CCL algorithms to accurately detect and classify all 14-known PCB defect types and determine the defect characteristics such as the location, area, and nature of defects. This information is helpful in electronic parts manufacturing for finding the root causes of PCB defects and appropriately adjusting the manufacturing process. Moreover, the algorithmic scheme can be integrated into machine vision to streamline the manufacturing process, improve the PCB quality, and lower the production cost.

Generalised spatially weighted autocorrelation approach for monitoring and diagnosing faults in 3D topographic surfaces

Digital transformation driven by artificial intelligence (AI) allows Industry 4.0 and the internet of things (IIoT) to make significant advancements in automating, controlling, and improving the quality of numerous manufacturing processes. Three-dimensional (3D) surface topography of manufactured products holds important information about the quality of manufacturing processes. Surface topography consists of unique properties, which makes the current monitoring approaches ineffective in identifying local and spatial surface faults. In this paper, we develop a generalised spatially weighted autocorrelation approach based on AI for monitoring changes in products based on their 3D topographic surfaces. We propose two effective algorithms to identify and assign spatial weights to the topographic regions with suspicious characteristics. The normal surface hard thresholding algorithm initially enhances the representation of surface characteristics through binarization, followed by the normal surface connected-component labelling algorithm, which utilises the obtained binary results to identify and assign spatial weights to the suspicious regions. We then introduce a generalised spatially weighted Moran index, which exploits the assigned weights to locally characterise and monitor changes in the spatial autocorrelation structure of identified regions. After an anomaly surface is detected, we extract different fault diagnostic information. The proposed approach proves its robustness and efficiency in characterising, monitoring, and diagnosing different patterns of faults in 3D topographic surfaces.

A novel and efficient method for wood–leaf separation from terrestrial laser scanning point clouds at the forest plot level

Abstract With the increasing use of terrestrial laser scanning (TLS) technology in the field of forest ecology, a large number of studies have been carried out on the separation of wood and leaves based on TLS point cloud data. However, most wood–leaf separation methods adopt the point‐wise classification strategy, which is not efficient for processing large‐volume TLS datasets acquired at the forest plot level. In this study, we proposed a segment‐wise classification strategy to improve the efficiency of the wood–leaf separation from large‐volume TLS point cloud datasets collected at the forest plot. The proposed method first decomposes the point cloud into three parts based on the threshold values of its local curvature. Then, the first two parts with lower local curvatures were segmented respectively by a connected component labelling algorithm. Finally, the segmented point clouds were classified into wood or leaf segments according to the segment‐wise geometric features of each segment. We tested our method on both needleleaf and broadleaf forest plots in temperate and tropical forests. We also compared our method with two other state‐of‐the‐art wood–leaf separation methods, that is, the CANUPO and LeWoS. The results showed that our method was more than 10 times faster than the compared methods while maintaining comparable and even higher accuracy. Our study demonstrates that the segment‐wise classification strategy applies to the large‐volume TLS datasets and can greatly improve the efficiency of the classification. The proposed method is simple, fast and universally applicable to the TLS data from various tree species and forest types at the plot level, which may facilitate the adoption of TLS technology by forest ecologists in their studies.

Spatiotemporal characteristics of regional extreme precipitation in Yangtze River basin

The frequency of extreme precipitation is increasing globally and poses a significant threat to human and natural systems. At the regional scale, these changes are becoming increasingly uncertain. Therefore, it is critical to understand the spatiotemporal evolution of extreme precipitation on a regional scale. In this study, we applied the connected component labelling algorithm to define regional extreme precipitation events (REPEs). This approach considers spatial coverage and its correlation with precipitation. We further investigated the spatiotemporal changes in extreme precipitation in the Yangtze River basin using the empirical orthogonal function and examined their potential relationships with large-scale climate indices, including the El Niño-Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO). The results show that REPEs in the study area exhibited an increasing trend in both frequency and spatial coverage since 1970. In addition, the integrated effects of PDO and ENSO have a significant impact on REPEs at various scales in summer and at large scales in winter. We also analysed the differences in these events between the current year and the following year for PDO and ENSO combinations and found that the position of REPEs change with different PDO and ENSO combinations in both summer and winter season. The spatiotemporal characteristics of REPEs in the study area reveal the corresponding connections with the East Asian continent–ocean 500-hPa pressure field. Based on our analysis and current predictions of these ocean–atmospheric oscillations, we suggest creating more REPE scenarios for upcoming decades, which may benefit forecasting and water resource management.

Computer vision-based citrus tree detection in a cultivated environment using UAV imagery

Manual inspection has been a common application for counting the trees and plants in orchards in precision agriculture processes. However, it is a time-consuming and, labour-intensive and expensive task. Recent remote sensing tools and methods provide a revolutionizing innovation for monitoring individual trees and crop recognition as an alternative to manual detection useful for long-term agricultural management. Our study adopted a Connected Components Labeling (CCL) algorithm to detect and count the citrus trees based on the high-resolution Unmanned Air Vehicles (UAV) images in two agricultural patches. The workflow consisted of applying morphological image operation algorithms on multi-spectral, 5-banded orthophoto imagery (derived from 1560 scenes) and 3,57 cm spatial resolution. Our approach was able to count 1462 out of 1506 trees resulting in accuracy and precision higher than 95% (average Recall: 0.97, Precision: 0.95) in heterogeneous agricultural patches (multiple trees and tree sizes). According to our understanding, the first time a CCL algorithm has been used with UAV multi-spectral images for detecting citrus trees. It performed significantly for geolocation and counting the trees individually in a heterogenous orchard. We concluded that our methodology provided satisfactory performance to predict the number of trees (in the citrus case study) in dense patches. Therefore it could be promising to replace the conventional tree detection techniques to detect the orchard trees in complex agricultural regions.

Improved Connected Component Labeling Algorithm for Remote Sensing Image Classification

The main objective of this paper is to carry out a detailed analysis of the most popular Connected Component Labeling (CCL) algorithms for remote sensing image classification. This algorithm searches line-by-line, top to bottom to assign a splotch label to each current pixel that is connected to a splotch. This paper presents two new strategies that can be used to greatly improve the speed of connected component labeling algorithms. It assigns a label to a new object, most labeling algorithms use a scanning step that examines some of its neighbors. The first strategy deeds the dependencies among the neighbors to reduce the number of neighbors examined. The second strategy uses an array to store the equivalence information among the labels. This replaces the pointer based deep rooted trees used to store the same equivalence information. It reduces the memory required and also produces consecutive final labels. The connected component labeling assigns labels to a pixel such that adjacent pixels of the same features are assigned the same label. The paper presents a modification of this algorithm that allows the resolution of merged labels and experimental results demonstrate that proposed method is much more efficient than conventional methods for various kinds of color images. This method is improving the labeling algorithms and also benefits for other applications in computer vision and pattern recognition