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Journal articles on the topic 'Lines detection and segmentation'
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1
Wang, Shengli, Zhangpeng Zhou, and Wenbin Zhao. "Semantic Segmentation and Defect Detection of Aerial Insulators of Transmission Lines." Journal of Physics: Conference Series 2185, no. 1 (2022): 012086. http://dx.doi.org/10.1088/1742-6596/2185/1/012086.
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Abstract Aiming at the problems of low accuracy and poor generalization ability of insulator defect detection in complex aerial images by existing insulator defect detection algorithms, the possibility of using semantic segmentation technology to simplify insulator features in complex images is explored. The semantic segmentation model DeepLabv3 is cascaded with the target detector yolov3 to realize the semantic segmentation of insulators in aerial images and the detection of defects. The experimental results show that the use of the strategy of semantic segmentation and target detection can increase the accuracy of insulator defect detection by 12.58%, which effectively improves the performance of the detection model.
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Tao, Zhen, Shiwei Ren, Yueting Shi, Xiaohua Wang, and Weijiang Wang. "Accurate and Lightweight RailNet for Real-Time Rail Line Detection." Electronics 10, no. 16 (2021): 2038. http://dx.doi.org/10.3390/electronics10162038.
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Railway transportation has always occupied an important position in daily life and social progress. In recent years, computer vision has made promising breakthroughs in intelligent transportation, providing new ideas for detecting rail lines. Yet the majority of rail line detection algorithms use traditional image processing to extract features, and their detection accuracy and instantaneity remain to be improved. This paper goes beyond the aforementioned limitations and proposes a rail line detection algorithm based on deep learning. First, an accurate and lightweight RailNet is designed, which takes full advantage of the powerful advanced semantic information extraction capabilities of deep convolutional neural networks to obtain high-level features of rail lines. The Segmentation Soul (SS) module is creatively added to the RailNet structure, which improves segmentation performance without any additional inference time. The Depth Wise Convolution (DWconv) is introduced in the RailNet to reduce the number of network parameters and eventually ensure real-time detection. Afterward, according to the binary segmentation maps of RailNet output, we propose the rail line fitting algorithm based on sliding window detection and apply the inverse perspective transformation. Thus the polynomial functions and curvature of the rail lines are calculated, and rail lines are identified in the original images. Furthermore, we collect a real-world rail lines dataset, named RAWRail. The proposed algorithm has been fully validated on the RAWRail dataset, running at 74 FPS, and the accuracy reaches 98.6%, which is superior to the current rail line detection algorithms and shows powerful potential in real applications.
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Song, Xiang, Xiaoyu Che, Huilin Jiang, et al. "A Robust Detection Method for Multilane Lines in Complex Traffic Scenes." Mathematical Problems in Engineering 2022 (March 8, 2022): 1–14. http://dx.doi.org/10.1155/2022/7919875.
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The robustness and stability of lane detection is vital for advanced driver assistance vehicle technology and even autonomous driving technology. To meet the challenges of real-time lane detection in complex traffic scenes, a simple but robust multilane detection method is proposed in this paper. The proposed method breaks down the lane detection task into two stages, that is, lane line detection algorithm based on instance segmentation and lane modeling algorithm based on adaptive perspective transform. Firstly, the lane line detection algorithm based on instance segmentation is decomposed into two tasks, and a multitask network based on MobileNet is designed. This algorithm includes two parts: lane line semantic segmentation branch and lane line Id embedding branch. The lane line semantic segmentation branch is mainly used to obtain the segmentation results of lane pixels and reconstruct the lane line binary image. The lane line Id embedding branch mainly determines which pixels belong to the same lane line, thereby classifying different lane lines into different categories and then clustering these different categories. Secondly, the adaptive perspective transformation model is adopted. In this model, the motion information is used to accurately convert the original image into a bird’s-eye view image, and then the least-squares second-order polynomial fitting is performed on the lane line pixels. Finally, experiments on the CULane dataset show that the proposed method achieved similar or better performance compared with several state-of-the-art methods, the F1 score of the proposed method in the normal test set and most challenge test sets is better than other algorithms, which verifies the effectiveness of the proposed method, and then the field experiments results show that the proposed method has good practical application value in various complex traffic scenes.
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Yan, Jichen, Xiaoguang Zhang, Siyang Shen, et al. "A Real-Time Strand Breakage Detection Method for Power Line Inspection with UAVs." Drones 7, no. 9 (2023): 574. http://dx.doi.org/10.3390/drones7090574.
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Power lines are critical infrastructure components in power grid systems. Strand breakage is a kind of serious defect of power lines that can directly impact the reliability and safety of power supply. Due to the slender morphology of power lines and the difficulty in acquiring sufficient sample data, strand breakage detection remains a challenging task. Moreover, power grid corporations prefer to detect these defects on-site during power line inspection using unmanned aerial vehicles (UAVs), rather than transmitting all of the inspection data to the central server for offline processing which causes sluggish response and huge communication burden. According to the above challenges and requirements, this paper proposes a novel method for detecting broken strands on power lines in images captured by UAVs. The method features a multi-stage light-weight pipeline that includes power line segmentation, power line local image patch cropping, and patch classification. A power line segmentation network is designed to segment power lines from the background; thus, local image patches can be cropped along the power lines which preserve the detailed features of power lines. Subsequently, the patch classification network recognizes broken strands in the image patches. Both the power line segmentation network and the patch classification network are designed to be light-weight, enabling efficient online processing. Since the power line segmentation network can be trained with normal power line images that are easy to obtain and the compact patch classification network can be trained with relatively few positive samples using a multi-task learning strategy, the proposed method is relatively data efficient. Experimental results show that, trained on limited sample data, the proposed method can achieve an F1-score of 0.8, which is superior to current state-of-the-art object detectors. The average inference speed on an embedded computer is about 11.5 images per second. Therefore, the proposed method offers a promising solution for conducting real-time on-site power line defect detection with computing sources carried by UAVs.
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Xing, Junyao, Xiaojun Bi, and Yu Weng. "A Multi-Scale Hybrid Attention Network for Sentence Segmentation Line Detection in Dongba Scripture." Mathematics 11, no. 15 (2023): 3392. http://dx.doi.org/10.3390/math11153392.
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Dongba scripture sentence segmentation is an important and basic work in the digitization and machine translation of Dongba scripture. Dongba scripture sentence segmentation line detection (DS-SSLD) as a core technology of Dongba scripture sentence segmentation is a challenging task due to its own distinctiveness, such as high inherent noise interference and nonstandard sentence segmentation lines. Recently, projection-based methods have been adopted. However, these methods are difficult when dealing with the following two problems. The first is the noisy problem, where a large number of noise in the Dongba scripture image interference detection results. The second is the Dongba scripture inherent characteristics, where many vertical lines in Dongba hieroglyphs are easily confused with the vertical sentence segmentation lines. Therefore, this paper aims to propose a module based on the convolutional neural network (CNN) to improve the accuracy of DS-SSLD. To achieve this, we first construct a tagged dataset for training and testing DS-SSLD, including 2504 real images collected from Dongba scripture books and sentence segmentation targets. Then, we propose a multi-scale hybrid attention network (Multi-HAN) based on YOLOv5s, where a multiple hybrid attention unit (MHAU) is used to enhance the distinction between important features and redundant features, and the multi-scale cross-stage partial unit (Multi-CSPU) is used to realize multi-scale and richer feature representation. The experiment is carried out on the Dongba scripture sentence segmentation dataset we built. The experimental results show that the proposed method exhibits excellent detection performance and outperforms several state-of-the-art methods.
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Chen, Yong, Yun-hui Wang, Song Li, and Meng Li. "Transmission Line Instance Segmentation Algorithm Based on YOLACT." Journal of Physics: Conference Series 2562, no. 1 (2023): 012018. http://dx.doi.org/10.1088/1742-6596/2562/1/012018.
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Abstract In the field of intelligent power patrol inspection, the transmission line is an important identification and detection target. The measurement of line spacing and ground distance are key technologies in the field of inspection. Therefore, it is necessary to quickly and accurately segment transmission lines. The transmission lines occupy a large span and vary widely in length. To improve the segmentation rate and accuracy of the transmission lines, we adopted EfficientNet as the main network. With the same accuracy, the number of parameters is reduced by 80% compared with ResNet 101. The network training cost is reduced, and the detection rate of the model is improved. To deal with the influence caused by the large change in transmission line length, we introduce adaptive anchor box calculation and the FPN + PAN structure. At the same time, multiple transmission lines are often overlapped, so the traditional NMS makes it easy to cause the omission or confusion of lines. We improve the NMS. Finally, we adopted the modified CIoU loss function to optimize the loss function. From the experimental results, our model has good performance for instance in segmenting transmission lines.
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Lee, Jaehyun, Keunwoo Lee, Jaewon Yang, Young-Jin Kim, and Seung-Woo Kim. "Comb segmentation spectroscopy for rapid detection of molecular absorption lines." Optics Express 27, no. 6 (2019): 9088. http://dx.doi.org/10.1364/oe.27.009088.
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Zhu, Yuhang, Zhezhuang Xu, Ye Lin, Dan Chen, Zhijie Ai, and Hongchuan Zhang. "A Multi-Source Data Fusion Network for Wood Surface Broken Defect Segmentation." Sensors 24, no. 5 (2024): 1635. http://dx.doi.org/10.3390/s24051635.
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Wood surface broken defects seriously damage the structure of wooden products, these defects have to be detected and eliminated. However, current defect detection methods based on machine vision have difficulty distinguishing the interference, similar to the broken defects, such as stains and mineral lines, and can result in frequent false detections. To address this issue, a multi-source data fusion network based on U-Net is proposed for wood broken defect detection, combining image and depth data, to suppress the interference and achieve complete segmentation of the defects. To efficiently extract various semantic information of defects, an improved ResNet34 is designed to, respectively, generate multi-level features of the image and depth data, in which the depthwise separable convolution (DSC) and dilated convolution (DC) are introduced to decrease the computational expense and feature redundancy. To take full advantages of two types of data, an adaptive interacting fusion module (AIF) is designed to adaptively integrate them, thereby generating accurate feature representation of the broken defects. The experiments demonstrate that the multi-source data fusion network can effectively improve the detection accuracy of wood broken defects and reduce the false detections of interference, such as stains and mineral lines.
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Cheng, Wangfeng, Xuanyao Wang, and Bangguo Mao. "Research on Lane Line Detection Algorithm Based on Instance Segmentation." Sensors 23, no. 2 (2023): 789. http://dx.doi.org/10.3390/s23020789.
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Aiming at the current lane line detection algorithm in complex traffic scenes, such as lane lines being blocked by shadows, blurred roads, and road sparseness, which lead to low lane line detection accuracy and poor real-time detection speed, this paper proposes a lane line detection algorithm based on instance segmentation. Firstly, the improved lightweight network RepVgg-A0 is used to encode road images, which expands the receptive field of the network; secondly, a multi-size asymmetric shuffling convolution model is proposed for the characteristics of sparse and slender lane lines, which enhances the ability to extract lane line features; an adaptive upsampling model is further proposed as a decoder, which upsamples the feature map to the original resolution for pixel-level classification and detection, and adds the lane line prediction branch to output the confidence of the lane line; and finally, the instance segmentation-based lane line detection algorithm is successfully deployed on the embedded platform Jetson Nano, and half-precision acceleration is performed using NVDIA’s TensorRT framework. The experimental results show that the Acc value of the lane line detection algorithm based on instance segmentation is 96.7%, and the FPS is 77.5 fps/s. The detection speed deployed on the embedded platform Jetson Nano reaches 27 fps/s.
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Tang, Yang Shan, Dao Hua Xia, Gui Yang Zhang, Li Na Ge, and Xin Yang Yan. "The Detection Method of Lane Line Based on the Improved Otsu Threshold Segmentation." Applied Mechanics and Materials 741 (March 2015): 354–58. http://dx.doi.org/10.4028/www.scientific.net/amm.741.354.
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For overcoming the shortage of Otsu method, proposed an improved Otsu threshold segmentation algorithm. On the basis of Otsu threshold segmentation algorithm, the gray level was divided into two classes according to the image segmentation, to determine the best threshold by comparing their center distance, so as to achieve peak line recognition under the condition of multiple gray levels. Then did experiments on image segmentation of the lane line with MATLAB by traditional Otsu threshold segmentation algorithm and the improved algorithm, the threshold of traditional Otsu threshold segmentation algorithm is 144 and the threshold of the improved Otsu threshold segmentation algorithm is 131, the processing time is within 0.453 s. Test results show that the white part markings appear more, the intersection place of white lines and the background is more clear, so this method can identify lane markings well and meet the real-time requirements.
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Zhu, Q., W. Jiang, and J. Zhang. "FEATURE LINE BASED BUILDING DETECTION AND RECONSTRUCTION FROM OBLIQUE AIRBORNE IMAGERY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-4/W5 (May 12, 2015): 199–204. http://dx.doi.org/10.5194/isprsarchives-xl-4-w5-199-2015.
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In this paper, a feature line based method for building detection and reconstruction from oblique airborne imagery is presented. With the development of Multi-View Stereo technology, increasing photogrammetric softwares are provided to generate textured meshes from oblique airborne imagery. However, errors in image matching and mesh segmentation lead to the low geometrical accuracy of building models, especially at building boundaries. To simplify massive meshes and construct accurate 3D building models, we integrate multi-view images and meshes by using feature lines, in which contour lines are used for building detection and straight skeleton for building reconstruction. Firstly, through the contour clustering method, buildings can be quickly and robustly detected from meshes. Then, a feature preserving mesh segmentation method is applied to accurately extract 3D straight skeleton from meshes. Finally, straight feature lines derived from multi-view images are used to rectify inaccurate parts of 3D straight skeleton of buildings. Therefore, low quality model can be refined by the accuracy improvement of mesh feature lines and rectification with feature lines of multi-view images. The test dataset in Zürich is provided by ISPRS/EuroSDR initiative Benchmark on High Density Image Matching for DSM Computation. The experiments reveal that the proposed method can obtain convincing and high quality 3D building models from oblique airborne imagery.
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Triwijoyo, Bambang Krismono. "Segmentasi Citra Pembuluh Darah Retina Menggunakan Metode Deteksi Garis Multi Skala." Jurnal Matrik 15, no. 1 (2017): 13. http://dx.doi.org/10.30812/matrik.v15i1.28.
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Changes in retinal blood vessels feature a sign of serious illnesses such as heart disease and stroke. Therefore, the analysis of retinal vascular features can help in detecting these changes and allow patients to take preventive measures at an early stage of this disease. Automation of this process will help reduce the costs associated with the specialist and eliminate inconsistencies that occur in manual detection system. Among the retinal image analysis, image extraction retinal blood vessels is a crucial step before measurement. In this paper, we use an effective methodof automatically extracting the blood vessels of the color images of the retina using a length detector line in several different scales, in order to maintain the strength and eliminates the weaknesses of each detector individual lines, the result of the detection lines on various scales combined to produce a segmentation of each image of the retina. The performance of the method is evaluated quantitatively using DRIVE dataset. Test results show that this method achieve high accuracy is 0.9407 approaching measurement results manually by experts, and this method produces accurate segmentation in detecting retinal blood vessels with effiiency by quickly segmenting time is 2.5 seconds per image.
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Yang, Ranbing, Yuming Zhai, Jian Zhang, et al. "Potato Visual Navigation Line Detection Based on Deep Learning and Feature Midpoint Adaptation." Agriculture 12, no. 9 (2022): 1363. http://dx.doi.org/10.3390/agriculture12091363.
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Potato machinery has become more intelligent thanks to advancements in autonomous navigation technology. The effect of crop row segmentation directly affects the subsequent extraction work, which is an important part of navigation line detection. However, the shape differences of crops in different growth periods often lead to poor image segmentation. In addition, noise such as field weeds and light also affect it, and these problems are difficult to address using traditional threshold segmentation methods. To this end, this paper proposes an end-to-end potato crop row detection method. The first step is to replace the original U-Net’s backbone feature extraction structure with VGG16 to segment the potato crop rows. Secondly, a fitting method of feature midpoint adaptation is proposed, which can realize the adaptive adjustment of the vision navigation line position according to the growth shape of a potato. The results show that the method used in this paper has strong robustness and can accurately detect navigation lines in different potato growth periods. Furthermore, compared with the original U-Net model, the crop row segmentation accuracy is improved by 3%, and the average deviation of the fitted navigation lines is 2.16°, which is superior to the traditional visual guidance method.
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Wang, W., F. Berholm, K. Hu, et al. "Lane Line Extraction in Raining Weather Images by Ridge Edge Detection with Improved MSR and Hessian Matrix." Information Technology and Control 50, no. 4 (2021): 722–35. http://dx.doi.org/10.5755/j01.itc.50.4.29094.
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To accurately detect lane lines in road traffic images at raining weather, a edge detection based method is studied, which mainly includes four algorithms. (1) Firstly an image is enhanced by an improved Retinex algorithm; (2) Then, an algorithm based on the Hessian matrix is applied to strengthen lane lines; (3) To extract the feature points of a lane line, a ridge edge detection algorithm based on five line detection in four directions is proposed, in which, in light on the possible positions of lane lines in the image, it detects the maximum gray level points in the local area of the detecting point within the pre-set valid detection region; and (4) After the noise removal based on the minimum circumscribed rectangles, the candidate points of lane lines are connected as segments, and for the gap filling between segments, in order to make connection correctly, the algorithm makes the filling in two steps, short gap and long gap fillings, and the long gap filling is made on the combination of segment angle difference and gap distance and gap angle. By testing hundreds of images of the lane lines at raining weather and by comparing several traditional image enhancement and segmentation algorithms, the new method of the lane line detection can produce the satisfactory results.
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Sindel, Aline, Thomas Klinke, Andreas Maier, and Vincent Christlein. "ChainLineNet: Deep-Learning-Based Segmentation and Parameterization of Chain Lines in Historical Prints." Journal of Imaging 7, no. 7 (2021): 120. http://dx.doi.org/10.3390/jimaging7070120.
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The paper structure of historical prints is sort of a unique fingerprint. Paper with the same origin shows similar chain line distances. As the manual measurement of chain line distances is time consuming, the automatic detection of chain lines is beneficial. We propose an end-to-end trainable deep learning method for segmentation and parameterization of chain lines in transmitted light images of German prints from the 16th Century. We trained a conditional generative adversarial network with a multitask loss for line segmentation and line parameterization. We formulated a fully differentiable pipeline for line coordinates’ estimation that consists of line segmentation, horizontal line alignment, and 2D Fourier filtering of line segments, line region proposals, and differentiable line fitting. We created a dataset of high-resolution transmitted light images of historical prints with manual line coordinate annotations. Our method shows superior qualitative and quantitative chain line detection results with high accuracy and reliability on our historical dataset in comparison to competing methods. Further, we demonstrated that our method achieves a low error of less than 0.7 mm in comparison to manually measured chain line distances.
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Muhammad Naufal Mansor, Mohd Zamri Hasan, Wan Azani Mustafa, et al. "Leukemia Blood Cells Detection using Neural Network Classifier." Journal of Advanced Research in Applied Sciences and Engineering Technology 33, no. 1 (2023): 152–62. http://dx.doi.org/10.37934/araset.33.1.152162.
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Image segmentation is an image processing operation performed on the image in order to partition the image into some images based on the information contained in the original image. Image segmentation plays an important role in many medical imaging applications, image segmentation facilitates the anatomy process in a particular body of human body. Classification and clustering are the methods used un data mining for analyzing the data sets and divide them on the basis of some particular classification rules. There are many image segmentation tools that used for medical purpose, so it is necessary to define and/or to improve the image segmentation methods in order to get the best method. In this study, the image of leukemia and red blood cells will be used as samples to determine the best algorithm in image segmentation. The procedure for doing segmentation itself is clustering image, edge detection on image, and image classification. The clustering is to extract important information from an image. The edge detection is to determine the existence of edges of lines in image in order to investigate and localize the desired edge features. Moreover, the classification analyzes the properties of some images and organizes the information into certain categories. In this study, the Neural Network and K-Nearest Neighbor are used for image classification by paired with Local Binary Pattern and Principal Component Analysis. The results revealed that the best method of proven in classifying images is from Local Binary Pattern feature extraction with the average accuracy of 94%.
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He, Lei, Shuang Wang, and Yongcun Guo. "Detection of Pits by Conjugate Lines: An Algorithm for Segmentation of Overlapping and Adhesion Targets in DE-XRT Sorting Images of Coal and Gangue." Applied Sciences 12, no. 19 (2022): 9850. http://dx.doi.org/10.3390/app12199850.
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In lump coal and gangue separation based on photoelectric technology, the prerequisite of using a dual-energy X-ray to locate and identify coal and gangue is to obtain the independent target area. However, with the increase in the input of the sorting system, the actual collected images had adhesion and overlapping targets. This paper proposes a pit point detection and segmentation algorithm to solve the problem of overlapping and adhesion targets. The adhesion forms are divided into open and closed-loop adhesion (OLA and CLA). Then, an open- and closed-loop crossing algorithm (OLCA and CLCA) is proposed. We used the conjugate lines to detect the pit and judge the position and distance of the pixel point relative to the conjugate lines. Then, we set the constraint of the distance of the pixel point and the relatively straight line position to complete the pit detection. Finally, the minimum distance search method was used to obtain the dividing line corresponding to the pit to complete the image segmentation. The experiment results demonstrate that the segmentation accuracy of the overlapping target was 90.73%, and the acceptable segmentation accuracy was 94.15%.
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Malik, Saud, Ahthasham Sajid, Arshad Ahmad, et al. "An Efficient Skewed Line Segmentation Technique for Cursive Script OCR." Scientific Programming 2020 (December 3, 2020): 1–12. http://dx.doi.org/10.1155/2020/8866041.
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Segmentation of cursive text remains the challenging phase in the recognition of text. In OCR systems, the recognition accuracy of text is directly dependent on the quality of segmentation. In cursive text OCR systems, the segmentation of handwritten Urdu language text is a complex task because of the context sensitivity and diagonality of the text. This paper presents a line segmentation algorithm for Urdu handwritten and printed text and subsequently to ligatures. In the proposed technique, the counting pixel approach is employed for modified header and baseline detection, in which the system first removes the skewness of the text page, and then the page is converted into lines and ligatures. The algorithm is evaluated on manually generated Urdu printed and handwritten dataset. The proposed algorithm is tested separately on handwritten and printed text, showing 96.7% and 98.3% line accuracy, respectively. Furthermore, the proposed line segmentation algorithm correctly extracts the lines when tested on Arabic text.
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Liu, Huaming, Rumeng Shi, Xuehui Bi, Xiuyou Wang, and Weilan Wang. "Line Segmentation of Tibetan Ancient Books Based on A* Algorithm." Journal of Physics: Conference Series 2356, no. 1 (2022): 012046. http://dx.doi.org/10.1088/1742-6596/2356/1/012046.
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Line segmentation is an important step in image character recognition. However, due to the problems of interline adhesion, overlapping, and skewing of document images, the effect of text line segmentation is not ideal. Therefore, further research on image line segmentation of Tibetan ancient books is urgently needed. The standard A* algorithm is not ideal for line segmentation of Tibetan ancient books, so this paper proposes a block-based A* algorithm for line segmentation of Tibetan ancient books. The method firstly preprocesses the image such as binarization and tilt correction and then performs horizontal projection. The obtained horizontal projection histogram is subjected to smoothing and peak detection to determine the position and number of text lines. Use the peak value to find the core area of the image text, extract the upper vowel and perform line attribution. Then, the image after removing the upper vowel is divided into 7 blocks, and each block is processed separately by the A* algorithm. When the algorithm finds the text line segmentation path, five cost functions are selected to calculate the cost from the starting point to the endpoint of the line segmentation, and the minimum cost path is found for text line segmentation. Finally, merge the segmented text lines, attribute the upper vowels, and rotate the text lines to obtain the final text line segmentation result. Experimental results show that our methos can effectively deal with the problems of text line adhesion and overlap, and achieve a good effect on text line segmentation.
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HIREMATH, P. S., and AJIT DANTI. "DETECTION OF MULTIPLE FACES IN AN IMAGE USING SKIN COLOR INFORMATION AND LINES-OF-SEPARABILITY FACE MODEL." International Journal of Pattern Recognition and Artificial Intelligence 20, no. 01 (2006): 39–61. http://dx.doi.org/10.1142/s021800140600451x.
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In this paper, human faces are detected using the skin color information and the Lines-of-Separability (LS) face model. The various skin color spaces based on widely used color models such as RGB, HSV, YCbCr, YUV and YIQ are compared and an appropriate color model is selected for the purpose of skin color segmentation. The proposed approach of skin color segmentation is based on YCbCr color model and sigma control limits for variations in its color components. The segmentation by the proposed method is found to be more efficient in terms of speed and accuracy. Each of the skin segmented regions is then searched for the facial features using the LS face model to detect the face present in it. The LS face model is a geometric approach in which the spatial relationships among the facial features are determined for the purpose of face detection. Hence, the proposed approach based on the combination of skin color segmentation and LS face model is able to detect single as well as multiple faces present in a given image. The experimental results and comparative analysis demonstrate the effectiveness of this approach.
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P V, Pearlsy, and Deepa Sankar. "Handwriting-Based Text Line Segmentation from Malayalam Documents." Applied Sciences 13, no. 17 (2023): 9712. http://dx.doi.org/10.3390/app13179712.
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Optical character recognition systems for Malayalam handwritten documents have become an open research area. A major hindrance in this research is the unavailability of a benchmark database. Therefore, a new database of 402 Malayalam handwritten document images and ground truth images of 7535 text lines is developed for the implementation of the proposed technique. This paper proposes a technique for the extraction of text lines from handwritten documents in the Malayalam language, specifically based on the handwriting of the writer. Text lines are extracted based on horizontal and vertical projection values, the size of the handwritten characters, the height of the text lines and the curved nature of the Malayalam alphabet. The proposed technique is able to overcome incorrect segmentation due to the presence of characters written with spaces above or below other characters and the overlapping of lines because of ascenders and descenders. The performance of the proposed method for text line extraction is quantitatively evaluated using the MatchScore value metric and is found to be 85.507%. The recognition accuracy, detection rate and F-measure of the proposed method are found to be 99.39%, 85.5% and 91.92%, respectively. It is experimentally verified that the proposed method outperforms some of the existing language-independent text line extraction algorithms.
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Chen, He, Nan Li, Tian Chen Huang, and Rong Xia Duan. "Research on TV Goniometer Object Extraction Algorithm Based on Threshold Segmentation." Advanced Materials Research 889-890 (February 2014): 1093–98. http://dx.doi.org/10.4028/www.scientific.net/amr.889-890.1093.
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In the TV goniometer detection system, to play the signal and field of view points line extraction is a key link in the process of parameter detection. Combination of target processing requirements, this article will target extraction algorithm based on gray level threshold and edge detection algorithm is studied, and through the experimental analysis to select the optimal algorithm was applied to the detection of TV goniometer; According to the characteristics of the standard signal and view points, lines, and put forward the corresponding methods of target recognition, and is verified through experiments
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Vrochidou, Eleni, George K. Sidiropoulos, Athanasios G. Ouzounis, et al. "Towards Robotic Marble Resin Application: Crack Detection on Marble Using Deep Learning." Electronics 11, no. 20 (2022): 3289. http://dx.doi.org/10.3390/electronics11203289.
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Cracks can occur on different surfaces such as buildings, roads, aircrafts, etc. The manual inspection of cracks is time-consuming and prone to human error. Machine vision has been used for decades to detect defects in materials in production lines. However, the detection or segmentation of cracks on a randomly textured surface, such as marble, has not been sufficiently investigated. This work provides an up-to-date systematic and exhaustive study on marble crack segmentation with color images based on deep learning (DL) techniques. The authors conducted a performance evaluation of 112 DL segmentation models with red–green–blue (RGB) marble slab images using five-fold cross-validation, providing consistent evaluation metrics in terms of Intersection over Union (IoU), precision, recall and F1 score to identify the segmentation challenges related to marble cracks’ physiology. Comparative results reveal the FPN model as the most efficient architecture, scoring 71.35% mean IoU, and SE-ResNet as the most effective feature extraction network family. The results indicate the importance of selecting the appropriate Loss function and backbone network, underline the challenges related to the marble crack segmentation problem, and pose an important step towards the robotic automation of crack segmentation and simultaneous resin application to heal cracks in marble-processing plants.
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Bojarczak, Piotr, Piotr Lesiak, and Waldemar Nowakowski. "Automatic Detection of Ballast Unevenness Using Deep Neural Network." Applied Sciences 14, no. 7 (2024): 2811. http://dx.doi.org/10.3390/app14072811.
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The amount of freight transported by rail and the number of passengers are increasing year by year. Any disruption to the passenger or freight transport stream can generate both financial and human losses. Such a disruption can be caused by the rail infrastructure being in poor condition. For this reason, the state of the infrastructure should be monitored periodically. One of the important elements of railroad infrastructure is the ballast. Its condition has a significant impact on the safety of rail traffic. The unevenness of the ballast surface is one of the indicators of its condition. For this reason, a regulation was introduced by Polish railway lines specifying the maximum threshold of ballast unevenness. This article presents an algorithm that allows for the detection of irregularities in the ballast. These irregularities are determined relative to the surface of the sleepers. The images used by the algorithm were captured by a laser triangulation system placed on a rail inspection vehicle managed by the Polish railway lines. The proposed solution has the following elements of novelty: (a) it presents a simple criterion for evaluating the condition of the ballast based on the measurement of its unevenness in relation to the level of the sleeper; (b) it treats ballast irregularity detection as an instance segmentation process and it compares two segmentation algorithms, Mask R-CNN and YOLACT, in terms of their application to ballast irregularity detection; and (c) it uses segmentation-related metrics—mAP (Mean Average Precision), IoU (Intersection over Union) and Pixel Accuracy—to evaluate the quality of the detection of ballast irregularity.
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Liu, Zhan Wen, Shan Lin, and Sheng Gen Dou. "A Novel Video Detection System on Traffic Flow Inspection." Applied Mechanics and Materials 182-183 (June 2012): 440–44. http://dx.doi.org/10.4028/www.scientific.net/amm.182-183.440.
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A prototype of video detection system applied to traffic flow inspection is developed, which uses CMOS linear image sensor with high resolution 2K pixels and wide dynamic range as the core of imaging device. It combines FPGA with DSP as the core of acquisition and processing of massive image data. Moreover, a novel multiscale and hierarchical clustering algorithm for image segmentation is presented. Based on the theory of graph spectral, the algorithm can construct a new graph by analyzing the feature of an original image at different clustering scales, so that image segmentation can be accomplished easily to segment the image. The simulation results show that the row scan speed of this system can reach to 1000 lines per second, the resolution being 2048 pixels.
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Droby, Ahmad, Berat Kurar Barakat, Reem Alaasam, Boraq Madi, Irina Rabaev, and Jihad El-Sana. "Text Line Extraction in Historical Documents Using Mask R-CNN." Signals 3, no. 3 (2022): 535–49. http://dx.doi.org/10.3390/signals3030032.
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Text line extraction is an essential preprocessing step in many handwritten document image analysis tasks. It includes detecting text lines in a document image and segmenting the regions of each detected line. Deep learning-based methods are frequently used for text line detection. However, only a limited number of methods tackle the problems of detection and segmentation together. This paper proposes a holistic method that applies Mask R-CNN for text line extraction. A Mask R-CNN model is trained to extract text lines fractions from document patches, which are further merged to form the text lines of an entire page. The presented method was evaluated on the two well-known datasets of historical documents, DIVA-HisDB and ICDAR 2015-HTR, and achieved state-of-the-art results. In addition, we introduce a new challenging dataset of Arabic historical manuscripts, VML-AHTE, where numerous diacritics are present. We show that the presented Mask R-CNN-based method can successfully segment text lines, even in such a challenging scenario.
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Guo, Jiayi, Xuelin Guo, and Limin Wang. "The Detection Algorithm of Broken Wires in Power Lines Based on Grabcut Segmentation." IOP Conference Series: Materials Science and Engineering 768 (March 31, 2020): 072017. http://dx.doi.org/10.1088/1757-899x/768/7/072017.
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Yu, Hao, Zhengyang Wang, Qingjie Zhou, et al. "Deep-Learning-Based Semantic Segmentation Approach for Point Clouds of Extra-High-Voltage Transmission Lines." Remote Sensing 15, no. 9 (2023): 2371. http://dx.doi.org/10.3390/rs15092371.
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The accurate semantic segmentation of point cloud data is the basis for their application in the inspection of extra high-voltage transmission lines (EHVTL). As deep learning evolves, point-wise-based deep neural networks have shown great potential for the semantic segmentation of EHVTL point clouds. However, EHVTL point cloud data are characterized by a large data volume and significant class imbalance. Therefore, the down-sampling method and point cloud feature extraction method used in current point-wise-based deep neural networks hardly meet the needs of computational accuracy and efficiency. In this paper, we proposed a two-step down-sampling method and a point cloud feature extraction method based on local feature aggregation of the point clouds after down-sampling in each layer of the model (LFAPAD). We then established a deep neural network named PowerLine-Net for the semantic segmentation of the EHVTL point clouds. Furthermore, in order to test and analyze the performance of PowerLine-Net, we constructed a point cloud dataset for the EHVTL scenes. Using this dataset and the Semantic3D dataset, we implemented network parameter testing, semantic segmentation, and an accuracy comparison of different networks based on PowerLine-Net. The results illustrate that the semantic segmentation model proposed in this paper has a high computational efficiency and accuracy in the semantic segmentation of EHVTL point clouds. Compared with conventional deep neural networks, including PointCNN, KPConv, SPG, PointNet++, and RandLA-Net, PowerLine-Net also achieves a higher accuracy in the semantic segmentation of EHVTL point clouds. Moreover, based on the results predicted by PowerLine-Net, the risk point detection for EHVTL point clouds has been achieved, which demonstrates the important value of this network in practical applications. In addition, as shown by the results of Semantic3D, PowerLine-Net also achieves a high segmentation accuracy, which proves its powerful capability and wide applicability in semantic segmentation for the point clouds of large-scale scenes.
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Zhang, Wenli, and Qingfeng Gao. "Recognition and Extraction of Power Transmission Lines Based on Infrared Image Processing for Line-following Robots." Academic Journal of Science and Technology 7, no. 1 (2023): 131–36. http://dx.doi.org/10.54097/ajst.v7i1.11299.
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To further improve the real-time performance and accuracy of power transmission line maintenance, this paper primarily focuses on the preliminary line recognition and extraction method based on thermal image processing of infrared images collected by line-following robots for thermal fault detection. Firstly, filtering and noise reduction techniques along with enhanced image processing are applied to preprocess the collected infrared images. This effectively addresses the noise and interference from background objects, which can affect the extraction of overheated areas on the lines, while also reducing the computational memory required for subsequent image processing. Subsequently, an improved Canny edge detection algorithm is employed to extract the edges of foreground objects in the images. Additionally, a region-growing algorithm combined with the geometric features of the lines is employed to filter out unwanted thermal sources, enabling the accurate segmentation and extraction of power transmission lines. This forms a solid foundation for subsequent detection and identification of abnormal hotspots on the extracted lines, and holds significance for the inspection and maintenance of existing thermal faults and potential hotspots in power transmission lines.
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Savage, C. J., and D. H. Foster. "Target Detection and Texture Segmentation in Briefly Presented Displays of Curved-line Elements." Perception 25, no. 1_suppl (1996): 135. http://dx.doi.org/10.1068/v96l0504.
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Similar pre-attentive processes are often thought to underlie rapid texture segmentation and target ‘pop-out’ in multi-element displays (but see Wolfe, 1992 Vision Research32 757 – 763). Performance in target-detection and texture-segmentation tasks was measured here for briefly presented displays of curved-line elements. In both tasks 49 curved-line elements, each subtending 1 deg of visual angle, were presented in a circular display for 100 ms and followed by a mask. The position of each element in the array was jittered to reduce any possible collinearity or luminance cues. In the target-detection task, observers determined whether the display contained a target which differed in curvature from the other, background elements. In the texture-segmentation task, observers determined the orientation, horizontal or vertical, of a foreground region of 4 × 2 elements which differed in curvature from the background elements. Performance, quantified as percent correct, was measured as a function of target (or foreground) and background curvatures. At small background curvatures, performance in the two tasks was very similar: performance was best when target or foreground curvature was large. Performance differed, however, at large background curvatures: for texture segmentation there was a marked peak in performance when foreground curvature was close to zero, but there was no corresponding peak for target detection. It seems that some additional, global cue can be extracted from a group of straight or slightly curved lines that is not available from a single line, thereby facilitating texture segmentation but not target detection.
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Chen, Tianao, and Aotian Chen. "Road Sign Recognition Method Based on Segmentation and Attention Mechanism." Mobile Information Systems 2022 (June 29, 2022): 1–11. http://dx.doi.org/10.1155/2022/6389580.
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With the development of autonomous driving, low-cost visual perception solutions have become a current research hotspot. However, the performance of the pure visual scheme in unfriendly environments such as low light, rain and fog, and complex traffic scenes has a large room for improvement. Moreover, with the development and application of deep learning, the balance between the accuracy and real-time performance of deep learning models is a difficult problem for current research. Aiming at the problems of large differences in the target scale of pavement signs and the difficulty of balancing model accuracy and real-time performance, a ground semantic cognition method based on segmentation and attention mechanism is proposed. The lightweight semantic segmentation model ERFNet is used to realize the semantic segmentation of pavement signs and the instantiation of lane lines. When only lane line detection is required, the prediction branch of lane line existence is introduced based on the lightweight semantic segmentation model ERFNet to realize lane line instantiation cognition, solve the imbalance of positive and negative lane line detection samples, and obtain the final lane line detection result via postprocessing. Deep features were used to guide shallow layers to extract semantic features at high resolution, and the model performance was further optimized without increasing the inference cost.
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Kim, JongBae. "Efficient Vanishing Point Detection for Driving Assistance Based on Visual Saliency Map and Image Segmentation from a Vehicle Black-Box Camera." Symmetry 11, no. 12 (2019): 1492. http://dx.doi.org/10.3390/sym11121492.
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Techniques for detecting a vanishing point (VP) which estimates the direction of a vehicle by analyzing its relationship with surrounding objects have gained considerable attention recently. VPs can be used to support safe vehicle driving in areas such as for autonomous driving, lane-departure avoidance, distance estimation, and road-area detection, by detecting points in which parallel extension lines of objects are concentrated at a single point in a 3D space. In this paper, we proposed a method of detecting the VP in real time for applications to intelligent safe-driving support systems. In order to support safe driving of autonomous vehicles, it is necessary to drive the vehicle with the VP in center of the road image in order to prevent the vehicle from moving out of the road area while driving. Accordingly, in order to detect the VP in the road image, a method of detecting a point where straight lines intersect in an area where edge directional feature information is concentrated is required. The visual attention model and image segmentation process are applied to quickly identify candidate VPs in the area where the edge directional feature-information is concentrated and the intensity contrast difference is large. In the proposed method, VPs are detected by analyzing the edges, visual-attention regions, linear components using the Hough transform, and image segmentation results in an input image. Our experimental results have shown that the proposed method could be applied to safe-driving support systems.
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Kim, Seongje, Van-Doi Truong, Kwang-Hee Lee, and Jonghun Yoon. "Revolutionizing Robotic Depalletizing: AI-Enhanced Parcel Detecting with Adaptive 3D Machine Vision and RGB-D Imaging for Automated Unloading." Sensors 24, no. 5 (2024): 1473. http://dx.doi.org/10.3390/s24051473.
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Detecting parcels accurately and efficiently has always been a challenging task when unloading from trucks onto conveyor belts because of the diverse and complex ways in which parcels are stacked. Conventional methods struggle to quickly and accurately classify the various shapes and surface patterns of unordered parcels. In this paper, we propose a parcel-picking surface detection method based on deep learning and image processing for the efficient unloading of diverse and unordered parcels. Our goal is to develop a systematic image processing algorithm that emphasises the boundaries of parcels regardless of their shape, pattern, or layout. The core of the algorithm is the utilisation of RGB-D technology for detecting the primary boundary lines regardless of obstacles such as adhesive labels, tapes, or parcel surface patterns. For cases where detecting the boundary lines is difficult owing to narrow gaps between parcels, we propose using deep learning-based boundary line detection through the You Only Look at Coefficients (YOLACT) model. Using image segmentation techniques, the algorithm efficiently predicts boundary lines, enabling the accurate detection of irregularly sized parcels with complex surface patterns. Furthermore, even for rotated parcels, we can extract their edges through complex mathematical operations using the depth values of the specified position, enabling the detection of the wider surfaces of the rotated parcels. Finally, we validate the accuracy and real-time performance of our proposed method through various case studies, achieving mAP (50) values of 93.8% and 90.8% for randomly sized and rotationally covered boxes with diverse colours and patterns, respectively.
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Pötzi, Werner, Gernot Riegler, Astrid Veronig, Thomas Pock, and Ute Möstl. "A system for near real-time detection of filament eruptions at Kanzelhöhe Observatory." Proceedings of the International Astronomical Union 8, S300 (2013): 519–20. http://dx.doi.org/10.1017/s1743921313011800.
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AbstractKanzelhöhe Observatory (kso.ac.at) performs regular high-cadence full-disk observations of the solar chromosphere in the Hα and CaIIK spectral lines as well as the solar photosphere in white-light. In the frame of ESA's Space Situational Awareness (SSA) activities, a new system for near real-time Hα image provision through the SSA Space Weather (SWE) portal (swe.ssa.esa.int) and for automatic alerting of flares and erupting filaments is under development. Image segmentation algorithms, based on optical flow image registration, for the automatic detection of solar filaments in real time Hα images have been developed and implemented at the Kanzelhöhe observing system. We present first results of this system with respect to the automatic recognition and segmentation of filaments and filament eruptions on the Sun.
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Moussaoui, Hanae, Nabil El Akkad, and Mohamed Benslimane. "License plate text recognition using deep learning, NLP, and image processing techniques." Statistics, Optimization & Information Computing 12, no. 3 (2024): 685–96. http://dx.doi.org/10.19139/soic-2310-5070-1966.
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Detecting license plates has never been easy, particularly with the proliferation of sophisticated radars on highways and roads. By 2021, the gendarmerie and National Security Road control agents will have access to more than 1 billion smart traffic radars worldwide. This research presents a revolutionary technique for detecting and recognizing Arabic and Latin license plates. After assembling the gathered images to create a novel dataset, we utilized YOLO v7 to locate and identify the number plate in the image as the first step of the suggested procedure. Before the dataset was fed to the detection system, it was manually labeled. Afterward, we improved the recognized license plate using machine learning methods. To do this, we used kernel methods as well as thresholding to get rid of the extra vertical lines on the plate. After that, we employed Arabic OCR along with Easy OCR methods to decipher the Latin and Arabic characters on the number plate. Eventually, the proposed method achieved an F1 score of 98%,with a precision and recall of 97% and 98%, respectively. We also obtained an accuracy of 99% for image segmentation. The segmentation and detection results from the suggested strategy have shown satisfactory results.
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Mapayi, Temitope, Jules-Raymond Tapamo, Serestina Viriri, and Adedayo Adio. "AUTOMATIC RETINAL VESSEL DETECTION AND TORTUOSITY MEASUREMENT." Image Analysis & Stereology 35, no. 2 (2016): 117. http://dx.doi.org/10.5566/ias.1421.
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As retinopathies continue to be major causes of visual loss and blindness worldwide, early detection and management of these diseases will help achieve significant reduction of blindness cases. However, an efficient automatic retinal vessel segmentation approach remains a challenge. Since efficient vessel network detection is a very important step needed in ophthalmology for reliable retinal vessel characterization, this paper presents study on the combination of difference image and K-means clustering for the segmentation of retinal vessels. Stationary points in the vessel center-lines are used to model the detection of twists in the vessel segments. The combination of arc-chord ratio with stationary points is used to compute tortuosity index. Experimental results show that the proposed K-means combined with difference image achieved a robust segmentation of retinal vessels. A maximum average accuracy of 0.9556 and a maximum average sensitivity of 0.7581 were achieved on DRIVE database while a maximum average accuracy of 0.9509 and a maximum average sensitivity of 0.7666 were achieved on STARE database. When compared with the previously proposed techniques on DRIVE and STARE databases, the proposed technique yields higher mean sensitivity and mean accuracy rates in the same range of very good specificity. In a related development, a non-normalized tortuosity index that combined distance metric and the vessel twist frequency proposed in this paper also achieved a strong correlation of 0.80 with the expert ground truth.
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Hliboký, Maroš, Ján Magyar, Marek Bundzel, et al. "Artifact Detection in Lung Ultrasound: An Analytical Approach." Electronics 12, no. 7 (2023): 1551. http://dx.doi.org/10.3390/electronics12071551.
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Lung ultrasound is used to detect various artifacts in the lungs that support the diagnosis of different conditions. There is ongoing research to support the automatic detection of such artifacts using machine learning. We propose a solution that uses analytical computer vision methods to detect two types of lung artifacts, namely A- and B-lines. We evaluate the proposed approach on the POCUS dataset and data acquired from a hospital. We show that by using the Fourier transform, we can analyze lung ultrasound images in real-time and classify videos with an accuracy above 70%. We also evaluate the method’s applicability for segmentation, showcasing its high success rate for B-lines (89% accuracy) and its shortcomings for A-line detection. We then propose a hybrid solution that uses a combination of neural networks and analytical methods to increase accuracy in horizontal line detection, emphasizing the pleura.
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Wan, Fang, and R. Yang. "Vanishing Point Detection Algorithm Based on Clustering Method." Advanced Materials Research 846-847 (November 2013): 1157–61. http://dx.doi.org/10.4028/www.scientific.net/amr.846-847.1157.
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Camera self-calibration is a key step in photo based reconstruction works. Vanishing point detection is a very important method in single photo based camera self-calibration. We research current vanishing point detection method and put forward a detection algorithm based on a new clustering idea: J-Linkage. First, we construct a similar concept space from straight lines from images by edge detection and segmentation method. Then, we cluster the similar concept space to decide several main directions. At last, we can easily estimate vanishing points from the clustered categories. Experiment prove that, the method has a high performance efficiency and with good accuracy.
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Zhang, Jingwei, Wei Lu, Xingliang Jian, Qingying Hu, and Dejian Dai. "Nondestructive Detection of Egg Freshness Based on Infrared Thermal Imaging." Sensors 23, no. 12 (2023): 5530. http://dx.doi.org/10.3390/s23125530.
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In this paper, we proposed a nondestructive detection method for egg freshness based on infrared thermal imaging technology. We studied the relationship between egg thermal infrared images (different shell colors and cleanliness levels) and egg freshness under heating conditions. Firstly, we established a finite element model of egg heat conduction to study the optimal heat excitation temperature and time. The relationship between the thermal infrared images of eggs after thermal excitation and egg freshness was further studied. Eight values of the center coordinates and radius of the egg circular edge as well as the long axis, short axis, and eccentric angle of the egg air cell were used as the characteristic parameters for egg freshness detection. After that, four egg freshness detection models, including decision tree, naive Bayes, k-nearest neighbors, and random forest, were constructed, with detection accuracies of 81.82%, 86.03%, 87.16%, and 92.32%, respectively. Finally, we introduced SegNet neural network image segmentation technology to segment the egg thermal infrared images. The SVM egg freshness detection model was established based on the eigenvalues extracted after segmentation. The test results showed that the accuracy of SegNet image segmentation was 98.87%, and the accuracy of egg freshness detection was 94.52%. The results also showed that infrared thermography combined with deep learning algorithms could detect egg freshness with an accuracy of over 94%, providing a new method and technical basis for online detection of egg freshness on industrial assembly lines.
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Ptak, Roman, Bartosz Żygadło, and Olgierd Unold. "Projection–Based Text Line Segmentation with a Variable Threshold." International Journal of Applied Mathematics and Computer Science 27, no. 1 (2017): 195–206. http://dx.doi.org/10.1515/amcs-2017-0014.
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Abstract Document image segmentation into text lines is one of the stages in unconstrained handwritten document recognition. This paper presents a new algorithm for text line separation in handwriting. The developed algorithm is based on a method using the projection profile. It employs thresholding, but the threshold value is variable. This permits determination of low or overlapping peaks of the graph. The proposed technique is shown to improve the recognition rate relative to traditional methods. The algorithm is robust in text line detection with respect to different text line lengths.
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Zhao, Wen Dong, Hui Qi, and Hai Yan Zhou. "A Kind of Vehicle Pressure Yellow Line Detection Algorithm Based on Wavelet Transform." Applied Mechanics and Materials 281 (January 2013): 65–70. http://dx.doi.org/10.4028/www.scientific.net/amm.281.65.
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For the Development of China's transportation industry, to develop video-based vehicle detection system prohibited the need for pressure line, the article compares and learns from the existing video surveillance technology on the basis of intelligent transportation system’s analysis and research. According to distribution characteristic of central yellow line area roads, the article proposes the algorithm based on wavelet transform pressure line detection. It has more practical significance on achieving the testing about vehicles rolling pressure and the central yellow line area roads. Firstly, the central road of the yellow line is separated to narrow the scope of testing by wavelet transform technique for image segmentation; and then using image segmentation based on color histogram matching algorithm judges whether the vehicle is rolling and pressing the yellow line area. On this basis, we developed a prototype vehicle contraband detection system pressure lines, the results show that the success rate in detection, false detection rate and timeliness are superior to other algorithms, and better meets the needs of engineering practice.
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Zhou, Wenlong, Xiangxiang Fu, Yunyuan Deng, Jinbiao Yan, Jialu Zhou, and Peilin Liu. "The Extraction of Roof Feature Lines of Traditional Chinese Village Buildings Based on UAV Dense Matching Point Clouds." Buildings 14, no. 4 (2024): 1180. http://dx.doi.org/10.3390/buildings14041180.
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Traditional Chinese buildings serve as a carrier for the inheritance of traditional culture and national characteristics. In the context of rural revitalization, achieving the 3D reconstruction of traditional village buildings is a crucial technical approach to promoting rural planning, improving living environments, and establishing digital villages. However, traditional algorithms primarily target urban buildings, exhibiting limited adaptability and less ideal feature extraction performance for traditional residential buildings. As a result, guaranteeing the accuracy and reliability of 3D models for different types of traditional buildings remains challenging. In this paper, taking Jingping Village in Western Hunan as an example, we propose a method that combines multiple algorithms based on the slope segmentation of the roof to extract feature lines. Firstly, the VDVI and CSF algorithms are used to extract the building and roof point clouds based on the MVS point cloud. Secondly, according to roof features, village buildings are classified, and a 3D roof point cloud is projected into 2D regular grid data. Finally, the roof slope is segmented via slope direction, and internal and external feature lines are obtained after refinement through Canny edge detection and Hough straight line detection. The results indicate that the CSF algorithm can effectively extract the roofs of I-shaped, L-shaped, and U-shaped traditional buildings. The accuracy of roof surface segmentation based on slope exceeds 99.6%, which is significantly better than the RANSAC algorithm and the region segmentation algorithm. This method is capable of efficiently extracting the characteristic lines of roofs in low-rise buildings within traditional villages. It provides a reference method for achieving the high-precision modeling of traditional village architecture at a low cost and with high efficiency.
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Refaey, Mohammed A. A. "Background Ruled-Lines Detection and Removal in Full-Colored Handwritten Image Documents." International Journal of Image and Graphics 15, no. 02 (2015): 1540006. http://dx.doi.org/10.1142/s0219467815400069.
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Automation becomes the standard in nearly all aspects of life. Some of these aspects are text analysis, translating and retrieval. This requires machine typed format as a preprocessing step. Converting the handwritten text into machine printed counterpart requires Optical Character Recognition (OCR) system, which requires clean text as input. One of the problems facing the process of getting clean handwritten text is the ruled background lines which are intersecting and mixed with the text. In this work, we present fast algorithms for detection and removal of these ruled lines. The detection stage use only the centralized and squared part of the image document instead of wasting time if the whole image document is used, and use Hough transform for getting the ruled lines location and direction. The removal algorithm uses the color histogram segmentation for separating the text from the ruled lines. The Hue of the color is used to represent colors instead of using all color components. Then the segmented image document is morphologically enhanced and converted to binary image that is suitable for OCR. The results show the benefits of the proposed algorithms with F1-measures 91.43% and 88.52% for detection and removal respectively.
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Droby, Ahmad, Berat Kurar Barakat, Raid Saabni, Reem Alaasam, Boraq Madi, and Jihad El-Sana. "Understanding Unsupervised Deep Learning for Text Line Segmentation." Applied Sciences 12, no. 19 (2022): 9528. http://dx.doi.org/10.3390/app12199528.
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We propose an unsupervised feature learning approach for segmenting text lines of handwritten document images with no labelling effort. Humans can easily group local text line features to global coarse patterns. We leverage this coherent visual perception of text lines as a supervising signal by formulating the feature learning as a global pattern differentiation task. The machine is trained to detect whether a document patch contains a similar global text line pattern with its identity or neighbours, and a different global text line pattern with its 90-degree-rotated identity or neighbours. Clustering the central windows of document image patches using their extracted features, forms blob lines which strike through the text lines. The blob lines guide an energy minimization function for extracting text lines in a binary image and guide a seam carving function for detecting baselines in a colour image. In identifying the aspect of the input patch that supports the actual prediction and clustering, we contribute toward the understanding of input patch functionality. We evaluate the method on several variants of text line segmentation datasets to demonstrate its effectiveness, visualize what it has learned, and enable it to comprehend its clustering strategy from a human perspective.
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Homainejad, A. S. "NEW APPROACH FOR SEGMENTATION AND EXTRACTION OF SINGLE TREE FROM POINT CLOUDS DATA AND AERIAL IMAGES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8 (June 24, 2016): 1287–92. http://dx.doi.org/10.5194/isprs-archives-xli-b8-1287-2016.
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This paper addresses a new approach for reconstructing a 3D model from single trees via Airborne Laser Scanners (ALS) data and aerial images. The approach detects and extracts single tree from ALS data and aerial images. The existing approaches are able to provide bulk segmentation from a group of trees; however, some methods focused on detection and extraction of a particular tree from ALS and images. Segmentation of a single tree within a group of trees is mostly a mission impossible since the detection of boundary lines between the trees is a tedious job and basically it is not feasible. In this approach an experimental formula based on the height of the trees was developed and applied in order to define the boundary lines between the trees. As a result, each single tree was segmented and extracted and later a 3D model was created. Extracted trees from this approach have a unique identification and attribute. The output has application in various fields of science and engineering such as forestry, urban planning, and agriculture. For example in forestry, the result can be used for study in ecologically diverse, biodiversity and ecosystem.
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Homainejad, A. S. "NEW APPROACH FOR SEGMENTATION AND EXTRACTION OF SINGLE TREE FROM POINT CLOUDS DATA AND AERIAL IMAGES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8 (June 24, 2016): 1287–92. http://dx.doi.org/10.5194/isprsarchives-xli-b8-1287-2016.
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This paper addresses a new approach for reconstructing a 3D model from single trees via Airborne Laser Scanners (ALS) data and aerial images. The approach detects and extracts single tree from ALS data and aerial images. The existing approaches are able to provide bulk segmentation from a group of trees; however, some methods focused on detection and extraction of a particular tree from ALS and images. Segmentation of a single tree within a group of trees is mostly a mission impossible since the detection of boundary lines between the trees is a tedious job and basically it is not feasible. In this approach an experimental formula based on the height of the trees was developed and applied in order to define the boundary lines between the trees. As a result, each single tree was segmented and extracted and later a 3D model was created. Extracted trees from this approach have a unique identification and attribute. The output has application in various fields of science and engineering such as forestry, urban planning, and agriculture. For example in forestry, the result can be used for study in ecologically diverse, biodiversity and ecosystem.
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Diaz-Escobar, Julia, and Vitaly Kober. "Natural Scene Text Detection and Segmentation Using Phase-Based Regions and Character Retrieval." Mathematical Problems in Engineering 2020 (June 19, 2020): 1–17. http://dx.doi.org/10.1155/2020/7067251.
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Multioriented text detection and recognition in natural scene images are still challenges in the document analysis and computer vision communities. In particular, character segmentation plays an important role in the complete end-to-end recognition system performance. In this work, a robust multioriented text detection and segmentation method based on a biological visual system model is proposed. The proposed method exploits the local energy model instead of a common approach based on variations of local image pixel intensities. Features such as lines and edges are obtained by searching for the maximum local energy utilizing the scale-space monogenic signal framework. The candidate text components are extracted from maximally stable extremal regions of the local phase information of the image. The candidate regions are filtered by their phase congruency and classified as text and nontext components by the AdaBoost classifier. Finally, misclassified characters are restored, and all final characters are grouped into words. Experimental results show that the proposed text detection and segmentation method is invariant to scale and rotation changes and robust to perspective distortions, blurring, low resolution, and illumination variations (low contrast, high brightness, shadows, and nonuniform illumination). Besides, the proposed method achieves often a better performance compared with state-of-the-art methods on typical natural scene datasets.
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Wang, Zhenzhou, Cunshan Zhang, Zhen Pan, et al. "Image Segmentation Approaches for Weld Pool Monitoring during Robotic Arc Welding." Applied Sciences 8, no. 12 (2018): 2445. http://dx.doi.org/10.3390/app8122445.
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There is a strong correlation between the geometry of the weld pool surface and the degree of penetration in arc welding. To measure the geometry of the weld pool surface robustly, many structured light laser line based monitoring systems have been proposed in recent years. The geometry of the specular weld pool could be computed from the reflected laser lines based on different principles. The prerequisite of accurate computation of the weld pool surface is to segment the reflected laser lines robustly and efficiently. To find the most effective segmentation solutions for the images captured with different welding parameters, different image processing algorithms are combined to form eight approaches and these approaches are compared both qualitatively and quantitatively in this paper. In particular, the gradient detection filter, the difference method and the GLCM (grey level co-occurrence matrix) are used to remove the uneven background. The spline fitting enhancement method is used to remove the fuzziness. The slope difference distribution-based threshold selection method is used to segment the laser lines from the background. Both qualitative and quantitative experiments are conducted to evaluate the accuracy and the efficiency of the proposed approaches extensively.
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Nanda, Riska, Sri Wulan Dari, and Ahmad Ihsan. "Segmentasi Citra Medis untuk Deteksi Objek FAM pada Payudara Menggunakan Metode Sobel." JURNAL MEDIA INFORMATIKA BUDIDARMA 3, no. 4 (2019): 248. http://dx.doi.org/10.30865/mib.v3i4.1232.
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Fibroa Adenoma Mammae (FAM) or called a benign tumor is the most common tumor found in the breast, often this disease is considered as breast cancer by some laymen, but this disease is different from cancer because Fibroadenoma (FAM) can grow in all parts of the breast, In identifying FAM, doctors or radiologists usually have to analyze carefully the images of Magnetic Resonance stored in the format of Digital Imaging Communication In Medicine (DICOM). This process is certainly quite time consuming. Thus the author feels the need to create a digital image processing application to help doctors or radiologists identify FAM in the breast by utilizing the segmentation process in medical images of USG results using the Sobel method. This method performs the original image segmentation process by detecting the edges of the image then the segmentation results are converted into binary images so the system can determine the FAM area. Medical image segmentation using the Sobel method is good for determining the edges of FAM objects because the edges can be clearly seen but for some image images with less resolution as previously tested, edge detection will be difficult to determine the edges of smooth objects and only form lines rough edges.
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Mahapatra, Aman Kumar. "Non-Destructive Defect Detection Using MASK R-CNN on Industrial Radiography of Machines." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (2022): 4638–52. http://dx.doi.org/10.22214/ijraset.2022.44930.
Full textAbstract:
Abstract: Many industrial processes, particularly those requiring casting or welding, rely heavily on quality control. Manual quality control processes, on the other hand, are frequently time-consuming and error-prone. To address the increased demand for high-quality products, sophisticated visual inspection technologies are becoming increasingly important in manufacturing lines. Convolutional Neural Networks have recently demonstrated exceptional performance in image classification and localization tasks. Based on the Mask Region-based CNN architecture, this research proposes a solution for detecting casting errors in X-ray pictures. The suggested defect detection system conducts flaw identification and segmentation on input pictures at the same time, making it appropriate for a variety of defect detection jobs. It is demonstrated that training the network to conduct defect detection and defect instance segmentation at the same time leads in greater defect detection accuracy than training on defect detection alone. Transfer learning is used to minimizetraining data requirements while increasing the trained model's prediction accuracy. More precisely, the model is trained using two huge publically available picture datasets before being fine-tuned using a relatively modest metal casting X-ray dataset.The trained model's accuracy outperforms state-of-the-art performance on the GRIMA database of Xray images (GDXray) Castings dataset and is quick enough to be deployed in production. On the GDXray Welds dataset, the system likewise works well.A variety of in-depth research are being undertaken to investigate how transfer learning, multi-task learning, and multi-class learning affect the trained system's performance.