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1
Danilov, V. V., O. M. Gerget, D. Y. Kolpashchikov, et al. "BOOSTING SEGMENTATION ACCURACY OF THE DEEP LEARNING MODELS BASED ON THE SYNTHETIC DATA GENERATION." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIV-2/W1-2021 (April 15, 2021): 33–40. http://dx.doi.org/10.5194/isprs-archives-xliv-2-w1-2021-33-2021.
Texte intégralRésumé :
Abstract. In the era of data-driven machine learning algorithms, data represents a new oil. The application of machine learning algorithms shows they need large heterogeneous datasets that crucially are correctly labeled. However, data collection and its labeling are time-consuming and labor-intensive processes. A particular task we solve using machine learning is related to the segmentation of medical devices in echocardiographic images during minimally invasive surgery. However, the lack of data motivated us to develop an algorithm generating synthetic samples based on real datasets. The concept of this algorithm is to place a medical device (catheter) in an empty cavity of an anatomical structure, for example, in a heart chamber, and then transform it. To create random transformations of the catheter, the algorithm uses a coordinate system that uniquely identifies each point regardless of the bend and the shape of the object. It is proposed to take a cylindrical coordinate system as a basis, modifying it by replacing the Z-axis with a spline along which the h-coordinate is measured. Having used the proposed algorithm, we generated new images with the catheter inserted into different heart cavities while varying its location and shape. Afterward, we compared the results of deep neural networks trained on the datasets comprised of real and synthetic data. The network trained on both real and synthetic datasets performed more accurate segmentation than the model trained only on real data. For instance, modified U-net trained on combined datasets performed segmentation with the Dice similarity coefficient of 92.6±2.2%, while the same model trained only on real samples achieved the level of 86.5±3.6%. Using a synthetic dataset allowed decreasing the accuracy spread and improving the generalization of the model. It is worth noting that the proposed algorithm allows reducing subjectivity, minimizing the labeling routine, increasing the number of samples, and improving the heterogeneity.
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Jin, Ran, Xiaozhen Han, and Tongrui Yu. "A Real-Time Image Semantic Segmentation Method Based on Multilabel Classification." Mathematical Problems in Engineering 2021 (May 31, 2021): 1–13. http://dx.doi.org/10.1155/2021/9963974.
Texte intégralRésumé :
Image semantic segmentation as a kind of technology has been playing a crucial part in intelligent driving, medical image analysis, video surveillance, and AR. However, since the scene needs to infer more semantics from video and audio clips and the request for real-time performance becomes stricter, whetherthe single-label classification method that was usually used before or the regular manual labeling cannot meet this end. Given the excellent performance of deep learning algorithms in extensive applications, the image semantic segmentation algorithm based on deep learning framework has been brought under the spotlight of development. This paper attempts to improve the ESPNet (Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation) based on the multilabel classification method by the following steps. First, the standard convolution is replaced by applying Receptive Field in Deep Convolutional Neural Network in the convolution layer, to the extent that every pixel in the covered area would facilitate the ultimate feature response. Second, the ASPP (Atrous Spatial Pyramid Pooling) module is improved based on the atrous convolution, and the DB-ASPP (Delate Batch Normalization-ASPP) is proposed as a way to reducing gridding artifacts due to the multilayer atrous convolution, acquiring multiscale information, and integrating the feature information in relation to the image set. Finally, the proposed model and regular models are subject to extensive tests and comparisons on a plurality of multiple data sets. Results show that the proposed model demonstrates a good accuracy of segmentation, the smallest network parameter at 0.3 M and the fastest speed of segmentation at 25 FPS.
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Xing, Yongfeng, Luo Zhong, and Xian Zhong. "DARSegNet: A Real-Time Semantic Segmentation Method Based on Dual Attention Fusion Module and Encoder-Decoder Network." Mathematical Problems in Engineering 2022 (June 6, 2022): 1–10. http://dx.doi.org/10.1155/2022/6195148.
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The convolutional neural network achieves excellent semantic segmentation results in artificially annotated datasets with complex scenes. However, semantic segmentation methods still suffer from several problems such as low use rate of the features, high computational complexity, and being far from practical real-time application, which bring about challenges for the image semantic segmentation. Two factors are very critical to semantic segmentation task: global context and multilevel semantics. However, generating these two factors will always lead to high complexity. In order to solve this, we propose a novel structure, dual attention fusion module (DAFM), by eliminating structural redundancy. Unlike most of the existing algorithms, we combine the attention mechanism with the depth pyramid pool module (DPPM) to extract accurate dense features for pixel labeling rather than complex expansion convolution. Specifically, we introduce a DPPM to execute the spatial pyramid structure in output and combine the global pool method. The DAFM is introduced in each decoder layer. Finally, the low-level features and high-level features are fused to obtain semantic segmentation result. The experiments and visualization results on Cityscapes and CamVid datasets show that, in real-time semantic segmentation, we have achieved a satisfactory balance between accuracy and speed, which proves the effectiveness of the proposed algorithm. In particular, on a single 1080ti GPU computer, ResNet-18 produces 75.53% MIoU at 70 FPS on Cityscapes and 73.96% MIoU at 109 FPS on CamVid.
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Xing, Yongfeng, Luo Zhong, and Xian Zhong. "DARSegNet: A Real-Time Semantic Segmentation Method Based on Dual Attention Fusion Module and Encoder-Decoder Network." Mathematical Problems in Engineering 2022 (June 6, 2022): 1–10. http://dx.doi.org/10.1155/2022/6195148.
Texte intégralRésumé :
The convolutional neural network achieves excellent semantic segmentation results in artificially annotated datasets with complex scenes. However, semantic segmentation methods still suffer from several problems such as low use rate of the features, high computational complexity, and being far from practical real-time application, which bring about challenges for the image semantic segmentation. Two factors are very critical to semantic segmentation task: global context and multilevel semantics. However, generating these two factors will always lead to high complexity. In order to solve this, we propose a novel structure, dual attention fusion module (DAFM), by eliminating structural redundancy. Unlike most of the existing algorithms, we combine the attention mechanism with the depth pyramid pool module (DPPM) to extract accurate dense features for pixel labeling rather than complex expansion convolution. Specifically, we introduce a DPPM to execute the spatial pyramid structure in output and combine the global pool method. The DAFM is introduced in each decoder layer. Finally, the low-level features and high-level features are fused to obtain semantic segmentation result. The experiments and visualization results on Cityscapes and CamVid datasets show that, in real-time semantic segmentation, we have achieved a satisfactory balance between accuracy and speed, which proves the effectiveness of the proposed algorithm. In particular, on a single 1080ti GPU computer, ResNet-18 produces 75.53% MIoU at 70 FPS on Cityscapes and 73.96% MIoU at 109 FPS on CamVid.
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Lessani, Mohammad Naser, Jiqiu Deng, and Zhiyong Guo. "A Novel Parallel Algorithm with Map Segmentation for Multiple Geographical Feature Label Placement Problem." ISPRS International Journal of Geo-Information 10, no. 12 (2021): 826. http://dx.doi.org/10.3390/ijgi10120826.
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Multiple geographical feature label placement (MGFLP) is an NP-hard problem that can negatively influence label position accuracy and the computational time of the algorithm. The complexity of such a problem is compounded as the number of features for labeling increases, causing the execution time of the algorithms to grow exponentially. Additionally, in large-scale solutions, the algorithm possibly gets trapped in local minima, which imposes significant challenges in automatic label placement. To address the mentioned challenges, this paper proposes a novel parallel algorithm with the concept of map segmentation which decomposes the problem of multiple geographical feature label placement (MGFLP) to achieve a more intuitive solution. Parallel computing is then utilized to handle each decomposed problem simultaneously on a separate central processing unit (CPU) to speed up the process of label placement. The optimization component of the proposed algorithm is designed based on the hybrid of discrete differential evolution and genetic algorithms. Our results based on real-world datasets confirm the usability and scalability of the algorithm and illustrate its excellent performance. Moreover, the algorithm gained superlinear speedup compared to the previous studies that applied this hybrid algorithm.
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Liu, Ning, Gang Liu, and Hong Sun. "Real-Time Detection on SPAD Value of Potato Plant Using an In-Field Spectral Imaging Sensor System." Sensors 20, no. 12 (2020): 3430. http://dx.doi.org/10.3390/s20123430.
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In this study, a SPAD value detection system was developed based on a 25-wavelength spectral sensor to give a real-time indication of the nutrition distribution of potato plants in the field. Two major advantages of the detection system include the automatic segmentation of spectral images and the real-time detection of SPAD value, a recommended indicating parameter of chlorophyll content. The modified difference vegetation index (MDVI) linking the Otsu algorithm (OTSU) and the connected domain-labeling (CDL) method (MDVI–OTSU–CDL) is proposed to accurately extract the potato plant. Additionally, the segmentation accuracy under different modified coefficients of MDVI was analyzed. Then, the reflectance of potato plants was extracted by the segmented mask images. The partial least squares (PLS) regression was employed to establish the SPAD value detection model based on sensitive variables selected using the uninformative variable elimination (UVE) algorithm. Based on the segmented spectral image and the UVE–PLS model, the visualization distribution map of SPAD value was drawn by pseudo-color processing technology. Finally, the testing dataset was employed to measure the stability and practicality of the developed detection system. This study provides a powerful support for the real-time detection of SPAD value and the distribution of crops in the field.
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Baskaran, S., L. Mubark Ali, A. Anitharani, E. Annal Sheeba Rani, and N. Nandhagopal. "Pupil Detection System Using Intensity Labeling Algorithm in Field Programmable Gate Array." Journal of Computational and Theoretical Nanoscience 17, no. 12 (2020): 5364–67. http://dx.doi.org/10.1166/jctn.2020.9429.
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Pupil detection techniques are an essential diagnostic technique in medical applications. Pupil detection becomes more complex because of the dynamic movement of the pupil region and it’s size. Eye-tracking is either the method of assessing the point of focus (where one sees) or the orientation of an eye relative to the head. An instrument used to control eye positions and eye activity is the eye tracker. As an input tool for human-computer interaction, eye trackers are used in research on the visual system, in psychology, psycholinguistics, marketing, and product design. Eye detection is one in all the applications in the image process. This is very important in human identification and it will improve today’s identification technique that solely involves the eye detection to spot individuals. This technology is still new, only a few domains are applying this technology as their medical system. The proposed work is developing an eye pupil detection method in real-time, stable, using an intensity labeling algorithm. The proposed hardware architecture is designed using the median filter, segmentation using the threshold process, and morphology to detect pupil shape. Finally, an intensity Labeling algorithm is done to locate an exact eye pupil region. A Real-time FPGA implementation is done by Altera Quartus II software with cyclone IV FPGA.
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Ji, Xing, Jia Yuan Zhuang, and Yu Min Su. "Marine Radar Target Detection for USV." Advanced Materials Research 1006-1007 (August 2014): 863–69. http://dx.doi.org/10.4028/www.scientific.net/amr.1006-1007.863.
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Unmanned surface vehicles (USV) have become an intense research area because of their extensive applications. Marine radar is the most important environmental perception sensor for USV. Aiming at the problems of noise jamming, uneven brightness, target lost in marine radar images, and the high-speed USV to the requirement of real-time and reliability, this paper proposes the radar image target detection algorithms which suitable for embedded marine radar target detection system. The smoothing algorithm can adaptive select filter in noise, border and background areas, improves the efficiency and smoothing effect. Based on the iterative threshold, the tolerance coefficient is selected by the histogram, ensures the robust of segmentation algorithm. The location, area and invariant moments features can be extracted from the radar image which after connected-component labeling. The actual radar image processing results demonstrate the effectiveness of the proposed algorithms.
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Woodward-Greene, M. Jennifer, Jason M. Kinser, Tad S. Sonstegard, Johann Sölkner, Iosif I. Vaisman, and Curtis P. Van Tassell. "PreciseEdge raster RGB image segmentation algorithm reduces user input for livestock digital body measurements highly correlated to real-world measurements." PLOS ONE 17, no. 10 (2022): e0275821. http://dx.doi.org/10.1371/journal.pone.0275821.
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Computer vision is a tool that could provide livestock producers with digital body measures and records that are important for animal health and production, namely body height and length, and chest girth. However, to build these tools, the scarcity of labeled training data sets with uniform images (pose, lighting) that also represent real-world livestock can be a challenge. Collecting images in a standard way, with manual image labeling is the gold standard to create such training data, but the time and cost can be prohibitive. We introduce the PreciseEdge image segmentation algorithm to address these issues by employing a standard image collection protocol with a semi-automated image labeling method, and a highly precise image segmentation for automated body measurement extraction directly from each image. These elements, from image collection to extraction are designed to work together to yield values highly correlated to real-world body measurements. PreciseEdge adds a brief preprocessing step inspired by chromakey to a modified GrabCut procedure to generate image masks for data extraction (body measurements) directly from the images. Three hundred RGB (red, green, blue) image samples were collected uniformly per the African Goat Improvement Network Image Collection Protocol (AGIN-ICP), which prescribes camera distance, poses, a blue backdrop, and a custom AGIN-ICP calibration sign. Images were taken in natural settings outdoors and in barns under high and low light, using a Ricoh digital camera producing JPG images (converted to PNG prior to processing). The rear and side AGIN-ICP poses were used for this study. PreciseEdge and GrabCut image segmentation methods were compared for differences in user input required to segment the images. The initial bounding box image output was captured for visual comparison. Automated digital body measurements extracted were compared to manual measures for each method. Both methods allow additional optional refinement (mouse strokes) to aid the segmentation algorithm. These optional mouse strokes were captured automatically and compared. Stroke count distributions for both methods were not normally distributed per Kolmogorov-Smirnov tests. Non-parametric Wilcoxon tests showed the distributions were different (p< 0.001) and the GrabCut stroke count was significantly higher (p = 5.115 e-49), with a mean of 577.08 (std 248.45) versus 221.57 (std 149.45) with PreciseEdge. Digital body measures were highly correlated to manual height, length, and girth measures, (0.931, 0.943, 0.893) for PreciseEdge and (0.936, 0. 944, 0.869) for GrabCut (Pearson correlation coefficient). PreciseEdge image segmentation allowed for masks yielding accurate digital body measurements highly correlated to manual, real-world measurements with over 38% less user input for an efficient, reliable, non-invasive alternative to livestock hand-held direct measuring tools.
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Gagliardi, Alessio, and Sergio Saponara. "AdViSED: Advanced Video SmokE Detection for Real-Time Measurements in Antifire Indoor and Outdoor Systems." Energies 13, no. 8 (2020): 2098. http://dx.doi.org/10.3390/en13082098.
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This paper proposes a video-based smoke detection technique for early warning in antifire surveillance systems. The algorithm is developed to detect the smoke behavior in a restricted video surveillance environment, both indoor (e.g., railway carriage, bus wagon, industrial plant, or home/office) or outdoor (e.g., storage area or parking area). The proposed technique exploits a Kalman estimator, color analysis, image segmentation, blob labeling, geometrical features analysis, and M of N decisor, in order to extract an alarm signal within a strict real-time deadline. This new technique requires just a few seconds to detect fire smoke, and it is 15 times faster compared to the requirements of fire-alarm standards for industrial or transport systems, e.g., the EN50155 standard for onboard train fire-alarm systems. Indeed, the EN50155 considers a response time of at least 60 s for onboard systems. The proposed technique has been tested and compared with state-of-art systems using the open access Firesense dataset developed as an output of a European FP7 project, including several fire/smoke indoor and outdoor scenes. There is an improvement of all the detection metrics (recall, accuracy, F1 score, precision, etc.) when comparing Advanced Video SmokE Detection (AdViSED) with other video-based antifire works recently proposed in literature. The proposed technique is flexible in terms of input camera type and frame size and rate and has been implemented on a low-cost embedded platform to develop a distributed antifire system accessible via web browser.
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Wang, Haoyu, Zhiming Ye, Dejiang Wang, Jiongyi Zhu, Haili Jiang, and Panpan Liu. "Synthetic Datasets for Rebar Instance Segmentation Using Mask R-CNN." Buildings 13, no. 3 (2023): 585. http://dx.doi.org/10.3390/buildings13030585.
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The construction and inspection of reinforcement rebar currently rely entirely on manual work, which leads to problems such as high labor requirements and labor costs. Rebar image detection using deep learning algorithms can be employed in construction quality inspection and intelligent construction; it can check the number, spacing, and diameter of rebar on a construction site, and guide robots to complete rebar tying. However, the application of deep learning algorithms relies on a large number of datasets to train models, while manual data collection and annotation are time-consuming and laborious. In contrast, using synthetic datasets can achieve a high degree of automation of annotation. In this study, using rebar as an example, we proposed a mask annotation methodology based on BIM software and rendering software, which can establish a large and diverse training set for instance segmentation, without manual labeling. The Mask R-CNN trained using both real and synthetic datasets demonstrated a better performance than the models trained using only real datasets or synthetic datasets. This synthetic dataset generation method could be widely used for various image segmentation tasks and provides a reference for other computer vision engineering tasks and deep learning tasks in related fields.
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Zhan, Wenqiang, Changshi Xiao, Yuanqiao Wen, et al. "Autonomous Visual Perception for Unmanned Surface Vehicle Navigation in an Unknown Environment." Sensors 19, no. 10 (2019): 2216. http://dx.doi.org/10.3390/s19102216.
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Robust detection and recognition of water surfaces are critical for autonomous navigation of unmanned surface vehicles (USVs), since any none-water region is likely an obstacle posing a potential danger to the sailing vehicle. A novel water region visual detection method is proposed in this paper. First, the input image pixels are clustered into different regions and each pixel is assigned a label tag and a confidence value by adaptive multistage segmentation algorithm. Then the resulting label map and associated confidence map are fed into a convolutional neural network (CNN) as training samples to train the network online. Finally, the online trained CNN is used to segment the input image again but with greater precision and stronger robustness. Compared with other deep-learning image segmentation algorithms, the proposed method has two advantages. Firstly, it dispenses with the need of manual labeling training samples which is a costly and painful task. Secondly, it allows real-time online training for CNN, making the network adaptive to the navigational environment. Another contribution of this work relates to the training process of neuro network. An effective network training method is designed to learn from the imperfect training data. We present the experiments in the lake with a various scene and demonstrate that our proposed method could be applied to recognize the water region in the unknown navigation environment automatically.
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Cheng, Zhenzhen, Lijun Qi, and Yifan Cheng. "Cherry Tree Crown Extraction from Natural Orchard Images with Complex Backgrounds." Agriculture 11, no. 5 (2021): 431. http://dx.doi.org/10.3390/agriculture11050431.
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Highly effective pesticide applications require a continual adjustment of the pesticide spray flow rate that attends to different canopy characterizations. Real-time image processing with rapid target detection and data-processing technologies is vital for precision pesticide application. However, the extant studies do not provide an efficient and reliable method of extracting individual trees with irregular tree-crown shapes and complicated backgrounds. This paper on our study proposes a Mahalanobis distance and conditional random field (CRF)-based segmentation model to extract cherry trees accurately in a natural orchard environment. This study computed Mahalanobis distance from the image’s color, brightness and location features to acquire an initial classification of the canopy and background. A CRF was then created by using the Mahalanobis distance calculations as unary potential energy and the Gaussian kernel function based on the image color and pixels distance as binary potential energy. Finally, the study completed image segmentation using mean-field approximation. The results show that the proposed method displays a higher accuracy rate than the traditional algorithms K-means and GrabCut algorithms and lower labeling and training costs than the deep learning algorithm DeepLabv3+, with 92.1%, 94.5% and 93.3% of the average P, R and F1-score, respectively. Moreover, experiments on datasets with different overlap conditions and image acquisition times, as well as in different years and seasons, show that this method performs well under complex background conditions, with an average F1-score higher than 87.7%.
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Wang, Zehui, Yu Meng, Jingbo Chen, Junxian Ma, Anzhi Yue, and Jiansheng Chen. "Learning Color Distributions from Bitemporal Remote Sensing Images to Update Existing Building Footprints." Remote Sensing 14, no. 22 (2022): 5851. http://dx.doi.org/10.3390/rs14225851.
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For most cities, municipal governments have constructed basic building footprint datasets that need to be updated regularly for the management and monitoring of urban development and ecology. Cities are capable of changing in a short period of time, and the area of change is variable; hence, automated methods for generating up-to-date building footprints are urgently needed. However, the labels of current buildings or changed areas are usually lacking, and the conditions for acquiring images from different periods are not perfectly consistent, which can severely limit deep learning methods when attempting to learn deep information about buildings. In addition, common update methods can ignore the strictly accurate historical labels of unchanged areas. To solve the above problem, we propose a new update algorithm to update the existing building database to the current state without manual relabeling. First, the difference between the data distributions of different time-phase images is reduced using the image color translation method. Then, a semantic segmentation model predicts the segmentation results of the images from the latest period, and, finally, a post-processing update strategy is applied to strictly retain the existing labels of unchanged regions to attain the updated results. We apply the proposed algorithm on the Wuhan University change detection dataset and the Beijing Huairou district land survey dataset to evaluate the effectiveness of the method in building surface and complex labeling scenarios in urban and suburban areas. The F1 scores of the updated results obtained for both datasets reach more than 96%, which proves the applicability of our proposed algorithm and its ability to efficiently and accurately extract building footprints in real-world scenarios.
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Wang, Tao, Juanli Wang, Jia Zhao, and Yanmin Zhang. "A Myocardial Segmentation Method Based on Adversarial Learning." BioMed Research International 2021 (February 26, 2021): 1–9. http://dx.doi.org/10.1155/2021/6618918.
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Congenital heart defects (CHD) are structural imperfections of the heart or large blood vessels that are detected around birth and their symptoms vary wildly, with mild case patients having no obvious symptoms and serious cases being potentially life-threatening. Using cardiovascular magnetic resonance imaging (CMRI) technology to create a patient-specific 3D heart model is an important prerequisite for surgical planning in children with CHD. Manually segmenting 3D images using existing tools is time-consuming and laborious, which greatly hinders the routine clinical application of 3D heart models. Therefore, automatic myocardial segmentation algorithms and related computer-aided diagnosis systems have emerged. Currently, the conventional methods for automatic myocardium segmentation are based on deep learning, rather than on the traditional machine learning method. Better results have been achieved, however, difficulties still exist such as CMRI often has, inconsistent signal strength, low contrast, and indistinguishable thin-walled structures near the atrium, valves, and large blood vessels, leading to challenges in automatic myocardium segmentation. Additionally, the labeling of 3D CMR images is time-consuming and laborious, causing problems in obtaining enough accurately labeled data. To solve the above problems, we proposed to apply the idea of adversarial learning to the problem of myocardial segmentation. Through a discriminant model, some additional supervision information is provided as a guide to further improve the performance of the segmentation model. Experiment results on real-world datasets show that our proposed adversarial learning-based method had improved performance compared with the baseline segmentation model and achieved better results on the automatic myocardium segmentation problem.
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Cao, Qifan, and Lihong Xu. "Unsupervised Greenhouse Tomato Plant Segmentation Based on Self-Adaptive Iterative Latent Dirichlet Allocation from Surveillance Camera." Agronomy 9, no. 2 (2019): 91. http://dx.doi.org/10.3390/agronomy9020091.
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It has long been a great concern in deep learning that we lack massive data for high-precision training sets, especially in the agriculture field. Plants in images captured in greenhouses, from a distance or up close, not only have various morphological structures but also can have a busy background, leading to huge challenges in labeling and segmentation. This article proposes an unsupervised statistical algorithm SAI-LDA (self-adaptive iterative latent Dirichlet allocation) to segment greenhouse tomato images from a field surveillance camera automatically, borrowing the language model LDA. Hierarchical wavelet features with an overlapping grid word document design and a modified density-based method quick-shift are adopted, respectively, according to different kinds of images, which are classified by specific proportions between fruits, leaves, and the background. We also utilize the feature correlation between several layers of the image to make further optimization through three rounds of iteration of LDA, with updated documents to achieve finer segmentation. Experiment results show that our method can automatically label the organs of the greenhouse plant under complex circumstances, fast and precisely, overcoming the difficulty of inferior real-time image quality caused by a surveillance camera, and thus obtain large amounts of valuable training sets.
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Wu and Xu. "Crop Organ Segmentation and Disease Identification Based on Weakly Supervised Deep Neural Network." Agronomy 9, no. 11 (2019): 737. http://dx.doi.org/10.3390/agronomy9110737.
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Object segmentation and classification using the deep convolutional neural network (DCNN) has been widely researched in recent years. On the one hand, DCNN requires large data training sets and precise labeling, which bring about great difficulties in practical application. On the other hand, it consumes a large amount of computing resources, so it is difficult to apply it to low-cost terminal equipment. This paper proposes a method of crop organ segmentation and disease recognition that is based on weakly supervised DCNN and lightweight model. While considering the actual situation in the greenhouse, we adopt a two-step strategy to reduce the interference of complex background. Firstly, we use generic instance segmentation architecture—Mask R-CNN to realize the instance segmentation of tomato organs based on weakly supervised learning, and then the disease recognition of tomato leaves is realized by depth separable multi-scale convolution. Instance segmentation algorithms usually require accurate pixel-level supervised labels, which are difficult to collect, so we propose a weakly supervised instance segmentation assignment to solve this problem. The lightweight model uses multi-scale convolution to expand the network width, which makes the extracted features richer, and depth separable convolution is adopted to reduce model parameters. The experimental results showed that our method reached higher recognition accuracy when compared with other methods, at the same time occupied less memory space, which can realize the real-time recognition of tomato diseases on low-performance terminals, and can be applied to the recognition of crop diseases in other similar application scenarios.
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Miao, Ying, Danyang Shao, and Zhimin Yan. "Privacy-Oriented Successive Approximation Image Position Follower Processing." Complexity 2021 (June 7, 2021): 1–12. http://dx.doi.org/10.1155/2021/6853809.
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In this paper, we analyze the location-following processing of the image by successive approximation with the need for directed privacy. To solve the detection problem of moving the human body in the dynamic background, the motion target detection module integrates the two ideas of feature information detection and human body model segmentation detection and combines the deep learning framework to complete the detection of the human body by detecting the feature points of key parts of the human body. The detection of human key points depends on the human pose estimation algorithm, so the research in this paper is based on the bottom-up model in the multiperson pose estimation method; firstly, all the human key points in the image are detected by feature extraction through the convolutional neural network, and then the accurate labelling of human key points is achieved by using the heat map and offset fusion optimization method in the feature point confidence map prediction, and finally, the human body detection results are obtained. In the study of the correlation algorithm, this paper combines the HOG feature extraction of the KCF algorithm and the scale filter of the DSST algorithm to form a fusion correlation filter based on the principle study of the MOSSE correlation filter. The algorithm solves the problems of lack of scale estimation of KCF algorithm and low real-time rate of DSST algorithm and improves the tracking accuracy while ensuring the real-time performance of the algorithm.
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Liu, Tao, Chunsheng Li, Zongbao Liu, et al. "Research on Image Identification Method of Rock Thin Slices in Tight Oil Reservoirs Based on Mask R-CNN." Energies 15, no. 16 (2022): 5818. http://dx.doi.org/10.3390/en15165818.
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Terrestrial tight oil has extremely strong diagenesis heterogeneity, so a large number of rock thin slices are needed to reveal the real microscopic pore-throat structure characteristics. In addition, difficult identification, high cost, long time, strong subjectivity and other problems exist in the identification of tight oil rock thin slices, and it is difficult to meet the needs of fine description and quantitative characterization of the reservoir. In this paper, a method for identifying the characteristics of rock thin slices in tight oil reservoirs based on the deep learning technique was proposed. The present work has the following steps: first, the image preprocessing technique was studied. The original image noise was removed by filtering, and the image pixel size was unified by a normalization technique to ensure the quality of samples; second, the self-labeling image data augmentation technique was constructed to solve the problem of sparse samples; third, the Mask R-CNN algorithm was introduced and improved to synchronize the segmentation and recognition of rock thin slice components in tight oil reservoirs; Finally, it was demonstrated through experiments that the SMR method has significant advantages in accuracy, execution speed and migration.
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Roth, Andreas, Konstantin Wüstefeld, and Frank Weichert. "A Data-Centric Augmentation Approach for Disturbed Sensor Image Segmentation." Journal of Imaging 7, no. 10 (2021): 206. http://dx.doi.org/10.3390/jimaging7100206.
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In the context of sensor-based data analysis, the compensation of image artifacts is a challenge. When the structures of interest are not clearly visible in an image, algorithms that can cope with artifacts are crucial for obtaining the desired information. Thereby, the high variation of artifacts, the combination of different types of artifacts, and their similarity to signals of interest are specific issues that have to be considered in the analysis. Despite the high generalization capability of deep learning-based approaches, their recent success was driven by the availability of large amounts of labeled data. Therefore, the provision of comprehensive labeled image data with different characteristics of image artifacts is of importance. At the same time, applying deep neural networks to problems with low availability of labeled data remains a challenge. This work presents a data-centric augmentation approach based on generative adversarial networks that augments the existing labeled data with synthetic artifacts generated from data not present in the training set. In our experiments, this augmentation leads to a more robust generalization in segmentation. Our method does not need additional labeling and does not lead to additional memory or time consumption during inference. Further, we find it to be more effective than comparable augmentations based on procedurally generated artifacts and the direct use of real artifacts. Building upon the improved segmentation results, we observe that our approach leads to improvements of 22% in the F1-score for an evaluated detection problem. Having achieved these results with an example sensor, we expect increased robustness against artifacts in future applications.
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Bassier, Maarten, Meisam Yousefzadeh, and Maarten Vergauwen. "Comparison of 2D and 3D wall reconstruction algorithms from point cloud data for as-built BIM." Journal of Information Technology in Construction 25 (March 2, 2020): 173–92. http://dx.doi.org/10.36680/j.itcon.2020.011.
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As-built Building Information Models (BIMs) are becoming increasingly popular in the Architectural, Engineering, Construction, Owner and Operator (AECOO) industry. These models reflect the state of the building up to as-built conditions. The production of these models for existing buildings with no prior BIM includes the segmentation and classification of point cloud data and the reconstruction of the BIM objects. The automation of this process is a must since the manual Scan-to-BIM procedure is both time-consuming and error prone. However, the automated reconstruction from point cloud data is still ongoing research with both 2D and 3D approaches being proposed. There currently is a gap in the literature concerning the quality assessment of the created entities. In this research, we present the empirical comparison of both strategies with respect to existing specifications. A 3D and a 2D reconstruction method are implemented and tested on a real life test case. The experiments focus on the reconstruction of the wall geometry from unstructured point clouds as it forms the basis of the model. Both presented approaches are unsupervised methods that segment, classify and create generic wall elements. The first method operates on the 3D point cloud itself and consists of a general approach for the segmentation and classification and a class-specific reconstruction algorithm for the wall geometry. The point cloud is first segmented into planar clusters, after which a Random Forests classifier is used with geometric and contextual features for the semantic labelling. The final wall geometry is created based on the 3D point clusters representing the walls. The second method is an efficient Manhattan-world scene reconstruction algorithm that simultaneously segments and classifies the point cloud based on point feature histograms. The wall reconstruction is considered an instance of image segmentation by representing the data as 2D raster images. Both methods have promising results towards the reconstruction of wall geometry of multi-story buildings. The experiments report that over 80% of the walls were correctly segmented by both methods. Furthermore, the reconstructed geometry is conform Level-of-Accuracy 20 for 88% of the data by the first method and for 55% by the second method despite the Manhattan-world scene assumption. The empirical comparison showcases the fundamental differences in both strategies and will support the further development of these methods.
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El Hassani, M., S. Jehan-Besson, L. Brun, et al. "A Time-Consistent Video Segmentation Algorithm Designed for Real-Time Implementation." VLSI Design 2008 (April 24, 2008): 1–12. http://dx.doi.org/10.1155/2008/892370.
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We propose a time-consistent video segmentation algorithm designed for real-time implementation. Our algorithm is based on a region merging process that combines both spatial and motion information. The spatial segmentation takes benefit of an adaptive decision rule and a specific order of merging. Our method has proven to be efficient for the segmentation of natural images with few parameters to be set. Temporal consistency of the segmentation is ensured by incorporating motion information through the use of an improved change-detection mask. This mask is designed using both illumination differences between frames and region segmentation of the previous frame. By considering both pixel and region levels, we obtain a particularly efficient algorithm at a low computational cost, allowing its implementation in real-time on the TriMedia processor for CIF image sequences.
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Shen, Jianbing, Xiaopeng Hao, Zhiyuan Liang, Yu Liu, Wenguan Wang, and Ling Shao. "Real-Time Superpixel Segmentation by DBSCAN Clustering Algorithm." IEEE Transactions on Image Processing 25, no. 12 (2016): 5933–42. http://dx.doi.org/10.1109/tip.2016.2616302.
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Song, Wei, Yifei Tian, Simon Fong, Kyungeun Cho, Wei Wang, and Weiqiang Zhang. "GPU-Accelerated Foreground Segmentation and Labeling for Real-Time Video Surveillance." Sustainability 8, no. 10 (2016): 916. http://dx.doi.org/10.3390/su8100916.
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Blokhinov, Y. B., V. A. Gorbachev, Y. O. Rakutin, and D. A. Nikitin. "A REAL-TIME SEMANTIC SEGMENTATION ALGORITHM FOR AERIAL IMAGERY." Computer Optics 42, no. 1 (2018): 141–48. http://dx.doi.org/10.18287/2412-6179-2018-42-1-141-148.
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We propose a novel effective algorithm for real-time semantic segmentation of images that has the best accuracy in its class. Based on a comparative analysis of preliminary segmentation methods, methods for calculating attributes from image segments, as well as various algorithms of machine learning, the most effective methods in terms of their accuracy and performance are identified. Based on the research results, a modular near real-time algorithm of semantic segmentation is constructed. Training and testing is performed on the ISPRS Vaihingen collection of aerial photos of the visible and IR ranges, to which a pixel map of the terrain heights is attached. An original method for obtaining a normalized nDSM for the original DSM is proposed.
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Jiang, Qunyan, Juying Dai, Ting Rui, et al. "Detail Guided Multilateral Segmentation Network for Real-Time Semantic Segmentation." Applied Sciences 12, no. 21 (2022): 11040. http://dx.doi.org/10.3390/app122111040.
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With the development of unmanned vehicles and other technologies, the technical demand for scene semantic segmentation is more and more intense. Semantic segmentation requires not only rich high-level semantic information, but also rich detail information to ensure the accuracy of the segmentation task. Using a multipath structure to process underlying and semantic information can improve efficiency while ensuring segmentation accuracy. In order to improve the segmentation accuracy and efficiency of some small and thin objects, a detail guided multilateral segmentation network is proposed. Firstly, in order to improve the segmentation accuracy and model efficiency, a trilateral parallel network structure is designed, including the context fusion path (CF-path), the detail information guidance path (DIG-path), and the semantic information supplement path (SIS-path). Secondly, in order to effectively fuse semantic information and detail information, a feature fusion module based on an attention mechanism is designed. Finally, experimental results on CamVid and Cityscapes datasets show that the proposed algorithm can effectively balance segmentation accuracy and inference speed.
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Cai Yu, 蔡雨, 黄学功 Huang Xuegong, 张志安 Zhang Zhian, 朱新年 Zhu Xinnian, and 马祥 Ma Xiang. "Real-Time Semantic Segmentation Algorithm Based on Feature Fusion Technology." Laser & Optoelectronics Progress 57, no. 2 (2020): 021011. http://dx.doi.org/10.3788/lop57.021011.
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Bibiloni, Pedro, Manuel González-Hidalgo, and Sebastia Massanet. "A real-time fuzzy morphological algorithm for retinal vessel segmentation." Journal of Real-Time Image Processing 16, no. 6 (2018): 2337–50. http://dx.doi.org/10.1007/s11554-018-0748-1.
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Robitaille, Michael C., Jeff M. Byers, Joseph A. Christodoulides, and Marc P. Raphael. "Robust optical flow algorithm for general single cell segmentation." PLOS ONE 17, no. 1 (2022): e0261763. http://dx.doi.org/10.1371/journal.pone.0261763.
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Cell segmentation is crucial to the field of cell biology, as the accurate extraction of single-cell morphology, migration, and ultimately behavior from time-lapse live cell imagery are of paramount importance to elucidate and understand basic cellular processes. In an effort to increase available segmentation tools that can perform across research groups and platforms, we introduce a novel segmentation approach centered around optical flow and show that it achieves robust segmentation of single cells by validating it on multiple cell types, phenotypes, optical modalities, and in-vitro environments with or without labels. By leveraging cell movement in time-lapse imagery as a means to distinguish cells from their background and augmenting the output with machine vision operations, our algorithm reduces the number of adjustable parameters needed for manual optimization to two. We show that this approach offers the advantage of quicker processing times compared to contemporary machine learning based methods that require manual labeling for training, and in most cases achieves higher quality segmentation as well. This algorithm is packaged within MATLAB, offering an accessible means for general cell segmentation in a time-efficient manner.
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Heo, Jiseong, and Hyoung woo Lim. "Sim-to-Real Reinforcement Learning for Autonomous Driving Using Pseudosegmentation Labeling and Dynamic Calibration." Journal of Robotics 2022 (June 26, 2022): 1–10. http://dx.doi.org/10.1155/2022/9916292.
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Applying reinforcement learning algorithms to autonomous driving is difficult because of mismatches between the simulation in which the algorithm was trained and the real world. To address this problem, data from global navigation satellite systems and inertial navigation systems (GNSS/INS) were used to gather pseudolabels for semantic segmentation. A very simple dynamics model was used as a simulator, and dynamic parameters were obtained from the linear regression of manual driving records. Segmentation and a dynamic calibration method were found to be effective in easing the transition from a simulation to the real world. Pseudosegmentation labels are found to be more suitable for reinforcement learning models. We conducted tests on the efficacy of our proposed method, and a vehicle using the proposed system successfully drove on an unpaved track for approximately 1.8 km at an average speed of 26.57 km/h without incident.
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Ribeiro, Miguel, Bruno Damas, and Alexandre Bernardino. "Real-Time Ship Segmentation in Maritime Surveillance Videos Using Automatically Annotated Synthetic Datasets." Sensors 22, no. 21 (2022): 8090. http://dx.doi.org/10.3390/s22218090.
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This work proposes a new system capable of real-time ship instance segmentation during maritime surveillance missions by unmanned aerial vehicles using an onboard standard RGB camera. The implementation requires two stages: an instance segmentation network able to produce fast and reliable preliminary segmentation results and a post-processing 3D fully connected Conditional Random Field, which significantly improves segmentation results by exploring temporal correlations between nearby frames in video sequences. Moreover, due to the absence of maritime datasets consisting of properly labeled video sequences, we create a new dataset comprising synthetic video sequences of maritime surveillance scenarios (MarSyn). The main advantages of this approach are the possibility of generating a vast set of images and videos, being able to represent real-world scenarios without the necessity of deploying the real vehicle, and automatic labels, which eliminate human labeling errors. We train the system with the MarSyn dataset and with aerial footage from publicly available annotated maritime datasets to validate the proposed approach. We present some experimental results and compare them to other approaches, and we also illustrate the temporal stability provided by the second stage in missing frames and wrong segmentation scenarios.
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Ji, Ronghua, Zetian Fu, and Lijun Qi. "Real‐time plant image segmentation algorithm under natural outdoor light conditions." New Zealand Journal of Agricultural Research 50, no. 5 (2007): 847–54. http://dx.doi.org/10.1080/00288230709510359.
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Kim, Hanguen, Jungmo Koo, Donghoon Kim, et al. "Vision-Based Real-Time Obstacle Segmentation Algorithm for Autonomous Surface Vehicle." IEEE Access 7 (2019): 179420–28. http://dx.doi.org/10.1109/access.2019.2959312.
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Cai, Jing, Ge Zhou, Mengkun Dong, Xinlei Hu, Guangda Liu, and Weiguang Ni. "Real-Time Arrhythmia Classification Algorithm Using Time-Domain ECG Feature Based on FFNN and CNN." Mathematical Problems in Engineering 2021 (May 17, 2021): 1–17. http://dx.doi.org/10.1155/2021/6648432.
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To solve the problem of real-time arrhythmia classification, this paper proposes a real-time arrhythmia classification algorithm using deep learning with low latency, high practicality, and high reliability, which can be easily applied to a real-time arrhythmia classification system. In the algorithm, a classifier detects the QRS complex position in real time for heartbeat segmentation. Then, the ECG_RRR feature is constructed according to the heartbeat segmentation result. Finally, another classifier classifies the arrhythmia in real time using the ECG_RRR feature. This article uses the MIT-BIH arrhythmia database and divides the 44 qualified records into two groups (DS1 and DS2) for training and evaluation, respectively. The result shows that the recall rate, precision rate, and overall accuracy of the algorithm’s interpatient QRS complex position prediction are 98.0%, 99.5%, and 97.6%, respectively. The overall accuracy for 5-class and 13-class interpatient arrhythmia classification is 91.5% and 75.6%, respectively. Furthermore, the real-time arrhythmia classification algorithm proposed in this paper has the advantages of practicability and low latency. It is easy to deploy the algorithm since the input is the original ECG signal with no feature processing required. And, the latency of the arrhythmia classification is only the duration of one heartbeat cycle.
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Panero Martinez, Ruben, Ionut Schiopu, Bruno Cornelis, and Adrian Munteanu. "Real-Time Instance Segmentation of Traffic Videos for Embedded Devices." Sensors 21, no. 1 (2021): 275. http://dx.doi.org/10.3390/s21010275.
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The paper proposes a novel instance segmentation method for traffic videos devised for deployment on real-time embedded devices. A novel neural network architecture is proposed using a multi-resolution feature extraction backbone and improved network designs for the object detection and instance segmentation branches. A novel post-processing method is introduced to ensure a reduced rate of false detection by evaluating the quality of the output masks. An improved network training procedure is proposed based on a novel label assignment algorithm. An ablation study on speed-vs.-performance trade-off further modifies the two branches and replaces the conventional ResNet-based performance-oriented backbone with a lightweight speed-oriented design. The proposed architectural variations achieve real-time performance when deployed on embedded devices. The experimental results demonstrate that the proposed instance segmentation method for traffic videos outperforms the you only look at coefficients algorithm, the state-of-the-art real-time instance segmentation method. The proposed architecture achieves qualitative results with 31.57 average precision on the COCO dataset, while its speed-oriented variations achieve speeds of up to 66.25 frames per second on the Jetson AGX Xavier module.
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Zhu, Zheng Tao, Bo Zhang, and Gang Yang. "Research on Fast Capsule Image Segmentation Algorithm." Applied Mechanics and Materials 236-237 (November 2012): 989–93. http://dx.doi.org/10.4028/www.scientific.net/amm.236-237.989.
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In on-line inspection for products based on machine vision, contrast between product defects and background is not always stable because of the discrepancy of environment where images are captured and the type, batch and innate structure of products to be detected. To perform accurate detection, image is usually divided into several parts which are of same gray value and later on sub-blocks the analysis for defected region where sudden gray value changes are occurring. The crucial step here is to have accurate regional image segmentation. Traditional edge detection is unlikely to ensure its accuracy, and at the same time, complicated image segmentation algorithms are time-consuming and cannot meet needs of real-time manufacturing. Images captured during on-line detection is relatively stable in structure. A new real-time image fast segmentation algorithm is proposed in this dissertation. This algorithm, combining with use of local image enhancement algorithm, morphological operation of simple structural operators and image thinning technology, can accurately find regions boundry of uniform region. Later, on-line image segmentation can be fulfilled by means of simple addition and subtraction for regions. This algorithm has been successfully applied to on-line capsule inspection. Experiments show that it can satisfy the need of on-line detection both with speed and accuracy.
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Ding, Xiao Ling, Qiang Zhao, Yi Bin Li, and Xin Ma. "A Real-Time and Effective Object Recognition and Localization Method." Applied Mechanics and Materials 615 (August 2014): 107–12. http://dx.doi.org/10.4028/www.scientific.net/amm.615.107.
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In this paper, we realize object recognition and localization in a real time based on appearance features of object. For object recognition, we proposed to use global feauture (color) of images, and with an improved color image segmentation algorithm to realize threshold segmentation based on pixels in the image’s HSV color model by using the tool OpenCV, so we can realize the special color object recognition. Further the object can be localized with the ground constrained method by using the camera perspective geometry model. In the lab conditions, we realized single color object recognition and localization by transplanting the algorithm into Amigobots mobile robot and proved this method is simple, effective and real-time.
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Wang, Zhen, Jing Junfeng, Huanhuan Zhang, and Yan Zhao. "Real-Time Fabric Defect Segmentation Based on Convolutional Neural Network." AATCC Journal of Research 8, no. 1_suppl (2021): 91–96. http://dx.doi.org/10.14504/ajr.8.s1.12.
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Automated visual inspection for quality control has widely-used deep convolutional neural networks (CNNs) in fabric defect detection. Most of the research on defect detection only focuses on increasing the accuracy of segmentation models with little attention to computationally efficient solutions. In this study, we propose a highly efficient deep learning-based method for pixel-level fabric defect classification algorithm based on a CNN. We started with the ShuffleNet V2 feature extractor, added five deconvolution layers as the decoder, and used a resize bilinear to produce the segmentation mask. To solve the sample imbalance problem, we used an improved loss function to guide network learning. We evaluated our model on the fabric defect data set. The proposed model outperformed the existing image segmentation models in both model efficiency and segmentation accuracy.
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Jin, Moon Yong, Jong Bin Park, Dong Suk Lee, and Dong Sun Park. "Real-Time Vehicle License Plate Recognition System Using Adaptive Heuristic Segmentation Algorithm." KIPS Transactions on Software and Data Engineering 3, no. 9 (2014): 361–68. http://dx.doi.org/10.3745/ktsde.2014.3.9.361.
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Tan Guanghong, 谭光鸿, 侯进 Hou Jin, 韩雁鹏 Han Yanpeng, and 罗朔 Luo Shuo. "Low-Parameter Real-Time Image Segmentation Algorithm Based on Convolutional Neural Network." Laser & Optoelectronics Progress 56, no. 9 (2019): 091003. http://dx.doi.org/10.3788/lop56.091003.
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Wang, Chunliang, Nils Dahlström, Sven-Göran Fransson, Claes Lundström, and örjan Smedby. "Real-Time Interactive 3D Tumor Segmentation Using a Fast Level-Set Algorithm." Journal of Medical Imaging and Health Informatics 5, no. 8 (2015): 1998–2002. http://dx.doi.org/10.1166/jmihi.2015.1685.
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Trieu, Dang Ba Khac, and Tsutomu Maruyama. "Real-time image segmentation based on a parallel and pipelined watershed algorithm." Journal of Real-Time Image Processing 2, no. 4 (2007): 319–29. http://dx.doi.org/10.1007/s11554-007-0051-z.
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Ye, Qing, Jun Feng Dong, and Yong Mei Zhang. "Real-Time Human Skeleton Extraction Based on Video Sequences." Applied Mechanics and Materials 401-403 (September 2013): 1410–14. http://dx.doi.org/10.4028/www.scientific.net/amm.401-403.1410.
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Thinning algorithm is widely used in image processing and pattern recognition.In this paper we proposed an optimized thinning algorithm based on Zhan-Suen thinning and applied it to video sequences of moving human body to extract real-time body skeleton. We firstly used background subtraction method to detect moving body, then made use of adaptive threshold segmentation to gain the binary moving body image, finally we used the optimized algorithm to the binary image and got its skeleton. The skeleton not only maintains the movement geometry and body image’s topological properties, also reduces image redundancy and computation cost, and helps us clearly recognize the moving body posture.
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Zhao, Wen Dong, Hui Qi, and Hai Yan Zhou. "Segmentation Algorithm of Traffic Prohibited Area Based on Wavelet." Advanced Materials Research 542-543 (June 2012): 1316–19. http://dx.doi.org/10.4028/www.scientific.net/amr.542-543.1316.
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The status of transportation industry development of China has shown the necessity and urgency of the development of intelligent transportation systems. This article proposed a segmentation algorithm of traffic prohibited region based on wavelet transform. Based on the de-noising and image enhancement, sharpening pretreatment of the traffic video image captured on real-time, the algorithm combines the method determining the quadrilateral based on the sample images manually with the image segmentation based on wavelet transform in order to get the segmentation of traffic prohibited region which will be used in the detection of vehicle pressing highway central line region. The experimental results show that in the algorithm not only meet the real-time requirement of foundations but also improves the successful rate of detection results.
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Liu, Zhijie, Yuanqiong Chen, Xiaohua Xiang, Zhan Li, Bolin Liao, and Jianfeng Li. "An End-to-End Real-Time Lightweight Network for the Joint Segmentation of Optic Disc and Optic Cup on Fundus Images." Mathematics 10, no. 22 (2022): 4288. http://dx.doi.org/10.3390/math10224288.
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Glaucoma is the second-most-blinding eye disease in the world and accurate segmentation of the optic disc (OD) and optic cup (OC) is essential for the diagnosis of glaucoma. To solve the problems of poor real-time performance, high algorithm complexity, and large memory consumption of fundus segmentation algorithms, a lightweight segmentation algorithm, GlauNet, based on convolutional neural networks, is proposed. The algorithm designs an efficient feature-extraction network and proposes a multiscale boundary fusion (MBF) module, which greatly improves the segmentation efficiency of the algorithm while ensuring segmentation accuracy. Experiments show that the algorithm achieves Dice scores of 0.9701/0.8959, 0.9650/0.8621, and 0.9594/0.8795 on three publicly available datasets—Drishti-GS, RIM-ONE-r3, and REFUGE-train—for both the optic disc and the optic cup. The number of model parameters is only 0.8 M, and it only takes 13 ms to infer an 800 × 800 fundus image on a GTX 3070 GPU.
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Li, Yanyi, Jian Shi, and Yuping Li. "Real-Time Semantic Understanding and Segmentation of Urban Scenes for Vehicle Visual Sensors by Optimized DCNN Algorithm." Applied Sciences 12, no. 15 (2022): 7811. http://dx.doi.org/10.3390/app12157811.
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The modern urban environment is becoming more and more complex. In helping us identify surrounding objects, vehicle vision sensors rely more on the semantic segmentation ability of deep learning networks. The performance of a semantic segmentation network is essential. This factor will directly affect the comprehensive level of driving assistance technology in road environment perception. However, the existing semantic segmentation network has a redundant structure, many parameters, and low operational efficiency. Therefore, to reduce the complexity of the network and reduce the number of parameters to improve the network efficiency, based on the deep learning (DL) theory, a method for efficient image semantic segmentation using Deep Convolutional Neural Network (DCNN) is deeply studied. First, the theoretical basis of the convolutional neural network (CNN) is briefly introduced, and the real-time semantic segmentation technology of urban scenes based on DCNN is recommended in detail. Second, the atrous convolution algorithm and the multi-scale parallel atrous spatial pyramid model are introduced. On the basis of this, an Efficient Symmetric Network (ESNet) of real-time semantic segmentation model for autonomous driving scenarios is proposed. The experimental results show that: (1) On the Cityscapes dataset, the ESNet structure achieves 70.7% segmentation accuracy for the 19 semantic categories set, and 87.4% for the seven large grouping categories. Compared with other algorithms, the accuracy has increased to varying degrees. (2) On the CamVid dataset, compared with segmentation networks of multiple lightweight real-time images, the parameters of the ESNet model are around 1.2 m, the highest FPS value is around 90 Hz, and the highest mIOU value is around 70%. In seven semantic categories, the segmentation accuracy of the ESNet model is the highest at around 98%. From this, we found that the ESNet significantly improves segmentation accuracy while maintaining faster forward inference speed. Overall, the research not only provides technical support for the development of real-time semantic understanding and segmentation of DCNN algorithms but also contributes to the development of artificial intelligence technology.
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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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Zhao, Fei, Lu Zhang, Zhi-yong Zhang, and Huan-zhang Lu. "A Hardware Acceleration Based Algorithm for Real-time Binary Image Connected-component Labeling." Journal of Electronics & Information Technology 33, no. 5 (2011): 1069–75. http://dx.doi.org/10.3724/sp.j.1146.2010.00793.
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Kwan, Paul W. H., Keisuke Kameyama, and Kazuo Toraichi. "On a relaxation-labeling algorithm for real-time contour-based image similarity retrieval." Image and Vision Computing 21, no. 3 (2003): 285–94. http://dx.doi.org/10.1016/s0262-8856(02)00159-2.
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Lu, Yisu, Jun Jiang, Wei Yang, Qianjin Feng, and Wufan Chen. "Multimodal Brain-Tumor Segmentation Based on Dirichlet Process Mixture Model with Anisotropic Diffusion and Markov Random Field Prior." Computational and Mathematical Methods in Medicine 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/717206.
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Brain-tumor segmentation is an important clinical requirement for brain-tumor diagnosis and radiotherapy planning. It is well-known that the number of clusters is one of the most important parameters for automatic segmentation. However, it is difficult to define owing to the high diversity in appearance of tumor tissue among different patients and the ambiguous boundaries of lesions. In this study, a nonparametric mixture of Dirichlet process (MDP) model is applied to segment the tumor images, and the MDP segmentation can be performed without the initialization of the number of clusters. Because the classical MDP segmentation cannot be applied for real-time diagnosis, a new nonparametric segmentation algorithm combined with anisotropic diffusion and a Markov random field (MRF) smooth constraint is proposed in this study. Besides the segmentation of single modal brain-tumor images, we developed the algorithm to segment multimodal brain-tumor images by the magnetic resonance (MR) multimodal features and obtain the active tumor and edema in the same time. The proposed algorithm is evaluated using 32 multimodal MR glioma image sequences, and the segmentation results are compared with other approaches. The accuracy and computation time of our algorithm demonstrates very impressive performance and has a great potential for practical real-time clinical use.