Relevant bibliographies by topics / Real time segmentation and labeling algorithm

Academic literature on the topic 'Real time segmentation and labeling algorithm'

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Published: 10 March 2023

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Journal articles on the topic "Real time segmentation and labeling algorithm"

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Danilov, V. V., O. M. Gerget, D. Y. Kolpashchikov, N. V. Laptev, R. A. Manakov, L. A. Hérnandez-Gómez, F. Alvarez, and M. J. Ledesma-Carbayo. "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.

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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.

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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.

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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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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 (December 6, 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 (June 17, 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 (December 1, 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 (October 13, 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 (April 23, 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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Dissertations / Theses on the topic "Real time segmentation and labeling algorithm"

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Abate, Francesco. "Innovative algorithms and data structures for signal treatment applied to ISO/IEC/IEEE 21451 smart transducers." Doctoral thesis, Universita degli studi di Salerno, 2016. http://hdl.handle.net/10556/2493.

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2014 - 2015
Technologies and, in particular sensors, permeate more and more application sectors. From energy management, to the factories one, to houses, environments, infrastructure, and building monitoring, to healthcare and traceability systems, sensors are more and more widespread in our daily life. In the growing context of the Internet of Things capabilities to acquire magnitudes of interest, to elaborate and to communicate data is required to these technologies. These capabilities of acquisition, elaboration, and communication can be integrated on a unique device, a smart sensor, which integrates the sensible element with a simple programmable logic device, capable of managing elaboration and communication. An efficient implementation of communication is required to these technologies, in order to better exploit the available bandwidth, minimizing energy consumption. Moreover, these devices have to be easily interchangeable (plug and play) in such a way that they could be easily usable. Nowadays, smart sensors available on the market reveal several problems such as programming complexity, for which depth knowledge is required, and limited software porting capability. The family of standards IEEE 1451 is written with the aim to define a set of common communication interfaces. These documents come from the Institute of Electric and Electronic Engineers (IEEE) with the aim to create a standard interface which allows devices interoperability produced from different manufacturers, but it is not concerned with problems related to bandwidth, management, elaboration and programming. For this family of standards, now under review, it is expected a further development, with the aim to renew applicable standards, and to add new layers of standardization. The draft of the ISO/IEC/IEEE 21451.001 proposes to solve problems related to the bandwidth and the elaboration management, relocating a part of processing in the point of acquisition, taking advantage of elaboration capabilities of smart sensors. This proposal is based on a Real Time Segmentation and Labeling algorithm, a new sampling technique, which allows to reduce the high number of samples to be transferred, with the same information content. This algorithm returns a data structure, according to which the draft expects two elaboration layers: a first layer, in order to elaborate basic information of the signal processing, and a second layer, for more complex elaboration. [edited by author]
XIV n.s.
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Chiou, Yung-Chuen, and 邱永椿. "The Study on Real-Time Video Object Segmentation Algorithm Based On Change Detection and Background Updating." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/72169261118433490566.

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碩士
國立高雄應用科技大學
電子與資訊工程研究所碩士班
93
Video object segmentation is key role for developing technique of content-based video processing. In practically, it can be implemented in pre-processing for contend-based video system in order to separate the video frame into video objects. Many proposed video segmentation algorithms which are aimed at specific sequence, e.g., shoulder-head sequence, or need an absolute background frame. Besides, the higher computational burden is requested because the complex operator is used in spatial domain. However, there restrictions hardly make it to be involved in real-time processing system. In this dissertation, we propose a video object segmentation algorithm based on change detection and background updating that can quickly obtain video object from video sequence. The change detection is used to analyze temporal information between successive frames more efficiently than motion estimation. The combining frame difference mask and background subtraction mask which is used to acquire the initial object mask and solve the uncovered background problem and still object problem. Moreover, the proposed boundary refinement is introduced that can overcome the shadow influence and residual background problem. Finally, subjective and objective evaluation of this algorithm is showed and demonstrates spatial accuracy of our algorithm can be hold above 95% and the time cost of boundary refinement is below 0.5 second in a single still camera environment.
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Yeh, Ruei-Cheng, and 葉睿誠. "Real-Time Processing Of Multiple Source Segmentation and Separation Using MUSIC Algorithm with Calibrated Array Manifold Vector." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/11584968399298425897.

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碩士
國立交通大學
工學院聲音與音樂創意科技碩士學位學程
104
A real-time system structure for multiple sound sources segmentation and separation using Multiple Signal Classification algorithm is proposed in this thesis. Using a calibrated array manifold vector, the proposed calibration method improves the accuracy of the MUSIC algorithm for wide-band detections, hence providing high accuracy source segmentation and separation results. And system structure using the Multiple Signal Classification algorithm to detect and estimate the localization of sound source’s spectrum distribution. And then using probability decision method to determine the direction of sound sources. Finally, multiple sources were extracted from array signals by using beamforming method. This proposed method can track and separate multiple sources at the same time and maintain high detection rate.
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Han-ChangChen and 陳漢昌. "Real-Time Human Position Tracking and Gesture Recognition System Based on Image Segmentation Algorithm and Its Application to Image Browser." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/65525731598906370826.

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碩士
國立成功大學
工程科學系碩博士班
101
Abstract Human vision is one of our most advanced senses; therefore, image for the human’s sense is very important. Along with the rapid improvement in the development of computer technology and execution speed, image processing techniques have also matured. However, in the past, positioning cameras have been used nearly exclusively for detecting and tracking moving objects. If the moving objects move outside the lens’ view area, it can not be tracked. In order to improve this weakness and reduce blind spots, this thesis proposes a real-time object tracking gesture recognition system. The system architecture is composed as follows: 1. Using a camera to capture images. 2. Using USB2.0 to transmit the images to a computer. 3. Using the YCbCr color space model to analyze and separate skin color from the background. 4. Removing the image noises with a morphological algorithm. 5. Calculating coordinates via the marginalization of the moving objects and histogram statistics. 6. Via USB2.0, the computer can determine movement trajectories to drive the servo motor, which can effectively track objects. 7. According to the vector analysis of moving coordinates, moving direction of hands can be recognized. The different sets of moving direction of hands can be defined as many gestures. In order to make users understand the actions recognized by the system easily, this thesis explores six types of gestures. 8. In addition, this thesis additionally focuses on a moving object and sets a moving mask, which can reduce system operation time and advance functions. This thesis invited ten participants, and through their cooperation it was verified that this system can detect and track moving objects and also recognize six types of gestures.
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Book chapters on the topic "Real time segmentation and labeling algorithm"

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Jau, U. L., and C. S. Teh. "Real-Time Object-Based Video Segmentation Using Colour Segmentation and Connected Component Labeling." In Lecture Notes in Computer Science, 110–21. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-05036-7_12.

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Hao, Zhifeng, Wen Wen, Zhou Liu, and Xiaowei Yang. "Real-Time Foreground-Background Segmentation Using Adaptive Support Vector Machine Algorithm." In Lecture Notes in Computer Science, 603–10. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-74695-9_62.

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Leclercq, Philippe, and Thomas Bräunl. "A Color Segmentation Algorithm for Real-Time Object Localization on Small Embedded Systems." In Robot Vision, 69–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44690-7_9.

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Ishii, Shun, Kizito Nkurikiyeyezu, Mika Luimula, Anna Yokokubo, and Guillaume Lopez. "ExerSense: Real-Time Physical Exercise Segmentation, Classification, and Counting Algorithm Using an IMU Sensor." In Smart Innovation, Systems and Technologies, 239–55. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-8944-7_15.

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Ding, Yuhua, George J. Vachtsevanos, Anthony J. Yezzi, Wayne Daley, and Bonnie S. Heck-Ferri. "A Real-Time Multisensory Image Segmentation Algorithm with an Application to Visual and X-Ray Inspection." In Lecture Notes in Computer Science, 192–201. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-36592-3_19.

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Eun, Jung, Jeonghyo Ha, Sung Hyun Baek, Sangkeun Moon, and Junmo Kim. "U-Net-Based Segmentation for Electrical Lines and Its Application to Real-Time Maintenance Algorithm for Electricity Facilities." In Lecture Notes in Mechanical Engineering, 386–95. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4803-8_38.

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Bagla, Kartikay, Amogh Dhar Diwan, and Kshitij Agarwal. "DarthYOLO: Using YOLO for Real-Time Image Segmentation." In Advances in Transdisciplinary Engineering. IOS Press, 2022. http://dx.doi.org/10.3233/atde220794.

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Image segmentation is a common use case of image processing. It enables a wide variety of activities, from self-driving cars to traffic systems that are capable of governing them. Most state of the art models like Mask R-CNN and other transformer-based models perform well but have very high inference times. Therefore in this paper, a novel model architecture is proposed that can run on consumer grade hardware while giving near real time image segmentation. This is done by creating masks on regions of interest proposed by a lightweight object detection algorithm that is the YOLOv4. Such an algorithm allows for image segmentation in near-real time for resource constrained environments as compared to other state of the art models that have high inference times are unsuitable for deployment in said environments.
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YU, X. S., and X. L. TANG. "THE ALGORITHM OF IMAGE SEGMENTATION BASED ON REAL-TIME DYNAMIC SCENE." In Information Sciences 2007, 909–15. WORLD SCIENTIFIC, 2007. http://dx.doi.org/10.1142/9789812709677_0127.

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K., Kanakambika, and Thamizhendhi G. "Application of Odd-Even Congruence Graph Labeling in Secured Cyber Physical Systems." In Real-Time Applications of Machine Learning in Cyber-Physical Systems, 77–92. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-7998-9308-0.ch006.

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Technological advancement in the recent decades enhanced the calibre of human life. Contemporary research in machine learning (ML) exhibits a mock-up to make decisions on its own and is applied in various fields including medical diagnosis, email filtering, banking, computer vision, financial marketing, image processing, cyber security. The systems inter-connected across the world via internet are attacked by hackers, and it is prevented by cyber security. The optimum solution for cyber-attacks is attained by collaborating ML techniques with cyber security and envisioned issues are designed by cyber machine learning models. In this chapter, an algorithm is proposed to defend data by encoding the text to an unintelligent text and decoding it to original text by applying graph labelling in cryptography. Symmetric key is designed based on the edge label of an odd-even congruence graph to achieve secured communication in CPS. In addition, a program is suggested using Python programming to attain cipher text and its converse.
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Jeffrey, Zoe, Soodamani Ramalingam, and Nico Bekooy. "Real-Time DSP-Based License Plate Character Segmentation Algorithm Using 2D Haar Wavelet Transform." In Advances in Wavelet Theory and Their Applications in Engineering, Physics and Technology. InTech, 2012. http://dx.doi.org/10.5772/35448.

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Conference papers on the topic "Real time segmentation and labeling algorithm"

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Abate, F., V. Paciello, A. Pietrosanto, and G. Monte. "Preliminary analysis of a real time segmentation and labeling algorithm." In 2015 IEEE Workshop on Environmental, Energy and Structural Monitoring Systems (EESMS). IEEE, 2015. http://dx.doi.org/10.1109/eesms.2015.7175880.

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Abate, F., A. Pietrosanto, V. Paciello, V. Huang, and G. Monte. "Uncertainty of a real time segmentation and labeling algorithm in signal period measurement." In 2016 IEEE 25th International Symposium on Industrial Electronics (ISIE). IEEE, 2016. http://dx.doi.org/10.1109/isie.2016.7744988.

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Choi, HyeOk, Yong-Suk Park, and Kyung-Taek Lee. "Re-Labeling for Real-time Semantic Segmentation in Specific Environments." In 2020 IEEE International Conference on Consumer Electronics - Asia (ICCE-Asia). IEEE, 2020. http://dx.doi.org/10.1109/icce-asia49877.2020.9276789.

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Gong, Wei, Ee-Peng Lim, Palakorn Achananuparp, Feida Zhu, David Lo, and Freddy Chong Tat Chua. "In-game action list segmentation and labeling in real-time strategy games." In 2012 IEEE Conference on Computational Intelligence and Games (CIG). IEEE, 2012. http://dx.doi.org/10.1109/cig.2012.6374150.

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Zhu, Song, Danhua Cao, Yubin Wu, and Shixiong Jiang. "A novel real-time superpixel segmentation algorithm." In International Conference on Optical Instruments and Technology (OIT2013), edited by Xinggang Lin and Jesse Zheng. SPIE, 2013. http://dx.doi.org/10.1117/12.2036679.

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Liu, Hanyu, Hongying Zhang, Junwen Li, and Yujun He. "Global Feature-Guided Real-Time Semantic Segmentation Algorithm." In 2022 5th International Conference on Pattern Recognition and Artificial Intelligence (PRAI). IEEE, 2022. http://dx.doi.org/10.1109/prai55851.2022.9904211.

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Gonzalez-Sosa, E., G. Robledo, D. Gonzalez-Morin, P. Perez-Garcia, and A. Villegas. "Real Time Egocentric Object Segmentation for Mixed Reality: THU-READ Labeling and Benchmarking Results." In 2022 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW). IEEE, 2022. http://dx.doi.org/10.1109/vrw55335.2022.00048.

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Zhao, Fei, and Zhi-yong Zhang. "Hardware acceleration based connected component labeling algorithm in real-time ATR system." In Fifth International Conference on Machine Vision (ICMV 12), edited by Yulin Wang, Liansheng Tan, and Jianhong Zhou. SPIE, 2013. http://dx.doi.org/10.1117/12.2014150.

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Long, Daniel T., Ikram E. Abdou, and Surachai Sutha. "Parallel algorithm for model-directed real-time image segmentation." In Orlando '90, 16-20 April, edited by Richard D. Juday. SPIE, 1990. http://dx.doi.org/10.1117/12.21216.

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Yu, Dengsha, Zifei Yan, and Baolin Ming. "Real-Time Instance Segmentation Tracking Algorithm in Mixed Reality." In 2021 IEEE 7th International Conference on Virtual Reality (ICVR). IEEE, 2021. http://dx.doi.org/10.1109/icvr51878.2021.9483810.

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