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Journal articles on the topic 'Super-pixel segmentation'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Haasa, M. K. Chandra. "Mixture Based Superpixel Segmentation and Classification of SAR Images." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 5052–57. http://dx.doi.org/10.22214/ijraset.2023.52827.

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Abstract: The proposed approach suggests a mixture-based super pixel segmentation technique for synthetic aperture radar (SAR) images. SAR is a radar system used to generate 2D or 3D representations of objects like landscapes. This method utilizes SAR image amplitudes and pixel coordinates as features. Super pixels are large irregular-shaped regions obtained through oversegmentation of an image. While super pixel segmentation methods are commonly designed for colour images, this approach adapts a finite mixture model (FMM) for single-channel SAR images. By employing finite mixture models, the

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Andrews, N. V., M. Manikandan, and M. Paranthaman. "SUPER PIXEL USING RANDOM WALK SEGMENTATION." Indian Journal of Science and Technology 11, no. 18 (2018): 1–7. http://dx.doi.org/10.17485/ijst/2018/v11i18/123043.

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Yi, Xiaochun, and Jing Hou. "Segmentation of Medical Image Based on Superpixel Boundary Perceptual Convolutional Network in Cancer Diagnosis and Treatment." Journal of Medical Imaging and Health Informatics 11, no. 1 (2021): 254–60. http://dx.doi.org/10.1166/jmihi.2021.3425.

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In order to reduce the computational complexity of breast tumor segmentation algorithms and improve the accuracy of breast segmentation, this paper proposes a breast tumor segmentation method based on super pixel boundary perceptual convolutional network. This method first uses super pixel segmentation convolutional network algorithm to segment breast medical images, and then uses region growth algorithm to achieve breast tumor segmentation at super pixel level. The research results show that in the classification of breast tumors, the fusion efficiency based on the classifier level is better

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Jiang, Quanchun, Olamide Timothy Tawose, Songwen Pei, et al. "Weakly-Supervised Image Semantic Segmentation Based on Superpixel Region Merging." Big Data and Cognitive Computing 3, no. 2 (2019): 31. http://dx.doi.org/10.3390/bdcc3020031.

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In this paper, we propose a semantic segmentation method based on superpixel region merging and convolutional neural network (CNN), referred to as regional merging neural network (RMNN). Image annotation has always been an important role in weakly-supervised semantic segmentation. Most methods use manual labeling. In this paper, super-pixels with similar features are combined using the relationship between each pixel after super-pixel segmentation to form a plurality of super-pixel blocks. Rough predictions are generated by the fully convolutional networks (FCN) so that certain super-pixel blo

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Wang, Mingjun, Shaodong Feng, and Jigang Wu. "Pixel super-resolution lensless in-line holographic microscope with hologram segmentation." Chinese Optics Letters 17, no. 11 (2019): 110901. http://dx.doi.org/10.3788/col201917.110901.

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Liu, C., H. Sui, Y. Peng, L. Hua, and Q. Li. "EARTHQUAKE-DAMAGED REGIONS DETECTION FROM HIGH RESOLUTION IMAGE BASED ON SUPER-PIXEL SEGMENTATION AND DEEP LEARNING." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-3-2020 (August 3, 2020): 45–51. http://dx.doi.org/10.5194/isprs-annals-v-3-2020-45-2020.

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Abstract. Accurate detection and automatic processing of earthquake-damaged regions is essential for effective rescue and post-disaster reconstruction. In this study, we proposed a Combined Super-pixel Segmentation and AlexNet Detection approach (CSSAD) for automatically extracting damaged regions from post-earthquake high-resolution images. Simple Linear Iterative Clustering (SLIC) algorithm was used to segment the high resolution images to obtain more homogeneous geo-objects. Multiscale samples database, which took the different scale effect of damaged regions into account, was constructed b

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Gu, Xiaohong, and Shuyuan Shang. "Pattern segmentation method based on super-pixel multi-feature fusion." SHS Web of Conferences 166 (2023): 01068. http://dx.doi.org/10.1051/shsconf/202316601068.

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The paper presents an optimized segmentation algorithm for clothing patterns. It uses the depth model to improve the image quality. Super-pixel pre-segmentation enhances the ability to capture the edge information of patterns.LBP texture information, HSV color information and image pixel space information are used to optimize the parameters of Gaussian mixture model. The energy function is optimized by maxflow/mincut to realize the fast segmentation of clothing patterns. The simulation results show that the segmentation accuracy and precision of the algorithm are improved.

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Gong, Liang, Chenrui Yu, Ke Lin, and Chengliang Liu. "A Lightweight Powdery Mildew Disease Evaluation Model for Its In-Field Detection with Portable Instrumentation." Agronomy 12, no. 1 (2021): 97. http://dx.doi.org/10.3390/agronomy12010097.

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Powdery mildew is a common crop disease and is one of the main diseases of cucumber in the middle and late stages of growth. Powdery mildew causes the plant leaves to lose their photosynthetic function and reduces crop yield. The segmentation of powdery mildew spot areas on plant leaves is the key to disease detection and severity evaluation. Considering the convenience for identification of powdery mildew in the field environment or for quantitative analysis in the lab, establishing a lightweight model for portable equipment is essential. In this study, the plant-leaf disease-area segmentatio

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Subha, K. "Cross-Correlation Driven Aerial Image Segmentation: Leveraging Multi-Scale Features and Edge Information." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (2024): 416–21. http://dx.doi.org/10.22214/ijraset.2024.61574.

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Abstract: Semantic segmentation of remote sensing images is crucial for interpreting these large, rich in information scenes and our study introduces a new method for segmenting remote sensing imagery (RSI) when faced with limited training data and imbalanced classes. Our approach utilizes a unique potential function that merges information from both super pixel segmentation and edge detection. This combination allows the model to effectively analyze features at various scales and reduce the influence of potential errors in super pixel segmentation. Furthermore, the inclusion of edge details e

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Shahsavarani, Sara, Clemente Ibarra-Castanedo, Fernando Lopez, and Xavier P. V. Maldague. "Deep Learning-Based Superpixel Texture Analysis for Crack Detection in Multi-Modal Infrastructure Images." NDT 2, no. 2 (2024): 128–42. http://dx.doi.org/10.3390/ndt2020008.

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Infrared and visible imaging play crucial roles in non-destructive testing, where accurate defect segmentation and detection are paramount. However, the scarcity of annotated training data or the limited number of data availability often poses a challenge. To address this, we propose an innovative framework tailored to the domain of infrared and visible imaging, integrating segmentation and detection tasks. The proposed approach eliminates the dependency on annotated defect data during training, enabling models to adapt to real-world scenarios with limited annotations. By utilizing super-pixel

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Feng, W., H. Sui, and X. Chen. "SALIENCY-GUIDED CHANGE DETECTION OF REMOTELY SENSED IMAGES USING RANDOM FOREST." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (April 30, 2018): 341–48. http://dx.doi.org/10.5194/isprs-archives-xlii-3-341-2018.

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Studies based on object-based image analysis (OBIA) representing the paradigm shift in change detection (CD) have achieved remarkable progress in the last decade. Their aim has been developing more intelligent interpretation analysis methods in the future. The prediction effect and performance stability of random forest (RF), as a new kind of machine learning algorithm, are better than many single predictors and integrated forecasting method. In this paper, we present a novel CD approach for high-resolution remote sensing images, which incorporates visual saliency and RF. First, highly homogen

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Kim, In-Kyu, Seung-Jun Hwang, Jong-Pil Na, Seung-Je Park, and Joong-Hwan Baek. "Super-Pixel-Based Segmentation and Classification for UAV Image." Journal of Korea Navigation Institute 18, no. 2 (2014): 151–57. http://dx.doi.org/10.12673/jant.2014.18.2.151.

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Liu, Han, Hang Du, Dan Zeng, and Qi Tian. "Cloud Detection Using Super Pixel Classification and Semantic Segmentation." Journal of Computer Science and Technology 34, no. 3 (2019): 622–33. http://dx.doi.org/10.1007/s11390-019-1931-y.

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14

Kiruthika, V., and R. Karpagam. "Super Pixel Segmentation Using Fuzzy Genetic Filter (FGF) with Gaussian Mixture Models (GMMs)." Journal of Advanced Research in Dynamical and Control Systems 11, no. 9 (2019): 89–100. http://dx.doi.org/10.5373/jardcs/v11i9/20192918.

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MP, Ayyoob, and Muhamed Ilyas.P. "Combination of Auto-encoder architecture and super resolution for better segmentation of thinned and cursive handwritten documents." Journal of Physics: Conference Series 2236, no. 1 (2022): 012007. http://dx.doi.org/10.1088/1742-6596/2236/1/012007.

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Abstract In neural networks an auto-encoder architecture has several applications such as image denoising, feature reduction, data compression, image colorization, dimensanality reduction, segmentation and so on. Super-resolution is used to upgrade the low resolution images into high resolution. In order to get a better result on segmentation of thinned hand written images, this paper proposes a method of combination of associative auto-encoder architecture and super resolution for pixel expansion. Experimental results show that the combination of proposed network and super resolution method a

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Zhu, Yiwei. "Natural image segmentation based on mutual information." MATEC Web of Conferences 309 (2020): 03030. http://dx.doi.org/10.1051/matecconf/202030903030.

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Natural image segmentation plays an important role in the fields of image processing and computer vision. Image segmentation based on clustering is an important method in unsupervised image segmentation algorithms. But there are two problems with this type of approach. First, feature extraction is generally pixel-based, which results in poor segmentation results and boundary fitting. In order to solve this problem, it is proposed to introduce super pixel to be segmented image preprocessing. Second, the number of partitions is difficult to determine. Aiming at this problem, an energy difference

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HAN, Shou-Dong, Yong ZHAO, Wen-Bing TAO, and Nong SANG. "Gaussian Super-pixel Based Fast Image Segmentation Using Graph Cuts." Acta Automatica Sinica 37, no. 1 (2011): 11–20. http://dx.doi.org/10.3724/sp.j.1004.2011.00011.

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Xu Yanbing, 许言兵, 周阳 Zhou Yang, 李灿标 Li Canbiao, 郑楚君 Zheng Chujun, 张润谷 Zhang Rungu, and 王文斌 Wang Wenbin. "Retinal Vessel Segmentation Based on Super-Pixel Affinity Propagation Clustering." Acta Optica Sinica 40, no. 2 (2020): 0210002. http://dx.doi.org/10.3788/aos202040.0210002.

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Khan, Khalil, Nasir Ahmad, Irfan Uddin, Muhammad Ehsan Mazhar, and Rehan Ullah Khan. "FaceParser – A new face segmentation approach and labeleddatabase." International Journal of Engineering & Technology 7, no. 2.5 (2018): 1. http://dx.doi.org/10.14419/ijet.v7i2.5.10043.

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Background and objective: A novel face parsing method is proposed in this paper which partition facial image into six semantic classes. Unlike previous approaches which segmented a facial image into three or four classes, we extended the class labels to six. Materials and Methods: A data-set of 464 images taken from FEI, MIT-CBCL, Pointing’04 and SiblingsDB databases was annotated. A discriminative model was trained by extracting features from squared patches. The built model was tested on two different semantic segmentation approaches – pixel-based and super-pixel-based semantic segmentation

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Yu, Junbo, Tinghua Ai, Haijiang Xu, Lingrui Yan, and Yilang Shen. "Inconsistency Detection in Cross-Layer Tile Maps with Super-Pixel Segmentation." ISPRS International Journal of Geo-Information 12, no. 6 (2023): 244. http://dx.doi.org/10.3390/ijgi12060244.

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The consistency of geospatial data is of great significance for the application and updating of geographic information in web maps. Due to the multiple data sources and different temporal versions, the tile web maps usually meet the inconsistency question across different layers. This study tries to develop a method to detect this kind of inconsistency utilizing a raster-based scaling approach. Compared with vector-based handling, this method can be directly available for multi-level tile images in a pixel representation form. The proposed cross-layer raster tile map rendering method (CRTMRM)

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ZHOU, Xiwei, Huifeng WANG, Saisai LI, Haonan PENG, and Jianfeng WU. "Complex Traffic Scene Image Classification Based on Sparse Optimization Boundary Semantics Deep Learning." Wuhan University Journal of Natural Sciences 28, no. 2 (2023): 150–62. http://dx.doi.org/10.1051/wujns/2023282150.

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With the rapid development of intelligent traffic information monitoring technology, accurate identification of vehicles, pedestrians and other objects on the road has become particularly important. Therefore, in order to improve the recognition and classification accuracy of image objects in complex traffic scenes, this paper proposes a segmentation method of semantic redefine segmentation using image boundary region. First, we use the SegNet semantic segmentation model to obtain the rough classification features of the vehicle road object, then use the simple linear iterative clustering (SLI

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Prabakar, Catherina Vasanthalin, and Chella Kavitha Nagarajan. "A novel approach of surface crack detection using super pixel segmentation." Materials Today: Proceedings 42 (2021): 1043–49. http://dx.doi.org/10.1016/j.matpr.2020.12.114.

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Ding, Shifei, Lijuan Wang, and Lin Cong. "Super‐pixel image segmentation algorithm based on adaptive equalisation feature parameters." IET Image Processing 14, no. 17 (2020): 4461–67. http://dx.doi.org/10.1049/iet-ipr.2020.0475.

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., Harathi, and Boyella Mala Konda Reddy. "Super pixel segmentation and classification of SAR images for brightness enhancement." International Journal of Computing, Programming and Database Management 2, no. 2 (2021): 30–35. http://dx.doi.org/10.33545/27076636.2021.v2.i2a.31.

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Rajalakshmi, C., Al M. Germanus, and R. Balasubramanian. "Copy move forgery detection using key point localized super pixel based on texture features." Computer Optics 43, no. 2 (2019): 270–76. http://dx.doi.org/10.18287/2412-6179-2019-43-2-270-276.

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The most important barrier in the image forensic is to ensue a forgery detection method such can detect the copied region which sustains rotation, scaling reflection, compressing or all. Traditional SIFT method is not good enough to yield good result. Matching accuracy is not good. In order to improve the accuracy in copy move forgery detection, this paper suggests a forgery detection method especially for copy move attack using Key Point Localized Super Pixel (KLSP). The proposed approach harmonizes both Super Pixel Segmentation using Lazy Random Walk (LRW) and Scale Invariant Feature Transfo

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Lei, Yubin, Xianjun Gao, Yuan Kou, Baifa Wu, Yue Zhang, and Bo Liu. "Balanced Cloud Shadow Compensation Method in High-Resolution Image Combined with Multi-Level Information." Applied Sciences 13, no. 16 (2023): 9296. http://dx.doi.org/10.3390/app13169296.

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As clouds of different thicknesses block sunlight, large areas of cloud shadows with varying brightness can appear on the ground. Cloud shadows in high-resolution remote sensing images lead to uneven loss of image feature information. However, cloud shadows still retain feature information, and how to compensate for and restore unbalanced cloud shadow occlusion is of great significance in improving image quality. Though traditional shadow compensation methods can enhance the shaded brightness, the results are inconsistent in a single shadow region with over-compensated or insufficient compensa

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Ho-Phuoc, Tien, and Dung-Nghi Truong Cong. "Segmentation-Based Video Upscaling without Motion Estimation." Journal of Science and Technology: Issue on Information and Communications Technology 1 (August 31, 2015): 20. http://dx.doi.org/10.31130/jst.2015.7.

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 This paper shows an effective method for video upscaling or super resolution (SR) without using an explicit motion estimation step. Exploiting the Non-Local Means (NLM) algorithm in order to bypass motion estimation, which is often complicated, our method proposes some modifications to ensure a good compromise between noise cancelling and detail preservation. A detailed consideration of the NLM algorithm is carried out to propose an efficient distance computation and the best eighbors for the reconstruction of each SR pixel. Moreover, efficient segmentation algorithms are also considere

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Wang, Shupeng, and Yao Zhao. "A Novel Patch-Based Multi-Exposure Image Fusion Using Super-Pixel Segmentation." IEEE Access 8 (2020): 39034–45. http://dx.doi.org/10.1109/access.2020.2975896.

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Jin, Lianghai. "Complex impulse noise removal from color images based on super pixel segmentation." Journal of Visual Communication and Image Representation 48 (October 2017): 54–65. http://dx.doi.org/10.1016/j.jvcir.2017.05.012.

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Merlin Inbamalar, T., Dhandapani Samiappan, and R. Ramesh. "SNCDM: Spinal Tumor Detection from MRI Images Using Optimized Super-Pixel Segmentation." Intelligent Automation & Soft Computing 36, no. 2 (2023): 1899–913. http://dx.doi.org/10.32604/iasc.2023.031202.

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Haiyan Yu, Haiyan Yu, and Jihong Wang Haiyan Yu. "Infrared Image Segmentation for Power Equipment Using Linear Spectral Clustering and Maximal Similarity-based Region Merging." 電腦學刊 33, no. 1 (2022): 043–53. http://dx.doi.org/10.53106/199115992022023301005.

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<p>The diagnostic method of power equipment based on infrared images is widely used because it has the advantages of non-contact and does not affect the online operation of power equipment. However, in actual using, the power equipment diagnosis method based on infrared image still relies on manual judgment, that is, the detection personnel can judge the fault according to the obtained infrared image of power equipment by experience. This process consumes a lot of time, and the subjectivity is strong, misjudgment rate is higher, which cannot meet the requirements of modern smart grid dev

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Hadavand, A., M. Saadatseresht, and S. Homayouni. "A NEW FRAMEWORK FOR OBJECT-BASED IMAGE ANALYSIS BASED ON SEGMENTATION SCALE SPACE AND RANDOM FOREST CLASSIFIER." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1-W5 (December 11, 2015): 263–68. http://dx.doi.org/10.5194/isprsarchives-xl-1-w5-263-2015.

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In this paper a new object-based framework is developed for automate scale selection in image segmentation. The quality of image objects have an important impact on further analyses. Due to the strong dependency of segmentation results to the scale parameter, choosing the best value for this parameter, for each class, becomes a main challenge in object-based image analysis. We propose a new framework which employs pixel-based land cover map to estimate the initial scale dedicated to each class. These scales are used to build segmentation scale space (SSS), a hierarchy of image objects. Optimiz

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Cui, Jiang, and Wang. "Unsupervised Moving Object Segmentation from Stationary or Moving Camera based on Multi-frame Homography Constraints." Sensors 19, no. 19 (2019): 4344. http://dx.doi.org/10.3390/s19194344.

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Moving object segmentation is the most fundamental task for many vision-based applications. In the past decade, it has been performed on the stationary camera, or moving camera, respectively. In this paper, we show that the moving object segmentation can be addressed in a unified framework for both type of cameras. The proposed method consists of two stages: (1) In the first stage, a novel multi-frame homography model is generated to describe the background motion. Then, the inliers and outliers of that model are classified as background trajectories and moving object trajectories by the desig

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Li, Bing, Shaoyong Wu, Siqin Zhang, Xia Liu, and Guangqing Li. "Fast Segmentation of Vertebrae CT Image Based on the SNIC Algorithm." Tomography 8, no. 1 (2022): 59–76. http://dx.doi.org/10.3390/tomography8010006.

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Automatic image segmentation plays an important role in the fields of medical image processing so that these fields constantly put forward higher requirements for the accuracy and speed of segmentation. In order to improve the speed and performance of the segmentation algorithm of medical images, we propose a medical image segmentation algorithm based on simple non-iterative clustering (SNIC). Firstly, obtain the feature map of the image by extracting the texture information of it with feature extraction algorithm; Secondly, reduce the image to a quarter of the original image size by downscali

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Prasad Kondisetty, Durga, and Mohammed Ali Hussain. "A novel approach for cDNA image segmentation using SLIC based SOM methodology." International Journal of Engineering & Technology 7, no. 2.8 (2018): 52. http://dx.doi.org/10.14419/ijet.v7i2.8.10323.

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In the segmentation of computer vision images, Super pixels are act as key role from last decade. There are multiple algorithms and techniques to analyze the Super pixels but amount all of them the best super pixel analyzing method is Simple Linear Iterative Clustering (SLIC) have come to pivot increasingly in recent years. The studying of micro array gene expression from MRI imaging is more useful to detect tumors or any other cancer diseases, so that the complementary DNA (cDNA) microarray is a well established tool for studying the same. The segmentation of microarray images is the main ste

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Liu, Xiaoming, Yan Yang, Yuanhong Zhong, Dong Xiong, and Zhiyong Huang. "Super-Pixel Guided Low-Light Images Enhancement with Features Restoration." Sensors 22, no. 10 (2022): 3667. http://dx.doi.org/10.3390/s22103667.

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Dealing with low-light images is a challenging problem in the image processing field. A mature low-light enhancement technology will not only be conductive to human visual perception but also lay a solid foundation for the subsequent high-level tasks, such as target detection and image classification. In order to balance the visual effect of the image and the contribution of the subsequent task, this paper proposes utilizing shallow Convolutional Neural Networks (CNNs) as the priori image processing to restore the necessary image feature information, which is followed by super-pixel image segm

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Saravanan, A., and S. Sathiamoorthy. "Image Denoising for the Detection of Follicle in Polycystic Ovarian Syndrome Images." Asian Journal of Computer Science and Technology 7, no. 2 (2018): 118–22. http://dx.doi.org/10.51983/ajcst-2018.7.2.1868.

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Polycystic Ovarian Syndrome is an endocrine ailment affecting women of reproductive age. This syndrome is largely found in women whose age is in between 25 and 35. Without knowing the accurate region of a follicle in ovary, the hazard rigorousness of the patient cannot be exposed. Since, super-pixels can be functional on segmentation and image representation, it has turned out to be essential for refining the competence in computer vision systems. Thus, in this paper, a novel image denoising methodology for the detection of a follicle in the PCOS has been suggested by exploring the super-pixel

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Zhang, Sanxing, Zhenhuan Ma, Gang Zhang, Tao Lei, Rui Zhang, and Yi Cui. "Semantic Image Segmentation with Deep Convolutional Neural Networks and Quick Shift." Symmetry 12, no. 3 (2020): 427. http://dx.doi.org/10.3390/sym12030427.

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Semantic image segmentation, as one of the most popular tasks in computer vision, has been widely used in autonomous driving, robotics and other fields. Currently, deep convolutional neural networks (DCNNs) are driving major advances in semantic segmentation due to their powerful feature representation. However, DCNNs extract high-level feature representations by strided convolution, which makes it impossible to segment foreground objects precisely, especially when locating object boundaries. This paper presents a novel semantic segmentation algorithm with DeepLab v3+ and super-pixel segmentat

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Chang, Zhenliang, Xiaogang Yang, Ruitao Lu, Hao Zhuang, and Pan Huang. "Change detection based on dimension reduction SLIC and image matching for remote sensing images." Journal of Physics: Conference Series 2078, no. 1 (2021): 012040. http://dx.doi.org/10.1088/1742-6596/2078/1/012040.

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Abstract The detection accuracy of traditional change detection algorithms is seriously affected by the low accuracy and high rate of omission, the radiometric correction accuracy, and the classification threshold for difference image. A change detection method based on image segmentation and image matching was proposed for remote sensing images. In this method, super-pixel-based dimension reduction SLIC image segmentation algorithm and SURF algorithms were used. The homogeneous region was used as the segmentation standard, and the homogeneity method was proposed to suppress the impact of inco

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Guo Qiang, 国强, та 彭龙 Peng Long. "基于三维卷积神经网络与超像素分割的高光谱分类". Acta Optica Sinica 41, № 22 (2021): 2210001. http://dx.doi.org/10.3788/aos202141.2210001.

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Lefèvre, Sébastien, David Sheeren, and Onur Tasar. "A Generic Framework for Combining Multiple Segmentations in Geographic Object-Based Image Analysis." ISPRS International Journal of Geo-Information 8, no. 2 (2019): 70. http://dx.doi.org/10.3390/ijgi8020070.

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The Geographic Object-Based Image Analysis (GEOBIA) paradigm relies strongly on the segmentation concept, i.e., partitioning of an image into regions or objects that are then further analyzed. Segmentation is a critical step, for which a wide range of methods, parameters and input data are available. To reduce the sensitivity of the GEOBIA process to the segmentation step, here we consider that a set of segmentation maps can be derived from remote sensing data. Inspired by the ensemble paradigm that combines multiple weak classifiers to build a strong one, we propose a novel framework for comb

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TAN, Le-Yi, and Shou-Jue WANG. "A Fast Image Segmentation Based on Path-based Similarity and Dual Super-pixel Sets." Acta Automatica Sinica 39, no. 10 (2013): 1653. http://dx.doi.org/10.3724/sp.j.1004.2013.01653.

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Gu, Anqi, Xinxin Shan, and Ying Wen. "An image segmentation model in combination with dissimilarity criterion and entropy rate super-pixel." Journal of Image and Graphics 27, no. 11 (2022): 3267–79. http://dx.doi.org/10.11834/jig.210566.

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Xiao, Feng, and Hao Sun. "Analysis and Design of Image Segmentation Algorithm Based on Super-pixel and Graph Cut." International Journal of Advanced Network, Monitoring and Controls 3, no. 4 (2019): 25–30. http://dx.doi.org/10.21307/ijanmc-2019-017.

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Sun, Zhen, Shaojie Sun, Jun Zhao, Bin Ai, and Qingshu Yang. "Detection of Massive Oil Spills in Sun Glint Optical Imagery through Super-Pixel Segmentation." Journal of Marine Science and Engineering 10, no. 11 (2022): 1630. http://dx.doi.org/10.3390/jmse10111630.

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Large volumes of crude oil accidentally released into the sea may cause irreversible adverse impacts on marine and coastal environments. Large swath optical imagery, acquired using platforms such as the moderate-resolution imaging spectroradiometer (MODIS), is frequently used for massive oil spill detection, attributing to its large coverage and short global revisit, providing rich data for oil spill monitoring. The aim of this study was to develop a suitable approach for massive oil spill detection in sun glint optical imagery. Specifically, preprocessing procedures were conducted to mitigate

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Penza, Veronica, Jesús Ortiz, Leonardo S. Mattos, Antonello Forgione, and Elena De Momi. "Dense soft tissue 3D reconstruction refined with super-pixel segmentation for robotic abdominal surgery." International Journal of Computer Assisted Radiology and Surgery 11, no. 2 (2015): 197–206. http://dx.doi.org/10.1007/s11548-015-1276-0.

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Liu, Xiaoming, Shuxu Guo, Bingtao Yang, et al. "Automatic Organ Segmentation for CT Scans Based on Super-Pixel and Convolutional Neural Networks." Journal of Digital Imaging 31, no. 5 (2018): 748–60. http://dx.doi.org/10.1007/s10278-018-0052-4.

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LI, Jing. "SLIC super-pixel segmentation algorithm base on NMI features used in remote sensing image." Optics and Precision Engineering 30, no. 6 (2022): 734–42. http://dx.doi.org/10.37188/ope.20223006.0734.

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Wang, Yu, Yansheng Li, Wei Chen, Yunzhou Li, and Bo Dang. "DNAS: Decoupling Neural Architecture Search for High-Resolution Remote Sensing Image Semantic Segmentation." Remote Sensing 14, no. 16 (2022): 3864. http://dx.doi.org/10.3390/rs14163864.

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Deep learning methods, especially deep convolutional neural networks (DCNNs), have been widely used in high-resolution remote sensing image (HRSI) semantic segmentation. In literature, most successful DCNNs are artificially designed through a large number of experiments, which often consume lots of time and depend on rich domain knowledge. Recently, neural architecture search (NAS), as a direction for automatically designing network architectures, has achieved great success in different kinds of computer vision tasks. For HRSI semantic segmentation, NAS faces two major challenges: (1) The task

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Gajula, Srinivasarao. "MRI BRAIN IMAGE SEGMENTATION BY USING A DEEP SPECTRUM IMAGE TRANSLATION NETWORK." Journal of Medical pharmaceutical and allied sciences 10, no. 4 (2021): 3097–100. http://dx.doi.org/10.22270/jmpas.v10i4.1103.

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Now a days medical image processing is challenging task. Because of its structure, flexibility in place, and irregular borders, manual identification and segmentation of brain tumours is difficult. The proposed work uses the super pixel technique to identify and segment brain tumours based on transfer learning. This process is called as dense prediction because we are predicting for each pixel in the image. It is important to identify these tumours early to provide better treatment to patients. Early detection improves the patient's chances of survival. The primary goal of this study is to use

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