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Relevant bibliographies by topics / Super-pixel segmentation

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Academic literature on the topic 'Super-pixel segmentation'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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Journal articles on the topic "Super-pixel segmentation"

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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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Book chapters on the topic "Super-pixel segmentation"

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Yang, Yadong, and Xiaofeng Wang. "Improved CNN Based on Super-Pixel Segmentation." In IFIP Advances in Information and Communication Technology. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68121-4_33.

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chengkun, Wang, Zhang jinqiu, Yang jiale, and Feng kaiyue. "Smoke Segmentation Method Based on Super Pixel Segmentation and Convolutional Neural Network." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-53404-1_23.

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Amine Bechar, Mohammed El, Nesma Settouti, and Inês Domingues. "Deep Learning vs. Super Pixel Classification for Breast Masses Segmentation." In Deep Learning for Biomedical Applications. CRC Press, 2021. http://dx.doi.org/10.1201/9780367855611-7.

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Wang, Juan, Hao Shi, Min Liu, Wei Xiong, Kaiwen Cheng, and Yuhan Jiang. "Image Semantic Segmentation Algorithm Based on Self-learning Super-Pixel Feature Extraction." In Advances in Internet, Data & Web Technologies. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-75928-9_69.

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Liu, Tao, Xi Zhong, and Lixin Zhang. "Unstructured Road Segmentation Method Based on Super Pixel and Region Growing Algorithm." In Lecture Notes in Electrical Engineering. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-8458-9_44.

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Kaur, Inderjeet, and Rana Gill. "Super-Pixel Based Segmentation of Urban Area and Its Detection Using Machine Learning Techniques." In Lecture Notes in Networks and Systems. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3812-9_31.

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Subhashini, D., and V. B. S. Srilatha Indira Dutt. "A Hybrid Optimal Technique for Road Extraction Using Entropy Rate Super-Pixel Segmentation and Probabilistic Neural Networks." In Intelligent Manufacturing and Energy Sustainability. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8497-6_1.

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Pinheiro da Rosa Rafaelo and Cordeiro d'Ornellas Marcos. "An Optimized Superpixel Clustering Approach for High-Resolution Chest CT Image Segmentation." In Studies in Health Technology and Informatics. IOS Press, 2015. https://doi.org/10.3233/978-1-61499-564-7-1045.

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Lung segmentation is a fundamental step in many image analysis applications for lung diseases and abnormalities in thoracic computed tomography (CT). However, due to the large variations in pathology that may be present in thoracic CT images, it is difficult to extract the lung regions accurately. A major insight to deal with this problem is the existence of new approaches to cope with quality and performance. This poster presents an optimized superpixel clustering approach for high-resolution chest CT segmentation. The proposed algorithm is compared against some super-pixel algorithms while a

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Huang, Junzhou, and Ruoyu Li. "Fast Regions-of-Interest Detection in Whole Slide Histopathology Images." In Histopathology and Liquid Biopsy [Working Title]. IntechOpen, 2020. http://dx.doi.org/10.5772/intechopen.94238.

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Detecting and localizing pathological region of interest (ROI) over whole slide pathological image (WSI) is a challenging problem. To reduce computational complexity, we introduced a two-stage superpixel-based ROI detection approach. To efficiently construct superpixels with fine details preserved, we utilized a novel superpixel clustering algorithm which cluster blocks of pixel in a hierarchical fashion. The major reduction of complexity is attributed to the combination of boundary update and coarse-to-fine refinement in superpixel clustering. The former maintains the accuracy of segmentation

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Giuliani, Donatella. "Segmentation and Edge Extraction of Grayscale Images Using Firefly and Artificial Bee Colony Algorithms." In Handbook of Research on Advancements of Swarm Intelligence Algorithms for Solving Real-World Problems. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-3222-5.ch005.

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This chapter proposes an unsupervised grayscale image segmentation method based on the Firefly and Artificial Bee Colony algorithms. The Firefly Algorithm is applied in a histogram-based research of cluster centroids to determine the number of clusters and the gray levels, successively used in the initialization step for the parameter estimation of a Gaussian Mixture Model. The coefficients of the linear super-position of Gaussians can be thought of as prior probabilities of each component. Applying the Bayes rule, the posterior probabilities of the grayscale intensities are evaluated and thei

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Conference papers on the topic "Super-pixel segmentation"

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Almusawi, Muntather, Sowmya M, S. Sureshkumar, V. Saganya, and Y. Nagendar. "Maximum Entropy Scaled Super-Pixel Segmentation for Effective Text Detection." In 2024 International Conference on Data Science and Network Security (ICDSNS). IEEE, 2024. http://dx.doi.org/10.1109/icdsns62112.2024.10691129.

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Bharati, Subrato, M. Omair Ahmad, and M. N. S. Swamy. "A Novel Super-pixel Grid Mixing-Based Augmentation with a Feature Fusion of Convolutional Networks for Breast Ultrasound Image Segmentation." In 2024 IEEE 67th International Midwest Symposium on Circuits and Systems (MWSCAS). IEEE, 2024. http://dx.doi.org/10.1109/mwscas60917.2024.10658872.

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Cigla, Cevahir, and A. Aydin Alatan. "Efficient super pixel extraction for image segmentation." In 2012 20th Signal Processing and Communications Applications Conference (SIU). IEEE, 2012. http://dx.doi.org/10.1109/siu.2012.6204483.

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Wan, Jianqiang, Yang Liu, Donglai Wei, Xiang Bai, and Yongchao Xu. "Super-BPD: Super Boundary-to-Pixel Direction for Fast Image Segmentation." In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2020. http://dx.doi.org/10.1109/cvpr42600.2020.00927.

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Ofir, Nati. "Multispectral Image Fusion based on Super Pixel Segmentation." In ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2023. http://dx.doi.org/10.1109/icassp49357.2023.10095874.

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Surya, T., Chitra Selvi S, Arumugam Palanivel, and R. Bhuvaneswari. "Super-pixel Segmentation based Skin texture pattern recognition." In 2021 5th International Conference on Electronics, Communication and Aerospace Technology (ICECA). IEEE, 2021. http://dx.doi.org/10.1109/iceca52323.2021.9675925.

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Dhore, Sanskruti, and Deepa Abin. "Chest X-ray Segmentation Using Watershed and Super Pixel Segmentation Technique." In 2021 International Conference on Communication information and Computing Technology (ICCICT). IEEE, 2021. http://dx.doi.org/10.1109/iccict50803.2021.9510078.

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Lv, Linjue, and Xingwei Li. "Super-pixel Semantic Segmentation Method for Urban Complex Scenes." In 2019 IEEE 4th International Conference on Image, Vision and Computing (ICIVC). IEEE, 2019. http://dx.doi.org/10.1109/icivc47709.2019.8981089.

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Liao, Lianjun, Yongbin Hao, Xiangyang Su, and Shihong Xia. "Alignment and Super Pixel Segmentation of RGB-D Video Stream." In 2016 International Conference on Virtual Reality and Visualization (ICVRV). IEEE, 2016. http://dx.doi.org/10.1109/icvrv.2016.24.

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Sugitha, T. S., and Jose K. T. Gladwin. "Optical Flow Based on Feature Match and Super Pixel Segmentation." In 2014 Fourth International Conference on Advances in Computing and Communications (ICACC). IEEE, 2014. http://dx.doi.org/10.1109/icacc.2014.65.

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