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Relevant bibliographies by topics / Global consistency error (GCE)

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Journal articles
Dissertations / Theses
Conference papers

Academic literature on the topic 'Global consistency error (GCE)'

Author: Grafiati

Published: 4 June 2021

Last updated: 8 February 2022

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Journal articles on the topic "Global consistency error (GCE)"

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Eliyani, Eliyani, and Fakhlul Nizam. "PEMILIHAN METODE SEGMENTASI PADA CITRA ULTRASONOGRAFI OVARIUM." E-Link: Jurnal Teknik Elektro dan Informatika 16, no. 1 (2021): 14. http://dx.doi.org/10.30587/e-link.v16i1.2731.

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Penelitian ini membandingkan metode segmentasi untuk mengenali folikel pada citra ultrasonografi ovarium, metode segmentasi yang paling baik akan digunakan untuk proses perhitungan jumlah folikel. Penilaian kinerja metode segmentasi active contour dan active contour without edge dievaluasi menggunakan Probabilistic Rand Index (PRI) dan Global Consistency Error (GCE). Hasil penelitian ini menunjukkan metode segmentasi yang terbaikadalah active contour without edge karena memiliki nilai PRI lebih tinggi dan pada nilai GCE lebih rendah dari pada hasil metode segmentasi active contour.

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Gunasekara, Shanaka Ramesh, H. N. T. K. Kaldera, and Maheshi B. Dissanayake. "A Systematic Approach for MRI Brain Tumor Localization and Segmentation Using Deep Learning and Active Contouring." Journal of Healthcare Engineering 2021 (February 28, 2021): 1–13. http://dx.doi.org/10.1155/2021/6695108.

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One of the main requirements of tumor extraction is the annotation and segmentation of tumor boundaries correctly. For this purpose, we present a threefold deep learning architecture. First, classifiers are implemented with a deep convolutional neural network (CNN) and second a region-based convolutional neural network (R-CNN) is performed on the classified images to localize the tumor regions of interest. As the third and final stage, the concentrated tumor boundary is contoured for the segmentation process by using the Chan–Vese segmentation algorithm. As the typical edge detection algorithm

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Kumar, Rajesh, Rajeev Srivastava, and Subodh Srivastava. "Microscopic Biopsy Image Segmentation Using Hybrid Color K-Means Approach." International Journal of Computer Vision and Image Processing 7, no. 1 (2017): 79–90. http://dx.doi.org/10.4018/ijcvip.2017010105.

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The color image segmentation is a fundamental requirement for microscopic biopsy image analysis and disease detection. In this paper, a hybrid combination of color k-means and marker control watershed based segmentation approach is proposed to be applied for the segmentation of cell and nuclei of microscopic biopsy images. The proposed approach is tested on breast cancer microscopic data set with ROI segmented ground truth images. Finally, the results obtained from proposed framework are compared with the results of popular segmentation algorithms such as Fuzzy c-means, color k-means, texture

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Jyothirmayi, T., K. Srinivasa Rao, P. Srinivasa Rao, and Ch Satyanarayana. "Image Segmentation Based on Doubly Truncated Generalized Laplace Mixture Model and K Means Clustering." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 5 (2016): 2188. http://dx.doi.org/10.11591/ijece.v6i5.10682.

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The present paper aims at performance evaluation of Doubly Truncated Generalized Laplace Mixture Model and K-Means clustering (DTGLMM-K) for image analysis concerned to various practical applications like security, surveillance, medical diagnostics and other areas. Among the many algorithms designed and developed for image segmentation the dominance of Gaussian Mixture Model (GMM) has been predominant which has the major drawback of suiting to a particular kind of data. Therefore the present work aims at development of DTGLMM-K algorithm which can be suitable for wide variety of applications a

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Jyothirmayi, T., K. Srinivasa Rao, P. Srinivasa Rao, and Ch Satyanarayana. "Image Segmentation Based on Doubly Truncated Generalized Laplace Mixture Model and K Means Clustering." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 5 (2016): 2188. http://dx.doi.org/10.11591/ijece.v6i5.pp2188-2196.

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The present paper aims at performance evaluation of Doubly Truncated Generalized Laplace Mixture Model and K-Means clustering (DTGLMM-K) for image analysis concerned to various practical applications like security, surveillance, medical diagnostics and other areas. Among the many algorithms designed and developed for image segmentation the dominance of Gaussian Mixture Model (GMM) has been predominant which has the major drawback of suiting to a particular kind of data. Therefore the present work aims at development of DTGLMM-K algorithm which can be suitable for wide variety of applications a

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Sun, Bing, Chuying Fang, Hailun Xu, and Anqi Gao. "A New Synthetic Aperture Radar (SAR) Imaging Method Combining Match Filter Imaging and Image Edge Enhancement." Sensors 18, no. 12 (2018): 4133. http://dx.doi.org/10.3390/s18124133.

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In general, synthetic aperture radar (SAR) imaging and image processing are two sequential steps in SAR image processing. Due to the large size of SAR images, most image processing algorithms require image segmentation before processing. However, the existence of speckle noise in SAR images, as well as poor contrast and the uneven distribution of gray values in the same target, make SAR images difficult to segment. In order to facilitate the subsequent processing of SAR images, this paper proposes a new method that combines the back-projection algorithm (BPA) and a first-order gradient operato

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Phornphatcharaphong, Wutthichai, and Nawapak Eua-Anant. "Edge-Based Color Image Segmentation Using Particle Motion in a Vector Image Field Derived from Local Color Distance Images." Journal of Imaging 6, no. 7 (2020): 72. http://dx.doi.org/10.3390/jimaging6070072.

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This paper presents an edge-based color image segmentation approach, derived from the method of particle motion in a vector image field, which could previously be applied only to monochrome images. Rather than using an edge vector field derived from a gradient vector field and a normal compressive vector field derived from a Laplacian-gradient vector field, two novel orthogonal vector fields were directly computed from a color image, one parallel and another orthogonal to the edges. These were then used in the model to force a particle to move along the object edges. The normal compressive vec

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Et. al., Rajendra Prasad Bellapu,. "PERFORMANCE COMPARISON OF UNSUPERVISED SEGMENTATION ALGORITHMS ON RICE, GROUNDNUT, AND APPLE PLANT LEAF IMAGES." INFORMATION TECHNOLOGY IN INDUSTRY 9, no. 2 (2021): 1090–105. http://dx.doi.org/10.17762/itii.v9i2.457.

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This paper focuses on plant leaf image segmentation by considering the aspects of various unsupervised segmentation techniques for automatic plant leaf disease detection. The segmented plant leaves are crucial in the process of automatic disease detection, quantification, and classification of plant diseases. Accurate and efficient assessment of plant diseases is required to avoid economic, social, and ecological losses. This may not be easy to achieve in practice due to multiple factors. It is challenging to segment out the affected area from the images of complex background. Thus, a robust s

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Gardner-McTaggart, Alexander. "Leadership of international schools and the International Baccalaureate learner profile." Educational Management Administration & Leadership 47, no. 5 (2018): 766–84. http://dx.doi.org/10.1177/1741143217745883.

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Purpose:this study offers a rare insight into senior leadership in International Baccalaureate (IB) international schools. The IB international school profits from the perceived quality and consistency of the IB brand; international schools, however, suffer from an endemic culture of change and reinterpretation. The IB learner profile (IBLP) offers scope for consistency and an overarching ethos, and research finds that ‘buy-in’ to the IBLP and modelling of it in all aspects of school life are essential in achieving this. It emerges that buy-in to the IBLP in directors is split between the pers

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Tang, Youmin, Richard Kleeman, and Sonya Miller. "ENSO Predictability of a Fully Coupled GCM Model Using Singular Vector Analysis." Journal of Climate 19, no. 14 (2006): 3361–77. http://dx.doi.org/10.1175/jcli3771.1.

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Abstract Using a recently developed method of computing climatically relevant singular vectors (SVs), the error growth properties of ENSO in a fully coupled global climate model are investigated. In particular, the authors examine in detail how singular vectors are influenced by the phase of ENSO cycle—the physical variable under consideration as well as the error norm deployed. Previous work using SVs for studying ENSO predictability has been limited to intermediate or hybrid coupled models. The results show that the singular vectors share many of the properties already seen in simpler models

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Dissertations / Theses on the topic "Global consistency error (GCE)"

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Khelifi, Lazhar. "Contributions à la fusion de segmentations et à l’interprétation sémantique d’images." Thèse, 2017. http://hdl.handle.net/1866/20490.

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Conference papers on the topic "Global consistency error (GCE)"

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Luo, Xiao, Daqing Wu, Zeyu Ma, et al. "CIMON: Towards High-quality Hash Codes." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/125.

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Recently, hashing is widely used in approximate nearest neighbor search for its storage and computational efficiency. Most of the unsupervised hashing methods learn to map images into semantic similarity-preserving hash codes by constructing local semantic similarity structure from the pre-trained model as the guiding information, i.e., treating each point pair similar if their distance is small in feature space. However, due to the inefficient representation ability of the pre-trained model, many false positives and negatives in local semantic similarity will be introduced and lead to error p

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Liu, Jian, Jinan Xu, Yufeng Chen, and Yujie Zhang. "Discourse-Level Event Temporal Ordering with Uncertainty-Guided Graph Completion." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/533.

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Learning to order events at discourse-level is a crucial text understanding task. Despite many efforts for this task, the current state-of-the-art methods rely heavily on manually designed features, which are costly to produce and are often specific to tasks/domains/datasets. In this paper, we propose a new graph perspective on the task, which does not require complex feature engineering but can assimilate global features and learn inter-dependencies effectively. Specifically, in our approach, each document is considered as a temporal graph, in which the nodes and edges represent events and ev

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Ren, Weiju, David Cebon, and Steven M. Arnold. "Effective Materials Property Information Management for the 21st Century." In ASME 2009 Pressure Vessels and Piping Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/pvp2009-77314.

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This paper discusses key principles for the development of materials property information management software systems. There are growing needs for automated materials information management in various organizations. In part these are fuelled by the demands for higher efficiency in material testing, product design and engineering analysis. But equally important, organizations are being driven by the need for consistency, quality and traceability of data, as well as control of access to sensitive information such as proprietary data. Further, the use of increasingly sophisticated nonlinear, anis

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De, Soumen, Nagarajan Sethuraman, and Chengyin Yuan. "A Formal Approach on Specification Modeling to Support Industrial PLC Program Verification." In ASME 2008 International Mechanical Engineering Congress and Exposition. ASMEDC, 2008. http://dx.doi.org/10.1115/imece2008-67458.

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Intensive global competition requires the automotive manufacturers to launch new vehicle models with a shorter launch time, better quality, lower cost and more customization. One of the key enablers for achieving these objectives is to have an efficient & error-free manufacturing automation system which is typically controlled by Programmable Logic Controllers (PLC). The current PLC logic code testing process in automotive industry is usually performed manually by individual engineer, and the overall testing quality highly depends on the engineer’s expertise and experience. The PLC logic c

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