8,241 research outputs found
A Two-Stage Image Segmentation Method Using a Convex Variant of the Mumford--Shah Model and Thresholding
No full textThe Mumford–Shah model is one of the most important image segmentation models and has been studied extensively in the last twenty years. In this paper, we propose a two-stage segmentation method based on the Mumford–Shah model. The first stage of our method is to find a smooth
solution g to a convex variant of the Mumford–Shah model. Once g is obtained, then in the second stage the segmentation is done by thresholding g into different phases. The thresholds can be given by the users or can be obtained automatically using any clustering methods. Because of the convexity of the model, g can be solved efficiently by techniques like the split-Bregman algorithm or the Chambolle–Pock method. We prove that our method is convergent and that the solution g is always unique. In our method, there is no need to specify the number of segments K (K ≥ 2) before finding g. We can obtain any K-phase segmentations by choosing (K − 1) thresholds after g is found in the first stage, and in the second stage there is no need to recompute g if the thresholds are changed to reveal different segmentation features in the image.Experimental results show that our two-stage method performs better than many standard two-phase or multiphase segmentation methods for very general images, including antimass, tubular, MRI, noisy, and blurry images
Marianne Chan: 47th Annual ODU Literary Festival
No full textMarianne Chan grew up in Stuttgart, Germany, and Lansing, Michigan. She is the author of All Heathens (Sarabande Books, 2020), which was the winner of the 2021 GLCA New Writers Award. Her second collection, Leaving Biddle City, was published from Sarabande Books in July of this year. Her poems have appeared in Poetry, Best American Poetry, New England Review, Kenyon Review, Michigan Quarterly Review, and elsewhere. She is an assistant professor of creative writing at Old Dominion University and teaches poetry in the Warren Wilson College MFA program for Writers
A study on idiomatic phrases in Chan texts
No full textChan documents, as an important source for conducting research in Medieval Chinese, contain a large number of colloquial words. Idioms, most of them deriving from the popular expressions, abound in Chan texts reflecting the colloquialisms and regional words of Medieval Chinese and Early Mandarin. Whereas some of the idioms found in medieval Chan texts are inherited or adapted from literary works of previous times, the majority of words reflect the colloquial language of the respective period. Some of these idioms are used only in Chan documents, and some expressions also had a certain impact on the spoken language of subsequent period. In this thesis idiomatic phrases in Chan texts are the main research object, and the contents include the following aspects:
The first chapter consists of the introduction, which briefly discusses the definition, scope and nature of idioms, debates the style of idioms, examines the relationship between Chan texts and idioms, and determines the research method and research content of the thesis.
The second chapter focuses on the origin of idioms. According to the origin of the idioms, the idioms are divided into four groups: secular literature, Buddhist sutras, Chan texts and contemporary colloquialisms.
The third chapter focuses on the diachronic development of idioms, and discusses the relationship between idioms in Chan texts and the previous literary works, as well as their composition and their influence.
The fourth chapter summarizes the linguistic features of Chan idioms, including the various semantic levels, the flexible morphology, and the patterns they occur in.
The fifth chapter discusses the research on the interpretation of idioms, evaluates the interpretation of some entries in relevant dictionaries, and focuses on the interpretation of idioms that are not included in dictionaries and other reference works.
The sixth chapter discusses the relationship between Chan idioms and the compilation of dictionaries, and discusses the deficiencies of existing idiom dictionaries. It also views Chan texts from the angle of compiling word lists, as well as focusing on the identification of dictionary entries and the documentation of reference material
Inauguración del XXIII Simposio Román Piña Chan. Zonas Arqueológicas en Contextos Urbanos. <p>XXIII Simposio Román Piña Chan.Zonas Arqueológicas en Contextos Urbanos<p>
No full textEl acto inaugural del XXIII Simposio Román Piña Chan “Zonas arqueológicas en contextos urbanos”, tuvo lugar el 6 de noviembre de 2018, en la Escuela Nacional de Antropología e Historia (ENAH). El Simposio fue inaugurado por el Antrop. Diego Prieto Hernández, Director General del Instituto Nacional de Antropología e Historia, en compañía de otras autoridades del INAH así como investigadores, docentes, alumnos y público en general.</p
Anyuon Chan
No full textabstract: Anyuon left his village in 1989 during the middle of the night.
“Lost Boys Found” is an ongoing, interdisciplinary project that is collecting, recording and archiving the oral histories of the Lost Boys/Girls of Sudan. The collection is a work-in-progress, seeking to record the oral history of as many Lost Boys/Girls as are willing, and will be used in a future book.Age: 22Region: Bahr al GhazalThis picture and bio was donated to the Lost Boys Found project from The Arizona Lost Boys Cente
Marianne Chan, 46th Annual ODU Literary Festival
No full textMarianne Chan grew up in Stuttgart, Germany, and Lansing, Michigan. After she earned her B.A. in English from Michigan State University, she went on to study poetry at the University of Nevada, Las Vegas, where she earned her MFA.
Marianne is the author of All Heathens, which was the winner of the 2021 GLCA New Writers Award in Poetry, the 2021 Ohioana Book Award in Poetry, and the 2022 Association for Asian American Studies Book Award for Outstanding Achievement. Her poems have appeared in Poetry Magazine, New England Review, Kenyon Review, Michigan Quarterly Review, and elsewhere. Between 2017-2019, she served as poetry editor for Split Lip Magazine. She is a Kundiman fellow.
She lives in Norfolk, Virginia . She is married to the fiction writer Clancy McGilligan
Clausura del XXIII Simposio Román Piña Chan. Zonas Arqueológicas en Contextos Urbanos. <p>XXIII Simposio Román Piña Chan.Zonas Arqueológicas en Contextos Urbanos<p>
No full textDel 6 al 8 de noviembre de 2018, se llevó a cabo el XXIII Simposio Román Piña Chan “Zonas arqueológicas en contextos urbanos”. Durante su desarrollo se contó con un amplio programa de trabajo que incluyó 31 ponencias, cinco conferencias magistrales y nueve sesiones de carteles. Se trató de un evento en el que participaron reconocidos académicos a nivel nacional e internacional en el ámbito de la arqueología; además de que se logró debatir y aportar ideas en un mismo foro acerca de la construcción de soluciones para la problemática de las zonas arqueológicas en contextos urbanos. El acto de clausura se efectuó con la presencia de diversas autoridades del INAH en compañía de investigadores, docentes, alumnos y público en general.</p
Judicial deference at work: Some reflections on Chan Kin Sum and Kong Yun Ming
No full text"Due deference" - the giving of appropriate weight to the government's judgment in the court's reasoning - is a tool that courts use to maintain the separation of powers in constitutional rights review. This note aims to provide a theoretical framework for understanding the issue of deference, and to analyse the Court of First Instance (CFI)'s approach to deference in two recent cases, Chan Kin Sum and Kong Yun Ming. The author argues that the CFI has adopted a spatial approach that failed to specify the contested issues that called for deference, inappropriately considered democratic legitimacy as a factor for deference and made broad presumptions about the democratic character of primary decisions. This approach may lead to an over-deferential attitude that threatens the separation of powers, and the malleability of the approach may be subject to courts' manipulation. The author argues for a more context-sensitive approach based purely on institutional factors.published_or_final_versio
An efficient and versatile variational method for high-dimensional data classification
No full textHigh-dimensional data classification is a fundamental task in machine learning and imaging science. In this paper, we propose an efficient and versatile multi-class semi-supervised classification method for classifying high-dimensional data and unstructured point clouds. To begin with, a warm initialization is generated by using a fuzzy classification method such as the standard support vector machine or random labeling. Then an unconstraint convex variational model is proposed to purify and smooth the initialization, followed by a step which is to project the smoothed partition obtained previously to a binary partition. These steps can be repeated, with the latest result as a new initialization, to keep improving the classification quality. We show that the convex model of the smoothing step has a unique solution and can be solved by a specifically designed primal–dual algorithm whose convergence is guaranteed. We test our method and compare it with the state-of-the-art methods on several benchmark data sets. Thorough experimental results demonstrate that our method is superior in both the classification accuracy and computation speed for high-dimensional data and point clouds.</p
A two-stage classification method for high-dimensional data and point clouds
No full textHigh-dimensional data classification is a fundamental task in machine learning and imaging science. In this paper, we propose a two-stage multiphase semi-supervised classification method for classifying high-dimensional data and unstructured point clouds. To begin with, a fuzzy classification method such as the standard support vector machine is used to generate a warm initialization. We then apply a two-stage approach named SaT (smoothing and thresholding) to improve the classification. In the first stage, an unconstraint convex variational model is implemented to purify and smooth the initialization, followed by the second stage which is to project the smoothed partition obtained at stage one to a binary partition. These two stages can be repeated, with the latest result as a new initialization, to keep improving the classification quality. We show that the convex model of the smoothing stage has a unique solution and can be solved by a specifically designed primal-dual algorithm whose convergence is guaranteed. We test our method and compare it with the state-of-the-art methods on several benchmark data sets. The experimental results demonstrate clearly that our method is superior in both the classification accuracy and computation speed for high-dimensional data and point clouds
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