Relevant bibliographies by topics / Textured images

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Textured images'

Author: Grafiati
Published: 1 June 2024
Last updated: 31 July 2025

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Journal articles on the topic "Textured images"

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Oliveira, Miguel, Gi-Hyun Lim, Tiago Madeira, Paulo Dias, and Vítor Santos. "Robust Texture Mapping Using RGB-D Cameras." Sensors 21, no. 9 (2021): 3248. http://dx.doi.org/10.3390/s21093248.

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The creation of a textured 3D mesh from a set of RGD-D images often results in textured meshes that yield unappealing visual artifacts. The main cause is the misalignments between the RGB-D images due to inaccurate camera pose estimations. While there are many works that focus on improving those estimates, the fact is that this is a cumbersome problem, in particular due to the accumulation of pose estimation errors. In this work, we conjecture that camera poses estimation methodologies will always display non-neglectable errors. Hence, the need for more robust texture mapping methodologies, ca
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Hemalatha, S., and S. Margret Anouncia. "A Computational Model for Texture Analysis in Images with Fractional Differential Filter for Texture Detection." International Journal of Ambient Computing and Intelligence 7, no. 2 (2016): 93–113. http://dx.doi.org/10.4018/ijaci.2016070105.

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This paper is dedicated to the modelling of textured images influenced by fractional derivatives for texture detection. As most of the images contain textures, texture analysis becomes the most important for image understanding and it is a key solution for many computer vision applications. Hence, texture must be suitably detected and represented. Nevertheless, existing texture detection algorithms consider texture as a unique feature from edges. The proposed model explores a novel way of developing texture detection algorithm by mimicking edge detection algorithms. The method assumes that tex
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Dal’Col, Lucas, Daniel Coelho, Tiago Madeira, Paulo Dias, and Miguel Oliveira. "A Sequential Color Correction Approach for Texture Mapping of 3D Meshes." Sensors 23, no. 2 (2023): 607. http://dx.doi.org/10.3390/s23020607.

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Texture mapping can be defined as the colorization of a 3D mesh using one or multiple images. In the case of multiple images, this process often results in textured meshes with unappealing visual artifacts, known as texture seams, caused by the lack of color similarity between the images. The main goal of this work is to create textured meshes free of texture seams by color correcting all the images used. We propose a novel color-correction approach, called sequential pairwise color correction, capable of color correcting multiple images from the same scene, using a pairwise-based method. This
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Bhaumik, Shubrajit, Viorel Paleu, Dhrubajyoti Chowdhury, et al. "Tribological Investigation of Textured Surfaces in Starved Lubrication Conditions." Materials 15, no. 23 (2022): 8445. http://dx.doi.org/10.3390/ma15238445.

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The present work investigates the friction reduction capability of two types of micro-textures (grooves and dimples) created on steel surfaces using a vertical milling machine. The wear studies were conducted using a pin-on-disc tribometer, with the results indicating a better friction reduction capacity in the case of the dimple texture as compared to the grooved texture. The microscopic images of the pin surface revealed deep furrows and significant damage on the pin surfaces of the groove-textured disc. An optimization of the textured surfaces was performed using an artificial neural networ
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Coelho, Daniel, Lucas Dal’Col, Tiago Madeira, Paulo Dias, and Miguel Oliveira. "A Robust 3D-Based Color Correction Approach for Texture Mapping Applications." Sensors 22, no. 5 (2022): 1730. http://dx.doi.org/10.3390/s22051730.

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Texture mapping of 3D models using multiple images often results in textured meshes with unappealing visual artifacts known as texture seams. These artifacts can be more or less visible, depending on the color similarity between the used images. The main goal of this work is to produce textured meshes free of texture seams through a process of color correcting all images of the scene. To accomplish this goal, we propose two contributions to the state-of-the-art of color correction: a pairwise-based methodology, capable of color correcting multiple images from the same scene; the application of
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Akl, Adib. "Adaptation of Symmetric Positive Semi-Definite Matrices for the Analysis of Textured Images." Cybernetics and Information Technologies 18, no. 1 (2018): 51–68. http://dx.doi.org/10.2478/cait-2018-0005.

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Abstract This paper addresses the analysis of textured images using the symmetric positive semi-definite matrix. In particular, a field of symmetric positive semi-definite matrices is used to estimate the structural information represented by the local orientation and the degree of anisotropy in structured and sinusoid-like textured images. In order to ensure faithful local structure estimation, an adaptive algorithm for the regularization of the extent of gradient fields smoothing is proposed. Results obtained on different texture samples show the strength of the proposed method in accurately
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Beschastnov, Nikolay P., Irina V. Rybaulina, and Evdokia N. Dergileva. "FACTURE, TEXTURE AND TEHNO-ORNAMENT IN MODERN DESIGN: FUNCTION AND ARTISTIC MEANING." Technologies & Quality 51, no. 1 (2021): 40–45. http://dx.doi.org/10.34216/2587-6147-2021-1-51-40-45.

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The article is devoted to defining the place and role of textured formations and techno-ornament in modern design, setting out the features of their use and methods of obtaining. The sources of the artistic attitude to the texture and texture of the material in the creation of works of decorative and applied art, interiors are briefly outlined, the importance of increased attention to them in the modern period is revealed. A special role is assigned to techno-ornamentation, which has arisen in high-tech culture and has become an exponent of new rhythmic-plastic images that are in tune with mod
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Barburiceanu, Stefania, Romulus Terebes, and Serban Meza. "3D Texture Feature Extraction and Classification Using GLCM and LBP-Based Descriptors." Applied Sciences 11, no. 5 (2021): 2332. http://dx.doi.org/10.3390/app11052332.

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Lately, 3D imaging techniques have achieved a lot of progress due to recent developments in 3D sensor technologies. This leads to a great interest regarding 3D image feature extraction and classification techniques. As pointed out in literature, one of the most important and discriminative features in images is the textural content. Within this context, we propose a texture feature extraction technique for volumetric images with improved discrimination power. The method could be used in textured volumetric data classification tasks. To achieve this, we fuse two complementary pieces of informat
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Wen, Mingyun, Jisun Park, and Kyungeun Cho. "Textured Mesh Generation Using Multi-View and Multi-Source Supervision and Generative Adversarial Networks." Remote Sensing 13, no. 21 (2021): 4254. http://dx.doi.org/10.3390/rs13214254.

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This study focuses on reconstructing accurate meshes with high-resolution textures from single images. The reconstruction process involves two networks: a mesh-reconstruction network and a texture-reconstruction network. The mesh-reconstruction network estimates a deformation map, which is used to deform a template mesh to the shape of the target object in the input image, and a low-resolution texture. We propose reconstructing a mesh with a high-resolution texture by enhancing the low-resolution texture through use of the super-resolution method. The architecture of the texture-reconstruction
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Nishad, N., R. Meenakshi, R. Ramakrishnan, and A. Chirputkar. "Texture analysis for skin cancer diagnosis using dermoscopic images." CARDIOMETRY, no. 25 (February 14, 2023): 287–91. http://dx.doi.org/10.18137/cardiometry.2022.25.287-291.

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This paper provides a foundation to examine the dermoscopic images for skin cancer diagnosis. A dermoscopic image will often include textured areas that make up a major amount of the image. It is conceivable to organize and categorize such textures according to whether they are related with artifacts or if they reflect biological structure. Given the connection between structure, disease, and texture, it seems likely that quantitative measurements of texture might make it possible to characterize the tissues included inside a dermoscopic image. It has been shown that texture is a valuable char
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Dissertations / Theses on the topic "Textured images"

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Li, Zhongqiang. "Segmentation of textured images." Thesis, University of Central Lancashire, 1991. http://clok.uclan.ac.uk/20270/.

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This study is dedicated to the problem of segmenting monochrome images into distinct homogeneous regions by texture properties. The principle of the approaches to texture segmentation adopted in this thesis is mapping a textured image into a grey level image so that conventional segmentation techniques by intensity can be applied. Three novel approaches to texture segmentation have been developed in this thesis. They are called the Local Feature Statistics Approach (LFS), the Local Spectral Mapping Approach (LSM) and the Multichannel Spatial Filtering Approach (MSF). In the LFS approach, a mul
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Leng, Xiaoling. "Analysis of some textured images by transputer." Thesis, University of Glasgow, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.324405.

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Noriega, Leonardo Antonio. "The colorimetric segmentation of textured digital images." Thesis, Southampton Solent University, 1998. http://ssudl.solent.ac.uk/2444/.

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This study approaches the problem of colour image segmentation as a pattern recognition task. This leads to the problem being broken down into two component parts: feature extraction and classification algorithms. Measures to enable the objective assessment of segmentation algorithms are considered. In keeping with this pattern-recognition based philosophy, the issue of texture is approached by a consideration of features, follwed by experimentation based on classification. Techniques based on Gabor filters and fractal dimension are compared. Also colour is considered in terms of its features,
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Bradbury, Teresa Ann. "Textured imprints, images, social change, and cultural memory." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ29144.pdf.

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Williams, Ian Anthony. "Edge detection of textured images using multiple scales and statistics." Thesis, Manchester Metropolitan University, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.491176.

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Texture is often the discriminator for different regions of an image. It can allow a region, or an object's edges to be represented as a difference in the pixel texture properties, as opposed to a difference in intensity. When analysing images with significant levels of noise, clutter or texture, the inadequacies of many common edge detectors has been noted. Where these traditional techniques fail, texture based edge detection proves more appropriate. In this work novel statistical edge detectors particularly suited for textured images are designed, presented and analysed. These are based on t
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Văcar, Cornelia Paula. "Inversion for textured images : unsupervised myopic deconvolution, model selection, deconvolution-segmentation." Thesis, Bordeaux, 2014. http://www.theses.fr/2014BORD0131/document.

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Ce travail est dédié à la résolution de plusieurs problèmes de grand intérêt en traitement d’images : segmentation, choix de modèle et estimation de paramètres, pour le cas spécifique d’images texturées indirectement observées (convoluées et bruitées). Dans ce contexte, les contributions de cette thèse portent sur trois plans différents : modéle, méthode et algorithmique.Du point de vue modélisation de la texture, un nouveaumodèle non-gaussien est proposé. Ce modèle est défini dans le domaine de Fourier et consiste en un mélange de Gaussiennes avec une Densité Spectrale de Puissance paramétriq
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Meléndez, Rodríguez Jaime Christian. "Supervised and unsupervised segmentation of textured images by efficient multi-level pattern classification." Doctoral thesis, Universitat Rovira i Virgili, 2010. http://hdl.handle.net/10803/8487.

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This thesis proposes new, efficient methodologies for supervised and unsupervised image segmentation based on texture information. For the supervised case, a technique for pixel classification based on a multi-level strategy that iteratively refines the resulting segmentation is proposed. This strategy utilizes pattern recognition methods based on prototypes (determined by clustering algorithms) and support vector machines. In order to obtain the best performance, an algorithm for automatic parameter selection and methods to reduce the computational cost associated with the segmentation proces
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Dura, Martinez Esther. "Reconstruction and classification of man-made objects and textured seafloors from side-scan sonar images." Thesis, Heriot-Watt University, 2002. http://hdl.handle.net/10399/409.

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Achddou, Raphaël. "Synthetic learning for neural image restoration methods." Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAT006.

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La photographie occupe aujourd'hui une place prépondérante dans nos vies. De plus, les attentes en terme de qualité des images augmentent tandis que la taille des appareils imageurs diminuent. Dans ce contexte, l'amélioration des algorithmes de traitement d'image est primordial.Dans ce manuscrit, on s'intéresse particulièrement aux tâches de restauration des images. Le but est de produire une image propre à partir d'une ou plusieurs observations bruitées de la même scène. Pour ces problèmes, les méthodes d'apprentissage profond ont connu un essor spectaculaire dans la dernière décennie, surpas
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Casaca, Wallace Correa de Oliveira [UNESP]. "Restauração de imagens digitais com texturas utilizando técnicas de decomposição e equações diferenciais parciais." Universidade Estadual Paulista (UNESP), 2010. http://hdl.handle.net/11449/94247.

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Made available in DSpace on 2014-06-11T19:26:56Z (GMT). No. of bitstreams: 0 Previous issue date: 2010-02-25Bitstream added on 2014-06-13T19:06:36Z : No. of bitstreams: 1 casaca_wco_me_sjrp.pdf: 5215634 bytes, checksum: 291e2a21fdb4d46a11de22f18cc97f93 (MD5)<br>Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)<br>Neste trabalho propomos quatro novas abordagens para tratar o problema de restauração de imagens reais contendo texturas sob a perspectiva dos temas: reconstrução de regiões danificadas, remoção de objetos, e eliminação de ruídos. As duas primeiras abor dagens são design
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Books on the topic "Textured images"

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Hung, Chih-Cheng, Enmin Song, and Yihua Lan. Image Texture Analysis. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13773-1.

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Harris, David Earl. Texture analysis of skin cancer images. UMI, 1991.

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Gimel’farb, Georgy L. Image Textures and Gibbs Random Fields. Springer Netherlands, 1999. http://dx.doi.org/10.1007/978-94-011-4461-2.

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Gimel'farb, Georgy L. Image Textures and Gibbs Random Fields. Springer Netherlands, 1999.

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Wood, E. J. Carpet texture measurement using image analysis. Wronz, 1987.

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Spann, Michael. Texture description and segmentation in image processing. University of Aston. Department of Electrical and Electronic Engineering, 1985.

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Chaki, Jyotismita, and Nilanjan Dey. Texture Feature Extraction Techniques for Image Recognition. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-0853-0.

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Rand, Robert S. Texture analysis and cartographic feature extraction. U.S. Army Corps of Engineers, Engineer Topographic Laboratories, 1985.

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Korn, Christopher A. Markov random field textures and applications in image processing. Naval Postgraduate School, 1997.

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National Institute of Standards and Technology (U.S.), ed. Singular integrals, image smoothness, and the recovery of texture in image deblurring. U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, 2003.

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Book chapters on the topic "Textured images"

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Rouquet, Catherine, and Pierre Bonton. "Region-based segmentation of textured images." In Image Analysis and Processing. Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/3-540-60298-4_230.

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Mičušík, Branislav, and Allan Hanbury. "Steerable Semi-automatic Segmentation of Textured Images." In Image Analysis. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11499145_5.

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Santos, Roi, Xosé R. Fdez-Vidal, and Xosé M. Pardo. "Adaptive Line Matching for Low-Textured Images." In Pattern Recognition and Image Analysis. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19390-8_22.

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Casadei, Stefano, Sanjoy Mitter, and Pietro Perona. "Boundary detection in piecewise homogeneous textured images." In Computer Vision — ECCV'92. Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/3-540-55426-2_20.

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Kjell, Bradley P., and Charles R. Dyer. "Segmentation of Textured Images by Pyramid Linking." In Pyramidal Systems for Computer Vision. Springer Berlin Heidelberg, 1986. http://dx.doi.org/10.1007/978-3-642-82940-6_17.

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Manfredi, Guido, Michel Devy, and Daniel Sidobre. "Textured Object Recognition: Balancing Model Robustness and Complexity." In Computer Analysis of Images and Patterns. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23192-1_5.

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Grau, Antoni, and Jordi Saludes. "Improved textured images segmentation using an energy functional." In Image Analysis and Processing. Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-63507-6_186.

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Azencott, R., C. Graffrigne, and C. Labourdette. "Edge Detection and Segmentation of Textured Plane Images." In Stochastic Models, Statistical Methods, and Algorithms in Image Analysis. Springer New York, 1992. http://dx.doi.org/10.1007/978-1-4612-2920-9_4.

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Taha, Bilal, Munawar Hayat, Stefano Berretti, and Naoufel Werghi. "Fused Geometry Augmented Images for Analyzing Textured Mesh." In Lecture Notes in Computer Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-54407-2_1.

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Lee, Yun-Seok, Seung-Hun Yoo, and Chang-Sung Jeong. "Modified Hough Transform for Images Containing Many Textured Regions." In Rough Sets and Current Trends in Computing. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11908029_85.

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Conference papers on the topic "Textured images"

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Sanzharov, Vadim Vladimirovich, and Vladimir Alexandrovich Frolov. "Viewpoint Selection for Texture Reconstruction with Inverse Rendering." In 33rd International Conference on Computer Graphics and Vision. Keldysh Institute of Applied Mathematics, 2023. http://dx.doi.org/10.20948/graphicon-2023-66-77.

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Viewpoint selection methods have a variety of applications in different fields of computer graphics and computer vision, including shape retrieval, scientific visualization, image-based modeling and others. In this paper we investigate the applicability of existing viewpoint selection methods to the problem of textures reconstruction using inverse rendering. First, we use forward rendering to produce path-traced images of a textured object. Then we apply different view quality metrics to select a set of images for texture reconstruction. Finally, we perform material and texture reconstruction
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Turner, Mark R. "Gabor functions and textural segmentation." In OSA Annual Meeting. Optica Publishing Group, 1985. http://dx.doi.org/10.1364/oam.1985.wj38.

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This paper investigates the applicability of Gabor functions to textural segmentation. Gabor functions are sinusoidal plane waves in 2-D Gaussian envelopes. The choice of parameters characterizing the geometry of an individual Gabor function affects its spatial extent as well as orientation and spatial frequency tuning. Daugman has indicated that these functions belong to a class of filters having optimal joint resolution in the 2-D space and 2-D frequency domains. They are, therefore, appropriate filter choices for tasks which require selective measurement in these domains. Textural segmentat
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Weber, Allan G., and Alexander A. Sawchuk. "Segmentation of Textured Images." In Machine Vision. Optica Publishing Group, 1985. http://dx.doi.org/10.1364/mv.1985.fb1.

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A requirement of any vision system is to segment the image into regions having a set of common characteristics. In many applications, this common characteristic is texture. Texture is a higher order image property, and depends on the statistics of pixels in a local neighborhood. To perform segmentation, the regions of homogeneous higher order statistics must be identified and the spatial boundaries where statistical properties change must be located. These local statistics are derived from measurements within a window whose dimensions are subject to a conflicting set of requirements. Using a s
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WU, DONG-SHENG, LE-NAN WU, and BO HUANG. "AUTOMATION TEXTURED AND NON-TEXTURED IMAGES CLASSIFICATION AND RETRIEVAL." In Proceedings of the Second International Conference. WORLD SCIENTIFIC, 2003. http://dx.doi.org/10.1142/9789812704313_0050.

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"COLOR AND TEXTURE BASED SEGMENTATION ALGORITHM FOR MULTICOLOR TEXTURED IMAGES." In International Conference on Computer Vision Theory and Applications. SciTePress - Science and and Technology Publications, 2007. http://dx.doi.org/10.5220/0002042502580263.

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Zeng, Yu, and Biyu Wan. "Saliency Detection in Textured Images." In 2020 15th International Conference on Computer Science & Education (ICCSE). IEEE, 2020. http://dx.doi.org/10.1109/iccse49874.2020.9201616.

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He, Qiang, and Chee-Hung Henry Chu. "Shadow removal from textured images." In SPIE Defense, Security, and Sensing, edited by Daniel J. Henry. SPIE, 2009. http://dx.doi.org/10.1117/12.818983.

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Giovannelli, Jean-Francois, and Cornelia Vacar. "Deconvolution-segmentation for textured images." In 2017 25th European Signal Processing Conference (EUSIPCO). IEEE, 2017. http://dx.doi.org/10.23919/eusipco.2017.8081195.

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Dolez, Benoit, and Nicole Vincent. "Sample Selection in Textured Images." In 2007 IEEE International Conference on Image Processing. IEEE, 2007. http://dx.doi.org/10.1109/icip.2007.4379132.

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Turtinen, M., and M. Pietikainen. "Contextual Analysis of Textured Scene Images." In British Machine Vision Conference 2006. British Machine Vision Association, 2006. http://dx.doi.org/10.5244/c.20.87.

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Reports on the topic "Textured images"

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Alhasan, Ahmad, Brian Moon, Doug Steele, Hyung Lee, and Abu Sufian. Chip Seal Quality Assurance Using Percent Embedment. Illinois Center for Transportation, 2023. http://dx.doi.org/10.36501/0197-9191/23-029.

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This study investigates the use of macrotexture as an indicator of the percent embedment (PE) of aggregate in a chip seal and ultimately, as a quality assurance tool for chip seals. The study included an extensive field- and controlled-testing program from 24 chip seal sections constructed in Illinois. Surface texture measurements were acquired using a high-speed texture profiler and a stationary laser texture device. The analysis showed that stationary texture measurements were more consistent and reliable for estimating PE and characterizing chip seals in the field. Moreover, the ground trut
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Maloney, Megan, Sarah Becker, Andrew Griffin, Susan Lyon, and Kristofer Lasko. Automated built-up infrastructure land cover extraction using index ensembles with machine learning, automated training data, and red band texture layers. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/49370.

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Automated built-up infrastructure classification is a global need for planning. However, individual indices have weaknesses, including spectral confusion with bare ground, and computational requirements for deep learning are intensive. We present a computationally lightweight method to classify built-up infrastructure. We use an ensemble of spectral indices and a novel red-band texture layer with global thresholds determined from 12 diverse sites (two seasonally varied images per site). Multiple spectral indexes were evaluated using Sentinel-2 imagery. Our texture metric uses the red band to s
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McKay, Paul, and C. A. Blain. An Automated Approach to Extracting River Bank Locations from Aerial Imagery Using Image Texture. Defense Technical Information Center, 2013. http://dx.doi.org/10.21236/ada609737.

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LaCascia, Marco, John Isidoro, and Stan Sclaroff. Head Tracking via Robust Registration in Texture Map Images. Defense Technical Information Center, 1998. http://dx.doi.org/10.21236/ada366993.

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Carasso, Alfred S. Singular integrals, image smoothness, and the recovery of texture in image deblurring. National Institute of Standards and Technology, 2003. http://dx.doi.org/10.6028/nist.ir.7005.

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Wendelberger, James G. Localized Similar Image Texture in Images of Sample Laser Confocal Microscope for Area: FY15 DE07 SW C1 Zone 1 & 2 Section b. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1496724.

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Gletsos, M., S. G. Mougiakakou, G. K. Matsopoulos, K. S. Nikita, and D. Kelekis. Classification of Hepatic Lesions From CT Images Using Texture Features and Neural Networks. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada412422.

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Rosenblum, W. I., C. Salvaggio, and J. R. Schott. Selection of optimal textural features for maximum likelihood image classification. Office of Scientific and Technical Information (OSTI), 1990. http://dx.doi.org/10.2172/5098367.

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Du, Li-Jen. Segmentation of Synthetic Aperture Radar (SAR) Images of Ocean Surface by the Texture Energy Transform Method. Defense Technical Information Center, 1988. http://dx.doi.org/10.21236/ada199536.

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Pe-Piper, G., D. J W Piper, J. Nagle, and P. Opra. Petrography of bedrock and ice-rafted granules: Flemish Cap, offshore Newfoundland and Labrador. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/331224.

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Abstract:
This Open File report provides petrographic information from a scanning electron microscope study of granules and small pebbles in four selected trawl samples from Flemish Cap. The mineral composition of the granules was determined by energy dispersive spectroscopy (EDS) and textures are shown in backscattered electron images (BSE). It complements Open File 8359 on the heavy mineral assemblage on Flemish Cap. Granules on the central shoals appear to be derived from outcropping Avalonian basement; those to the east and west are predominantly ice-rafted in origin. These data improve our understa
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