A Medial Axis Based Thinning Strategy for Character Images
Indian Institute of Technology Kharagpur（印度理工学院卡拉格普尔分校）
Soumen Bag, Gaurav Harit
备注：
6 pages, 5 figures. In proceedings of the second National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG), pp. 67-72, Jaipur, India, 2010
Thinning of character images is a big challenge. Removal of strokes or deformities in thinning is a difficult problem. In this paper, we have proposed a medial axis based thinning strategy used for performing skeletonization of printed and handwritten character images. In this method, we have used shape characteristics of text to get skeleton of nearly same as the true character shape. This approach helps to preserve the local features and true shape of the character images. The proposed algorithm produces one pixel width thin skeleton. As a by-product of our thinning approach, the skeleton also gets segmented into strokes in vector form. Hence further stroke segmentation is not required. Experiment is done on printed English and Bengali characters and we obtain less spurious branches comparing with other thinning methods without any post processing.
University of Pennsylvania（宾夕法尼亚大学）；National University of Colombia（哥伦比亚国立大学）
We present the CPMA, a new method for medial axis pruning with noise robustness and equivariance to isometric transformations. Our method leverages the discrete cosine transform to create smooth versions of a shape $Ω$. We use the smooth shapes to compute a score function $\scorefunction$ that filters out spurious branches from the medial axis. We extensively compare the CPMA with state-of-the-art pruning methods and highlight our method's noise robustness and isometric equivariance. We found that our pruning approach achieves competitive results and yields stable medial axes even in scenarios…
We introduce Appearance-MAT (AMAT), a generalization of the medial axis transform for natural images, that is framed as a weighted geometric set cover problem. We make the following contributions: i) we extend previous medial point detection methods for color images, by associating each medial point with a local scale; ii) inspired by the invertibility property of the binary MAT, we also associate each medial point with a local encoding that allows us to invert the AMAT, reconstructing the input image; iii) we describe a clustering scheme that takes advantage of the additional scale and appear…
Quasi-Medial Distance Field (Q-MDF): A Robust Method for Approximating and Discretizing Neural Medial Axes
S-Lab, Nanyang Technological University（南洋理工大学S实验室）；School of Mathematics, Nanjing University of Aeronautics and Astronautics（南京航空航天大学数学学院）；School of Computer Science and Technology, Shandong University（山东大学计算机科学与技术学院）；Key Laboratory of System Software (CAS), Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所系统软件重点实验室）；University of Chinese Academy of Sciences（中国科学院大学）；SenseTime Research（商汤科技研究院）
McGill University（麦吉尔大学）；University of Toronto（多伦多大学）；Samsung Toronto AI Research Center（三星多伦多人工智能研究中心）；Vector Institute for Artificial Intelligence（向量人工智能研究所）
We combine ideas from shock graph theory with more recent appearance-based methods for medial axis extraction from complex natural scenes, improving upon the present best unsupervised method, in terms of efficiency and performance. We make the following specific contributions: i) we extend the shock graph representation to the domain of real images, by generalizing the shock type definitions using local, appearance-based criteria; ii) we then use the rules of a Shock Grammar to guide our search for medial points, drastically reducing run time when compared to other methods, which exhaustively …
Deep Medial Voxels: Learned Medial Axis Approximations for Anatomical Shape Modeling
Graz University of Technology, Institute of Computer Graphics and Vision（格拉茨技术大学计算机图形学与视觉研究所）；Ruhr University Bochum, Faculty of Mathematics（博德大学数学系）；Essen University Hospital, Institute for Artificial Intelligence in Medicine（埃森大学医院人工智能医学研究所）；University of Stuttgart（斯图加特大学）
McGill University（麦吉尔大学）；University of Toronto（多伦多大学）
The computer vision community has witnessed recent advances in scene categorization from images, with the state-of-the art systems now achieving impressive recognition rates on challenging benchmarks such as the Places365 dataset. Such systems have been trained on photographs which include color, texture and shading cues. The geometry of shapes and surfaces, as conveyed by scene contours, is not explicitly considered for this task. Remarkably, humans can accurately recognize natural scenes from line drawings, which consist solely of contour-based shape cues. Here we report the first computer v…
The University of Hong Kong（香港大学）；RWTH Aachen University（亚琛工业大学）；Tsinghua University（清华大学）；Beijing National Research Center for Information Science and Technology（北京信息科学与技术国家研究中心）；Shandong University（山东大学）
Segmenting arbitrary 3D objects into constituent parts that are structurally meaningful is a fundamental problem encountered in a wide range of computer graphics applications. Existing methods for 3D shape segmentation suffer from complex geometry processing and heavy computation caused by using low-level features and fragmented segmentation results due to the lack of global consideration. We present an efficient method, called SEG-MAT, based on the medial axis transform (MAT) of the input shape. Specifically, with the rich geometrical and structural information encoded in the MAT, we are able…
University of British Columbia（不列颠哥伦比亚大学）；Simon Fraser University（西蒙菲莎大学）；Google Research（谷歌研究院）；Stanford University（斯坦福大学）；University of Toronto（多伦多大学）
Implicit representations of geometry, such as occupancy fields or signed distance fields (SDF), have recently re-gained popularity in encoding 3D solid shape in a functional form. In this work, we introduce medial fields: a field function derived from the medial axis transform (MAT) that makes available information about the underlying 3D geometry that is immediately useful for a number of downstream tasks. In particular, the medial field encodes the local thickness of a 3D shape, and enables O(1) projection of a query point onto the medial axis. To construct the medial field we require nothin…
Transferable and Undefendable Point Cloud Attacks via Medial Axis Transform
Cyberspace Institute of Advanced Technology, Guangzhou University（高级科技 cyber 空间研究所，广州大学）；School of Computer Science and Cyber Engineering, Guangzhou University（计算机科学与网络工程学院，广州大学）；Department of Automation, University of Science and Technology of China（自动化系，中国科学技术大学）；School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University（人工智能、光学与电子学（iOPEN）学院，西北工业大学）
University of Toronto（多伦多大学）；Epson（爱普生）；Lund University（隆德大学）
The role of symmetry in computer vision has waxed and waned in importance during the evolution of the field from its earliest days. At first figuring prominently in support of bottom-up indexing, it fell out of favor as shape gave way to appearance and recognition gave way to detection. With a strong prior in the form of a target object, the role of the weaker priors offered by perceptual grouping was greatly diminished. However, as the field returns to the problem of recognition from a large database, the bottom-up recovery of the parts that make up the objects in a cluttered scene is critica…
RibPull: Implicit Occupancy Fields and Medial Axis Extraction for CT Ribcage Scans
Electrical and Computer Engineering Department, University of California, Santa Cruz（加州大学圣克ruz分校电子与计算机工程系）；Inria, Univ. Rennes, CNRS, IRISA, M2S, Rennes（Inria，里尔大学，CNRS，IRISA，M2S，里尔）；LIRIS - CNRS - Université Claude Bernard Lyon 1（LIRIS-CNRS-里尔1大学）；Computer Science Engineering Department, University of California, Santa Cruz（加州大学圣克ruz分校计算机科学工程系）
Segmentation of 3D pore space from CT images using curvilinear skeleton: application to numerical simulation of microbial decomposition
IRD（法国发展研究院）；UMMISCO（复杂系统数学与信息建模单元）；Cadi Ayyad University（卡迪·阿亚德大学）；Faculty of Sciences Semlalia（塞姆拉利亚科学学院）；LIB（自由研究所）；UFR Sciences et Techniques（科学与技术培训与研究单位）；University of Burgundy（勃艮第大学）；INRA（法国国家农业研究院）
Université Paris Descartes（巴黎第五大学）；Computer Vision Center（计算机视觉中心）；University of Barcelona（巴塞罗那大学）
The investigation of spatio-temporal dynamics of bacterial cells and their molecular components requires automated image analysis tools to track cell shape properties and molecular component locations inside the cells. In the study of bacteria aging, the molecular components of interest are protein aggregates accumulated near bacteria boundaries. This particular location makes very ambiguous the correspondence between aggregates and cells, since computing accurately bacteria boundaries in phase-contrast time-lapse imaging is a challenging task. This paper proposes an active skeleton formulatio…
