Vectorizing hand-drawn sketches is a challenging task, which is of paramount importance for creating CAD vectorized versions for the fashion and creative workflows. This paper proposes a complete framework that automatically transforms noisy and complex hand-drawn sketches with different stroke types in a precise, reliable and highly-simplified vectorized model. The proposed framework includes a novel line extraction algorithm based on a multi-resolution application of Pearson's cross correlation and a new unbiased thinning algorithm that can get rid of scribbles and variable-width strokes to obtain clean 1-pixel lines. Other contributions include variants of pruning, merging and edge linking procedures to post-process the obtained paths. Finally, a modification of the original Schneider's vectorization algorithm is designed to obtain fewer control points in the resulting Bezier splines. All the proposed steps of the framework have been extensively tested and compared with state-of-the-art algorithms, showing (both qualitatively and quantitatively) its outperformance.
@cite_11 reported a first proposal of a framework transforming raw images to full vectorized representations. However, the binarization'' step is not considered at all, by presenting directly the skeleton processing and vectorization steps. In addition to this limitation, this paper also bases the vectorization to the simple fitting of straight lines and circular arcs (instead of using Bezier interpolation), which represents a too simplified and limited representation of the resulting path.
@cite_11
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The problem of large-scale image search has been traditionally addressed with the bag-of-visual-words (BOV). In this article, we propose to use as an alternative the Fisher kernel framework. We first show why the Fisher representation is well-suited to the retrieval problem: it describes an image by what makes it different from other images. One drawback of the Fisher vector is that it is high-dimensional and, as opposed to the BOV, it is dense. The resulting memory and computational costs do not make Fisher vectors directly amenable to large-scale retrieval. Therefore, we compress Fisher vectors to reduce their memory footprint and speed-up the retrieval. We compare three binarization approaches: a simple approach devised for this representation and two standard compression techniques. We show on two publicly available datasets that compressed Fisher vectors perform very well using as little as a few hundreds of bits per image, and significantly better than a very recent compressed BOV approach. Abstract Being motivated by the multilayer RECOS (REctified-COrrelations on a Sphere) transform, we develop a data-driven Saak (Subspace approximation with augmented kernels) transform in this work. The Saak transform consists of three steps: (1) building the optimal linear subspace approximation with orthonormal bases using the second-order statistics of input vectors, (2) augmenting each transform kernel with its negative, (3) applying the rectified linear unit (ReLU) to the transform output. The Karhunen-Loeve transform (KLT) is used in the first step. The integration of Steps 2 and 3 is powerful since they resolve the sign confusion problem, remove the rectification loss and allow a straightforward implementation of the inverse Saak transform at the same time. Multiple Saak transforms are cascaded to transform images of a larger size. All Saak transform kernels are derived from the second-order statistics of input random vectors in a one-pass feedforward manner. Neither data labels nor backpropagation is used in kernel determination. Multi-stage Saak transforms offer a family of joint spatial-spectral representations between two extremes; namely, the full spatial-domain representation and the full spectral-domain representation. We select Saak coefficients of higher discriminant power to form a feature vector for pattern recognition, and use the MNIST dataset classification problem as an illustrative example. This paper addresses the problem of converting a rasterized floorplan image into a vector-graphics representation. Unlike existing approaches that rely on a sequence of lowlevel image processing heuristics, we adopt a learning-based approach. A neural architecture first transforms a rasterized image to a set of junctions that represent low-level geometric and semantic information (e.g., wall corners or door end-points). Integer programming is then formulated to aggregate junctions into a set of simple primitives (e.g., wall lines, door lines, or icon boxes) to produce a vectorized floorplan, while ensuring a topologically and geometrically consistent result. Our algorithm significantly outperforms existing methods and achieves around 90 precision and recall, getting to the range of production-ready performance. The vector representation allows 3D model popup for better indoor scene visualization, direct model manipulation for architectural remodeling, and further computational applications such as data analysis. Our system is efficient: we have converted hundred thousand production-level floorplan images into the vector representation and generated 3D popup models. One. Introduction: On the Absence of Geometry from Morphometrics.- First Part. The Measurement of Biological Shape.- Two. Shapes and Measures of Shape.- A. Properties of the Euclidean Plane and Euclidean Space.- B. Outlines and Homologous Landmarks.- C. Definitions of Shape, Shape Change, Shape Measurement.- D. Shapes as Data.- Three. Critique of an Applied Field: Conventional Cephalometrics.- A. Landmarks, Curvature, and Growth.- B. Registration.- Four. New Statistical Methods for Shape.- A. Analysis of the Tangent Angle Function.- 1. History.- 2. Sampling from the tangent angle function.- 3. Conic replacement curves and their estimation Fit of a conic to a point - Geometric interpretation - Estimator for a circle - Estimation for the general conic - Computation of the extremum -Linear constraints.- 4. Conic splining Joint conic fitting under constraint -An example - Application - Analysis of parameters.- B. Extension to Three Dimensions: A Sketch.- C. Skeletons Definition - A multivariate statistical method - Bibliographic note.- Second Part. The Measurement of Shape Change Using Biorthogonal Grids.- Five. The Study of Shape Transformation after D'Arcy Thompson.- A. The Original Method.- 1. Thompson's own work.- 2. Later examples.- 3. Difficulties.- B. Analysis of Growth Gradients.- C. Simulations.- D. Other Morphometric Schemes Vector displacements - Multivariate morphometrics.- Six. The Method of Biorthogonal Grids.- A. Representation of Affine Transformations.- B. General Lines of Growth and Biorthogonal Grids.- C. Summarizing the Grids.- Technical Note 1. Existence and Form of Biorthogonal Grids.- Technical Note 2. Interpolation from Landmark Locations and Arcs The measure of roughness - The vector space and its associated functions - Interpolation from boundary values - Interpolation from boundary values and interior points - Note on computation.- Technical Note 3. Construction of Integral Curves.- Technical Note 4. On Homologous Points.- Seven. Examples of Biorthogonal Analysis.- A. Comparison of Square and Biorthogonal Grids: Thompson's Diodon Figure.- B. Phylogeny and Ontogeny of Primate Crania.- 1. Functional craniology and craniometrics.- 2. Data for this exercise.- 3. Two types of transformations.- 4. Comparative ontogeny of the apes and man.- Eight. Future Directions for Transformation Analysis.- A. Statistical Methods The symmetric tensor field - Concordance -Linear methods.- B. Computation Other kinds of information about homology -Three dimensions.- C. Likely Applications Computed tomography - Orthodontics - Developmental biology.- Nine. Envoi.- Literature Cited.