215 research outputs found
Paper-based scribble simplification : where do we stand?
This research is funded by the University of Malta under theresearch grant IED 73-529 and is part of the project Innovative ‘Early Design’ Product Prototyping (InPro).This paper presents a formal evaluation of the paper-based scribble simplification algorithm described in [BCFB07] and [BCFB08]. A comparative analysis of different aspects of the algorithm with other algorithms described in the literature such as Sparse Pixel Vectorization, spatial moving average filtering and Principal Component Analysis is performed, hence establishing the qualities of this paper-based scribble simplification algorithm. To quantify the performance of the algorithm, performance measures established in the literature, such as the Pixel Recovery Index are used when suitable. However, since there exists no quantitative measure which measures scribble simplification, this paper proposes a new methodology with which scribble simplification may be quantitatively assessed. Through the evaluation described in this paper, we will be able to determine remaining difficulties in the interpretation of paper-based scribbles and hence identify future research areas.peer-reviewe
A structural representation for understanding line-drawing images
International audienceIn this paper, we are concerned with the problem of finding a good and homogeneous representation to encode line-drawing documents (which may be handwritten). We propose a method in which the problems induced by a first-step skeletonization have been avoided. First, we vectorize the image, to get a fine description of the drawing, using only vectors and quadrilateral primitives. A structural graph is built with the primitives extracted from the initial line-drawing image. The objective is to manage attributes relative to elementary objects so as to provide a description of the spatial relationships (inclusion, junction, intersection, etc.) that exist between the graphics in the images. This is done with a representation that provides a global vision of the drawings. The capacity of the representation to evolve and to carry highly semantic information is also highlighted. Finally, we show how an architecture using this structural representation and a mechanism of perceptive cycles can lead to a high-quality interpretation of line drawings
Scribble vectorization using concentric sampling circles
In this paper we introduce a path extraction algorithm for multi-stroke scribbled paths by making use of path-centred concentric sampling circles. Circle and line geometry is then exploited to efficiently obtain piece-wise linear models of the multi-stroke segments in the drawing. Parzen-window estimation is used to obtain the probability distribution of the grey-level profile of the sampling circles to determine the intersecting angle of the sampling circle with the stroke segments and hence determine the line model parameters. The results obtained show that the algorithm identifies the line models accurately while reducing considerably the computational time required to obtain the line models.peer-reviewe
Analysis of Digital Logic Schematics Using Image Recognition
This thesis presents the results of research in the area of automated recognition of digital logic schematics. The adaptation of a number of existing image processing techniques for use with this kind of image is discussed, and the concept of using sets of tokens to represent the overall drawing i s explained in detail. Methods are given for using tokens to describe schematic component shapes, to represent the connections between components, and to provide sufficient information to a parser so that an equation can be generated. A Microsoft Windows-based test program which runs under Windows 95 or Windows NT has been written to implement the ideas presented. This program accepts either scanned images of digital schematics, or computer-generated images in Microsoft Windows bitmap format as input. It analyzes the input schematic image for content, and produces a corresponding logical equation as output. It also provides the functionality necessary to build and maintain an image token library
Component Segmentation of Engineering Drawings Using Graph Convolutional Networks
We present a data-driven framework to automate the vectorization and machine
interpretation of 2D engineering part drawings. In industrial settings, most
manufacturing engineers still rely on manual reads to identify the topological
and manufacturing requirements from drawings submitted by designers. The
interpretation process is laborious and time-consuming, which severely inhibits
the efficiency of part quotation and manufacturing tasks. While recent advances
in image-based computer vision methods have demonstrated great potential in
interpreting natural images through semantic segmentation approaches, the
application of such methods in parsing engineering technical drawings into
semantically accurate components remains a significant challenge. The severe
pixel sparsity in engineering drawings also restricts the effective
featurization of image-based data-driven methods. To overcome these challenges,
we propose a deep learning based framework that predicts the semantic type of
each vectorized component. Taking a raster image as input, we vectorize all
components through thinning, stroke tracing, and cubic bezier fitting. Then a
graph of such components is generated based on the connectivity between the
components. Finally, a graph convolutional neural network is trained on this
graph data to identify the semantic type of each component. We test our
framework in the context of semantic segmentation of text, dimension and,
contour components in engineering drawings. Results show that our method yields
the best performance compared to recent image, and graph-based segmentation
methods.Comment: Preprint accepted to Computers in Industr
An evolutionary approach to determining hidden lines from a natural sketch
This paper focuses on the identification of hidden lines and junctions from natural sketches of drawings that exhibit an extended-trihedral geometry. Identification of hidden lines and junctions is essential in the creation of a complete 3D model of the sketched object, allowing the interpretation algorithms to infer what the unsketched back of the object should look like. This approach first labels the sketched visible edges of the object with a geometric edge label, obtaining a labelled junction at each of the visible junctions of the object. Using a dictionary of junctions with visible and hidden edges, these labelled visible junctions are then used to deduce the edge interpretation and orientation of some of the hidden edges. A genetic algorithm is used to combine these hidden edges into hidden junctions, evolving the representation of the hidden edges and junctions until a feasible hidden view representation of the object is obtained.peer-reviewe
- …