This paper presents a new approach to classify, index and retrieve technical drawings by content. Our work uses spatial relationships, visual elements and high-dimensional indexing mechanisms to retrieve complex drawings from CAD databases. This contrasts with conventional approaches which use mostly textual metadata for the same purpose. Creative designers and draftspeople often re–use data from previous projects, publications and libraries of ready to use components. Usually, retrieving these drawings is a slow, complex and error–prone endeavor, requiring either exhaustive visual examination, a solid memory, or both. Unfortunately, the widespread use of CAD systems, while making it easier to create and edit drawings, exacerbates this problem, insofar as the number of projects and drawings grows enormously, without providing adequate retrieval mechanisms to support retrieving these documents. In this paper we describe an approach that supports automatic indexation of technical drawing databases through drawing simplification techniques based on geometric features and efficient algorithms to index large amounts of data. We describe in detail the indexing structure (NB–Tree) we have developed within the context of a more general approach. Experimental evaluation reveals that our approach outperforms some of the best indexing structures published, enabling us to search very large drawing databases.

CALI is a fast, simple and compact online recognizer that identifies Scribbles (multi-stroke geometric shapes) drawn with a stylus on a digitizing tablet. Our method is able to identify shapes of different sizes and rotated at arbitrary angles, drawn with dashed, continuous strokes or overlapping lines. We use temporal adjacency to allow users to input the most common shapes in drawing such as triangles, lines, rectangles, circles, diamonds and ellipses, using multiple strokes.

This paper presents a new approach to classify, index and retrieve technical draw-ings by content. Our work uses spatial relationships, shape geometry and high-dimensional indexing mechanisms to retrieve complex drawings from CAD databa-ses. This contrasts with conventional approaches which use mostly textual metadata for the same purpose. Creative designers and draftspeople often re-use data from previous projects, publications and libraries of ready-to-use components. Usually, retrieving these drawings is a slow, complex and error-prone endeavor, requiring either exhaustive visual examination, a solid memory, or both. Unfortunately, the widespread use of CAD systems, while making it easier to create and edit drawings, exacerbates this problem, insofar as the number of projects and drawings grows enormously, without providing adequate searching mechanisms to support retrieving these documents. We describe an approach that supports automatic indexation of technical drawing databases through drawing simplification, feature extraction and efficient algorithms to index large amounts of data. We describe in detail all the steps of our classification 1 process to content-based retrieval of technical drawings (CAD) and present results from usability tests on our prototype.

On-line pen input benefits greatly from mode detection when the user is in a free writing situation, where he is allowed to write, to draw, and to generate gestures. Mode detection is performed before recognition to restrict the classes that a classifier has to consider, thereby increasing the performance of the overall recognition. In this paper we present a hybrid system which is able to achieve a mode detection performance of 95.6 % on seven classes; handwriting, lines, arrows, ellipses, rectangles, triangles, and diamonds. The system consists of three kNN classifiers which use global and structural features of the pen trajectory and a fitting algorithm for verifying the different geometrical objects. Results are presented on a significant amount of data, acquired in different contexts like scribble matching and design applications. 1.

In this paper we present an approach to allow indexing and retrieving vector graphics by content, using topological and geometric information automatically extracted from figures. The method described retrieves WMF ClipArt files as an instance of the general problem of searching vector drawings. Our prototype can already handle databases with thousands of drawings using commodity hardware. Our scheme uses pattern recognition to identify shapes ensuring good precision vs recall trade-o#s, when compared to other approaches. Preliminary usability assessments show promising results and suggest good acceptance of sketching as a query mechanism by users.

Current user interfaces of CAD systems are still not suited to the initial stages of product development, where freehand drawings are used by engineers and designers to express their visual thinking. In order to exploit these sketching skills, we present a sketch based modeling system, which provides a reduced instruction set calligraphic interface to create orthogonal polyhedra, and an extension of them we named quasi-normalons. Our system allows users to draw lines on free-hand axonometric-like drawings, which are automatically tidied and beautified. These line drawings are then converted into a threedimensional model in real time because we implemented a fast reconstruction process, suited for quasi-normalon objects and so-called axonometric inflation method, providing in this way an innovative integrated 2D sketching and 3D view visualization work environment.

These days there are a lot of vector drawings available for people to integrate into documents. These come in a variety of formats, such as Corel, Postscript, CGM, WMF and recently SVG. Typically, such ClipArt drawings tend to be archieved and accessed by categories (e.g. food, shapes, transportation, etc.). However, to find a drawing among hundreds of thousands is not an easy task. While text-driven attempts at classifying image data have been recently supplemented with query-by-image content, these have been developed for bitmap-type data and cannot handle vectorial information. In this paper we present an approach to allow indexing and retrieving vector drawings by content from large datasets. Our prototype can already handle databases with thousands of drawings using commodity hardware. Furthermore, preliminary usability assessments show promising results and suggest good acceptance of sketching as a query mechanism by users.

Nowadays there are a lot of vector drawings available for inclusion into documents, which tend to be achieved and accessed by categories. However, to find a drawing among hundreds of thousands is not easy. While text-driven attempts at classifying image data have been recently supplemented with query-by-image content, these have been developed for bitmap-type data and cannot handle vectorial information. In this paper we present an approach to index and retrieve ClipArt images by content, using topological and geometric information automatically extracted from drawings. Additionally, we introduce a set of simplification heuristics to eliminate redundant information and useless elements.

Abstract. We present a shape classification technique for structural content–based retrieval of two-dimensional vector drawings. Our method has two distinguishing features. For one, it relies on explicit hierarchical descriptions of drawing structure by means of spatial relationships and shape characterization. However, unlike other approaches which attempt rigid shape classification, our method relies on estimating the likeness of a given shape to a restricted set of simple forms. It yields for a given shape, a feature vector describing its geometric properties, which is invariant to scale, rotation and translation. This provides the advantage of being able to characterize arbitrary two–dimensional shapes with few restrictions. Moreover, our technique seemingly works well when compared to established methods for two dimensional shapes. 1

Many indexing approaches for high–dimensional data points have evolved into very complex and hard to code algorithms. Sometimes this complexity is not matched by increase in perfor-mance. Motivated by these ideas, we take a step back and look at simpler approaches to indexing multimedia data. In this paper we propose a simple, (not simplistic) yet efficient indexing struc-ture for high–dimensional data points of variable dimension, using dimension reduction. Our approach maps multidimensional points to a 1D line by computing their Euclidean Norm. In a second step we sort these using a B +-Tree on which we perform all subsequent operations. We exploit B +-Tree efficient indexed sequential search to develop simple, yet performant methods to implement point, range and nearest-neighbor queries. To evaluate our technique we conducted a set of experiments, using both synthetic and real data. We analyze creation, insertion and query times as a function of data set size and dimension. Results so far show that our simple scheme outperforms current approaches, such as the Pyramid Technique, the A-Tree and the SR-Tree, for many data distributions. Moreover, our approach seems to scale better both with growing dimensionality and data set size, while exhibiting low insertion and search times. 1