DESIGNING A NEW TOY TO FIT OTHER TOY PIECES- A shape-matching toy design based on existing building blocks 1(a) Building blocks

Yuki IGARASHI1 and Hiromasa SUZUKI2The University of Tokyo, Japan / JSPS research fellow
2
The University of Tokyo, Japan(b) Input photograph(c) The user designs a toyusing sketching interface(d) Traced outlines(e) block-shape matching toyFigure 1: Overview of our method
Introduction
Shape-matching toys are popular items for infants, and consist of boxes withmany holes in different shapes along with corresponding blocks of the same shapes. To play withthe toy, an infant finds and inserts a block matching the shape of a particular hole. It is very difficult
to design such toys because the toy body Onew must closely fit the shape of the existing blocks
Oexisting. The designer usually takes the measurements of the blocks Oexisting and draws
corresponding feature sizes, then makes a construction diagram for the toy body Onew. We propose acomputer-based method for novices to enable the design of a new toy to fit other toy pieces. The
method involves making a construction diagram for Onew on a photograph of the blocks Oexisting
using a computer. The user first takes a photograph of the blocks on a checkerboard, then designs
the form of the new toy Onew on the photograph using a sketching interface. From the photograph on
the checkerboard, the system automatically fits the designed toy Onew to the actual measurements
and exports the results in vector form. The system supports SVG and DXF formats. Finally, the user
cuts the shapes from a wooden board using a cutting plotter or laser printer.
System Overview Figure 1 shows a real toy made using the proposed method. Our goal was todesign a new shape-matching toy (Fig. 1 (e)) from existing building blocks (Fig. 1 (a)). Figure 2
shows the proposed user interface. The user first prints a checkerboard and takes a photograph ofthe existing building blocks Oexisting on it.

(d) Convert DXF format(b) The user traces outlines(c) Traced outlines(h) Cut using laser plotter(a) Input photograph(g) Adjustment full scale(e) Edge extraction(f) Marking grids
manuallyFigure 2: Our algorithm

The user draws free-form strokes on the input photograph using the sketching interface (Fig. 2 (b)).
The system also provides some basic shapes (line, rectangle and ellipse), and exports the user-input
strokes in vector form as shown Fig. 2 (c).
The checkerboard is used for scale adjustment. Although various methods have been proposed forfeature point extraction in 2D [1], [2], [3], no method currently enables precise extraction.
Accordingly, we adopt manual input by the user. The system first extracts the grid of thecheckerboard, and the user can easily mark the corners of the grid using the extracted edge
detection image (Fig. 2 (e)). The user has to mark corners at more than two unbroken points (Fig. 2(f)). The system calculates the ratio of the image to the actual size (Fig. 2 (g)). We applied themethod to the design of a new toy using existing real toys (Figs. 1 and 3).

Figure 3: Example of a puzzle cover design based on an existing puzzle
Evaluation We tried the system with three test-users who designed a shape-matching toy as shownin Fig. 1. Graph 1 shows a comparison of our method and implementation using existing software(Adobe Illustrator). In our method, the user first takes a photograph of the building blocks Oexisting
on a checkerboard, then draws strokes to create a diagram for the shape-matching toy Onew on thephotograph and marks its corners. In the existing method, the user first measures the building
blocks of Oexisting and draws a construction diagram for the shape-matching toy Onew. It is only
necessary to mark 5 – 6 points from the results of the user study as shown in Fig. 4. It took about 10– 13 seconds, and the test users reported that they found the system easy to use.
Graph 1: Comparison of timing data
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6Figure 4: Number of markers
Keywords: design, user interface, craft
References[1] S. Carlson, “Sketch-based image coding of gray-level images,” Signal Processing, Vol. 15, pp. 57 – 83, 1988.
[2] J. H. Elder, R. M. Goldberg, “Image Editing in the Contour Domain,” IEEE Trans. on Pattern Analysis and Machine Intelligence,Vol. 23, No. 3, pp. 291 – 296, March 2001.
[3] Bradski, G., Kaehler, A. (2008), Learning OpenCV: Computer Vision with the OpenCV Library.