Pyramid of arclength descriptor for generating collage of shapes

Abstract

This paper tackles a challenging 2D collage generation problem, focusing on shapes: we aim to fill a given region by packing irregular and reasonably-sized shapes with minimized gaps and overlaps. To achieve this nontrivial problem, we first have to analyze the boundary of individual shapes and then couple the shapes with partially-matched boundary to reduce gaps and overlaps in the collages. Second, the search space in identifying a good coupling of shapes is highly enormous, since arranging a shape in a collage involves a position, an orientation, and a scale factor. Yet, this matching step needs to be performed for every single shape when we pack it into a collage. Existing shape descriptors are simply infeasible for computation in a reasonable amount of time. To overcome this, we present a brand new, scale- and rotation-invariant 2D shape descriptor, namely pyramid of arclength descriptor (PAD). Its formulation is locally supported, scalable, and yet simple to construct and compute. These properties make PAD efficient for performing the partial-shape matching. Hence, we can prune away most search space with simple calculation, and efficiently identify candidate shapes. We evaluate our method using a large variety of shapes with different types and contours. Convincing collage results in terms of visual quality and time performance are obtained.

TransHist: Occlusion-robust shape detection in cluttered images

“…Recently, Kwan et al [12] proposed a locally scale-invariant shape descriptor, the pyramid of arclength descriptor (PAD). The locally scale-invariant property enables more robust detection of shapes in the presence of cluttered edges and noise.…”

“…For the queried shape, we simply identify all boundary pixels as edges of the shape. We then calculate the local shape feature of each each point using an existing point-based shape descriptor, the pyramid of arc-length descriptor (PAD) [12]. Note that PAD can be computed for all points no matter whether they are on closed or open curves.…”

“…Given the edge maps from an image and a query shape, we first extract the shape features of all reference points on the edges by a local point-based shape descriptor, pyramid of arclength descriptor (PAD) [12]. The features extracted from PAD is locally scale invariant, rotational invariant, and translational invariant.…”

“…Recently, Kwan et al [12] proposed a point-based shape descriptor called the pyramid of arc-length descriptor (PAD) to tackle partial occlusion, and their method is also applicable to open curves. However, their method is greatly affected by cluttered backgrounds since they rely on distance fields for final shape matching.…”

“…In order to find the optimal transformation for each point efficiently, we adopt PAD [12] as the shape descriptor for each point and find the optimal corresponding point in the image by comparing similarity of PAD. We use PAD because of its scale, rotational, and translational invariance.…”