A Stochastic Grammar for Natural Shapes

TL;DR

This paper introduces a stochastic grammar model for natural shapes, unifying object recognition and grouping processes in computer vision to improve object detection.

Contribution

It proposes a novel stochastic grammar framework that bridges model-based recognition and grouping, offering a unified approach for shape analysis.

Findings

Demonstrates the effectiveness of the stochastic grammar in shape detection

Shows that the model can serve as a mid-level vision grouping process

Provides a new perspective on combining recognition and grouping mechanisms

Abstract

We consider object detection using a generic model for natural shapes. A common approach for object recognition involves matching object models directly to images. Another approach involves building intermediate representations via a generic grouping processes. We argue that these two processes (model-based recognition and grouping) may use similar computational mechanisms. By defining a generic model for shapes we can use model-based techniques to implement a mid-level vision grouping process.