Object Detection by Two-Dimensional Linear Prediction.
MASSACHUSETTS INST OF TECH LEXINGTON LINCOLN LAB
Pagination or Media Count:
An important component of any automated image analysis system is the detection and classification of objects. In this report, the authors consider the first of these problems where the specific goal is to detect anomalous areas e.g., man-made objects in textured backgrounds such as trees, grass, and fields of aerial photographs. Their detection algorithm relies on a significance test which adapts itself to the changing background in such a way that a constant false alarm rate is maintained. Furthermore, this test has a potentially practical implementation since it can be expressed in terms of the residuals of an adaptive two-dimensional linear predictor. The algorithms is demonstrated with both synthetic and real-world images.
- Theoretical Mathematics