Statistical Edge Detection of Concealed Weapons Using Artificial Neural Networks
| Authors | |
|---|---|
| Year of publication | 2008 |
| Type | Article in Proceedings |
| Conference | Proceedings of SPIE-IS&T Electronic Imaging |
| MU Faculty or unit | |
| Citation | |
| Web | http://dx.doi.org/10.1117/12.766472 |
| Field | Use of computers, robotics and its application |
| Keywords | statistical edge detection; neural networks; image processing |
| Description | A novel edge detector has been developed that utilizes statistical masks and neural networks for the optimal detection of edges over a wide range of image types. The failure of many common edge detection techniques has been observed when analyzing concealed weapons X-ray images, biomedical images or images with significant levels of noise, clutter or texture. This novel technique is based on a statistical edge detection filter that uses a range of two-sample statistical tests to evaluate any local image texture differences. The range and type of tests has been greatly expanded from the previous works. This process is further enhanced by applying combined multiple scale pixel masks and multiple statistical tests, to Artificial Neural Networks (ANN) trained to classify different edge types. Through the use of Artificial Neural Networks (ANN) we can combine the output results of several statistical mask scales into one detector. Furthermore we can allow the combination of several two sample statistical tests of varying properties (for example; mean based, variance based and distribution based). This combination of both scales and tests allows the optimal response from a variety of statistical masks. From this we can produce the optimum edge detection output for a wide variety of images, and the results of this are presented. |
| Related projects: |