Comparative Evaluation of Texture Analysis Algorithms for Defect Inspection of Textile Products
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
A novel approach to the detection of small objects with low contrast
Signal Processing
Target identification method by adaptive GO-CFAR processor and DSP implementation for radar system
SSIP'07 Proceedings of the 7th WSEAS International Conference on Signal, Speech and Image Processing
Computers and Electrical Engineering
A learning-based spatial processing method for the detection of point targets
ICMLC'05 Proceedings of the 4th international conference on Advances in Machine Learning and Cybernetics
| Hi-index | 0.01 |
Two-dimensional (2-D) adaptive filtering is a technique that can be applied to many image processing applications. This paper will focus on the development of an improved 2-D adaptive lattice algorithm (2-D AL) and its application to the removal of correlated clutter to enhance the detectability of small objects in images. The two improvements proposed here are increased flexibility in the calculation of the reflection coefficients and a 2-D method to update the correlations used in the 2-D AL algorithm. The 2-D AL algorithm is shown to predict correlated clutter in image data and the resulting filter is compared with an ideal Wiener-Hopf filter. The results of the clutter removal will be compared to previously published ones for a 2-D least mean square (LMS) algorithm. 2-D AL is better able to predict spatially varying clutter than the 2-D LMS algorithm, since it converges faster to new image properties. Examples of these improvements are shown for a spatially varying 2-D sinusoid in white noise and simulated clouds. The 2-D LMS and 2-D AL algorithms are also shown to enhance a mammogram image for the detection of small microcalcifications and stellate lesions