A Neural Network Classifier for Occluded Images

This paper proposes a neural network classifier which can automatically detect the occluded regions in the given image and replace that regions with the estimated values. An auto-associative memory is used to detect outliers such as pixels in the occluded regions. Certainties of each pixels are estimated by comparing the input pixels with the outputs of the auto-associative memory. The input values to the associative memory are replaced with the new values which are defined depending on the certainties. By repeating this process, we can get an image in which the pixel values of the occluded regions are replaced with the estimates. The proposed classifier is designed by integrating this associative memory with a simple classifier.

• Type: Conference
• Year: 2002
• Where: ICPR
• Authors: Takio Kurita, Takashi Takahashi, Yukifumi Ikeda
• Keywords: Associative Memory, Computer Vision, ICPR 2002, Neural Network Classifier, Occluded Regions