2D object segmentation from fovea images based on eigen-subspace learning

In this paper, we consider the problem of segmenting 2D objects from intensity fovea images based on learning. During the training, we apply the Karhunen-Loeve projection to the training set to obtain a set of eigenvectors and also construct a space decomposition tree to achieve logarithmic retrieval time complexity. The eigenvectors are used to reconstruct the test fovea image. Then we apply a spring network model to the reconstructed image to generate a polygon mask. After applying the mask to the test image, we search the space decomposition tree to find the nearest neighbor to segment the object from background. The system is tested to segment 25 classes of different hand shapes. The experimental results show 97% correct rate for the hands presented in the training (because of the background effect) and 93% correct rate for the hands that have not been used in the training phase.