Showing papers by "Kuo-Chin Fan published in 2006"
20 Aug 2006
TL;DR: Two detectors, one for face and the other for license plates, are proposed, both based on a modified convolutional neural network (CNN) verifier, and Pyramid-based localization techniques were applied to fuse the candidates and to identify the regions of faces or license plates.
Abstract: In this paper, two detectors, one for face and the other for license plates, are proposed, both based on a modified convolutional neural network(CNN) verifier. In our proposed verifier, a single feature map and a fully connected MLP were trained by examples to classify the possible candidates. Pyramid-based localization techniques were applied to fuse the candidates and to identify the regions of faces or license plates. In addition, geometrical rules filtered out false alarms in license plate detection. Some experimental results are given to show the effectiveness of the approach. Keywords: Face detection, license plate detection, convolution neural network, feature map.
84 citations
TL;DR: Experimental results validate that the proposed approach to the detection of small objects by employing watershed-based transformation is indeed feasible and effective in detecting objects with small size and low contrast.
19 citations
11 Sep 2006
TL;DR: A hybrid approach for vector quantization (VQ) is proposed for obtaining the better codebook modified and improved based on the centroid neural network adaptive resonance theory (CNN-ART) and the enhanced LBG approaches.
Abstract: In this paper, a hybrid approach for vector quantization (VQ) is proposed for obtaining the better codebook. It is modified and improved based on the centroid neural network adaptive resonance theory (CNN-ART) and the enhanced LBG (Linde-Buzo-Gray) approaches. Three modules, a neuronal net (NN) based clustering, a mean shift (MS) based refinement, and a principal component analysis (PCA) based seed assignment, are repeatedly utilized. Basically, the seed assignment module generates a new initial codebook to replace the low utilized codewords during the iteration. The NN-based clustering module clusters the training vectors using a competitive learning approach. The clustered results are refined using the mean shift operation. Some experiments in image compression applications were conducted to show the effectiveness of the proposed approach
2 citations