| dc.description.abstract | Recently, the researches and applications about object detection and recognition grow rapidly in the area of computer vision. Among these, the detection and recognition of human face and license plate are typical applications. To achieve the detection goal, an object is firstly detected by a trained classifier. The commonly used classifiers are neural networks, support vector machines, etc. However, the computation load is very heavy. To remedy the drawback, researcher proposed a cascade classifier trained by Adaboost and combined Haar-like features with integral images to quickly filter out background regions to achieve real-time detection task. However, it is still time consuming in training the classifiers.
In this dissertation, an object detector is proposed based on a convolution/sub-sampling feature map and a two-level cascade classifier. First, a convolution/subsampling operation is proposed to alleviate the suffering of the illumination, rotation, and distortion variances. Then, two classifiers are concatenated to check a large number of windows using a coarse-to-fine strategy. Since the sub-sampled feature map with enhanced pixels is fed into the coarse-level classifier, the size of the feature map is drastically reduced to a quarter of the original image. A few surviving windows with detailed data are further checked with the fine-level classifier. In addition to improving the detection process, the proposed mechanism also speeds up the training process. A few features generated from the prototypes within the small window are selected and trained to obtain the coarse-level classifier. Moreover, a feature ranking algorithm is proposed to reduce the huge feature pool to a small set for speeding up the training process without losing the generality of the feature pool. Finally, some experiments were conducted to show the feasibility of the proposed method.
As to the recognition, a novel manifold learning algorithm, called orthogonal nearest neighbour feature line embedding (ONNFLE), for face recognition is also proposed. In the proposed ONNFLE, two drawbacks of our earlier proposed nearest feature line embedding (NFLE) method are resolved. They are the extrapolation/interpolation error, and high computational load. The extrapolation error occurs if the distance from a specified point to one line is small when that line passes through two farther points. The scatter matrix generated by the invalid discriminant vectors does not efficiently preserve the locally topological structure which results in incorrect selection while reducing recognition performance. To remedy this problem, the nearest neighbour (NN) selection strategy is used in the proposed method. In addition, the high computational load is also reduced using a selection strategy. Finally, some experiments were conducted to demonstrate the effectiveness of the proposed algorithm. | en_US |