Fast Haar Transform Based Feature Extraction for Face Representation and Recognition

Subspace learning is the process of finding a proper feature subspace and then projecting high-dimensional data onto the learned low-dimensional subspace. The projection operation requires many floating-point multiplications and additions, which makes the projection process computationally expensive. To tackle this problem, this paper proposes two simple-but-effective fast subspace learning and image projection methods, fast Haar transform (FHT) based principal component analysis and FHT based spectral regression discriminant analysis. The advantages of these two methods result from employing both the FHT for subspace learning and the integral vector for feature extraction. Experimental results on three face databases demonstrated their effectiveness and efficiency.

An Obstacle Cluster Based Data Fusion Method for Mobile Robot

首先给出了一种通过融合多个超声波传感器和一台激光全局定位系统的数据建立机器人环境地图的方法 ,并在此基础上 ,首次提出了机器人在非结构环境下识别障碍物的一种新方法 ,即基于障碍物群的方法 .该方法的最大特点在于它可以更加简洁、有效地提取和描述机器人的环境特征 ,这对于较好地实现机器人的导航、避障 ,提高系统的自主性和实时性是至关重要的 .大量的实验结果表明了该方法的有效性 .