A multistage hierarchical algorithm for hand shape recognition

This paper represents a multistage hierarchical algorithm for hand shape recognition using principal component analysis (PCA) as a dimensionality reduction and a feature extraction method. The paper discusses the effect of image blurring to build data manifolds using PCA and the different ways to construct these manifolds. In_order to classify the hand shape of an incoming sign object and to be invariant to linear transformations like translation and rotation, a multistage hierarchical classifier structure is used. Computer generated images for different Irish Sign Language shapes are used in testing. Experimental results are given to show the accuracy and performance of the proposed algorithm

Nonlinearity reduction of manifolds using Gaussian blur for handshape recognition based on multi-dimensional grids

This paper presents a hand-shape recognition algorithm based on using multi-dimensional grids (MDGs) to divide the feature space of a set of hand images. Principal Component Analysis (PCA) is used as a feature extraction and dimensionality reduction method to generate eigenspaces from example images. Images are blurred by convolving with a Gaussian kernel as a low pass filter. Image blurring is used to reduce the non-linearity in the manifolds within the eigenspaces where MDG structure can be used to divide the spaces linearly. The algorithm is invariant to linear transformations like rotation and translation. Computer generated images for different hand-shapes in Irish Sign Language are used in testing. Experimental results show accuracy and performance of the proposed algorithm in terms of blurring level and MDG size

Principal component pyramids using image blurring for nonlinearity reduction in hand shape recognition

The thesis presents four algorithms using a multistage hierarchical strategy for hand shape recognition. The proposed multistage hierarchy analyzes new patterns by projecting them into the different levels of a data pyramid, which consists of different principal component spaces. Image blurring is used to reduce the nonlinearity in manifolds generated by a set of example images. Flattening the space helps in classifying different hand shapes more accurately. Four algorithms using different pattern recognition techniques are proposed. The first algorithm is based on using perpendicular distance to measure the distance between new patterns and the nearest manifold. The second algorithm is based on using supervised multidimensional grids. The third algorithm uses unsupervised multidimensional grids to cluster the space into cells of similar objects. The fourth algorithm is based on training a set of simple architecture multi-layer neural networks at the different levels of the pyramid to map new patterns to the closest class. The proposed algorithms are categorized as example-based approaches where a large set of computer generated images are used to densely sample the space. Experimental results are presented to examine the accuracy and performance of the proposed algorithms. The effect of image blurring on reducing the nonlinearity in manifolds is examined. The results are compared with the exhaustive search scenario. The experimental results show that the proposed algorithms are applicable for real time applications with high accuracy measures. They can achieve frame rates of more than 10 frames per second and accuracies of up to 98% on test data

Multistage classification of multispectral Earth observational data: The design approach

An algorithm is proposed which predicts the optimal features at every node in a binary tree procedure. The algorithm estimates the probability of error by approximating the area under the likelihood ratio function for two classes and taking into account the number of training samples used in estimating each of these two classes. Some results on feature selection techniques, particularly in the presence of a very limited set of training samples, are presented. Results comparing probabilities of error predicted by the proposed algorithm as a function of dimensionality as compared to experimental observations are shown for aircraft and LANDSAT data. Results are obtained for both real and simulated data. Finally, two binary tree examples which use the algorithm are presented to illustrate the usefulness of the procedure