Ling-Zhi Liao

An investigation into using singular value decomposition as a method of image compression

The purpose of this paper is to discuss the usage possibility of singular value decomposition in image compression applications. A mistake viewpoint that is about SVD-based image compression scheme is demonstrated. The paper goes deep to study three schemes of SVD-based image compression and prove the usage feasibility of SVD-based image compression.

3D object recognition and pose estimation using kernel PCA

Kernel principal component analysis (PCA) is proposed as a nonlinear technique for dimensionality reduction. The basic idea is to map the input space into a feature space via nonlinear mapping and then compute the principal component in the feature space. In this paper, we utilize kernel PCA technique into 3D object recognition and pose estimation, and present results of appearance-based object recognition accomplished by employing a neural network architecture on the base of kernel PCA. Through adopting a polynomial kernel, the principal component can be computed in the space spanned by high-order correlations of input pixels. We illustrate the potential of kernel PCA on a database of 1,440 images of 20 different objects. The excellent recognition rates achieved in all of the performed experiments indicate that the proposed method is well-suited for object recognition and pose estimation.

Top-down attention guided object detection

Existing attention models concentrate on bottom-up attention guidance, and lack of effective definition of top-down attention information. In this paper we define a new holistic scene representation and use it as top-down attention information which works in three ways. The first is to discriminate between close-up and open scene categories. The second and the third are to provide reliable priors for the presence or absence of object and the location of it. Compared with traditional attention guidance algorithms, our algorithm shows how scene classification and basing directly on entire scene without segmentation stages, facilitate the object detection. Two stages of pre-attention and focus attention enhance the detecting performance and are more suitable for vision information processing in high level. Experiment results prove the effectiveness of our algorithm.

Fast and adaptive low-pass whitening filters for natural images

A fast and simple solution was suggested to reduce the inter-pixels correlations in natural images, of which the power spectra roughly fell off with the increasing spatial frequency f according to a power law; but the 1/f exponent, α, was different from image to image. The essential of the proposed method was to flatten the decreasing power spectrum of each image by using an adaptive low-pass and whitening filter. The act of low-pass filtering was just to reduce the effects of noise usually took place in the high frequencies. The act of whitening filtering was a special processing, which was to attenuate the low frequencies and boost the high frequencies so as to yield a roughly flat power spectrum across all spatial frequencies. The suggested method was computationally more economical than the geometric covariance matrix based PCA method. Meanwhile, the performance degradations accompanied with the computational economy improvement were fairly insignificant.

“What” and “where” information based attention guidance model

Visual system can be defined as consisting of two pathways. The classic definition labeled a “what” pathway to process object information and a “where” pathway to process spatial information. In this paper, we propose a novel attention guidance model based on “what” and “where” information. Context-centered “where” information is used to control top-down attention, and guide bottom-up attention which is driven by “what” information. The procedure of top-down attention can be divided into two stages: pre-attention and focus attention. In the stage of pre-attention, “where” information can be used to provide prior knowledge of presence or absence of objects which decides whether search operation is followed. By integrating the result of focus attention with “what” information, attention is directed to the region that is most likely to contain the object and series of salient regions are detected. Results of experiment on natural images demonstrate its effectiveness.

Learning overcomplete representations with a generalized gaussian prior

Overcomplete representations have been advocated because they allow a basis to better approximate the underlying statistical density of the data which can lead to representations that better capture the underlying structure in the data. The prior distributions for the coefficients of these models, however, are assumed to be fixed, not adaptive to the data, and hereby inaccurate. Here we describe a method for learning overcomplete representations with a generalized Gaussian prior, which can fit a broader range of statistical distributions by varying the value of the steepness parameter β. Using this distribution in overcomplete representations, empirical results were obtained for the blind source separation of more sources than mixtures, which show that the accuracy of the density estimation is improved.

Efficient Coding for Natural Images Based on the Sparseness of Neural Coding in V1 across the Stimuli

The sparse coding and independent component analysis for natural scenes, in recent years, have succeeded in for finding a set of basis functions that can effectively represent the input data, by supposing that the feature vectors of images should be sparse or independent. In this paper, we investigated the efficient coding for natural images by making assumptions of sparseness and independence on the activities of basis functions over the image ensemble, without considering directly the statistics of the feature vectors of images. Experimental results show that the approach can also produce basis functions which have similar properties with the receptive fields of simple cells in V1 and thereby be effective