Improved classification robustness for noisy cell images represented as principal-component projections in a hybrid recognition system

Analysis of spectroscopic measurements of leaf water content at terahertz frequencies using linear transforms

We provide a unified framework for a range of linear transforms that can be used for the analysis of terahertz spectroscopic data, with particular emphasis on their application to the measurement of leaf water content. The use of linear transforms for filtering, regression, and classification is discussed. For illustration, a classification problem involving leaves at three stages of drought and a prediction problem involving simulated spectra are presented. Issues resulting from scaling the data set are discussed. Using Lagrange multipliers, we arrive at the transform that yields the maximum separation between the spectra and show that this optimal transform is equivalent to computing the Euclidean distance between the samples. The optimal linear transform is compared with the average for all the spectra as well as with the Karhunen-Loève transform to discriminate a wet leaf from a dry leaf. We show that taking several principal components into account is equivalent to defining new axes in which data are to be analyzed. The procedure shows that the coefficients of the Karhunen-Loève transform are well suited to the process of classification of spectra. This is in line with expectations, as these coefficients are built from the statistical properties of the data set analyzed.

Noise-driven manifestation of learning in mature neural networks

We show that the generalization capability of a mature thresholding neural network to process above-threshold disturbances in a noise-free environment is extended to subthreshold disturbances by ambient noise without retraining. The ability to benefit from noise is intrinsic and does not have to be learned separately. Nonlinear dependence of sensitivity with noise strength is significantly narrower than in individual threshold systems. Noise has a minimal effect on network performance for above-threshold signals. We resolve two seemingly contradictory responses of trained networks to noise-their ability to benefit from its presence and their robustness against noisy strong disturbances.

Statistical performance of cascaded linear shift-invariant processing

The cascaded correlator architecture comprises a series of traditional linear correlators separated by nonlinear threshold functions, trained with neural-network techniques. We investigate the shift-invariant classification performance of cascaded correlators in comparison with optimum Bayes classifiers. Inputs are formulated as randomly generated sample members of known statistical class distributions. It is shown that when the separability of true and false classes is varied in both the first and the second orders, the two-stage cascaded correlator shows performance similar to that of the optimum quadratic Bayes classifier throughout the studied range. It is shown that this is due to the similar decision boundaries implemented by the two nonlinear classifiers.

Image compression by vector quantization with noniterative derivation of a codebook: applications to video and confocal images

We demonstrate an image-compression technique that uses what we believe is a new noniterative codebook generation algorithm for vector quantization. The technique supports rapid decompression and is equally applicable to individual images or to a set of images without the need for interframe processing. Compression with a single-image codebook is tested on (1) ten confocal images of the hindbrain of a mouse embryo, (2) video images of a polystyrene microsphere that is manipulated by a focused laser light, and (3) five fluorescence images of the embryo eye lens taken at different magnifications. The reconstructions are assessed with the normalized mean-squared error and with Linfoot's criteria of fidelity, structural content, and correlation quality. Experimental results with single-image compression show that the technique produces fewer local artifacts than JPEG compression, especially with noisy images. Results with video and confocal image series indicate that single-image codebook generation is sufficient at practical compression ratios for producing acceptable reconstructions for mouse embryo analysis and for viewing optically trapped microspheres. Experiments with the magnified images also reveal that the compression scheme is robust to scaling.