Chulhee Lee

Anterior Paralimbic Mediation of Procaine-Induced Emotional and Psychosensory Experiences

BACKGROUND Procaine activates limbic structures in animals. In humans, acute intravenous administration of procaine yields emotional and psychosensory experiences and temporal lobe fast activity. We studied procaines acute effects on cerebral blood flow (CBF) in relationship to clinical responses. METHODS Cerebral blood flow was assessed by positron emission tomography with oxygen-15-labeled water in 32 healthy volunteers. Data were analyzed with statistical parametric mapping and magnetic resonance imaging-directed regions of interest. RESULTS Procaine increased global CBF and, to a greater extent, anterior paralimbic CBF. Subjects with intense procaine-induced fear compared with those with euphoria had greater increases in left amygdalar CBF. Absolute and normalized left amygdalar CBF changes tended to correlate positively with fear and negatively with euphoria intensity. Procaine-induced visual hallucinations appeared associated with greater global and occipital CBF increases. Absolute occipital CBF increases appeared to correlate positively with visual hallucination intensity. CONCLUSIONS Procaine increased anterior paralimbic CBF, and different clinical responses appeared to be associated with different patterns of CBF changes.

Interpolation using neural networks for digital still cameras

In this paper we present a color interpolation technique based on artificial neural networks for a single-chip CCD (charge-coupled device) camera with a Bayer color filter array (CFA). Single-chip digital cameras use a color filter array and an interpolation method in order to produce high quality color images from sparsely sampled images. We have applied 3-layer feedforward neural networks in order to interpolate a missing pixel from surrounding pixels. And we compare the proposed method with conventional interpolation methods such as the bilinear interpolation method and cubic spline interpolation method. Experiments show that the proposed interpolation algorithm based on neural networks provides a better performance than the conventional interpolation algorithms.

Registration and statistical analysis of PET images using the wavelet transform

We have described a general procedure for the processing and analysis of PET data. We have used the multiresolution framework of the wavelet transform to derive new solutions for the two main processing steps. The first task was to align the various brain images using a general affine deformation model. Our registration procedure uses a continuous polynomial spline image model and takes advantage of the multiresolution structure of the underlying function spaces. This method implements a nonlinear least squares optimization technique with a coarse-to-fine iteration strategy that substantially improves the overall performance of the algorithm. The second task was to analyze the series of registered images and to detect the between group differences in metabolic brain activity. We chose to take advantage of the orthogonality and localization properties of the wavelet transform. Our approach was to apply this transform to the group-difference image and identify the wavelet channels that are globally significantly different from noise. >

Optimizing feature extraction for speech recognition

We propose a method to minimize the loss of information during the feature extraction stage in speech recognition by optimizing the parameters of the mel-cepstrum transformation, a transform which is widely used in speech recognition. Typically, the mel-cepstrum is obtained by critical band filters whose characteristics play an important role in converting a speech signal into a sequence of vectors. First, we analyze the performance of the mel-cepstrum by changing the parameters of the filters such as shape, center frequency, and bandwidth. Then we propose an algorithm to optimize the parameters of the filters using the simplex method. Experiments with Korean digit words show that the recognition rate improved by about 4-7%.

Objective video quality assessment

We propose a new method for an objective measurement of video quality. By analyzing subjective scores of various video sequences, we find that the human visual system is particularly sensitive to degradation around edges. In other words, when edge areas of a video sequence are degraded, evaluators tend to give low quality scores to the video, even though the overall mean squared error is not large. Based on this observation, we propose an objective video quality measurement method based on degradation around edges. In the proposed method, we first apply an edge detection algorithm to videos and locate edge areas. Then, we measure degradation of those edge areas by computing mean squared errors and use it as a video quality metric after some postprocessing. Experiments show that the proposed method significantly outperforms the conventional peak signal-to-noise ratio (PSNR). This method was also independently evaluated by independent laboratory groups in the Video Quality Experts Group (VQEG) Phase 2 test. The method consistently provided good performances. As a result, the method was included in international recommendations for objective video quality measurement.

Principal component analysis for compression of hyperspectral images

In this paper, we explore the possibility to use the principal component analysis for compression of hyperspectral images. When the principal component analysis is applied to AVIRIS data that has 220 channels, we found that most energy is concentrated on a few eigenvalues, indicating that it may be possible to compress hyperspectral images significantly. The performance of the proposed algorithm is evaluated in terms of SNR and classification accuracies of selected classes. Experiments with AVIRIS data show promising results.

Compression for hyperspectral images using three dimensional wavelet transform

In this paper, we apply the three dimensional wavelet transform to hyperspectral images. In particular, in order to compress hyperspectral data, we propose to use the three dimensional version of the set partitioning in hierarchical trees (SPIHT) algorithm, which has been successfully applied to 2 dimensional images and video signals. In order to evaluate the performance of the three dimensional SPIHT algorithm, we compute the SNR of compressed images and classification accuracies in the original images and the reconstructed images. Experiments with AVIRIS data show that high compression is possible with negligible information loss.

Auto-stereoscopic 3D displays with reduced crosstalk

In this paper, we propose new auto-stereoscopic 3D displays that substantially reduce crosstalk. In general, it is difficult to eliminate crosstalk in auto-stereoscopic 3D displays. Ideally, the parallax barrier can eliminate crosstalk for a single viewer at the ideal position. However, due to variations in the viewing distance and the interpupillary distance, crosstalk is a problem in parallax barrier displays. In this paper, we propose 3-dimensional barriers, which can significantly reduce crosstalk.

Rapid computation of the continuous wavelet transform by oblique projections

We introduce a fast simple method for computing the real continuous wavelet transform (CWT). The approach has the following attractive features: it achieves O(N) complexity per scale, the filter coefficients can be analytically obtained by a simple integration, and the algorithm is faster than a least squares approach with negligible loss in accuracy. Our method is to use P wavelets per octave and to approximate them with their oblique projection onto a space defined by a compactly supported scaling function. The wavelet templates are expanded to larger sizes (octaves) using the two-scale relation and zero-padded filtering. Error bounds are presented to justify the use of an oblique projection over an orthogonal one. All the filters are FIR with the exception of one filter, which is implemented using a fast recursive algorithm.

Objective measurements of video quality using the wavelet transform

We propose a new method for objective measurement of video quality using the wavelet transform. We exploit the fact that the sensitivity of the human visual system varies over spatiotemporal frequencies. First, in order to compute spatial-frequency components, the wavelet transform is applied to each frame of source and processed videos. Then, the difference (squared error) of the wavelet coefficients in each subband is computed and summed, producing a difference vector for each frame. By repeating this procedure for all the frames of a video, we obtain a sequence of difference vectors, and then the average vector is computed. Each component of this average vector represents a difference in a certain spatial frequency. In order to take into account the temporal frequencies, we also propose a modified 3-D wavelet transform. In either case, a single vector represents the difference between the source and the processed video. From this vector, a number is computed as a weighted sum of its elements and is used as an objective score. In order to find the optimal weight vector, an optimization procedure is proposed. Experimental results show that the proposed method compares favorably with the current objective methods for video quality measurement.