Biomedical subjects

Cheng-Lin Liu

Publications and source records attributed to Cheng-Lin Liu.

4 recordsLinked to original sources

PubMed2005

[Preparation and spectral characteristic of TiO2/SnO2 composite oxides].

To study the spectral characteristic of TiO2/SnO2, TiO2/SnO2 composite oxide with SnO2 inside kernel was fabricated by colloid chemistry method, and was studied with UV-visible absorption spectrum, X-ray diffraction and infrared spectrum. The results showed that the bandgap widths of TiO2 and TiO2/SnO2 were 4.13 and 3.86 eV, respectively. TiO2/SnO2 had a widest and strong absorption band in the ultraviolet range. There was a quantum effect. In the X-ray diffraction, the station of TiO2 (110) diffraction peak moved 1.6 degrees, and its intensity changed. Compared with TiO2 infrared spectrum, the Ti--O stretching vibration of TiO2/SnO2 moved from 500 to 656 cm(-1), and had 560 cm(-1) shoulder peak. The intensity of the peaks near 1 650 and 3 420 cm(-1) corresponding to water absorbing peaks increased.

English Abstract↗

PubMed2004

Discriminative learning quadratic discriminant function for handwriting recognition.

In character string recognition integrating segmentation and classification, high classification accuracy and resistance to noncharacters are desired to the underlying classifier. In a previous evaluation study, the modified quadratic discriminant function (MQDF) proposed by Kimura et al. was shown to be superior in noncharacter resistance but inferior in classification accuracy to neural networks. This paper proposes a discriminative learning algorithm to optimize the parameters of MQDF with aim to improve the classification accuracy while preserving the superior noncharacter resistance. We refer to the resulting classifier as discriminative learning QDF (DLQDF). The parameters of DLQDF adhere to the structure of MQDF under the Gaussian density assumption and are optimized under the minimum classification error (MCE) criterion. The promise of DLQDF is justified in handwritten digit recognition and numeral string recognition, where the performance of DLQDF is comparable to or superior to that of neural classifiers. The results are also competitive to the best ones reported in the literature.

Discrimination Learning↗

PubMed2004

Effects of classifier structures and training regimes on integrated segmentation and recognition of handwritten numeral strings.

In integrated segmentation and recognition of character strings, the underlying classifier is trained to be resistant to noncharacters. We evaluate the performance of state-of-the-art pattern classifiers of this kind. First, we build a baseline numeral string recognition system with simple but effective presegmentation. The classification scores of the candidate patterns generated by presegmentation are combined to evaluate the segmentation paths and the optimal path is found using the beam search strategy. Three neural classifiers, two discriminative density models, and two support vector classifiers are evaluated. Each classifier has some variations depending on the training strategy: maximum likelihood, discriminative learning both with and without noncharacter samples. The string recognition performances are evaluated on the numeral string images of the NIST Special Database 19 and the zipcode images of the CEDAR CDROM-1. The results show that noncharacter training is crucial for neural classifiers and support vector classifiers, whereas, for the discriminative density models, the regularization of parameters is important. The string recognition results compare favorably to the best ones reported in the literature though we totally ignored the geometric context. The best results were obtained using a support vector classifier, but the neural classifiers and discriminative density models show better trade-off between accuracy and computational overhead.

Algorithms↗

PubMed2004

Online recognition of Chinese characters: the state-of-the-art.

Online handwriting recognition is gaining renewed interest owing to the increase of pen computing applications and new pen input devices. The recognition of Chinese characters is different from western handwriting recognition and poses a special challenge. To provide an overview of the technical status and inspire future research, this paper reviews the advances in online Chinese character recognition (OLCCR), with emphasis on the research works from the 1990s. Compared to the research in the 1980s, the research efforts in the 1990s aimed to further relax the constraints of handwriting, namely, the adherence to standard stroke orders and stroke numbers and the restriction of recognition to isolated characters only. The target of recognition has shifted from regular script to fluent script in order to better meet the requirements of practical applications. The research works are reviewed in terms of pattern representation, character classification, learning/adaptation, and contextual processing. We compare important results and discuss possible directions of future research.

Algorithms↗