Cheol-Ki Kim

Recognition of Car License Plate by Using Dynamical Thresholding Method and Enhanced Neural Networks

In this paper, for the implementation of the recognition system of car license plates, the region extraction algorithm based on the contour tracking and the new enhanced neural networks learning algorithm are proposed, which extracts the areas of car license plate and the character areas from the car images and recognizes the car license numbers from the extracted areas. And a candidate area was selected, whose density rate was corresponding to the properties of the car license plate obtained in the condition of the car license plate. The contour tracking algorithm extracted the feature areas covering the areas of characters from the car license plate. As well, the enhanced neural networks learning algorithm, combining the modified ART1 and supervised learning algorithm, recognized the car license numbers from the feature areas.

Recognition of passports using FCM-based RBF network

This paper proposes a novel method for the recognition of passports based on a FCM-based RBF network. First, for the extraction of individual codes for recognizing, this paper targets code sequence blocks including individual codes by applying Sobel masking, horizontal smearing and a contour tracking algorithm on the passport image. As the last step, individual codes are recovered and extracted from the binarized areas by applying CDM masking and vertical smearing. This paper also proposes a FCM-based RBF network that adapts the FCM algorithm for the middle layer. This network is applied to the recognition of individual codes. The results of the experiments for performance evaluation on the real passport images showed that the proposed method has the better performance compared with other approaches.

Non-stationary Movement Analysis Using Wavelet Transform

This paper presents a method that automatically detects the insect’s abnormal movements. In general, the ecological data are difficult for analysis due to complexity residing in the systems with the variables varying in nonstationary fashion. Therefore, we needs to efficient methods that are able to measure from various environmental conditions. In this paper the wavelet transform are introduced as an alternative tool for extracting local and global information out of complex ecological data. And we discuss the method that is applicable to various relative fields.

Model type recognition using de-interlacing and block code generation

This paper presents a method that automatically recognizes the shoe’s outsole products into model type, which flows through the conveyor belts from right to left. The interlaced pixels are displayed when we use the NTSC based camera in experiments. So, we require a suitable post-processing. For the purpose of this processing, it decides to find rectangle region of object by thresholding after removing interlaced pixels using de-interlacing method. And then, after rectangle region is separated into blocks through edge detection, we calculates pixel number per each block, re-classifies using its average, and classifies products into model type.

Intelligent Pattern Recognition by Feature Selection through Combined Model of DWT and ANN

This paper presented a combined model of Discrete Wavelet Transform(DWT) and Self-Organizing Map(SOM) to select features from irregular insect’s movement patterns. In the proposed method, the DWT was implemented to characterize different movement patterns in order to detect behavioral changes of insects. The extracted parameters based on combined model of DWT and SOM were subsequently provided to artificial neural networks to be trained to represent different patterns of the movement tracks before and after treatments of the insecticide. Finally, the proposed combined model of DWT and SOM was able to point out the occurrence of characteristic movement patterns, and could be a method for automatically detecting irregular patterns for nonlinear movements.

Applications of the ecological visualization system using artificial neural network and mathematical analysis

This paper presents a 3D visualization system with artificial neural network algorithm that tracks the motion of particles flowing in the water, where we get a great deal of variable information, and predicts the distribution of particles according to the flowing of water and the pattern of their precipitation. Various particles and their mutual collision influencing the force such as buoyancy force, gravitational force, and the pattern of precipitation are considered in this system and we control it by normalizing momentum equation and sequential equation. Flowing particles whose motion is changed with the environment can be visualized in the system presented here as they are in real water. We can track the motion of the particles efficiently and predict the pattern of the particles as well.