• Journal of the Korean Institute of Telematics and Electronics S
• /
• v.35S no.12
• /
• pp.17-26
• /
• 1998

Ultrasonic sensors are widely used in various applications due to advantages of low cost, simplicity in construction, mechanical robustness, and little environmental restriction in usage. But for the application of object recognition, ultrasonic sensors exhibit several shortcomings of poor directionality which results in low spatial resolution of objects, and specularity which gives frequent erroneous range readings. The time-of-flight(TOF) method generally used for distance measurement can not distinguish small object patterns of plane, corner or edge. To resolve the problem, an increased number of the sensors in the forms of a linear array or 2-dimensional array of the sensors has been used. Also better resolution has been obtained by shifting the array in several steps using mechanical actuators. Also simple patterns are classified based on analyzing signal reflections. In this paper we propose a method of a sensor array system with improved capability in pattern distinction using electronic circuits accompanying the sensor array, and intelligent algorithm based on neuro-fuzzy processing of data fusion. The circuit changes transmitter output voltages of array elements in several steps. A set of different return signals from neighborhood sensors is manipulated to provide enhanced pattern recognition in the aspects of inclination angle, size and shift as well as distance of objects. The results show improved resolution of the measurements for smaller targets.

• Journal of Korea Multimedia Society
• /
• v.13 no.3
• /
• pp.438-456
• /
• 2010

Iris recognition uses a unique iris pattern of user to identify person. In order to enhance the performance of iris recognition, it is reported that the diameter of iris region should be greater than 200 pixels in the captured iris image. So, the previous iris system used zoom lens camera, which can increase the size and cost of system. To overcome these problems, we propose a new method of enhancing the accuracy of iris recognition on low-resolution iris images which are captured without a zoom lens. This research is novel in the following two ways compared to previous works. First, this research is the first one to analyze the performance degradation of iris recognition according to the decrease of the image resolution by excluding other factors such as image blurring and the occlusion of eyelid and eyelash. Second, in order to restore a high-resolution iris image from single low-resolution one, we propose a new method based on multiple multi-layered perceptrons (MLPs) which are trained according to the edge direction of iris patterns. From that, the accuracy of iris recognition with the restored images was much enhanced. Experimental results showed that when the iris images down-sampled by 6% compared to the original image were restored into the high resolution ones by using the proposed method, the EER of iris recognition was reduced as much as 0.133% (1.485% - 1.352%) in comparison with that by using bi-linear interpolation

• Journal of Broadcast Engineering
• /
• v.16 no.3
• /
• pp.448-461
• /
• 2011

H.264/AVC significantly outperforms the previous video coding standards with many new coding tools. Among these tools, several intra-block coding tools can particularly improve coding efficiency. For intra prediction, H.264/AVC supports most probable mode in the entropy coding process to reduce syntax elements indicating intra prediction modes and most probable mode selection ratio is very high. Also, in general, natural images and videos have many homogeneous regions whose high correlation with neighbouring blocks. In this paper, we propose intra prediction mode SKIP mode using decoder-side prediction to improve the coding efficiency. The proposed method is determined the optimal prediction mode using only neighbouring block's information and coded on the basis of the conventional prediction/transform coding. And the prediction modes are not send to decoder at all. Skipped intra prediction mode is determined by decoder. Experimental results show that the proposed method achieves coding gains of 1.40% for common intermediate format(CIF), 3.24% for 720p sequences against the H.264/AVC JM 17.0 reference software.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.45 no.6
• /
• pp.70-77
• /
• 2008

This paper proposes a new text candidate region detection method that uses genetic algorithm based on information of the segmented regions. In image segmentation, a classification of the pixels at each color channel and a reclassification of the region-unit for reducing inhomogeneous clusters are performed. EWFCM(Entropy-based Weighted C-Means) algorithm to classify the pixels at each color channel is an improved FCM algorithm added with spatial information, and therefore it removes the meaningless regions like noise. A region-based reclassification based on a similarity between each segmented region of the most inhomogeneous cluster and the other clusters reduces the inhomogeneous clusters more efficiently than pixel- and cluster-based reclassifications. And detecting text candidate regions is performed by genetic algorithm based on energy and variance of the directional edge components, the number, and a size of the segmented regions. The region information-based detection method can singles out semantic text candidate regions more accurately than pixel-based detection method and the detection results will be more useful in recognizing the text regions hereafter. Experiments showed the results of the segmentation and the detection. And it confirmed that the proposed method was superior to the existing methods.