Geun-Jun Kim

Color Image Enhancement Based on Adaptive Nonlinear Curves of Luminance Features

This paper proposes an image-dependent color image enhancement method that uses adaptive luminance enhancement and color emphasis. It effectively enhances details of low-light regions while maintaining well-balanced luminance and color information. To compare the structure similarity and naturalness, we used the tone mapped image quality index (TMQI). The proposed method maintained better structure similarity in the enhanced image than did the space-variant luminance map (SVLM) method or the adaptive and integrated neighborhood dependent approach for nonlinear enhancement (AINDANE). The proposed method required the smallest computation time among the three algorithms. The proposed method can be easily implemented using the field-programmable gate array (FPGA), with low hardware resources and with better performance in terms of similarity.

Depth Image Distortion Correction Method according to the Position and Angle of Depth Sensor and Its Hardware Implementation

The motion recognition system has been broadly studied in digital image and video processing fields. Recently, method using th depth image is used very useful. However, recognition accuracy of depth image based method will be loss caused by size and shape of object distorted for angle of the depth sensor. Therefore, distortion correction of depth sensor is positively necessary for distinguished performance of the recognition system. In this paper, we propose a pre-processing algorithm to improve the motion recognition system. Depth data from depth sensor converted to real world, performed the corrected angle, and then inverse converted to projective world. The proposed system make progress using the OpenCV and the window program, and we test a system using the Kinect in real time. In addition, designed using Verilog-HDL and verified through the Zynq-7000 FPGA Board of Xilinx.

Automation System Architecture For Barcode Region Detection Algorithm

Conventional barcode region detections are generally using guideline at computer vision. Guideline is the simple method but efficient. As the demand increases in the automation system, guideline method needs to replace. Hardware has strong point at the parallel operation. Using parallel operation, hardware can process iterative operation efficiently. In this paper, we propose system architecture for barcode region detection algorithm using APU and FPGA. This architecture can easily adapted application system. FPGA reduce overload easily and efficiently for APU.

Identification of In-Home Appliances through Analysis of Current Consumption

Non-intrusive load monitoring (NILM) has been being the method to estimate and disaggregate information about the power consumption of individual electric appliances in a building or home by aggregate measurements of voltage and current. The power consumption plays a key component in the NILM system to monitor, and reduces the overall energy consumption in home or building. This paper proposes a method to extract the characteristic fingerprint of root-mean-square (RMS) current consumption of household appliances and discusses the feasibility of the classifying as well as identifying the household appliance with the expert system. The proposed system consists of two main parts, including an integrated circuit for measuring the RMS current consumption in real-time, and analysis toolkit with data acquisition for gathering the raw data and extracting current profile. Furthermore, the measurement methodology and simple algorithm for identifying the appliance type are validated through the experimental results.