Hosang Cho

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.

A Color Gamut Mapping System Using the RGB Primaries and White-Point Correction for a Wide Gamut Display Device

Organic light emitting diodes (OLEDs) are considered to be among the best flat panel display technologies owing to their wide viewing angles, high-speed response, high resolution, and simple structure. OLEDs exhibit a wide color gamut exceeding 100 % of the national television system committee (NTSC) sRGB gamut ratio. However, most movies and images are made to comply with standard specifications of the sRGB color gamut. If a sRGB image is displayed on a wide gamut display, the color tone will be distorted. In this paper, we propose a color gamut mapping system using the CIE-1931 XYZ color space for digital image processing. White point and RGB primaries correction provide color gamut correction for different measurement devices of the color gamut. An evaluation using images shows that the proposed system can convert a wide color gamut such as that of OLEDs to the color gamut of the user’s choice without color distortion.

A Face-Detection Postprocessing Scheme Using a Geometric Analysis for Multimedia Applications

Abstract—Human faces have been broadly studied in digital image and video processing fields. An appearance-based method, the adaptive boosting learning algorithm using integral image representations has been successfully employed for face detection, taking advantage of the feature extraction’s low computational complexity. In this paper, we propose a face-detection postprocessing method that equalizes instantaneous facial regions in an efficient hardware architecture for use in real-time multimedia applications. The proposed system requires low hardware resources and exhibits robust performance in terms of the movements, zooming, and classification of faces. A series of experimental results obtained using video sequences collected under dynamic conditions are discussed. Index Terms—Face detection, adaptive boosting algorithm, face-region stabilization, face recognition, single-port line memory I. I NTRODUCTION Human faces have been broadly studied in digital image and video processing fields. As one of the most detailed attributes in the human-computer interface, the face conveys a great deal of the information including behavioral cues, emotional state, identity, human race, age, and so on. Face-based algorithms thus have been used in a wide range of multimedia applications [1-3]. In particular, most digital imaging systems have integrated face-based auto-focusing functions to provide users with a convenient interface from the perspective of visual attention. In addition, the face-recognition systems require a correct face-detection scheme to extract facial features fed into pre-trained classifiers [4]. Detection and feature extraction are concurrently performed while searching human faces in digital images under dynamic visual deformation conditions such as position, scale, in-plane rotation, orientation, pose, and illumination. Depending upon the specific applications, robust face segmentation has been employed to solve several problems including the non-rigid shape of faces, clever alignment, and occlusion. In order to successfully achieve face detection in tiny mobile multimedia platforms, the hardware architecture must be computationally optimized in terms of memory usage and operation time. An appearance-based method, the adaptive boosting (AdaBoost) learning algorithm has been widely accepted in face detection using integral image representations. It is ideal for real-time hardware systems such as digital cameras and mobile phones [5] and requires low computational complexity for feature extraction. The AdaBoost employs local binary pattern (LBP) images to avoid the effects of illumination and to express detailed textures. The pyramid representation is assigned for multifarious sizes of human faces. To segment face regions according to the in-plane rotations of the human face, pre-defined feature factors are used. Although AdaBoost exhibits acceptable performance, instantaneous face detection in the spatial domain caused by cascaded structures limits its performance. For example, the pyramid representation is used to compare

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.

A Region of Interest Labeling Algorithm Using Three Mask Patterns

Labeling is one of the most basic and important processes in image analysis, which is used to identify detached objects by assigning the same number (labels) to all adjacent connected pixels in a binary image. Labeling algorithms have long been studied, and a variety of algorithms have been developed. Two scans method is easy to implement hardware. The two scans method requires memory for 1-D and 2-D tables to perform labeling. In this paper, three masks are used to assign label values to minimize memory usage, and an algorithm to increase computation speed by separating the inputted image into regions of interest and non-interest is proposed. As a result of experiment that is continuous image of 100 frames, Assigned provisional label is that conventional algorithm is 7657, [9] is 14665 and proposed algorithm is 5710. Processing times is required of conventional algorithm 341.6 ms, [9] 621.328 ms, proposed algorithm 275.18 ms. To verify the performance of the proposed algorithm, an experiment has been performed using a variety of binary images.

Color extension and noise reduction method for the smallest module system

Adaptive tone mapping improves the luminance value of the low-light area in an image. A captured image in low light environments has high probability that noise is generated. Therefore, when Luminance of Low-light is enhanced, color noise is increased and also original color tone is changed. This paper proposes color extension and noise reduction method that not uses memories in the event of hardware implementation and can apply to in small module system.