Changwoo Ha

Vision-Based Smoke Detection Algorithm for Early Fire Recognition in Digital Video Recording System

The digital video recording (DVR) closed circuit television (CCTV) system is quickly becoming one of the most accepted security, surveillance documentation, and monitoring in service today. Particularly a lot of catastrophes such as tsunami, volcano, and terrorism cause huge casualty and property loss. In this paper, we concentrate on the smoke detection system in video for early fire alarming. The proposed block-based smoke detection algorithm consists of three basic steps, which has simple operation and provides good performance. In the first step, we discover motion vector utilizing several motion estimation schemes and discern the suspect as smoke region. This process does not require huge computational cost because of using the H.264/AVC during the recoding in DVR system. In the second step, we conduct block based chromatic detection. In the third step, we employ motion information for detecting the correct smoke block by using the characteristics that smoke goes almost upward. Experimental results provide that the proposed method gives good performance for real smoke detection.

Vision-Based Fire Detection Algorithm Using Optical Flow

Recently automatic video surveillance in the digital video recording CCTV system is rapidly becoming one of the most accepted security system. The dangerous situations such as forest fire, flood, and terrorism are increasing, and cause serious casualty and property loss. In this paper, we particularly focus on the fire detection system in video. The proposed block-based fire detection algorithm consists of three basic steps. In the first step, we find motion vector utilizing optical flow and distinguish the suspect as fire region. In the second step, we conduct chromatic detection based on Lab color space. In the final step, we employ motion information for detecting the correct fire block by using the characteristics that fire goes almost upward. Experimental results show that the proposed method yields good performance for fire detection.

Contrast enhancement and noise elimination using singular value decomposition for stereo imaging

Abstract. A new stereoscopic satellite image enhancement method based on singular value decomposition (SVD) is presented. The proposed stereoscopic image enhancement technique consists of four steps; SVD based on image analysis, image enhancement, noise reduction, and tone consistency. The goal of this work is to enhance low-contrast stereo satellite images with noise elimination, and obtain identical luminance levels from slightly different luminance in stereo images. Experimental results show the proposed method improves contrast performance while maintaining the luminance continuity. It also exhibits less noise than conventional methods.

Remote sensing image enhancement based on singular value decomposition

A satellite image enhancement method based on singular value decomposition (SVD) is presented. The proposed method consists mainly of four steps: image decomposition, adjustable contrast enhancement, noise reduction, and image composition. The method decomposes an image into its singular values, from which the luminance information can be obtained using SVD. In turn, adjustable contrast enhancement is applied by changing the singular values, which are controlled by characteristic-based contrast-level parameters. In the noise reduction process, the high-frequency elements, such as noise, are removed using rank approximation. In the final image composition process, the proposed algorithm combines color and luminance components in order to preserve color consistency. Experimental results show that the proposed scheme has less noise than the conventional methods and improves contrast performance while preserving details.

Human Perception of Asymmetrical 3-D Inputs

The 3DTV services can be seen as a general case of the multi-view video that has been receiving a significant attention lately. However, the key factors that influence the success of 3DTV are the availability of content, the ease of use, the quality of contents, and the reduction of cost. This paper deals primarily with the perceptual improvement in image quality, especially based on human factors. An optimal asymmetrical coding method for binocular and multi-view images is presented. The quantitative value of asymmetrical rate to maintain optimized subjective image quality is explored. Also we analyze how edges of 2D images affect on 3D perceptions and propose an edge-preserving algorithm to perform perceptual improvements. Experimental results demonstrate that the proposed algorithm enhances subjective image quality much better than conventional methods.

An adaptive fast multiple reference frame selection algorithm for H.264/AVC using reference region data

Multiple reference frame motion estimation to improve video coding performance is newly adopted in the H.264/AVC video coding standard. However, much complexity is burdened in these features. Therefore, we propose a fast reference frame selection algorithm based on information from the reference region used for a motion estimation process. Simulation results show that the proposed algorithm effectively reduces the complexity of multiple reference frame motion estimation with negligible degradation of video quality.

Human Perception of Asymmetrical Three-Dimensional Image

The 3DTV services can be seen as a general case of the multi-view video that has been receiving a significant attention lately. However, the key factors that influence the success of 3DTV are the availability of content, the ease of use, the quality of contents, and the reduction of cost. This paper deals primarily with the perceptual improvement in image quality, especially based on human factors. An optimal asymmetrical coding method for binocular and multi-view images is presented. The quantitative value of asymmetrical rate to maintain optimized subjective image quality is explored. Also we analyze how edges of 2D images affect on 3D perceptions and propose an edge-preserving algorithm to perform perceptual improvements. Experimental results demonstrate that the proposed algorithm enhances subjective image quality much better than conventional methods.

Tone reproduction for high-dynamic range imaging based on adaptive filtering

Abstract. A tone reproduction algorithm with enhanced contrast of high-dynamic range images on conventional low-dynamic range display devices is presented. The proposed algorithm consists mainly of block-based parameter estimation, a characteristic-based luminance adjustment, and an adaptive Gaussian filter using minimum description length. Instead of relying only on the reduction of the dynamic range, a characteristic-based luminance adjustment process modifies the luminance values. The Gaussian-filtered luminance value is obtained from appropriate value of variance, and the contrast is then enhanced through the use of a relation between the adjusted luminance and Gaussian-filtered luminance values. In the final tone-reproduction process, the proposed algorithm combines color and luminance components in order to preserve the color consistency. The experimental results demonstrate that the proposed algorithm achieves a good subjective quality while enhancing the contrast of the image details.

Transcoding MPEG-2 to H.264/AVC in the GOP Structure Conversion

Currently, H.264/AVC is used in many multimedia applications. Also, The MPEG-2 main profile which supports B pictures for bi-directional motion prediction is widely used in applications such as HDTV and DVD’s. Therefore, transcoding the MPEG-2 main profile to the H.264/AVC baseline is necessary for universal multimedia access. In this transcoding architecture including the GOP structure conversion, the proposed algorithms adopt the adaptive search range selection through the linearity test of a predictive motion vector and adaptive mode selection using the reference region complexity information. The proposed algorithms extremely reduce the computational complexity while maintaining the video quality.