• Journal of Institute of Control, Robotics and Systems
• /
• v.21 no.11
• /
• pp.996-1002
• /
• 2015

In this paper, we present an effective system for the 3D scene labeling of objects from RGB-D videos. Our system uses a Markov Random Field (MRF) over a voxel representation of the 3D scene. In order to estimate the correct label of each voxel, the probabilistic graphical model integrates both scores from sliding window-based object detectors and also from object location prior maps. Both the object detectors and the location prior maps are pre-trained from manually labeled RGB-D images. Additionally, the model integrates the scores from considering the geometric constraints between adjacent voxels in the label estimation. We show excellent experimental results for the RGB-D Scenes Dataset built by the University of Washington, in which each indoor scene contains tabletop objects.

• Journal of Broadcast Engineering
• /
• v.25 no.3
• /
• pp.439-448
• /
• 2020

In this paper, we propose a modified optimization algorithm for point cloud matching of multi-view RGB-D cameras. In general, in the computer vision field, it is very important to accurately estimate the position of the camera. The 3D model generation methods proposed in the previous research require a large number of cameras or expensive 3D cameras. Also, the methods of obtaining the external parameters of the camera through the 2D image have a large error. In this paper, we propose a matching technique for generating a 3D point cloud and mesh model that can provide omnidirectional free viewpoint using 8 low-cost RGB-D cameras. We propose a method that uses a depth map-based function optimization method with RGB images and obtains coordinate transformation parameters that can generate a high-quality 3D model without obtaining initial parameters.

• Journal of IKEEE
• /
• v.23 no.1
• /
• pp.44-52
• /
• 2019

We implement an educational indoor autonomous mobile robot system that integrates LiDAR sensing information with RGB-D camera image information and exploits the integrated information. This system uses the existing sensing method employing a LiDAR with a small number of scan channels to acquire LiDAR sensing information. To remedy the weakness of the existing LiDAR sensing method, we propose the 3D structure recognition technique using depth images from a RGB-D camera and the deep learning based object recognition algorithm and apply the proposed technique to the system.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.20 no.1
• /
• pp.167-174
• /
• 2016

In this paper, we propose a generation of 2D elemental images for 3D objects using Kinect in 3D depth-priority integral imaging (DPII) display. First, we analyze a principle to pickup elemental images based on ray optics. Based on our analysis, elemental images are generated with both RGB image and depth image recorded from Kinect. We reconstruct 3D images from the elemental images with the computational integral imaging reconstruction technique and then compare various perspective images. To show the usefulness of the proposed method, we carried out the preliminary experiments. The experimental results reveal that our method can provide correct 3D images with full parallax.

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.31 no.4
• /
• pp.207-213
• /
• 2018

This paper investigates applicability of Microsoft $Kinect^{(R)}$, RGB-depth camera, to implement a 3D image and spatial information for sensing a target. The relationship between the image of the Kinect camera and the pixel coordinate system is formulated. The calibration of the camera provides the depth and RGB information of the target. The intrinsic parameters are calculated through a checker board experiment and focal length, principal point, and distortion coefficient are obtained. The extrinsic parameters regarding the relationship between the two Kinect cameras consist of rotational matrix and translational vector. The spatial images of 2D projection space are converted to a 3D images, resulting on spatial information on the basis of the depth and RGB information. The measurement is verified through comparison with the length and location of the 2D images of the target structure.

• Current Optics and Photonics
• /
• v.5 no.1
• /
• pp.32-39
• /
• 2021

We propose a three-dimensional (3D) streaming system based on a lamina display that can convey field information in real-time by creating floating 3D images that can satisfy the accommodation cue. The proposed system is mainly composed of three parts, namely: a 3D vision camera unit to obtain and provide RGB and depth data in real-time, a 3D image engine unit to realize the 3D volume with a fast response time by using the RGB and depth data, and an optical floating unit to bring the implemented 3D image out of the system and consequently increase the sense of presence. Furthermore, we devise the streaming method required for implementing augmented reality (AR) images by using a multilayered image, and the proposed method for implementing AR 3D video in real-time non-face-to-face communication has been experimentally verified.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.13 no.6
• /
• pp.313-319
• /
• 2018

We propose a post-processing algorithm to improve the quality of the RGB image generated by deep learning based colorization from the gray-scale image of an infrared camera. Wavelet fusion is used to generate a new luminance component of the RGB image luminance component from the deep learning model and the luminance component of the infrared camera. PSNR is increased for all experimental images by applying the proposed algorithm to RGB images generated by two deep learning models of SegNet and DCGAN. For the SegNet model, the average PSNR is improved by 1.3906dB at level 1 of the Haar wavelet method. For the DCGAN model, PSNR is improved 0.0759dB on the average at level 5 of the Daubechies wavelet method. It is also confirmed that the edge components are emphasized by the post-processing and the visibility is improved.

• KIPS Transactions on Software and Data Engineering
• /
• v.5 no.1
• /
• pp.13-20
• /
• 2016

In this paper, we propose an object recognition system that can effectively find out its category, its instance name, and several attributes from the color and depth images of an object with hierarchical feature learning. In the preprocessing stage, our system transforms the depth images of the object into the surface normal vectors, which can represent the shape information of the object more precisely. In the feature learning stage, it extracts a set of patch features and image features from a pair of the color image and the surface normal vector through two-layered learning. And then the system trains a set of independent classification models with a set of labeled feature vectors and the SVM learning algorithm. Through experiments with UW RGB-D Object Dataset, we verify the performance of the proposed object recognition system.

• Journal of information and communication convergence engineering
• /
• v.2 no.2
• /
• pp.123-127
• /
• 2004

The most popular technique for image retrieval in a heterogeneous collection of color images is the comparison of images based on their color histogram. The color histogram describes the distribution of colors in the color space of a color image. In the most image retrieval systems, the color histogram is used to compute similarities between the query image and all the images in a database. But, small changes in the resolution, scaling, and illumination may cause important modifications of the color histogram, and so two color images may be considered to be very different from each other even though they have completely related semantics. A new method of color feature representation based on the 3-dimensional RGB color map is proposed to improve the defects of the color histogram. The proposed method is based on the three 2-dimensional projection map evaluated by projecting the RGB color space on the RG, GB, and BR surfaces. The experimental results reveal that the proposed is less sensitive to small changes in the scene and that achieve higher retrieval performances than the traditional color histogram.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.21 no.2
• /
• pp.1-8
• /
• 2007

This study is to generate optimal values of brightness and achieve more realistic images by varying such values according to individual materials in order to create better quality simulation images using Lightscape. Because there are discolorations of materials by adding the radiation of luminaires to that of daylight during the daytime, when low brightness values are inputted in case of daytime, the results are founded that images of materials can be seen much similar with real images. And in most of the materials, when low values in the daytime and high values in the nighttime are inputted this study verifies that the realest simulation images can be obtained using Lightscape, because the RGB data of simulation images are almost similar with that of real materials.