• Journal of Korea Society of Digital Industry and Information Management
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• v.12 no.1
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• pp.27-33
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• 2016

In this study we propose a method for 3D landform reconstruction and object modeling method by generating contour lines on the map using a depth sensor which abstracts characteristics of geological layers from the depth map. Unlike the common visual camera, the depth-sensor is not affected by the intensity of illumination, and therefore a more robust contour and object can be extracted. The algorithm suggested in this paper first abstracts the characteristics of each geological layer from the depth map image and rearranges it into the proper order, then creates contour lines using the Bezier curve. Using the created contour lines, 3D images are reconstructed through rendering by mapping RGB images of the visual camera. Experimental results show that the proposed method using depth sensor can reconstruct contour map and 3D modeling in real-time. The generation of the contours with depth data is more efficient and economical in terms of the quality and accuracy.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.6
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• pp.1175-1184
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• 2015

A depth map is an image which contains 3D distance information. Generally, it is difficult to acquire a high resolution (HD), noise-removed, good quality depth map directly from the camera. Therefore, many researches have been focused on acquisition of the high resolution and the good quality depth map by up-sampling and pre/post image processing of the low resolution depth map. However, many researches are lack of effective up-sampling for the edge region which has huge impact on image perceptual-quality. In this paper, we propose an up-sampling method, based on joint bilateral filter, which improves up-sampling of the edge region and visual quality of synthetic images by adopting different weights for the edge parts that is sensitive to human perception characteristics. The proposed method has gains in terms of PSNR and subjective video quality compared to previous researches.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.10
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• pp.875-880
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• 2013

When we generate 3D video that provides immersive and realistic feeling to users, depth information of the scene is essential. Since the resolution of the depth map captured by a depth sensor is lower than of the color image, we need to upsample the low-resolution depth map for high-resolution 3D video generation. In this paper, we propose a depth upsampling method using depth-discontinuity information. Using the high-resolution color image and the low-resolution depth map, we detect depth-discontinuity regions. Then, we define an energy function for the depth map upsampling and optimize it using the belief propagation method. Experimental results show that the proposed method outperforms other depth upsampling methods in terms of the bad pixel rate.

• Journal of Broadcast Engineering
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• v.14 no.5
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• pp.601-615
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• 2009

This paper proposes an efficient depth-map coding method for generating virtual-view images in 3D-Video. Virtual-view images can be generated by the view-interpolation based on the depth-map of the image. A conventional video coding method such as H.264 has been used. However, a conventional video coding method does not consider the image characteristics of the depth-map. Therefore, this paper proposes an adaptive depth-map coding method that can select between the H.264/AVC coding scheme and the proposed gray-coded bit plane-based coding scheme in a unit of block. This improves the coding efficiency of the depth-map data. Simulation results show that the proposed method, in comparison with the H.264/AVC coding scheme, improves the average BD-rate savings by 7.43% and the average BD-PSNR gains by 0.5dB. It also improves the subjective picture quality of synthesized virtual-view images using decoded depth-maps.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.6
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• pp.8-17
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• 2011

Recently, fusion camera systems that consist of depth sensors and color cameras have been widely developed with the advent of a new type of sensor, time-of-flight (TOF) depth sensor. The physical limitation of depth sensors usually generates low resolution images compared to corresponding color images. Therefore, the pre-processing module, such as camera calibration, three dimensional warping, and hole filling, is necessary to generate the high resolution depth map that is placed in the image plane of the color image. However, the result of the pre-processing step is usually inaccurate due to errors from the camera calibration and the depth measurement. Therefore, in this paper, we present a depth map upsampling method robust these errors. First, the confidence of the measured depth value is estimated by the interrelation between the color image and the pre-upsampled depth map. Then, the detailed depth map can be generated by the modified kernel regression method which exclude depth values having low confidence. Our proposed algorithm guarantees the high quality result in the presence of the camera calibration errors. Experimental comparison with other data fusion techniques shows the superiority of our proposed method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.05a
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• pp.818-819
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• 2013

This paper propose a contents security technique for digital holographic display service. Digital holographic video system assumes the existing service frame for 2-dimensional or 3-dimensional video, which includes data acquisition, processing, transmission, reception, and reconstruction. In this paper, we perform the encryption of RGB image and depth-map for such a system. The experimental results showed that encrypting only 0.048% of the entire data was enough to hide the constants of the RGB image and depth-map.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.12
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• pp.2530-2538
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• 2015

The pixel value of depth image is depth value so that different objects which are placed on nearby position have similar pixel value. Moreover, the pixels of depth image have distinct pixel values compared to adjacent pixels while those of color image has very similar values. Accordingly distorted depth image of multiview video plus depth (MVD) needs proper error concealment methods considering the characteristics of depth image when transmission errors are happened. In this paper, classifying regions of depth image to consider edge directions and then applying adaptive error concealment methods to each region are proposed. Recovered depth images utilize with multiview video data to synthesize intermediate-view point video. The synthesized view is evaluated by objective quality metrics to demonstrate proposed method performance.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.10a
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• pp.147-148
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• 2010

Recently, advancement in the visual technology has lead to the further development of the three dimensional (3D) imaging systems. The visual perception to view a pair of images simultaneously, is a crucial factor to build a stereoscopic 3D image. In this paper, we present the depth cues between the intensities of the two images when viewing with both eyes. Due to this stereoscopic effect, objects at different distances from the eyes differ in their horizontal positions, giving the depth cue of horizontal disparity. By simple image processing technique, we also present the binocular disparity map between the two images. A median filter has been used to filter out all the noises occurring in the disparity map image.

• Aerospace Engineering and Technology
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• v.11 no.2
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• pp.140-148
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• 2012

For autonomous navigation, map, pose tracking, and finding the shortest path are required. Because there is no GPS signal in indoor environment, the current position should be recognized in the 3D map by using image processing or something. In this paper, we explain 3D map creation technology by using depth camera like Kinect and pose tracking in 3D map by using 2D image taking from camera. In addition, the mechanism of avoiding obstacles is discussed.

• Journal of Digital Convergence
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• v.10 no.4
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• pp.191-197
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• 2012

In this paper we present creating 3D image from 2D image by extract initial depth values calculated from focal values. The initial depth values are created by using the extracted focal information, which is calculated by the comparison of original image and Gaussian filtered image. This initial depth information is allocated to the object segments obtained from normalized cut technique. Then the depth of the objects are corrected to the average of depth values in the objects so that the single object can have the same depth. The generated depth is used to convert to 3D image using DIBR(Depth Image Based Rendering) and the generated 3D image is compared to the images generated by other techniques.