• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.627-630
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• 1999

For depth extraction from the focus and recovery the shape, determination of criterion function for focus measure and size of the criterion window are very important. However, Texture, illumination, and magnification have an effect on focus measure. For that reason, depth map has a partial high and low peak. In this paper, we propose a depth extraction method from focused images using the error interpolation. This method is modified the error depth into mean value between two normal depth in order to improve the depth map.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.12
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• pp.1115-1121
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• 2012

This paper proposes a method to extract accurate plane of an object in unstructured environment for a humanoid robot by using a laser scanner. By panning and tilting 2D laser scanner installed on the head of a humanoid robot, 3D depth map of unstructured environment is generated. After generating the 3D depth map around a robot, the proposed plane extraction method is applied to the 3D depth map. By using the hierarchical clustering method, points on the same plane are extracted from the point cloud in the 3D depth map. After segmenting the plane from the point cloud, dimensions of the planes are calculated. The accuracy of the extracted plane is evaluated with experimental results, which show the effectiveness of the proposed method to extract planes around a humanoid robot in unstructured environment.

• Journal of Korea Multimedia Society
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• v.22 no.5
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• pp.547-557
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• 2019

To extract the depth map from a single image, a number of CNN-based deep learning methods have been performed in recent research. In this study, the GAN structure of Pix2Pix is maintained. this model allows to converge well, because it has the structure of the generator and the discriminator. But the convolution in this model takes a long time to compute. So we change the convolution form in the generator to a depthwise convolution to improve the speed while preserving the result. Thus, the seven down-sizing convolutional hidden layers in the generator U-Net are changed to depthwise convolution. This type of convolution decreases the number of parameters, and also speeds up computation time. The proposed model shows similar depth map prediction results as in the case of the existing structure, and the computation time in case of a inference is decreased by 64%.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37C no.9
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• pp.751-756
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• 2012

In this paper, we propose a foreground extraction and depth map generation method using a time-of-flight (TOF) depth camera. Although, the TOF depth camera captures the scene's depth information in real-time, it has a built-in noise and distortion. Therefore, we perform several preprocessing steps such as image enhancement, segmentation, and 3D warping, and then use the TOF depth data to generate the depth-discontinuity regions. Then, we extract the foreground object and generate the depth map as of the color image. The experimental results show that the proposed method efficiently generates the depth map even for the object boundary and textureless regions.

• Electronics and Telecommunications Trends
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• v.35 no.5
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• pp.112-122
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• 2020

In 3D computer graphics, a depth map is an image that provides information related to the distance from the viewpoint to the subject's surface. Stereo sensors, depth cameras, and imaging systems using an active illumination system and a time-resolved detector can perform accurate depth measurements with their own light sources. The 3D image information obtained through the depth map is useful in 3D modeling, autonomous vehicle navigation, object recognition and remote gesture detection, resolution-enhanced medical images, aviation and defense technology, and robotics. In addition, the depth map information is important data used for extracting and restoring multi-view images, and extracting phase information required for digital hologram synthesis. This study is oriented toward a recent research trend in deep learning-based 3D data analysis methods and depth map information extraction technology using a convolutional neural network. Further, the study focuses on 3D image processing technology related to digital hologram and multi-view image extraction/reconstruction, which are becoming more popular as the computing power of hardware rapidly increases.

• Journal of Korea Multimedia Society
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• v.13 no.12
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• pp.1855-1862
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• 2010

In virtual reality, there are increasing qualitative development in service technologies for realtime interaction system development, 3- dimensional contents, 3D TV and augment reality services. These services experience difficulties to generate depth value that is essential to recover 3D space to form solidity on existing contents. Hence, research for the generation of effective depth-map using 2D is necessary. This thesis will describe a shortcoming of an existing depth-map generation for the recovery of 3D space using 2D image and will propose an enhanced depth-map generation algorithm that complements a shortcoming of existing algorithms and utilizes the definition of depth direction based on the vanishing point within image.

• Journal of Korea Society of Industrial Information Systems
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• v.7 no.1
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• pp.81-88
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• 2002

This paper proposes an extraction method which extracts two different thematic maps, which have different line thickness from each other in a raster image that contains the two thematic maps. In the proposed method, the depth of each pixel is calculated according to the amount of pixels in its surrounding neighborhood, and then the thinning is performed. By using depth threshold, two thematic maps are first extracted from the thinning result. There are noise images and skeleton disconnection in the lines of each extracted thematic map. Each thematic map extraction is finally completed after removing the noise images and connecting the disconnected lines. Through the experiment, we showed that the proposed method could be used for the extraction of each thematic map of a raster image which included two thematic maps.

• Journal of Korea Multimedia Society
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• v.14 no.2
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• pp.329-338
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• 2011

Recent augmentation reality demands more realistic multimedia data with the mixture of various media. High-technology for multimedia data which combines existing media data with various media such as audio and video dominates entire media industries. In particular, there is a growing need to serve augmentation reality, 3-dimensional contents and realtime interaction system development which are communication method and visualization tool in Internet. The existing services do not correspond to generate depth value for 3-dimensional space structure recovery which is to form solidity in existing contents. Therefore, it requires research for effective depth-map generation using 2-dimensional video. Complementing shortcomings of existing depth-map generation method using 2-dimensional video, this paper proposes an enhanced depth-map generation method that defines the depth direction in regard to loss location in a video in which none of existing algorithms has defined.

• The Journal of the Korea Contents Association
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• v.18 no.11
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• pp.447-454
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• 2018

This paper proposes a depth image generation algorithm of stereo images using a deep learning model composed of a CNN (convolutional neural network). The proposed algorithm consists of a feature extraction unit which extracts the main features of each parallax image and a depth learning unit which learns the parallax information using extracted features. First, the feature extraction unit extracts a feature map for each parallax image through the Xception module and the ASPP(Atrous spatial pyramid pooling) module, which are composed of 2D CNN layers. Then, the feature map for each parallax is accumulated in 3D form according to the time difference and the depth image is estimated after passing through the depth learning unit for learning the depth estimation weight through 3D CNN. The proposed algorithm estimates the depth of object region more accurately than other algorithms.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.4
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• pp.1897-1903
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• 2011

This paper discusses a 2D/3D conversion of images using technologies like object extraction and depth-map creation. The general procedure for converting 2D images into a 3D image is extracting objects from 2D image, recognizing the distance of each points, generating the 3D image and correcting the image to generate with less noise. This paper proposes modified new methods creating a depth-map from 2D image and recognizing the distance of objects in it. Depth-map information which determines the distance of objects is the key data creating a 3D image from 2D images. To get more accurate depth-map data, noise filtering is applied to the optical flow. With the proposed method, better depth-map information is calculated and better 3D image is constructed.