• The KIPS Transactions:PartB
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• v.12B no.5 s.101
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• pp.567-576
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• 2005

This paper proposes an image-based modeling method which recovers 3D models using projected line segments in multiple images. Using the method, a user obtains accurate 3D model data via several steps of simple manual works. The embedded nonlinear minimization technique in the model parameter estimation stage is based on the distances between the user provided image line segments and the projected line segments of primitives. We define an error using a finite line segment and thus increase accuracy in the model parameter estimation. The error is defined as the sum of differences between the observed image line segments provided by the user and the predicted image line segments which are computed using the current model parameters and camera parameters. The method is robust in a sense that it recovers 3D structures even from partially occluded objects and it does not be seriously affected by small measurement errors in the reconstruction process. This paper also describesexperimental results from real images and difficulties and tricks that are found while implementing the image-based modeler.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.7
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• pp.817-822
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• 2005

Recently tile study that exploits visual information for tile services of robot in indoor environments is active. Conventional image processing approaches are based on the pre-defined geometric models, so their performances are likely to decrease when they are applied to the uncertain and dynamic environments. For this, diverse researches to manage the uncertainty based on the knowledge for improving image recognition performance have been doing. In this paper we propose a Bayesian network modeling method for predicting the existence of target objects when they are occluded by other ones for improving the object detection performance of the service robots. The proposed method makes object relationship, so that it allows to predict the target object through observed ones. For this, we define the design method for small size Bayesian networks (primitive Bayesian netqork), and allow to integrate them following to the situations. The experiments are performed for verifying the performance of constructed model, and they shows $82.8\%$ of accuracy in 5 places.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.7C
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• pp.554-564
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• 2012

The vehicle recognition consists of two steps; the vehicle region detection step and the vehicle identification step based on the feature extracted from the detected region. Features using linear transformations have the effect of dimension reduction as well as represent statistical characteristics, and show the robustness in translation and rotation of objects. Among the linear transformations, the NMF(Non-negative Matrix Factorization) is one of part-based representation. Therefore, we can extract NMF features with sparsity and improve the vehicle recognition rate by the representation of local features of a car as a basis vector. In this paper, we propose a feature extraction using NMF suitable for the vehicle recognition, and verify the recognition rate with it. Also, we compared the vehicle recognition rate for the occluded area using the SNMF(sparse NMF) which has basis vectors with constraint and LVQ2 neural network. We showed that the feature through the proposed NMF is robust in the urban scene where occlusions are frequently occur.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.1
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• pp.190-196
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• 2013

Curved integral imaging is a simple method to display 3D images in space using lens array and provides wide viewing angle. In this paper, we propose a nonlinear 3D correlator based on the direct pixel-mapping (DPM) method in order to improve the recognition performance of 3D target object in curving-effective integral imaging. With this scheme, the elemental image array (EIA) originally picked up from a partially occluded 3-D target object can be converted into a resolution enhanced new EIA by using DPM method. Then, through nonlinear cross-correlations between the reconstructed reference and the target plane images, the improved pattern recognition can be performed from the correlation outputs. To show the feasibility of the proposed method, some preliminary experiments are carried out and results are presented by comparing the conventional method.

• Korean Journal of Optics and Photonics
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• v.20 no.1
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• pp.16-22
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• 2009

We propose a method to convert a two-dimensional movie to a three-dimensional movie using normalized cut and optical flow. In this paper, we segment an image of a two-dimensional movie to objects first, and then estimate the depth of each object. Normalized cut is one of the image segmentation algorithms. For improving speed and accuracy of normalized cut, we used a watershed algorithm and a weight function using optical flow. We estimate the depth of objects which are segmented by improved normalized cut using optical flow. Ordinal depth is estimated by the change of the segmented object label in an occluded region which is the difference of absolute values of optical flow. For compensating ordinal depth, we generate the relational depth which is the absolute value of optical flow as motion parallax. A final depth map is determined by multiplying ordinal depth by relational depth, then dividing by average optical flow. In this research, we propose the two-dimensional/three-dimensional movie conversion method which is applicable to all three-dimensional display devices and all two-dimensional movie formats. We present experimental results using sample two-dimensional movies.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.41 no.2
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• pp.113-130
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• 2004

Most multimedia data contain text to emphasize the meaning of the data, to present additional explanations about the situation, or to translate different languages. But, the left makes it difficult to reuse the images, and distorts not only the original images but also their meanings. Accordingly, this paper proposes a support vector machines (SVMs) and spatiotemporal restoration-based approach for automatic text detection and removal in video sequences. Given two consecutive frames, first, text regions in the current frame are detected by an SVM-based texture classifier Second, two stages are performed for the restoration of the regions occluded by the detected text regions: temporal restoration in consecutive frames and spatial restoration in the current frame. Utilizing text motion and background difference, an input video sequence is classified and a different temporal restoration scheme is applied to the sequence. Such a combination of temporal restoration and spatial restoration shows great potential for automatic detection and removal of objects of interest in various kinds of video sequences, and is applicable to many applications such as translation of captions and replacement of indirect advertisements in videos.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.6
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• pp.48-60
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• 2011

Nowadays, the 3D community is actively researching on 3D imaging and free-viewpoint video (FVV). The free-viewpoint rendering in multi-view video, virtually move through the scenes in order to create different viewpoints, has become a popular topic in 3D research that can lead to various applications. However, there are restrictions of cost-effectiveness and occupying large bandwidth in video transmission. An alternative to solve this problem is to generate virtual views using a single texture image and a corresponding depth image. A critical issue on generating virtual views is that the regions occluded by the foreground (FG) objects in the original views may become visible in the synthesized views. Filling this disocclusions (holes) in a visually plausible manner determines the quality of synthesis results. In this paper, a new approach for handling disocclusions using depth based inpainting algorithm in synthesized views is presented. Patch based non-parametric texture synthesis which shows excellent performance has two critical elements: determining where to fill first and determining what patch to be copied. In this work, a noise-robust filling priority using the structure tensor of Hessian matrix is proposed. Moreover, a patch matching algorithm excluding foreground region using depth map and considering epipolar line is proposed. Superiority of the proposed method over the existing methods is proved by comparing the experimental results.

• Korean Journal of Remote Sensing
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• v.29 no.5
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• pp.499-508
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• 2013

This article presents a new method for semi-automatic extraction of building information (height, shape, and footprint location) from monoscopic urban scenes. The proposed method is to expand Semi-automatic Volumetric Shadow Analysis (SVSA), which can handle occluded building footprints or shadows semi-automatically. SVSA can extract wrong building information from a single high resolution satellite image because SVSA is influenced by extracted shadow area, image noise and objects around a building. The proposed method can reduce the disadvantage of SVSA by using multi-spectral images. The proposed method applies SVSA to panchromatic and multi-spectral images. Results of SVSA are used as parameters of a cost function. A building height with maximum value of the cost function is determined as actual building height. For performance evaluation, building heights extracted by SVSA and the proposed method from Kompsat-2 images were compared with reference heights extracted from stereo IKONOS. The result of performance evaluation shows the proposed method is a more accurate and stable method than SVSA.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.6
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• pp.201-211
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• 2013

In this paper, we propose a hand tracking and gesture recognition system. Our system employs a depth capture device to obtain 3D geometric information of user's bare hand. In particular, we build a flexible tracking volume and restrict the hand tracking area, so that we can avoid diverse problems caused by conventional object detection/tracking systems. The proposed system computes running average of the hand position, and tracking volume is actively adjusted according to the statistical information that is computed on the basis of uncertainty of the user's hand motion in the 3D space. Once the position of user's hand is obtained, then the system attempts to detect stretched fingers to recognize finger gesture of the user's hand. In order to test the proposed framework, we built a NUI system using the proposed technique, and verified that our system presents very stable performance even in the case that multiple objects exist simultaneously in the crowded environment, as well as in the situation that the scene is occluded temporarily. We also verified that our system ensures running speed of 24-30 frames per second throughout the experiments.