• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.11
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• pp.47-55
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• 2012

In this paper, we propose a method to reconstruct accurate 3D shape object by using multi-view images which are disturbed by motion blur. In multi-view deblurring, more precise PSF estimation can be done by using the geometric relationship between multi-view images. The proposed method first estimates initial 2D PSFs from individual input images. Then 3D PSF candidates are projected on the input images one by one to find the best one which are mostly consistent with the initial 2D PSFs. 3D PSF consists with direction and density and it represents the 3D trajectory of object motion. 야to restore 3D shape by using multi-view images computes the similarity map and estimates the position of 3D point. The estimated 3D PSF is again projected to input images and they replaces the intial 2D PSFs which are finally used in image deblurring. Experimental result shows that the quality of image deblurring and 3D reconstruction improves significantly compared with the result when the input images are independently deblurred.

• Journal of International Society for Simulation Surgery
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• v.2 no.1
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• pp.13-16
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• 2015

Purpose We conducted a study on the reconstruction of the head's shape in 3D using the ToF depth sensor. A time-of-flight camera (ToF camera) is a range imaging camera system that resolves distance based on the known speed of light, measuring the time-of-flight of a light signal between the camera and the subject for each point of the image. The above method is the safest way of measuring the head shape of plagiocephaly patients in 3D. The texture, appearance and size of the head were reconstructed from the measured data and we used the SDF method for a precise reconstruction. Materials and Methods To generate a precise model, mesh was generated by using Marching cube and SDF. Results The ground truth was determined by measuring 10 people of experiment participants for 3 times repetitively and the created 3D model of the same part from this experiment was measured as well. Measurement of actual head circumference and the reconstructed model were made according to the layer 3 standard and measurement errors were also calculated. As a result, we were able to gain exact results with an average error of 0.9 cm, standard deviation of 0.9, min: 0.2 and max: 1.4. Conclusion The suggested method was able to complete the 3D model by minimizing errors. This model is very effective in terms of quantitative and objective evaluation. However, measurement range somewhat lacks 3D information for the manufacture of protective helmets, as measurements were made according to the layer 3 standard. As a result, measurement range will need to be widened to facilitate production of more precise and perfectively protective helmets by conducting scans on all head circumferences in the future.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.4
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• pp.187-194
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• 2015

In this paper, we present an effective visual odometry estimation system to track the real-time pose of a camera moving in 3D space. In order to meet the real-time requirement as well as to make full use of rich information from color and depth images, our system adopts a feature-based sparse odometry estimation method. After matching features extracted from across image frames, it repeats both the additional inlier set refinement and the motion refinement to get more accurate estimate of camera odometry. Moreover, even when the remaining inlier set is not sufficient, our system computes the final odometry estimate in proportion to the size of the inlier set, which improves the tracking success rate greatly. Through experiments with TUM benchmark datasets and implementation of the 3D scene reconstruction application, we confirmed the high performance of the proposed visual odometry estimation method.

• Journal of Korea Multimedia Society
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• v.1 no.1
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• pp.26-36
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• 1998

It is very important study field in computer vision 'How we obtain 3D information from 2D image'. For this purpose, we must know position of camera, direction of light source, and surface reflectance property before we take the image, which are intrinsic information of the object in the scene. Among them, surface reflectance property presents very important clues. Most previous researches assume that objects have only Lambertian reflectance, but many real world objects have Non-Lambertian reflectance property. In this paper the new method for analyzing the properties of surface reflectance and reconstructing the shape of object through estimation of reflectance parameters is proposed. We have interest in Non-Lambertian reflectance surface that has specular reflection and diffuse reflection which can be explained by Torrance-Sparrow model. Photometric matching method proposed in this paper is robust method because it match reference image and object image considering the neighbor brightness distribution. Also in this thesis, the neural network based shaped reconstruction method is proposed, which can be performed in the absence of reflectance information. When brightness obtained by each light is inputted, neural network is trained by surface normal and can determine the surface shape of object.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.3 no.3
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• pp.8-13
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• 2010

Shape form focus (SFF) is a technique that reconstructs 3D shape of an object using image focus. Although many SFF methods have been proposed, there are still notable inaccuracy effects due to noise and non-optimization of image characteristics. In this paper, we propose a noise filter technique for noise reduction and genetic algorithm (GA) for focus measure optimization. The proposed method is analyzed with a statistical criteria such as Root Mean Square Error (RMSE) and correlation.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.3D
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• pp.535-540
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• 2006

This paper presented an effective strategy to extract the buildings and to reconstruct 3-D buildings using high-resolution multispectral stereo satellite images. Proposed scheme contained three major steps: building enhancement and segmentation using both BDT (Background Discriminant Transformation) and ISODATA algorithm, conjugate building identification using the object matching with Hausdorff distance and color indexing, and 3-D building reconstruction using photogrammetric techniques. IKONOS multispectral stereo images were used to evaluate the scheme. As a result, the BDT technique was verified as an effective tool for enhancing building areas since BDT suppressed the dominance of background to enhance the building as a non-background. In building recognition, color information itself was not enough to identify the conjugate building pairs since most buildings are composed of similar materials such as concrete. When both Hausdorff distance for edge information and color indexing for color information were combined, most segmented buildings in the stereo images were correctly identified. Finally, 3-D building models were successfully generated using the space intersection by the forward RFM (Rational Function Model).

• Journal of the Korean Society of Clothing and Textiles
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• v.33 no.3
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• pp.401-409
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• 2009

The shape of crotch area is very important to develop functional clothing as well as other ergonomic goods such as chair or saddle etc. However, it is inevitable that 3D scanned image of crotch would have missing part due to its folded shape including overlapping legs nearby. Therefore, the objectives of this research was to compare reconstruction methods of missing parts at crotch using seven dummies of real men's replicas. Two reconstruction methods adopted were kinds of 'fill- hole' in Rapidform 2004, one was 'smooth' and the other was 'curvature'. Each restored image was compared with the original shape of the dummies. As results, the average distance was 0.66mm between original and 'smooth' treated images and 0.59mm between original and 'curvature' treated, which was not statistically different. Average area of restored crotch region was $8740.04cm^2$ by 'smooth' method and $8405.02cm^2$ by 'curvature' method which is close to the original area of $8413.76cm^2$. Statistical difference was found between images of original and 'smooth' ones$(p=0.04^*)$. However, there was no difference between original and 'curvature' treated images, which indicates that 'curvature' method is more useful to fill the hole compared with 'smooth' method.

• 한국HCI학회:학술대회논문집
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• 2007.02a
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• pp.982-987
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• 2007

The necessity of data compression scheme for volume data has been increased because of the increase of data capacity and the amount of network uses. Now we have various kinds of compression schemes, and we can choose one of them depending on the data types, application fields, the preferences, etc. However, the capacity of data which is produced by application scientists has been excessively increased, and the format of most scientific data is 3D volume. For 2D image or 3D moving pictures, many kinds of standards are established and widely used, but for 3D volume data, specially time-varying volume data, it is very difficult to find any applicable compression schemes. In this paper, we present a compression scheme for encoding time-varying volume data. This scheme is aimed to encoding time-varying volume data for visualization. This scheme uses MPEG's I- and P-frame concept for raising compression ratio. Also, it transforms volume data using Daubechies D4 filter before encoding, so that the image quality is better than other wavelet-based compression schemes. This encoding scheme encodes time-varying volume data composed of single precision floating-point data. In addition, this scheme provides the random reconstruction accessibility for an unit, and can be used for compressing large time-varying volume data using correlation between frames while preserving image qualities.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.2
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• pp.135-140
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• 2012

The mirror neuron system in the cerebrum, which are handled by visual information-based imitative learning. When we observe the observer's range of mirror neuron system, we can assume intention of performance through progress of neural activation as specific range, in include of partially hidden range. It is goal of our paper that imitative learning is applied to 3D vision-based intelligent system. We have experiment as stereo camera-based restoration about acquired 3D image our previous research Using Optical flow, unscented Kalman filter. At this point, 3D input image is sequential continuous image as including of partially hidden range. We used Hidden Markov Model to perform the intention recognition about performance as result of restoration-based hidden range. The dynamic inference function about sequential input data have compatible properties such as hand gesture recognition include of hidden range. In this paper, for proposed intention recognition, we already had a simulation about object outline and feature extraction in the previous research, we generated temporal continuous feature vector about feature extraction and when we apply to Hidden Markov Model, make a result of simulation about hand gesture classification according to intention pattern. We got the result of hand gesture classification as value of posterior probability, and proved the accuracy outstandingness through the result.

• Proceedings of the Korean Society of Computer Information Conference
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• 2018.07a
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• pp.389-390
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• 2018

본 논문에서는 3차원 복원을 위한 특징점 추출 및 매칭에 대한 보다 정확한 방법을 제안한다. 이 방법은 컴퓨터 비전의 기본이 되는 분야로 복원뿐 만 아니라 SLAM과 같은 지도 작성 및 자율 운행에도 필요한 방법이다. 본 연구는 3차원 물체 복원을 위해서 사용하는 방법 중 하나인 Column space fitting(CSF)을 이용하여 turntable-image data에 적용하여 성능을 평가하여 정확성을 검증을 한다. 오늘날 3D scanner를 이용하여 물체를 3차원 모델을 획득하고 3D프린터를 이용하여 다양한 분야에 적용한다. 그러나 고가의 장비이기 때문에 접근성이 떨어진다. 본 연구는 영상들만을 가지고 기하학적 계산을 통해 3차원 모델을 획득한다. 본 연구결과는 기존의 방법인 KLT 알고리즘과 비교하여 RMSE의 값을 약 5배를 줄이는 성능 향상을 보인다.