• Journal of information and communication convergence engineering
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• v.15 no.3
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• pp.193-198
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• 2017

In this study, we propose a three-dimensional (3D) visualization technique of occluded objects using integral imaging with a plenoptic camera. In previous studies, depth map estimation from elemental images was used to remove occlusion. However, the resolution of these depth maps is low. Thus, the occlusion removal accuracy is not efficient. Therefore, we use a plenoptic camera to obtain a high-resolution depth map. Hence, individual depth map for each elemental image can also be generated. Finally, we can regenerate a more accurate depth map for 3D objects with these separate depth maps, allowing us to remove the occlusion layers more efficiently. We perform optical experiments to prove our proposed technique. Moreover, we use MSE and PSNR as a performance metric to evaluate the quality of the reconstructed image. In conclusion, we enhance the visual quality of the reconstructed image after removing the occlusion layers using the plenoptic camera.

• Journal of the Optical Society of Korea
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• v.19 no.3
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• pp.248-254
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• 2015

In this paper we propose an improved version of the computational integral imaging reconstruction (CIIR) for visualizing a partially occluded object by utilizing an image inpainting technique. In the proposed method the elemental images for a partially occluded three-dimensional (3D) object are recorded through the integral imaging pickup process. Next, the depth of occlusion within the elemental images is estimated using two different CIIR methods, and the weight mask pattern for occlusion is generated. After that, we apply our image inpainting technique to the recorded elemental images to fill in the occluding area with reliable data, using information from neighboring pixels. Finally, the inpainted elemental images for the occluded region are reconstructed using the CIIR process. To verify the validity of the proposed system, we carry out preliminary experiments in which faces are the objects. The experimental results reveal that the proposed system can dramatically improve the quality of a reconstructed CIIR image.

• Proceedings of the Optical Society of Korea Conference
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• 2009.02a
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• pp.259-260
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• 2009

• The Journal of the Acoustical Society of Korea
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• v.12 no.2E
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• pp.80-87
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• 1993

본 논문에서는 겹침이 있는 물체의 인식을 위하여 겹침을 당한 물체의 가려진 부분에 대한 추정을 행하는 방법을 제안한다. 주어진 입력화상에서 정점의 호수점의 개수를 셈으로 겹침의 생성 여부를 알 수 있다. 또한 특징추출을 행하기전에 도형의 형태량에 기초하여 겹침을 당한 물체의 가려진 부분을 추정하여 본 논문의 유용성을 여러 실험에 의해 입증하였다.

• Journal of IKEEE
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• v.28 no.1
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• pp.65-71
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• 2024

This paper proposes a neural network that can reduce the number of parameters while reducing matching errors in occluded regions to increase the accuracy of depth maps in stereo matching. Stereo matching-based object recognition is utilized in many fields to more accurately recognize situations using images. When there are many objects in a complex image, an occluded area is generated due to overlap between objects and occlusion by background, thereby lowering the accuracy of the depth map. To solve this problem, existing research methods that create context information and combine it with the cost volume or RoIselect in the occluded area increase the complexity of neural networks, making it difficult to learn and expensive to implement. In this paper, we create a depthwise seperable neural network that enhances regional feature extraction before cost volume generation, reducing the number of parameters and proposing a neural network that is robust to occlusion errors. Compared to PSMNet, the proposed neural network reduced the number of parameters by 30%, improving 5.3% in color error and 3.6% in test loss.

• The KIPS Transactions:PartB
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• v.14B no.5
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• pp.351-360
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• 2007

Video Completion refers to a computer vision technique which restores damaged images by filling missing pixels with suitable color in a video sequence. We propose a new video completion technique to fill in image holes which are caused by removing an unnecessary object in a video sequence, where two objects cross each other in the presence of camera motion. We remove the closer object from a camera which results in image holes. Then these holes are filled by color information of some others frames. First of all, spatio-temporal volumes of occluding and occluded objects are created according to the centroid of the objects. Secondly, a temporal search technique by voxel matching separates and removes the occluding object. Finally. these holes are filled by using spatial search technique. Seams on the boundary of completed pixels we removed by a simple blending technique. Experimental results using real video sequences show that the proposed technique produces new completed videos.

• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.1
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• pp.20-28
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• 1989

The recognition of partially occluded objects is of prime importance for industrial machine vision applications and to solve real provlems in factory automation. This paper describes a method tc solve the problem of occlusion in a two dimensional scene. The technique consists of three steps: searching of border, extracting of line segments and clustering of hypotheses by matching probability. Computer simulation results have been tested for 20 scenes contained the 80 models, and have obtained 95% of properly correct recognition rate on the average.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.16 no.1
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• pp.23-34
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• 1991

This paper is studied for the recognition of occluded objects by fuzzy inference. The images are transformed a group of linear line segments, which is formed local features extracted from curvature points, using polygonal approximation. The features extracted from images are representes to the fuzzified data which is mapped into fuzzy concepts to represent the fuzziness, and the recognition of a model from scenes is performed by fuzzy inference using the production rulse which is generated from the model image. It is considered that the recognition results according to the change of degree of fuzziness in the experiments, and the experimental results for 30 scenes contained 120 models is obtained 92.5% of recognition rate.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.1
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• pp.60-65
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• 2012

This paper presents a method for occluded object based motion estimation and tracking system in dynamic image sequences using particle filter with 3D reconstruction. A unique characteristic of this study is its ability to cope with partial occlusion based continuous motion estimation using particle filter inspired from the mirror neuron system in human brain. To update a prior knowledge about the shape or motion of objects, firstly, fundamental 3D reconstruction based occlusion tracing method is applied and object landmarks are determined. And optical flow based motion vector is estimated from the movement of the landmarks. When arbitrary partial occlusions are occurred, the continuous motion of the hidden parts of object can be estimated by particle filter with optical flow. The resistance of the resulting estimation to partial occlusions enables the more accurate detection and handling of more severe occlusions.

• 한국정보디스플레이학회:학술대회논문집
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• 2008.10a
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• pp.761-762
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• 2008

In this paper, a novel approach for robust recognition of partially occluded 3-D target objects from computationally reconstructed integral images is proposed. The occluding object noises are selectively removed from the picked-up elemental images and performance of the proposed integral imaging-based 3-D target recognition system can be improved.