• Journal of the Optical Society of Korea
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• v.18 no.5
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• pp.517-522
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• 2014

In this paper we propose an axially distributed image-sensing method with a wide-angle lens to capture the wide-area scene of 3D objects. A lot of parallax information can be collected by translating the wide-angle camera along the optical axis. The recorded wide-area elemental images are calibrated using compensation of radial distortion. With these images we generate volumetric slice images using a computational reconstruction algorithm based on ray back-projection. To show the feasibility of the proposed method, we performed optical experiments for visualization of a partially occluded 3D object.

• Journal of the Optical Society of Korea
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• v.13 no.1
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• pp.139-145
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• 2009

Computational integral imaging (CII) is a new method for 3D imaging and visualization. However, it suffers from seriously poor image quality of the reconstructed image as the reconstructed image plane increases. In this paper, to overcome this problem, we propose a CII method based on a smart pixel mapping (SPM) technique for partially occluded 3D object recognition, in which the object to be recognized is located at far distance from the lenslet array. In the SPM-based CII, the use of SPM moves a far 3D object toward the near lenslet array and then improves the image quality of the reconstructed image. To show the usefulness of the proposed method, we carry out some experiments for occluded objects and present the experimental results.

• 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

• 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.

• 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.

• Journal of the Korea Institute of Military Science and Technology
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• v.25 no.4
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• pp.381-389
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• 2022

Detecting partially occluded objects is difficult due to the appearances and shapes of occluders are highly variable. These variabilities lead to challenges of localizing accurate bounding box or classifying objects with visible object parts. To address these problems, we propose a two-stage part-based attention approach for robust object detection under partial occlusion. First, our part attention network(PAN) captures the important object parts and then it is used to generate weighted object features. Based on the weighted features, the re-weighted object features are produced by our reinforced PAN(RPAN). Experiments are performed on our collected military vehicle dataset and synthetic occlusion dataset. Our method outperforms the baselines and demonstrates the robustness of detecting objects under partial occlusion.

• Journal of the Korea Society of Computer and Information
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• v.19 no.10
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• pp.63-70
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• 2014

Process of recognizing objects in binary images consists of image segmentation and pattern matching. If binary objects in the image are assumed to be separated, global features such as area, length of perimeter, or the ratio of the two can be used to recognize the objects in the image. However, if such an assumption is not valid, the global features can not be used but local features such as points or line segments should be used to recognize the objects. In this paper points with large curvature along the perimeter are chosen to be the feature points, and pairs of points selected from them are used as local features. Similarity of two local features are defined using elastic deformation energy for making the lengths and angles between gradient vectors at the end points same. Neighbour support value is defined and used for robust recognition of partially occluded binary objects. An experiment on Kimia-25 data showed that the proposed algorithm runs 4.5 times faster than the maximum clique algorithm with same recognition rate.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1920-1925
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• 2004

This paper describes a new method for 3D object recognition which used surface segment-based stereo vision. The position and orientation of an objects is identified accurately enabling a robot to pick up, even though the objects are multiple and partially occluded. The stereo vision is used to get the 3D information as 3D sensing, and CAD model with its post processing is used for building models. Matching is initially performed using the model and object features, and calculate roughly the object's position and orientation. Though the fine adjustment step, the accuracy of the position and orientation are improved.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.3
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• pp.79-87
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• 1996

A new method for recognizing and locating partially occluded or overlapped two-dimensional objects regardless of their size, translation, and rotation, is presented. Dominant points approximating occuluding contoures of objects are generated by finding local maxima of smoothed k-cosine function, and then used to guide the contour segment matching procedure. Primitives between the dominant points are produced by projecting the local contours onto the line between the dominant points. Robust classification of primitives. Which is crucial for reliable partial shape matching, is performed using adaptive resonance theory (ART2). The matched primitives having similar scale factors and rotation angles are detected in the hough space to identify the presence of the given model in the object scene. Finally the translation vector is estimated by minimizing the mean squred error of the matched contur segment pairs. This model-based matching algorithm may be used in diveerse factory automation applications since models can be added or changed simply by training ART2 adaptively without modifying the matching algorithm.

• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.6
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• pp.954-962
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• 1990

Experties of recognizing an object despite of every possible occlusions among objects is difficult to be provided directly to a system. In this paper, we propose a method for inferring inherent shape-characteirstics of an object from training views provided. The method learns rules incrementally by alternating the rule induction process from limited number of training views and the rule verification process from the following taining views. The learned rules are represented using logical expressions to enhance the readability. Thr proposed method is tested by simulating occlusions on 2-dimensional objects to examine the learning process and to show improvement of recognition rate. Thr result shows that it can be applied to a practical system for 3-dimensional object recognition.