• The Journal of Korean Society for Radiation Therapy
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• v.33
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• pp.109-116
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• 2021

Purpose: The purpose of this study is to evaluate the usefulness of Surface-Guided Patient Setup by comparing the patient positioning accuracy when image-guided radiation therapy was used for Markerless patients(unmarked on the skin) using Surface-Guided Patient Setup and Marker patients(marked on the skin) using Laser-Based Patient Setup. Materials And Methods: The position error during IGRT was compared between a Markerless patient initially set up with SGPS using an optical surface scanning system using three cameras and a Marker patient initially set up with LBPS that aligns the laser with the marker drawn on the patient's skin. Both SGPS and LBPS were performed on 20 prostate cancer patients and 10 Stereotactic Radiation Surgery patients, respectively, and SGPS was performed on an additional 60 breast cancer patients. All were performed IGRT using CBCT or OBI. Position error of 6 degrees of freedom was obtained using Auto-Matching System, and comparison and analysis were performed using Offline-Review in the treatment planning system. Result: The difference between the root mean square (RMS) of SGPS and LBPS in prostate cancer patients was Vrt -0.02cm, Log -0.02cm, Lat 0.01cm, Pit -0.01°, Rol -0.01°, Rtn -0.01°, SRS patients was Vrt 0.02cm, Log -0.05cm, Lat 0.00cm, Pit -0.30°, Rol -0.15°, Rtn -0.33°. there was no significant difference between the two regions. According to the IGRT standard of breast cancer patients, RMS was Vrt 0.26, Log 0.21, Lat 0.15, Pit 0.81, Rol 0.49, Rtn 0.59. Conclusion:. As a result of this study, the position error value of SGPS compared to LBPS did not show a significant difference between prostate cancer patients and SRS patients. In the case of additionally performed SGPS breast cancer patients, the position error value was not large based on IGRT. Therefore, it is considered that it will be useful to replace LBPS with SGPS, which has the great advantage of not requiring patient skin marking..

• Journal of Internet Computing and Services
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• v.21 no.1
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• pp.33-43
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• 2020

Retrieving a 3D model from a 3D database and augmenting the retrieved model in the Augmented Reality system simultaneously became an issue in developing the plausible AR environments in a convenient fashion. It is considered that the sketch-based 3D object retrieval is an intuitive way for searching 3D objects based on human-drawn sketches as query. In this paper, we propose a novel deep learning based approach of retrieving a sketch-based 3D object as for an Augmented Reality Model. For this work, we introduce a new method which uses Sketch CNN, Wasserstein CNN and Wasserstein center loss for retrieving a sketch-based 3D object. Especially, Wasserstein center loss is used for learning the center of each object category and reducing the Wasserstein distance between center and features of the same category. The proposed 3D object retrieval and augmentation consist of three major steps as follows. Firstly, Wasserstein CNN extracts 2D images taken from various directions of 3D object using CNN, and extracts features of 3D data by computing the Wasserstein barycenters of features of each image. Secondly, the features of the sketch are extracted using a separate Sketch CNN. Finally, we adopt sketch-based object matching method to localize the natural marker of the images to register a 3D virtual object in AR system. Using the detected marker, the retrieved 3D virtual object is augmented in AR system automatically. By the experiments, we prove that the proposed method is efficiency for retrieving and augmenting objects.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.493-496
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• 2009

Many AR (Augmented Reality) applications have been interested in a marker-less tracking since the tracking methods give camera poses without attaching explicit markers. In this paper, we propose a new marker-less page recognition and tracking algorithm for an AR book application such as DigilogBook. The proposed method only requires orthogonal images of pages, which need not to be trained for a long time, and the algorithm works in real-time. The page recognition is done in two steps by using SIFT (Scale Invariant Feature Transform) descriptors and the comparison evaluation function. And also, the method provides real-time tracking with 25fps ~ 30fps by separating the page recognition and the frame-to-frame matching into two multi-cores. The proposed algorithm will be extended to various AR applications that require multiple objects tracking.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.3B
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• pp.339-348
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• 2004

Packet classification is an important factor to support various services such as QoS guarantee and VPN for users in Internet. Packet classification is a searching process for best matching rule on rule tables by employing multi-field such as source address, protocol, and port number as well as destination address in If header. In this paper, we propose hardware based packet classification algorithm by employing tree bitmap of multi-bit trio. We divided prefixes of searching fields and rule into multi-bit stride, and perform a rule searching with multi-bit of fixed size. The proposed scheme can reduce the access times taking for rule search by employing indexing key in a fixed size of upper bits of rule prefixes. We also employ a marker prefixes in order to remove backtracking during searching a rule. In this paper, we generate two dimensional random rule set of source address and destination address using routing tables provided by IPMA Project, and compare its memory usages and performance.

• Journal of Korea Game Society
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• v.12 no.4
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• pp.45-51
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• 2012

Current virtual game interfaces cannot comprehend our metaphor, cannot reflect on our natural behavior aspect, cannot make us immerse into a game, and makes a barrier between virtual game space and our real behavior. It is very meaningful issue to use real objects tightly related to human-being's behaviors or reactions for interacting with game applications. Interactive Augmented Reality interfaces may augment users' perception of the real world by adding virtual information to it. We attempted an experiment on camera-based non-marker interface for online TCG application. This experiment uses real TCG cards which are recognized by our two phases Image KeyPoint Extraction/Matching Algorithm. These initiative experiments not only enlarge immersion and reality to the game, but also make real and virtual world seamless.

• The KIPS Transactions:PartB
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• v.17B no.5
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• pp.345-354
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• 2010

Visual tracking techniques for Augmented Reality are classified as either a marker tracking approach or a natural feature tracking approach. Marker-based tracking algorithms can be efficiently implemented sufficient to work in real-time on mobile devices. On the other hand, natural feature tracking methods require a lot of computationally expensive procedures. Most previous natural feature tracking methods include heavy feature extraction and pattern matching procedures for each of the input image frame. It is difficult to implement real-time augmented reality applications including the capability of natural feature tracking on low performance devices. The required computational time cost is also in proportion to the number of patterns to be matched. To speed up the natural feature tracking process, we propose a novel fast tracking method based on optical flow. We implemented the proposed method on mobile devices to run in real-time and be appropriately used with mobile augmented reality applications. Moreover, during tracking, we keep up the total number of feature points by inserting new feature points proportional to the number of vanished feature points. Experimental results showed that the proposed method reduces the computational cost and also stabilizes the camera pose estimation results.

• Korean Journal of Breeding Science
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• v.43 no.5
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• pp.399-404
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• 2011

Genetic diversity among 28 Cymbidium varieties was evaluated by using a sequence-related amplified polymorphism (SRAP) marker system. The SRAP marker which was based on the open reading frames (ORFs) regions was developed primarily for Brassica species, but has been applied to various crops. A total of 30 SRAP primer combinations were initially screened. Twenty-eight SRAP primer combinations showed high polymorphism among the 28 Cymbidium varieties, which were consisted of breeding varieties and their parents in National Institute of Horticultural & Herbal Science (NIHHS). The amplified DNA fragments were separated by denaturing acrylamide gels and detected silver staining method. One hundred ninety six polymorphic bands (7 per primer) were generated and ranged from 0.3 to 1.0 kb in size. Polymorphic fragments were scored for calculating simple matching coefficient of genetic similarity and cluster analysis with multi-variate statistical package (MVSP) 3.1. The mean genetic similarity coefficient value was 0.588. The results showed that the correlation between $F_1$ varieties and their parents was high. These studied SRAP markers will be useful tools for genotype identification, germplasm conservation, genetic relationships in Cymbidium.

• Korean Journal of Remote Sensing
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• v.34 no.6_1
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• pp.1067-1087
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• 2018

In a study for LiDAR data based building boundary extraction, usually dense point cloud was used to cluster building rooftop area and extract building outline. However, when we used DSM generated from stereo image matching to extract building boundary, it is not trivial to cluster building roof top area automatically due to outliers and large holes of point cloud. Thus, we propose a technique to extract building boundary semi-automatically from the DSM created from stereo images. The technique consists of watershed segmentation for using user input as markers and recursive MBR algorithm. Since the proposed method only inputs simple marker information that represents building areas within the DSM, it can create building boundary efficiently by minimizing user input.

• Progress in Medical Physics
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• v.9 no.1
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• pp.47-53
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• 1998

Nuclear Medicine Images have comparatively poor spatial resolution, making it difficult to relate the functional information which they contain to precise anatomical structures. Anatomical structures useful in the interpretation of SPECT /PET Images were radiolabelled. PET/SPECT Images Provide functional information, whereas MRI mainly demonstrate morphology and anatomical. Fusion or Image Registration improves the information obtained by correlating images from various modalities. Brain Scan were studied on one or more occations using MRI and SPECT. The data were aligned using a point pair methods and surface matching. SPECT and MR Images was tested using a three dimensional water fillable Hoffman Brain Phantom with small marker and PET and MR Image was tested using a patient data. Registration of SPECT and MR Images is feasible and allows more accurate anatomic assessment of sites of abnormal uptake in radiolabeled studies. Point based registration was accurate and easily implemented three dimensional registration of multimodality data set for fusion of clinical anatomic and functional imaging modalities. Accuracy of a surface matching algorithm and homologous feature pair matching for three dimensional image registration of Single Photon Emission Computed Tomography Emission Computed Tomography (SPECT), Positron Emission Tomography (PET) and Magnetic Resonance Images(MRD was tested using a three dimensional water fill able brain phantom and Patients data. Transformation parameter for translation and scaling were determined by homologous feature point pair to match each SPECT and PET scan with MR images.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.5
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• pp.444-450
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• 2015

The accuracy of small and low cost CCD camera is insufficient to provide data for precisely tracking unmanned aerial vehicles(UAVs). This study shows how UAV can hover on a human targeted tracking object by using CCD camera rather than imprecise GPS data. To realize this, UAVs need to recognize their attitude and position in known environment as well as unknown environment. Moreover, it is necessary for their localization to occur naturally. It is desirable for an UAV to estimate of his attitude by environment recognition for UAV hovering, as one of the best important problems. In this paper, we describe a method for the attitude of an UAV using image information of a maker on the floor. This method combines the observed position from GPS sensors and the estimated attitude from the images captured by a fixed camera to estimate an UAV. Using the a priori known path of an UAV in the world coordinates and a perspective camera model, we derive the geometric constraint equations which represent the relation between image frame coordinates for a marker on the floor and the estimated UAV's attitude. Since the equations are based on the estimated position, the measurement error may exist all the time. The proposed method utilizes the error between the observed and estimated image coordinates to localize the UAV. The Kalman filter scheme is applied for this method. its performance is verified by the image processing results and the experiment.