• Proceedings of the KIEE Conference
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• 2006.04a
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• pp.306-308
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• 2006

We propose an attention based object recognition system, to recognize object fast and robustly. For this we calculate visual stimulus degrees and make saliency maps. Through this map we find a strongly attentive part of image by stimulus degrees, where local features are extracted to recognize objects.

• Journal of Korean Society for Atmospheric Environment
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• v.34 no.4
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• pp.625-625
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• 2018

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.41 no.1
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• pp.17-24
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• 2013

In this paper, a micro-mechanical failure prediction program is developed based on SIFT (Strain Invariant Failure Theory) by using the multi-scale modeling method for fiber-reinforced composite materials. And the failure analysis are performed for open-hole composite laminate specimen in order to verify the developed program. First of all, the critical strain invariants are obtained through the tensile tests for three types of specimens. Also, the matrices of strain amplification factors are determined through the finite element analysis for micro-mechanical model, RVE (Representative Volume Element). Finally, the microscopic failure analysis is performed for the open-hole composite laminate specimen model by applying a failure load obtained from tensile test, and the predicted failure indices are evaluated for verification of the developed program.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.9
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• pp.3782-3796
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• 2020

A three-dimensional (3D) reconstruction is an important research area in computer vision. The ability to detect and match features across multiple views of a scene is a critical initial step. The tracking matrix W obtained from a 3D reconstruction can be applied to structure from motion (SFM) algorithms for 3D modeling. We often fail to generate an acceptable number of features when processing face or medical images because such images typically contain large homogeneous regions with minimal variation in intensity. In this study, we seek to locate sufficient matching points not only in general images but also in face and medical images, where it is difficult to determine the feature points. The algorithm is implemented on an adaptive threshold value, a scale invariant feature transform (SIFT), affine SIFT, speeded up robust features (SURF), and affine SURF. By applying the algorithm to face and general images and studying the geometric errors, we can achieve quasi-dense matching points that satisfy well-functioning geometric constraints. We also demonstrate a 3D reconstruction with a respectable performance by applying a column space fitting algorithm, which is an SFM algorithm.

• Journal of Digital Contents Society
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• v.17 no.6
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• pp.603-611
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• 2016

Generally, the doctors manually delineated the prostate boundary seeing the image by their eyes, but the manual method not only needed quite much time but also had different boundaries depending on doctors. To reduce the effort like them the automatic delineating methods are needed, but detecting the boundary is hard to do since there are lots of uncertain textures or speckle noises. There have been studied in SVM, SIFT, Gabor texture filter, snake-like contour, and average-shape model methods. Besides, there were lots of studies about 2 and 3 dimension images and CT and MRI. But no studies have been developed superior to human experts and they need additional studies. For this, this paper proposes a method that delineates the boundary predicting its texture features and its average distribution on the prostate image. As result, we got the similar boundary as the method of human experts.

• Korean Journal of Poultry Science
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• v.45 no.3
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• pp.209-217
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• 2018

The innate immune recognition is based on the detection of microbial products. Toll-like receptors (TLRs) located on the cell surface and the endosome senses microbial components and nucleic acids, respectively. Chicken TLRs mediate immune responses by sensing ligands from pathogens, have been studied as immune adjuvants to increase the efficacy of vaccines. Single nucleotide polymorphisms (SNPs) of TLR3 and TLR4 genes in chicken were associated with resistance and susceptibility to viral infection. In this study, SNPs of chTLR3 and chTLR4 genes were retrieved from public database and annotated with chicken reference genome. Three-dimensional models of the chTLR3 and chTLR4 proteins were built using a Swiss modeler. We identified 35 and 13 nsSNPs in chTLR3 and chTLR4 genes respectively. Sorting Intolerant from Tolerant (SIFT) and Polymorphism Phenotyping v2 (Polyphen-2) analyses, suggested that, out of 35 and 13 nsSNPs, 4 and 2 SNPs were identified to be deleterious in chTLR3 and chTLR4 gene respectively. In chTLR3, 1 deleterious SNP was located in ectodomain and 3 were located in the Toll / IL-1 receptor (TIR) domain. Further structural model of chTLR3-TIR domain suggested that 1 deleterious SNP be present in the B-B loop region, which is important for TIR-TIR domain interactions in the downstream signaling. In chTLR4, the deleterious SNPs were located both in the ectodomain and TIR domain. SNPs predicted for chTLR3 and chTLR4 in this study, might be related to resistance or susceptible to viral infection in chickens. Results from this study will be useful to develop the effective measures in chicken against infectious diseases.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.11
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• pp.2271-2283
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• 2015

In this paper, we propose a method which infers an accurate disparity map for radiometrically varying stereo images. For this end, firstly, we transform the input color images to the log-chromaticity color space from which a linear relationship can be established during constructing a joint pdf between input stereo images. Based on this linear property, we present a new stereo matching cost by combining weighted mutual information and the SIFT (Scale Invariant Feature Transform) descriptor with segment-based plane-fitting constraints to robustly find correspondences for stereo image pairs which undergo radiometric variations. Experimental results show that our method outperforms previous methods and produces accurate disparity maps even for stereo images with severe radiometric differences.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.4
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• pp.173-180
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• 2006

A key component of autonomous navigation of intelligent home robot is localization and map building with recognized features from the environment. To validate this, accurate measurement of relative location between robot and features is essential. In this paper, we proposed relative localization algorithm based on 3D reconstruction of scale invariant features of two images which are captured from two parallel cameras. We captured two images from parallel cameras which are attached in front of robot and detect scale invariant features in each image using SIFT(scale invariant feature transform). Then, we performed matching for the two image's feature points and got the relative location using 3D reconstruction for the matched points. Stereo camera needs high precision of two camera's extrinsic and matching pixels in two camera image. Because we used two cameras which are different from stereo camera and scale invariant feature point and it's easy to setup the extrinsic parameter. Furthermore, 3D reconstruction does not need any other sensor. And the results can be simultaneously used by obstacle avoidance, map building and localization. We set 20cm the distance between two camera and capture the 3frames per second. The experimental results show :t6cm maximum error in the range of less than 2m and ${\pm}15cm$ maximum error in the range of between 2m and 4m.

• The KIPS Transactions:PartB
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• v.17B no.5
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• pp.333-344
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• 2010

This paper addresses the way to compose paronamic images from images taken the same objects. With the spread of digital camera, the panoramic image has been studied to generate with its interest. In this paper, we propose a panoramic image generation method using scaling and rotation invariant features. First, feature points are extracted from input images and matched with a RANSAC algorithm. Then, after the perspective model is estimated, the input image is registered with this model. Since the SURF feature extraction algorithm is adapted, the proposed method is robust against geometric distortions such as scaling and rotation. Also, the improvement of computational cost is achieved. In the experiment, the SURF feature in the proposed method is compared with features from Harris corner detector or the SIFT algorithm. The proposed method is tested by generating panoramic images using $640{\times}480$ images. Results show that it takes 0.4 second in average for computation and is more efficient than other schemes.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.3
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• pp.630-638
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• 2016

In this paper, a feature based panorama image generation algorithm using FAST(Features from Accelerated Segment Test) method that is faster than SIFT(Scale Invariant Feature Transform) and SURF(Speeded Up Robust Features) is proposed. Cylindrical projection is performed to generate natural panorama images with numerous images as input. The occurred error can be minimized by applying RANSAC(Random Sample Consensus) for the matching process. When we synthesize numerous images acquired from different camera angles, we use blending techniques to compensate the distortions by the heterogeneity of border line. In that way, we could get more natural synthesized panorama image. The proposed algorithm can generate natural panorama images regardless the order of input images and tilted images. In addition, the image matching can be faster than the conventional method. As a result of the experiments, distortion was corrected and natural panorama image was generated.