• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.12
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• pp.2363-2371
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• 2015

In this paper, we propose a new feature matching algorithm which is robust to the viewpoint change by using the FAST(Features from Accelerated Segment Test) feature detector and the SIFT(Scale Invariant Feature Transform) feature descriptor. The original FAST algorithm unnecessarily results in many feature points along the edges in the image. To solve this problem, we apply the principal curvatures for refining it. We use the SIFT descriptor to describe the extracted feature points and calculate the homography matrix through the RANSAC(RANdom SAmple Consensus) with the matching pairs obtained from the two different viewpoint images. To make feature matching robust to the viewpoint change, we classify the matching pairs by calculating the Euclidean distance between the transformed coordinates by the homography transformation with feature points in the reference image and the coordinates of the feature points in the different viewpoint image. Through the experimental results, it is shown that the proposed algorithm has better performance than the conventional feature matching algorithms even though it has much less computational load.

• The KIPS Transactions:PartB
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• v.17B no.5
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• pp.355-362
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• 2010

Face tracking is to estimate the motion of a non-rigid face together with a rigid head in 3D, and plays important roles in higher levels such as face/facial expression/emotion recognition. In this paper, we propose an AAM-based face tracking algorithm. AAM has been widely used to segment and track deformable objects, but there are still many difficulties. Particularly, it often tends to diverge or converge into local minima when a target object is self-occluded, partially or completely occluded. To address this problem, we utilize the scale invariant feature transform (SIFT). SIFT is an effective method for self and partial occlusion because it is able to find correspondence between feature points under partial loss. And it enables an AAM to continue to track without re-initialization in complete occlusions thanks to the good performance of global matching. We also register and use the SIFT features extracted from multi-view face images during tracking to effectively track a face across large pose changes. Our proposed algorithm is validated by comparing other algorithms under the above 3 kinds of occlusions.

• Journal of Korea Society of Industrial Information Systems
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• v.19 no.1
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• pp.77-85
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• 2014

Medicine-bottle classification algorithm to avoid medicine accidents must be robust to a geometric change such as rotation, size variation, location movement of the medicine bottles. In this paper, we propose an algorithm to classify the medicine bottles exactly in real-time by using SIFT(Scale Invariant Feature Transform) which is robust to the geometric change. In first, we classify medicine bottles by size using minimum boundary rectangle(MBR) of medicine bottles as a striking feature in order to classify the medicine bottles. We extract label region in the MBR and the region of interest(ROI) considering rotation. Then, we classify medicine bottles using SIFT for the extracted ROI. We also simplify the number of octave of SIFT in order to improve a process speed of SIFT. We confirm to classify all the medicine bottles exactly as a result of performance evaluation of the proposed algorithm about images of 250 medicine bottles. We also confirm to improve the process time more than twice the processing time by simplifying the number of octave of SIFT.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.46 no.3
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• pp.86-95
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• 2009

SIFT is an algorithm to extract vectors at pixels around keypoints, in which the pixel colors are very different from neighbors, such as vertices and edges of an object. The SIFT algorithm is being actively researched for various image processing applications including 3D image reconstructions and intelligent vision system for robots. In this paper, we implement a hardware to sift feature detection algorithm for real time processing in embedded systems. We estimate that the hardware implementation give a performance 25ms of $1,280{\times}960$ image and 5ms of $640{\times}480$ image at 100MHz. And the implemented hardware consumes 45,792 LUTs(85%) with Synplify 8.li synthesis tool.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.25 no.10
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• pp.1062-1068
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• 2014

In this paper, a novel approach estimating target's rotation velocity(RV) is proposed for inverse synthetic aperture radar(ISAR) cross-range scaling(CRS). Scale invariant feature transform(SIFT) is applied to two sequently generated ISAR images for extracting non-fluctuating scatterers. Considering the fact that the distance between target's rotation center(RC) and SIFT features is same, we can set a criterion for estimating RV. Then, the criterion is optimized through the proposed method based on particle swarm optimization(PSO) combined with exhaustive search method. Simulation results show that the proposed algorithm can precisely estimate RV of a scenario based maneuvering target without RC information. With the use of the estimated RV, ISAR image can be correctly re-scaled along the cross-range direction.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.1
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• pp.116-122
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• 2010

This paper proposes a method for classifying targets robust to geometric transformations of targets such as rotation, scale change, translation, and pose change. Targets which have rotation, scale change, and shift is firstly classified based on CM(Confidence Map) which is generated by similarity, scale ratio, and range of orientation for SIFT(Scale-Invariant Feature Transform) feature vectors. On the other hand, DB(DataBase) which is acquired in various angles is used to deal with pose variation of targets. Range of the angle is determined by comparing and analyzing the execution time and performance for sampling intervals. We experiment on various images which is geometrically changed to evaluate performance of proposed target classification method. Experimental results show that the proposed algorithm has a good classification performance.

• Journal of the Korea Society of Computer and Information
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• v.18 no.11
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• pp.69-75
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• 2013

This paper introduces a touch recognition method for touch screen systems based on the SIFT(Scale Invariant Feature Transform) algorithm for stable touch recognition under strong noises. This method provides strong robustness against the noises and makes it possible to effectively extract the various size of touches due to the SIFT algorithm. In order to verify our algorithm we simulate it on the Matlab with the channel data obtained from a real touch screen. It was found from the simulations that our method could stably recognize the touches without regard to the size and direction of the touches. But, our algorithm implemented on a real touch screen system does not support the realtime feature because the DoG(Difference of Gaussian) of the SIFT algorithm needs too many computations. We solved the problem using the DoM(Difference of Mean) which is a fast approximation method of DoG.

• Journal of Korea Multimedia Society
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• v.14 no.10
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• pp.1243-1251
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• 2011

This paper proposes a new vehicle model recognition using scale invariant feature transform to car's headlights image. Proposed vehicle model recognition raises the accuracy using "homogeneity" calculated from the distribution of features. In the experiment with 400 test images taken from 54 different vehicles, proposed method has 90% recognition rate and 16.45 homogeneity.

• International Journal of Contents
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• v.9 no.2
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• pp.1-7
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• 2013

This paper proposes an efficient matching algorithm based on ASIFT (Affine Scale-Invariant Feature Transform) which is fully invariant to affine transformation. In our approach, we proposed a method of reducing similar measure matching cost and the number of outliers. First, we combined the Manhattan and Chessboard metrics replacing the Euclidean metric by a linear combination for measuring the similarity of keypoints. These two metrics are simple but really efficient. Using our method the computation time for matching step was saved and also the number of correct matches was increased. By applying an Optimized Random Sampling Algorithm (ORSA), we can remove most of the outlier matches to make the result meaningful. This method was experimented on various combinations of affine transform. The experimental result shows that our method is superior to SIFT and ASIFT.

• Journal of the Korea Society of Computer and Information
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• v.18 no.9
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• pp.1-10
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• 2013

In this paper, we implement the SIFT(Scale-Invariant Feature Transform) algorithm for feature point extraction using a many-core processor, and analyze the performance, area efficiency, and system area efficiency of the many-core processor. In addition, we demonstrate the potential of the proposed many-core processor by comparing the performance of the many-core processor with that of high-performance CPU and GPU(Graphics Processing Unit). Experimental results indicate that the accuracy result of the SIFT algorithm using the many-core processor was same as that of OpenCV. In addition, the many-core processor outperforms CPU and GPU in terms of execution time. Moreover, this paper proposed an optimal model of the SIFT algorithm on the many-core processor by analyzing energy efficiency and area efficiency for different octave sizes.