• Journal of the Korea Society of Computer and Information
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• v.23 no.1
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• pp.41-48
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• 2018

Recently, surveillance systems are widely used, and one of the key technologies in this surveillance system is to recognize and track objects. In order to track a moving object robustly and efficiently in a complex environment, it is necessary to extract the feature points in the interesting object and to track the object using the feature points. In this paper, we propose a method to track interesting objects in real time by eliminating unnecessary information from objects, generating feature point descriptors using only key feature points, and reducing computational complexity for object recognition. Experimental results show that the proposed method is faster and more robust than conventional methods, and can accurately track objects in various environments.

• 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 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.

• The Journal of the Convergence on Culture Technology
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• v.4 no.1
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• pp.291-297
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• 2018

In this paper, we present an automatic image object tracking system for Studio cameras on the stage. For object tracking, we use the OpenCV-based Python program using PC, Raspberry Pi 3 and mobile devices. There are many methods of image object tracking such as mean-shift, CAMshift (Continuously Adaptive Mean shift), background modelling using GMM(Gaussian mixture model), template based detection using SURF(Speeded up robust features), CMT(Consensus-based Matching and Tracking) and TLD methods. CAMshift algorithm is very efficient for real-time tracking because of its fast and robust performance. However, in this paper, we implement an image object tracking system for studio cameras based CMT algorithm. This is an optimal image tracking method because of combination of static and adaptive correspondences. The proposed system can be applied to an effective and robust image tracking system for continuous object tracking on the stage in real time.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.6
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• pp.315-320
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• 2017

Estimating the correct pose of augmented objects on the real camera view efficiently is one of the most important questions in image tracking area. In computer vision, Homography is used for camera pose estimation in augmented reality system with markerless. To estimating Homography, several algorithm like SURF features which extracted from images are used. Based on extracted features, Homography is estimated. For this purpose, RANSAC algorithm is well used to estimate homography and DCS-RANSAC algorithm is researched which apply constraints dynamically based on Constraint Satisfaction Problem to improve performance. In DCS-RANSAC, however, the dataset is based on pattern of feature distribution of images manually, so this algorithm cannot classify the input image, pattern of feature distribution is not recognized in DCS-RANSAC algorithm, which lead to reduce it's performance. To improve this problem, we suggest the KCS-RANSAC algorithm using K-means clustering in CS-RANSAC to cluster the images automatically based on pattern of feature distribution and apply constraints to each image groups. The suggested algorithm cluster the images automatically and apply the constraints to each clustered image groups. The experiment result shows that our KCS-RANSAC algorithm outperformed the DCS-RANSAC algorithm in terms of speed, accuracy, and inlier rate.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.05a
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• pp.249-251
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• 2012

Augmented Reality (AR) enables the enhanced realism and interaction by providing the overlaid digital information on the user's view of the physical world. In this paper, we propose an AR-based ship design visualization system for presenting ship 3D model in smart phones or table PCs. The proposed system compute corner points and feature points by contour finding method and harris corner detector, and build a ship-design drawing database. By using SURF algorithm, key feature points are extracted from ship-design drawing image which is obtained by mobile camera. Then ship-design drawing image is recognized by matching the feature points stored in DB and extracted key feature points. 3D ship structures are visualized by overlaying the ship-design drawing image on the smart phone or table PC's screen. Compared to conventional 2D ship-design, proposed system helps to easily understand the structures of the ship and reduce the business design period. Thus, Enhanced competitiveness of business is expected.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.2
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• pp.893-898
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• 2013

It is always a dream to recreate the experience of a particular place, the Panorama Virtual Reality has been interpreted as a kind of technology to create virtual environments and the ability to maneuver angle for and select the path of view in a dynamic scene. In this paper we examined an efficient method for Image registration and stitching of captured imaged. Two approaches are studied in this paper. First, dynamic programming is used to spot the ideal key points, match these points to merge adjacent images together, later image blending is used for smooth color transitions. In second approach, FAST and SURF detection are used to find distinct features in the images and nearest neighbor algorithm is used to match corresponding features, estimate homography with matched key points using RANSAC. The paper also covers the automatically choosing (recognizing, comparing) images to stitching method.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.1
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• pp.65-70
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• 2013

Recently, the field of computer vision has been actively researched through digital image which can be easily generated as the development and expansion of digital camera technology. Especially, research that extracts and utilizes the feature in image has been actively carried out. The image stitching is a method that creates the high resolution image using features extract and match. Image stitching can be widely used in military and medical purposes as well as in variety fields of real life. In this paper, we have proposed efficient image stitching method using fast feature descriptor extraction and matching based on SURF algorithm. It can be accurately, and quickly found matching point by reduction of dimension of feature descriptor. The feature descriptor is generated by classifying of unnecessary minutiae in extracted features. To reduce the computational time and efficient match feature, we have reduced dimension of the descriptor and expanded orientation window. In our results, the processing time of feature matching and image stitching are faster than previous algorithms, and also that method can make natural-looking stitched image.

• The Journal of Korea Robotics Society
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• v.10 no.1
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• pp.16-23
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• 2015

This paper presents a method of improving the pose recognition accuracy of objects by using Kinect sensor. First, by using the SURF algorithm, which is one of the most widely used local features point algorithms, we modify inner parameters of the algorithm for efficient object recognition. The proposed method is adjusting the distance between the box filter, modifying Hessian matrix, and eliminating improper key points. In the second, the object orientation is estimated based on the homography. Finally the novel approach of Auto-scaling method is proposed to improve accuracy of object pose estimation. The proposed algorithm is experimentally tested with objects in the plane and its effectiveness is validated.

• Biomaterials and Biomechanics in Bioengineering
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• v.2 no.4
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• pp.249-269
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• 2015

A problem faced by oil companies is the maintenance of the location register of pipelines that cross the surf zone, the regular survey of their location, and also their inspection. A survey of the state of art did not allow identifying operating systems capable of executing such tasks. Commercial technologies available on the market also do not address this problem and/or do not satisfy the presented requirements. A possible solution is to use robotic systems which have the ability to walk on the shore and in the surf zone, subject to existing currents and ripples, and being able to withstand these ambient conditions. In this sense, the authors propose the development of a spider crab biologically inspired robot to achieve those tasks. Based on these ideas, this work presents a biomechanical study of the spider crab, its modeling and simulation using the SimMechanics toolbox of Matlab/Simulink, which is the first phase of this more vast project. Results show a robot model that is moving in an "animal like" manner, the locomotion, the algorithm presented in this paper allows the crab to walk sideways, in the desired direction.