• Advances in robotics research
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• v.2 no.1
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• pp.33-44
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• 2018

This paper presents a novel feature detection algorithm called depth-hybrid speeded-up robust features (DH-SURF) augmented by depth information in the speeded-up robust features (SURF) algorithm. In the keypoint detection part of classical SURF, the standard deviation of the Gaussian kernel is varied for its scale-invariance property, resulting in increased computational complexity. We propose a keypoint detection method with less variation of the standard deviation by using depth data from a red-green-blue depth (RGB-D) sensor. Our approach maintains a scale-invariance property while reducing computation time. An RGB-D simultaneous localization and mapping (SLAM) system uses a feature extraction method and depth data concurrently; thus, the system is well-suited for showing the performance of the DH-SURF method. DH-SURF was implemented on a central processing unit (CPU) and a graphics processing unit (GPU), respectively, and was validated through the real-time RGB-D SLAM.

• Journal of the Semiconductor & Display Technology
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• v.12 no.3
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• pp.59-64
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• 2013

Accurate and robust image registration is important task in many applications such as image retrieval and computer vision. To perform the image registration, essential required steps are needed in the process: feature detection, extraction, matching, and reconstruction of image. In the process of these function, feature extraction not only plays a key role, but also have a big effect on its performance. There are two representative algorithms for extracting image features, which are scale invariant feature transform (SIFT) and speeded up robust feature (SURF). In this paper, we present and evaluate two methods, focusing on comparative analysis of the performance. Experiments for accurate and robust feature detection are shown on various environments such like scale changes, rotation and affine transformation. Experimental trials revealed that SURF algorithm exhibited a significant result in both extracting feature points and matching time, compared to SIFT method.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.6
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• pp.122-129
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• 2016

There have been a lot of researches on object recognition in computer vision. The SURF(Speeded Up Robust Features) algorithm based on feature detection is faster and more accurate than others. However, this algorithm has a shortcoming of making an error due to feature point mismatching when extracting feature points. In order to increase a success rate of object recognition, we have created an object recognition system based on SURF and RANSAC(Random Sample Consensus) algorithm and proposed the pattern recognition filtering. We have also presented experiment results relating to enhanced the success rate of object recognition.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.6
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• pp.58-67
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• 2009

A correspondence matching is one of the important tasks in computer vision, and it is not easy to find corresponding points in variable environment where a scale, rotation, view point and illumination are changed. A SURF(Speeded Up Robust Features) algorithm have been widely used to solve the problem of the correspondence matching because it is faster than SIFT(Scale Invariant Feature Transform) with closely maintaining the matching performance. However, because SURF considers only gray image and local geometric information, it is difficult to match corresponding points on the image where similar local patterns are scattered. In order to solve this problem, this paper proposes an extended SURF algorithm that uses the invariant color and global geometric information. The proposed algorithm can improves the matching performance since the color information and global geometric information is used to discriminate similar patterns. In this paper, the superiority of the proposed algorithm is proved by experiments that it is compared with conventional methods on the image where an illumination and a view point are changed and similar patterns exist.

• Journal of Information Processing Systems
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• v.12 no.3
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• pp.502-510
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• 2016

Considering video copy transform diversity, a multi-feature video copy detection algorithm based on a Speeded-Up Robust Features (SURF) local descriptor is proposed in this paper. Video copy coarse detection is done by an ordinal measure (OM) algorithm after the video is preprocessed. If the matching result is greater than the specified threshold, the video copy fine detection is done based on a SURF descriptor and a box filter is used to extract integral video. In order to improve video copy detection speed, the Hessian matrix trace of the SURF descriptor is used to pre-match, and dimension reduction is done to the traditional SURF feature vector for video matching. Our experimental results indicate that video copy detection precision and recall are greatly improved compared with traditional algorithms, and that our proposed multiple features algorithm has good robustness and discrimination accuracy, as it demonstrated that video detection speed was also improved.

• Proceedings of the Korean Society of Computer Information Conference
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• 2012.07a
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• pp.43-46
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• 2012

컴퓨터 비전을 이용한 다양한 응용 분야에 있어서, 특징점을 이용한 응용 분야가 많이 이루어지고 있다. 그 중에 Global feature는 표현의 위험성과 부정확성으로 인해서 많이 사용되고 있지 않으며, Local feature를 이용한 연구가 주로 이루고 있다. 그 중에 SURF(Speeded-Up Robust Features) 알고리즘은 다수의 영상에서 같은 물리적 위치에 있는 동일한 특징점을 찾아서 매칭하는 방법으로 널리 알려진 특징점 매칭 알고리즘이다. 하지만 SURF 알고리즘을 이용하여 특징점을 매칭하여 정합 쌍을 구하였을 때 매칭되는 특징점들의 정확도가 떨어지는 단점이 있다. 본 논문에서는 특징점 매칭 알고리즘인 SURF를 사용하여 대응되는 특징점들을 들로네 삼각형의 기하학적 특징을 이용하여 정확도가 높은 특징점을 분류하여 SURF 알고리즘의 매칭되는 대응점들의 정확도를 높이는 방법을 제안한다.

• ETRI Journal
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• v.37 no.3
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• pp.595-605
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• 2015

This paper proposes a robust, imperceptible block-based digital video watermarking algorithm that makes use of the Speeded Up Robust Feature (SURF) technique. The SURF technique is used to extract the most important features of a video. A discrete multiwavelet transform (DMWT) domain in conjunction with a discrete cosine transform is used for embedding a watermark into feature blocks. The watermark used is a binary image. The proposed algorithm is further improved for robustness by an error-correction code to protect the watermark against bit errors. The same watermark is embedded temporally for every set of frames of an input video to improve the decoded watermark correlation. Extensive experimental results demonstrate that the proposed DMWT domain video watermarking using SURF features is robust against common image processing attacks, motion JPEG2000 compression, frame averaging, and frame swapping attacks. The quality of a watermarked video under the proposed algorithm is high, demonstrating the imperceptibility of an embedded watermark.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.1
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• pp.118-125
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• 2016

This study evaluates the performance of image to patient registration algorithm by using stereo vision and CT image for facial region surgical navigation. For the process of image to patient registration, feature extraction and 3D coordinate calculation are conducted, and then 3D CT image to 3D coordinate registration is conducted. Of the five combinations that can be generated by using three facial feature extraction methods and three registration methods on stereo vision image, this study evaluates the one with the highest registration accuracy. In addition, image to patient registration accuracy was compared by changing the facial rotation angle. As a result of the experiment, it turned out that when the facial rotation angle is within 20 degrees, registration using Active Appearance Model and Pseudo Inverse Matching has the highest accuracy, and when the facial rotation angle is over 20 degrees, registration using Speeded Up Robust Features and Iterative Closest Point has the highest accuracy. These results indicate that, Active Appearance Model and Pseudo Inverse Matching methods should be used in order to reduce registration error when the facial rotation angle is within 20 degrees, and Speeded Up Robust Features and Iterative Closest Point methods should be used when the facial rotation angle is over 20 degrees.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.3
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• pp.209-219
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• 2019

Satellite imagery occurs geometric and radiometric errors due to external environmental factors at the acquired time, which in turn causes false-alarm in change detection. These errors should be eliminated by geometric and radiometric corrections. In this study, we propose a methodology that automatically and simultaneously performs geometric and radiometric corrections by using the SURF (Speeded-Up Robust Feature) algorithm and the mask filter. The MPs (Matching Points), which show invariant properties between multi-temporal imagery, extracted through the SURF algorithm are used for automatic geometric correction. Using the properties of the extracted MPs, PIFs (Pseudo Invariant Features) used for relative radiometric correction are selected. Subsequently, secondary PIFs are extracted by generated mask filters around the selected PIFs. After performing automatic using the extracted MPs, we could confirm that geometric and radiometric errors are eliminated as the result of performing the relative radiometric correction using PIFs in geo-rectified images.

• Journal of Korea Multimedia Society
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• v.14 no.2
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• pp.201-209
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• 2011

This paper suggests a panorama image stitching system which consists of an image matching algorithm: modified SURF (Speeded Up Robust Feature) and an image blending algorithm: multi-band blending. In this paper, first, Modified SURF is described and SURF is compared with SIFT (Scale Invariant Feature Transform), which also gives the reason why modified SURF is chosen instead of SIFT. Then, multi-band blending is described, Lastly, the structure of a panorama image stitching system is suggested and evaluated by experiments, which includes stitching quality test and time cost experiment. According to the experiments, the proposed system can make the stitching seam invisible and get a perfect panorama for large image data, In addition, it is faster than the sift based stitching system.