• 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 KIISE
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• v.42 no.1
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• pp.33-43
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• 2015

Recently, patent images are growing importance and thus patent image retrieval is a growing area of research. However, most existing patent image retrieval systems use edges extracted in the images, whose performance is affected by the quality of edge detection in the image pre-processing step. To overcome this disadvantage, we propose a SURF-based patent image retrieval method which uses the morphological characteristics of the images. The proposed method detects SURF interest points with directions and computes regional histograms. We apply the proposed method to a patent image database with 2000 binary images and we show the proposed retrieval system achieves excellent results, even when the images have some loss or degradation.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.4
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• pp.586-591
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• 2010

In this paper, we propose the localization method of mobile robot using SURF(Speeded-Up Robust Features) and Particle filter. The proposed method is as follows: First, we seek the Landmark from the obtained image using SURF in order to find the first rigorous position of mobile robot. Second, we obtain the distance from obstacles using ultrasonic sensors in order to create the relative position of mobile robot. And then, we estimate the localization of mobile robot using Particle filter about movement of mobile robot. Finally, we show the feasibility of the proposed method through some experiments.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.5
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• pp.83-93
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• 2013

Recently, the SURF algorithm, which is conjugated for object tracking system as part of many computer vision applications, is a well-known scale- and rotation-invariant feature detection algorithm. The SURF, due to its high computational complexity, there is essential to develop a hardware accelerator in order to be used on an IP in embedded environment. However, the SURF requires a huge local memory, causing many problems that increase the chip size and decrease the value of IP in ASIC and SoC system design. In this paper, we proposed a way to design a SURF algorithm in hardware with greatly reduced local memory by partitioning the algorithms into several Sub-IPs using external memory and a DMA. To justify validity of the proposed method, we developed an example of simplified object tracking algorithm. The execution speed of the hardware IP was about 31 frame/sec, the logic size was about 74Kgate in the 30nm technology with 81Kbytes local memory in the embedded system platform consisting of ARM Cortex-M0 processor, AMBA bus(AHB-lite and APB), DMA and a SDRAM controller. Hence, it can be used to the hardware IP of SoC Chip. If the image processing algorithm akin to SURF is applied to the method proposed in this paper, it is expected that it can implement an efficient hardware design for target application.

• Korean Journal of Food Science and Technology
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• v.3 no.1
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• pp.48-51
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• 1971

The raw surf clam meat was pretreated with BHA, EDTA or $NaHSO_3$. The pretreated meat was chopped with chopper, and spread the chopped meat on nylon net, and then dehydrated with cabinet type hot air dryer. In the surf clam meat flake process, the pretreating and the copping steps prior to final dehydration improved the pigment retention and texture of the products. The chopping steps prior to dehydration reduced the dehydration time of surf clam meat. The BHA treatment prior to dehydration of mashed surf clam meat had an outstanding effect on the pigment retention during a process of dehydration and storage. The surf dam meat flakes reabsorbed water more rapidly about two times than the dehydrated natural surf clam meat products. The surf clam meat flakes stored for three months in the dark place showed less pigment loss than the exposed ones, and the former reabsorbed water more rapidly than the latter.

• Journal of Ocean Engineering and Technology
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• v.19 no.4 s.65
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• pp.49-55
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• 2005

We constructed and demonstrated a numerical CADMAS-SURF(V4.0) model that reproduces the wave run-up characteristics on the slope of coastal structures and applied it to a permeable coastal structure. We also compared the numerical model with published experimental results on the hydrodynamic phenomena of structures and some numerical results for a modified Pbreak model. In conclusion, the CADMAS-SURF model efficiently simulated wave run-up on the slope of a permeable coastal structure. The inflow/outflow effects from the porous structure boundary were approximately $15\%$ more than with the modified Pbreak model. Nevertheless, the descriptions of the internal hydraulic characteristics still could not be full!! exacted from the result(Fig. 1 참조)s obtained in our model experiment.

• IEMEK Journal of Embedded Systems and Applications
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• v.10 no.1
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• pp.49-55
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• 2015

In this paper, we propose a fast interest point detection method using SURF algorithm. Since the SURF algorithm needs a great computations to detect the interest points and obtain the corresponding descriptors, it is not suitable for real-time based applications. In order to overcome this problem, the interest point detection step is parallelized by OpenMP and SIMD based on analysis of the scale space representation process and localization one in the step. The simulation results demonstrate that processing speed is enhanced about 55% by applying the proposed method.

• 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 Broadcast Engineers Conference
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• 2011.11a
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• pp.161-164
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• 2011

영상 매칭은 컴퓨터 비전에서 기초적인 기술로써 영상 추적, 물체인식 등 다양한 분양에서 많이 사용되고 있다. 하지만 스케일, 시점변화, 조명 변화에 강인한 매칭점을 찾는 것은 어려운 일이다. 이러한 문제점을 보완하기 위해 SURF(Scale Invariant Feature Transform), SIFT(Speed up Robust Features) 등의 알고리즘이 제안 되었지만, 여전히 조명변화에 불안정하고 정확하지 못한 성능을 보인다. 본 논문에서는 이러한 조명변화에 대한 문제점을 해결하기 위해 히스토그램 평활화를 이용하여 영상을 보정 후, SURF를 통한 영상 매칭을 하였다. 열악한 조명환경 내에서 촬영된 영상에서 SURF를 이용하여 표현자(Descriptor)를 생성 할 때 특징점이 잘 추출되지 않는 문제점을 해결하기 위하여 히스토그램 평활화를 이용하였고, 보정 후 특징점 개수가 많이 증가하는 것을 보여 확인하였다. 기존의 SURF와 개량된 SURF를 조명이 서로 다른 영상간의 매칭 성능을 비교함으로써 제안한 알고리즘의 우수성을 확인하였다

• Journal of IKEEE
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• v.17 no.2
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• pp.202-207
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• 2013

This paper implemented the conventional FAST and BRIEF algorithm as hardware on Zynq-7000 SoC Platform. Previous feature-based hardware accelerator is mostly implemented using the SIFT or SURF algorithm, but it requires excessive internal memory and hardware cost. The proposed FAST & BRIEF accelerator reduces approximately 57% of internal memory usage and 70% of hardware cost compared to the conventional SIFT or SURF accelerator, and it processes 0.17 pixel per Clock.