• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.11
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• pp.64-73
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• 2012

In this paper, we propose a method to search for identical videos. The proposed method is spatio-temporal group feature fingerprinting. Frame of video is extracted from fixed rate method and is partitioned into vertical group and horizontal group. Descriptor is made of each group feature that is extracted from binary fingerprinting. Next, use descriptor of original video to build a two type of fingerprinting database and matching with query video. To efficient and effective video copy detection, method have high robustness, independence, matching speed. In proposed method, group feature have high robustness and independence in variable modification of video. Building a original fingerprinting database is able to fast matching with query video. The proposed method shows performance improvement in variable modifications in comparison to the existing methods. Especially, very singular performance in speed improvement is great advantage of this paper.

• Journal of Korea Multimedia Society
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• v.16 no.4
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• pp.483-492
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• 2013

SURF is an algorithm which extracts feature points and generates their descriptors from input images, and it is being used for many applications such as object recognition, tracking, and constructing panorama pictures. Although SURF is known to be robust to changes of scale, rotation, and view points, it is hard to implement it in real time due to its complex and repetitive computations. Using 3.3 GHz Pentium, in our experiment, it takes 240ms to extract feature points and create descriptors in a VGA image containing about 1,000 feature points, which means that software implementation cannot meet the real time requirement, especially in embedded systems. In this paper, we present a hardware architecture that can compute the SURF algorithm very fast while consuming minimum hardware resources. Two key concepts of our architecture are parallelism (for repetitive computations) and efficient line memory usage (obtained by analyzing memory access patterns). As a result of FPGA synthesis using Xilinx Virtex5LX330, it occupies 101,348 LUTs and 1,367 KB on-chip memory, giving performance of 30 frames per second at 100 MHz clock.

• Journal of Advanced Navigation Technology
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• v.16 no.2
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• pp.394-400
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• 2012

SURF is a robust local invariant feature descriptor and has been used in many applications such as object recognition. Even though this algorithm has similar matching accuracy compared to the SIFT, which is another popular feature extraction algorithm, it has advantage in matching time. However, these descriptors do not consider relative location information of extracted interesting points to guarantee rotation invariance. Also, since they use gray image of original color image, they do not use the color information of images, either. In this paper, we propose a method for improving matching performance of SURF descriptor using the color and relative location information of interest points. The location information is built from the angles between the line connecting the centers of interest points and the orientation line constructed for the center of each interest points. For the color information, color histogram is constructed for the region of each interest point. We show the performance of our scheme through experiments.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.759-760
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• 2008

In this paper, We propose the feature-based 3D model Retrieval System. 3D models are represented as triangle meshes. A first simple feature vector can be calculated from hull. After looking for meshes intersected with the hull, we compute the curvature of meshes. These curvature are used as the model descriptor.

• Journal of Broadcast Engineering
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• v.5 no.1
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• pp.10-22
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• 2000

In this paper, we present a texture description method as a standardization of multimedia contents description. Like color, shape, object and camera motion information, texture is one of very important information in the visual part of international standard (MPEG-7) in multimedia contents description. Current MPEG-7 texture descriptor has been designed to fit human visual system. Many psychophysical experiments give evidence that the brain decomposes the spectra into perceptual channels that are bands in spatial frequency. The MPEG-7 texture description method has employed Radon transform that fits with HVS behavior. By taking average energy and energy deviation of HVS channels, the texture descriptor is generated. To test the performance of current texture descriptor, experiments with MPEG-7 Texture data sets of T1 to T7 are performed. Results show that the current MPEG-7 texture descriptor gives better retrieval rate and fast and fast extraction time for texture feature.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.246-246
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• 2000

다른 생체기반 검증시스템에 비해 서명 검증 시스템에서 가장 문제점은 불안정한 특징 정보를 가진다는 것이다. 그러나, 서명은 인류역사를 통해 인간에게 가장 익숙한 방법이므로 사용자에게 거부감이 없어 수많은 연구가 진행되고 있다. 본 논문에서는 이 문제를 해결하기 위해 좀더 안정화 되어 있고 유용한 특징정보를 사용하여 서명 검증 시스뎀을 구현한다

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.3
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• pp.229-234
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• 2018

Texture is one of the most useful features in classifying and segmenting images. The LBP-based approach previously presented in the literature has been successful in many applications. However, it's theoretical foundation is based only on the difference of pixel values, and consequently it has a number of drawbacks like it performs poorly for the images corrupted with noise, and especially it cannot be used as a multiscale texture descriptor due to the exploding increase of feature vector dimension with increase of the number of neighbor pixels. In this paper, we present a method to address these drawbacks of LBP-based approach. More specifically, our approach quantizes the range of pixels values and construct a 3D histogram which captures the correlative information of pixels. This histogram is used as a texture feature. Several tests with texture images show that the proposed method outperforms the LBP-based approach in the problem of texture classification.

• Journal of Broadcast Engineering
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• v.8 no.1
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• pp.91-100
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• 2003

In this paper, to improve the retrieval Performance, an efficient combination of the MPEG-7 visual descriptors, such as the edge histogram descriptor (EHD), the color layout descriptor (CLD), and the homogeneous texture descriptor (HTD), is proposed in the framework of the relevance feedback approach. The EHD represents spatial distribution of edges in local image regions and it is considered as an important feature to represent the content of the image. The CLD specifies spatial distribution of colors and is widely used in image retrieval due to its simplicity and fast operation speed. The HTD describes precise statistical distribution of the image texture. Both the feature vector for the query image and the weighting factors among the combined descriptors are adaptively determined during the relevance feedback. Experimental results show that the proposed method improves the retrieval performance significantly tot natural images.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.3
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• pp.251-256
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• 2019

In this paper we proposed system and algorithm to recognize hand gestures based on the millimeter wave that is in 65GHz bandwidth. The proposed system is composed of millimeter wave radar board, analog to data conversion and data capture board and notebook to perform gesture recognition algorithms. As feature vectors in proposed algorithm. we used global and local zernike moment descriptor which are robust to distort by rotation of scaling of 2D data. As Experimental result, performance of the proposed algorithm is evaluated and compared with those of algorithms using single global or local zernike descriptor as feature vectors. In analysis of confusion matrix of algorithms, the proposed algorithm shows the better performance in comparison of precision, accuracy and sensitivity, subsequently total performance index of our method is 95.6% comparing with another two mehods in 88.4% and 84%.

• Journal of Korea Multimedia Society
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• v.17 no.3
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• pp.300-311
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• 2014

According to fast change of the environment, the structured study of the ecosystem by analyzing the plant leaves are needed. Expecially, the methodology that searches and classifies the leaves from captured from the smart device have received numerous concerns in the field of computer science and ecology. In this paper, we propose a plant leaf classification technique using shape descriptor by combining Scale Invarinat Feature Transform (SIFT) and Histogram of Oriented Gradient (HOG) from the image segmented from the background via Graphcut algorithm. The shape descriptor is coded in the field of Locality-constrained Linear Coding to optimize the meaningful features from a high degree of freedom. It is connected to Support Vector Machines (SVM) for efficient classification. The experimental results show that our proposed approach is very efficient to classify the leaves which have similar color, and shape.