• Journal of Korea Multimedia Society
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• v.19 no.2
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• pp.138-145
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• 2016

LLAH(Locally Likely Arrangement Hashing) is a method which describes image features by exploiting the geometric relationship between their neighbors. Inherently, it is more robust to large view change and poor scene texture than conventional texture-based feature description methods. However, LLAH strongly requires that image features should be detected with high repeatability. The problem is that such requirement is difficult to satisfy in real applications. To alleviate the problem, this paper proposes a method that improves the matching rate of LLAH by exploiting together the brightness of features. Then, it is verified that the matching rate is increased by about 5% in experiments with synthetic images in the presence of Gaussian noise.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.12
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• pp.90-97
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• 1997

This paper presents a new algorithm for surface segmentation and feature description. The algorithm extracts the signature of an edge image based on the signature technqique[12] in the first stage. If there exists a range in the angle axis where more than two signatures form a closed curve, we can conclude there is a surface inside the range. Using this feature of the signature, surfaces can be segmented. The surface features such as number of vertices, number of edges, and type of surfaces can also be extracted by finding the signatures of individual surfaces. This algorithm has distinguished advantages: it can easily recover the lost part occuring in the edge iage using the curve fitting method and it can extract surface features even when surfaces are rotated in 3-D space.

• IEIE Transactions on Smart Processing and Computing
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• v.5 no.3
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• pp.153-163
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• 2016

Local feature extraction methods for images and videos are widely applied in the fields of image understanding and computer vision. However, robust features are detected differently when using the latest feature detectors and descriptors because of diverse image environments. This paper analyzes various feature extraction methods by summarizing algorithms, specifying properties, and comparing performance. We analyze eight feature extraction methods. The performance of feature extraction in various image environments is compared and evaluated. As a result, the feature detectors and descriptors can be used adaptively for image sequences captured under various image environments. Also, the evaluation of feature detectors and descriptors can be applied to driving assistance systems, closed circuit televisions (CCTVs), robot vision, etc.

• Journal of Broadcast Engineering
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• v.20 no.6
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• pp.912-920
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• 2015

Unlike general texture-based feature description algorithms, Locally Likely Arrangement Hashing (LLAH) algorithm describes a feature based on the geometric relationship between its neighbors. Thus, even in poor-textured scenes or large camera pose changes, it can successfully describe and track features and enables to implement augmented reality. This paper aims to optimize the performance of LLAH algorithm for tracking random dots (= features) with Gaussian noise. For this purpose, images with different number of features and magnitude of Gaussian noise are prepared. Then, the performance of LLAH algorithm according to the conditions: the number of neighbors, the type of geometric invariants, and the distance between features, is analyzed, and the optimal conditions are determined. With the optimal conditions, each feature could be matched and tracked in real-time with a matching rate of more than 80%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.11
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• pp.4137-4152
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• 2014

This paper investigates the description ability of chaotic feature vector to dynamic textures. First a chaotic feature and other features are calculated from each pixel intensity series. Then these features are combined to a chaotic feature vector. Therefore a video is modeled as a feature vector matrix. Next by the aid of bag of words framework, we explore the representation ability of the proposed chaotic feature vector. Finally we investigate recognition rate between different combinations of chaotic features. Experimental results show the merit of chaotic feature vector for pixel intensity series representation.

• Journal of the HCI Society of Korea
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• v.2 no.2
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• pp.35-43
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• 2007

This paper presents a system for image matching and recognition based on image feature detection and description techniques from artificial satellite photographs. We propose some kind of parameters from the varied environmental elements happen by image handling process. The essential point of this experiment is analyzes that affects match rate and recognition accuracy when to change of state of each parameter. The proposed system is basically inspired by Lowe's SIFT(Scale-Invariant Transform Feature) algorithm. The descriptors extracted from local affine invariant regions are saved into database, which are defined by k-means performed on the 128-dimensional descriptor vectors on an artificial satellite photographs from Google earth. And then, a label is attached to each cluster of the feature database and acts as guidance for an appeared building's information in the scene from camera. This experiment shows the various parameters and compares the affected results by changing parameters for the process of image matching and recognition. Finally, the implementation and the experimental results for several requests are shown.

• Journal of Internet Computing and Services
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• v.10 no.4
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• pp.13-23
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• 2009

In this paper we propose an approach for localizing objects in satellite images. Our method exploits matching features based on description vectors. We applied Scale Invariant Feature Transform (SIFT) to object localization. First, we find keypoints of the satellite images and the objects and generate description vectors of the keypoints. Next, we calculate the similarity between description vectors, and obtain matched keypoints. Finally, we weight the adjacent pixels to the keypoints and determine the location of the matched object. The experiments of object localization by using SIFT show good results on various scale and affine transformed images. In this paper the proposed methods use Google Earth satellite images.

• Annual Conference on Human and Language Technology
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• 1994.11a
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• pp.419-425
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• 1994

문법체계내의 문법규칙은 규칙의 기술양식에 의해 언어특성이 결정된다. 본 논문에서는 문법 체계의 규칙기술을 위한 새로운 자질 집합 기술 (feature set description)을 제안하고, 이를 기반으로 한 파라메터화된 문맥자유문법 (parametrized context-free grammar : PCFG)을 정의하여, 자연언어의 문법규칙을 구성하는 방법에 대하여 기술한다. 자질 집합 기술은 간결한 규칙체계를 유지하면서 강력한 생성능력을 갖는 문법체계를 구현할 수 있어, 자연언어 처리 시스템에 효과적으로 적용할 수 있음을 보였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.2148-2161
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• 2019

In this paper, we present a real-time cattle action recognition algorithm to detect the estrus phase of cattle from a live video stream. In order to classify cattle movement, specifically, to detect the mounting action, the most observable sign of the estrus phase, a simple yet effective feature description exploiting motion history images (MHI) is designed. By learning the proposed features using the support vector machine framework, various representative cattle actions, such as mounting, walking, tail wagging, and foot stamping, can be recognized robustly in complex scenes. Thanks to low complexity of the proposed action recognition algorithm, multiple cattle in three enclosures can be monitored simultaneously using a single fisheye camera. Through extensive experiments with real video streams, we confirmed that the proposed algorithm outperforms a conventional human action recognition algorithm by 18% in terms of recognition accuracy even with much smaller dimensional feature description.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.892-895
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• 1992

This paper presents a methodology for automatic feature extraction used in a vision system of FMC (flexible Manufacturing Cell). To implement a robot vision system, it is important to make a feature database for object recognition, location, and orientation. For industrial applications, it is necessary to extract feature information from CAD database since the detail information about an object is described in CAD data. Generally, CAD description is three dimensional information but single image data from camera is two dimensional information. Because of this dimensiional difference, many problems arise. Our primary concern in this study is to convert three dimensional data into two dimensional data and to extract some features from them and store them into the feature database. Secondary concern is to construct feature selecting system that can be used for part recognition in a given set of objects.