• The Transactions of the Korea Information Processing Society
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• 제1권1호
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• pp.73-80
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• 1994

Fourier Descriptors(FD) is a common way for representing the boundary of an object. In this paper, an algorithm has been implemented to do object recognition by using FD. This is applied to various tool object, and is tested. This implementation contains two parts: image acquisition and object recognition. Appropriate lighting, viewing angle, and strong contrast of background and object are taken into account in this aspect. Minimum distances are calculated by using FD's and boundary matching among objects on the process of object recognition. Rotation, translation and scaling of the object will not influence the performance of the algorithm. Experiments show that we can use only one fourth of 1024 FD coefficients to do raped object recognition.

• Journal of Information Technology Services
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• 제20권6호
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• pp.97-105
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• 2021

Recently, with the development of IT technology, unmanned systems are being introduced in many industrial fields, and one of the most important factors for introducing unmanned systems in the automobile field is vehicle licence plate recognition(VLPR). The existing VLPR algorithms are configured to use image processing for a specific type of license plate to divide individual areas of a character within the plate to recognize each character. However, as the number of Korean vehicle license plates increases, the law is amended, there are old-fashioned license plates, new license plates, and different types of plates are used for each type of vehicle. Therefore, it is necessary to update the VLPR system every time, which incurs costs. In this paper, we use an object detection algorithm to detect character regardless of the format of the vehicle license plate, and apply a handwritten Hangul recognition(HHR) algorithm to enhance the recognition accuracy of a single Hangul character, which is called a Hangul unit. Since Hangul unit is recognized by combining initial consonant, medial vowel and final consonant, so it is possible to use other Hangul units in addition to the 40 Hangul units used for the Korean vehicle license plate.

• Journal of the Korean Geophysical Society
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• 제7권4호
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• pp.247-253
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• 2004

In this paper, 3D recognizing algorithm which is based on the external shape feature is presented. Since many objects have the regular shape, if we posses the database of pattern and we recognize the object using the database of the object's pattern, it is possible to inspect and/or recognize the objects of many fields. This paper handles on the 3D object recognition algorithm using the geometrical pattern matching by 3D database.

• Transactions of the Korean Society of Mechanical Engineers
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• 제17권11호
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• pp.2711-2722
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• 1993

Most of the current 2-D object recognition systems are model-based. In such systems, the representation of each of a known set of objects are precompiled and stored in a database of models. Later, they are used to recognize the image of an object in each instance. In this thesis, the approach method for the 2-D object recognition is treating an object boundary as a string of structral units and utilizing string matching to analyze the scenes. To reduce string matching time, models are rebuilt by means of fuzzy c-means clustering algorithm. In this experiments, the image of objects were taken at initial position of a robot from the CCD camera, and the models are consturcted by the proposed algorithm. After that the image of an unknown object is taken by the camera at a random position, and then the unknown object is identified by a comparison between the unknown object and models. Finally, the amount of translation and rotation of object from the initial position is computed.

• The Journal of Korea Robotics Society
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• 제4권3호
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• pp.185-191
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• 2009

This paper presents a new shape-based algorithm using affine category shape model for object category recognition and model learning. Affine category shape model is a graph of interconnected nodes whose geometric interactions are modeled using pairwise potentials. In its learning phase, it can efficiently handle large pose variations of objects in training images by estimating 2-D homography transformation between the model and the training images. Since the pairwise potentials are defined on only relative geometric relationship betweenfeatures, the proposed matching algorithm is translation and in-plane rotation invariant and robust to affine transformation. We apply spectral matching algorithm to find feature correspondences, which are then used as initial correspondences for RANSAC algorithm. The 2-D homography transformation and the inlier correspondences which are consistent with this estimate can be efficiently estimated through RANSAC, and new correspondences also can be detected by using the estimated 2-D homography transformation. Experimental results on object category database show that the proposed algorithm is robust to pose variation of objects and provides good recognition performance.

• ETRI Journal
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• 제45권5호
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• pp.811-821
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• 2023

We propose an adaptive unmanned aerial vehicle (UAV)-assisted object recognition algorithm for urban surveillance scenarios. For UAV-assisted surveillance, UAVs are equipped with learning-based object recognition models and can collect surveillance image data. However, owing to the limitations of UAVs regarding power and computational resources, adaptive control must be performed accordingly. Therefore, we introduce a self-adaptive control strategy to maximize the time-averaged recognition performance subject to stability through a formulation based on Lyapunov optimization. Results from performance evaluations on real-world data demonstrate that the proposed algorithm achieves the desired performance improvements.

• Journal of Broadcast Engineering
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• 제21권1호
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• pp.11-23
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• 2016

Recent days, touch interaction interface is the most widely used interaction interface to communicate with digital devices. Because of its usability, touch technology is applied almost everywhere from watch to advertising boards and it is growing much bigger. However, this technology has a critical weakness. Normally, touch input device needs a contact surface with touch sensors embedded in it. Thus, touch interaction through general objects like books or documents are still unavailable. In this paper, a human-object interaction framework based on RGB-D camera is proposed to overcome those limitation. The proposed framework can deal with occluded situations like hovering the hand on top of the object and also moving objects by hand. In such situations object recognition algorithm and hand gesture algorithm may fail to recognize. However, our framework makes it possible to handle complicated circumstances without performance loss. The framework calculates the status of the object with fast and robust object recognition algorithm to determine whether it is an object or a human hand. Then, the hand gesture recognition algorithm controls the context of each object by gestures almost simultaneously.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 한국정보통신학회 2018년도 추계학술대회
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• pp.536-537
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• 2018

A goal of this paper is doing fine grained recognition of breed of animal from pet images. Research about fine grained recognition from images is continuously developing, but it is not for animal object recognition because they have polymorphism. This paper proposes method of higher animal object recognition using Grab-cut algorithm for object segmentation and Fisher Vector for image encoding.

• Journal of Korea Multimedia Society
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• 제15권3호
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• pp.336-344
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• 2012

As an important part of the Computer Vision, the object recognition and tracking function has infinite possibilities range from motion recognition to aerospace applications. One of methods to improve accuracy of the object recognition, are uses colors which have robustness of orientation, scale and occlusion. Computational cost for extracting features can be reduced by using color. Also, for fast object recognition, predicting the location of the object recognition in a smaller area is more effective than lowering accuracy of the algorithm. In this paper, we propose a method that uses SURF descriptors which applied with color model for improving recognition accuracy and combines with Kalman filter which is Motion estimation algorithm for fast object tracking. As a result, the proposed method classified objects which have same patterns with different colors and showed fast tracking results by performing recognition in ROI which estimates future motion of an object.

• Journal of the Korean Institute of Telematics and Electronics C
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• 제35C권1호
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• pp.55-63
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• 1998

This paper presents a new maching algorithm by Hopfield Neural Network for 3-D object recognition. In the proposed method, a model object is represented by a set of polygons in a single coordinate. And each polygon is described by a set of features; feature attributes. In case of 3-D object recognition, the scale and poses of the object are important factors. So we propose a strategy for 3-D object recognition independently to its scale and poses. In this strategy, the respective features of the input or the model objects are changed to the startegical constants when they are compared with one another. Finally, we show that the proposed method has a robustness through the results of experiments which included the classification of the input objects and the matching sequence to its 3-D rotation and scale.