• IEIE Transactions on Smart Processing and Computing
• /
• 제3권2호
• /
• pp.59-64
• /
• 2014

This paper presents a method for estimating the bounding volume for image-based 3D object reconstruction. The bounding volume of an object is a three-dimensional space where the object is expected to exist, and the size of the bounding volume strongly affects the resolution of the reconstructed geometry. Therefore, the size of a bounding volume should be as small as possible while it encloses an actual object. To this end, the proposed method uses a set of silhouettes of an object and generates a point cloud using a point filter. A bounding volume is then determined as the minimum sphere that encloses the point cloud. The experimental results show that the proposed method generates a bounding volume that encloses an actual object as small as possible.

• 센서학회지
• /
• 제30권2호
• /
• pp.76-81
• /
• 2021

In this study, we propose a machine vision system with a high object recognition rate. By utilizing a multiple-exposure image sensing technique, the proposed deep learning-based machine vision system can cover a wide light intensity range without further learning processes on the various light intensity range. If the proposed machine vision system fails to recognize object features, the system operates in a multiple-exposure sensing mode and detects the target object that is blocked in the near dark or bright region. Furthermore, short- and long-exposure images from the multiple-exposure sensing mode are synthesized to obtain accurate object feature information. That results in the generation of a wide dynamic range of image information. Even with the object recognition resources for the deep learning process with a light intensity range of only 23 dB, the prototype machine vision system with the multiple-exposure imaging method demonstrated an object recognition performance with a light intensity range of up to 96 dB.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• 제11권2호
• /
• pp.118-123
• /
• 2011

Video based object tracking normally deals with non-stationary image streams that change over time. Robust and real time moving object tracking is considered to be a problematic issue in computer vision. Multiple object tracking has many practical applications in scene analysis for automated surveillance. In this paper, we introduce a specified object tracking based particle filter used in an environment of multiple moving objects. A differential image region based tracking method for the detection of multiple moving objects is used. In order to ensure accurate object detection in an unconstrained environment, a background image update method is used. In addition, there exist problems in tracking a particular object through a video sequence, which cannot rely only on image processing techniques. For this, a probabilistic framework is used. Our proposed particle filter has been proved to be robust in dealing with nonlinear and non-Gaussian problems. The particle filter provides a robust object tracking framework under ambiguity conditions and greatly improves the estimation accuracy for complicated tracking problems.

• 한국정밀공학회:학술대회논문집
• /
• 한국정밀공학회 2004년도 추계학술대회 논문집
• /
• pp.1087-1090
• /
• 2004

stereo vision system is applied to a mobile manipulator for effective tasks. The robot can recognize a target and compute the position of the target using a stereo vision system. In this paper we persent a visual approach to the problem of object grasping. First we propose object recognization method which can find the object position and pose using feature points. A robot recognizes the feature point to Object. So a number of feature point is the more, the better, but if it is overly many, the robot have to process many data, it makes real-time image processing ability weakly. In other to avoid this problem, the robot selects only two point and recognize the object by line made by two points. Second we propose trajectory planing of the robot manipulator. Using grometry of between object and gripper, robot can find a goal point to translate the robot manipulator, and then it can grip the object successfully.

• 전자공학회논문지C
• /
• 제34C권10호
• /
• pp.76-88
• /
• 1997

Based on the principles described in Part I, we have implemented a working prototype vision system using a feature structure called an LSG (local surface group) for generating object hypotheses. In order to verify an object hypothesis, we estimate the view of the hypothesized model object and render the model object for the computed view. The object hypothesis is then verified by finding additional features in the scene that match those present in the rendered image. Experimental results on synthetic and real range images show the effectiveness of the indexing scheme.

• 대한수학교육학회지:학교수학
• /
• 제18권3호
• /
• pp.441-455
• /
• 2016

수학 학습이 구체물이나 친숙한 상황을 다양하게 제시해주는 것으로부터 시작되어야 한다는 입장은 CSA(Concrete-Semiconcrete-Abstract)라는 이름으로 잘 알려져 있다. 이에 비하여 최근 Kaminski 등의 연구는, 다양한 맥락을 가진 구체물로 수학적 개념을 학습하는 것보다 추상적인 개념을 먼저 학습하는 것이 지식의 전이 측면에서 효과적일 수 있음을 주장한다. 본고에서는 이러한 상반된 관점을 고려하여, 구체물, 반구체물, 추상적 개념의 지도순서를 다르게 적용한 수업을 분석하고 그 교육적 시사점을 확인하고자 하였다. 연구 결과 구체물로 시작하여 개념을 도입한 수업은 수학에 대한 긍정적인 태도를 가지게 한 것으로 보였으나 그 효과가 지속적이지는 않았으며, 성취도 면에서도 유의미한 차이를 보이지 않았고, 오히려 구체물이 가지는 과도한 구체성으로 인해 오류를 보이는 경우가 관찰되었다. 이러한 오류는 반구체물로 개념을 도입한 수업에서는 발견되지 않았는데, 이는 비본질적 요소가 사상된 반구체물이 추상적인 개념 학습에 효율적으로 사용될 수 있음을 시사한다.

• 대한기계학회논문집
• /
• 제17권11호
• /
• pp.2711-2722
• /
• 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.

• 한국멀티미디어학회논문지
• /
• 제25권1호
• /
• pp.72-81
• /
• 2022

Visual Object Tracking is known as the most fundamental problem in the field of computer vision. Object tracking localize the region of target object with bounding box in the video. In this paper, a custom CNN is created to extract object feature that has strong and various information. This network was constructed as a Siamese network for use as a feature extractor. The input images are passed convolution block composed of a bottleneck layers, and features are emphasized. The feature map of the target object and the search area, extracted from the Siamese network, was input as a local proposal network. Estimate the object area using the feature map. The performance of the tracking algorithm was evaluated using the OTB2013 dataset. Success Plot and Precision Plot were used as evaluation matrix. As a result of the experiment, 0.611 in Success Plot and 0.831 in Precision Plot were achieved.

• 한국수학교육학회지시리즈B:순수및응용수학
• /
• 제17권2호
• /
• pp.137-143
• /
• 2010

Category Fuz of fuzzy sets has a similar function to the Category Set. But it forms a weak topos. We study a natural number object and a weak natural number object in the weak topos Fuz. Also we study the weak natural number object in $Fuz^C$.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 1990년도 한국자동제어학술회의논문집(국제학술편); KOEX, Seoul; 26-27 Oct. 1990
• /
• pp.933-938
• /
• 1990

A method of recognizing 2-dimensional polygonal object is proposed by using a concept of generalized incremental circle transform. The generalized incremental circle transform, which maps boundaries of an object into a circular disc, represents efficiently the shape of the boundaries that are obtained from digirized binary images of the objects. It is proved that the generalized incremental circle transform of an object is invariant to object translation, rotation, and size, and can be used as feature information for recognizing two dimensional polygonal object efficiently.