• Proceedings of the Korean Information Science Society Conference
• /
• 2005.07b
• /
• pp.634-636
• /
• 2005

지능형 로봇 기술이 발전하면서 영상에서 장면을 이해하는 연구가 많은 관심을 받고 있으며, 최근에는 불확실한 환경에서도 좋은 성능을 발휘할 수 있는 확률적 접근 방법이 많이 연구되고 있다. 본 논문에서는 확률적 모델링이 가능한 베이지안 네트워크(BN)를 이용해서 장면 인식 추론 모듈을 설계하고, 실제 환경에서 얻어진 증거 및 베이지안 추론 결과를 결합하여 분류 성능을 향상시키기 위한 방법을 제안한다. 영상 정보는 시간에 대해 연속성을 가지고 있기 때문에, 증거 정보와 베이지안 추론 결과들을 적절히 결합하면 더 좋은 결과를 예상할 수 있으며, 본 논문에서는 확신 요소(Certainty Factor: CF) 분석에 의한 결합 방법을 사용하였다. 성능 평가 실험을 위해서 SET (Scale Invariant Feature Transform) 기법을 이용하여 물체 인식 처리를 수행하고, 여기서 얻어진 데이터를 베이지안 추론의 증거로 사용하였으며, 전문가의 CF 값 정의에 의한 베이지안 네트워크 설계 방법을 이용하였다.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.26 no.8
• /
• pp.1121-1129
• /
• 2022

Object recognition is carried out using RGB images in various object recognition studies. However, RGB images in dark illumination environments or environments where target objects are occluded other objects cause poor object recognition performance. On the other hand, IR images provide strong object recognition performance in these environments because it detects infrared waves rather than visible illumination. In this paper, we propose an RGB-IR fusion model, high-frequency interchange network (HINet), which improves object recognition performance by combining only the strengths of RGB-IR image pairs. HINet connected two object detection models using a mutual high-frequency transfer (MHT) to interchange advantages between RGB-IR images. MHT converts each pair of RGB-IR images into a discrete cosine transform (DCT) spectrum domain to extract high-frequency information. The extracted high-frequency information is transmitted to each other's networks and utilized to improve object recognition performance. Experimental results show the superiority of the proposed network and present performance improvement of the multispectral object recognition task.

• The KIPS Transactions:PartD
• /
• v.13D no.2 s.105
• /
• pp.287-292
• /
• 2006

There are two problems associated with the existing principal component analysis, which is regarded as the most effective in object recognition technology. First, it brings about an increase in the volume of calculations in proportion to the square of image size. Second, it gives rise to a decrease in accuracy according to illumination changes. In order to solve these problems, this paper proposes wavelet transformation and histogram equalization. Wavelet transformation solves the first problem by using the images of low resolution. To solve the second problem the histogram equalization enlarges the contrast of images and widens the distribution of brightness values. The proposed technology improves recognition rate by minimizing the effect of illumination change. It also speeds up the processing and reduces its area by wavelet transformation.

• Journal of Advanced Navigation Technology
• /
• v.24 no.3
• /
• pp.192-197
• /
• 2020

Recently, the technologies related to autonomous drive has studying the goal for safe operation and prevent accidents of vehicles. There is radar and camera technologies has used to detect obstacles in these autonomous vehicle research. Now a day, the method for using LiDAR sensor has considering to detect nearby objects and accurately measure the separation distance in the autonomous navigation. It is calculates the distance by recognizing the time differences between the reflected beams and it allows precise distance measurements. But it also has the disadvantage that the recognition rate of object in the atmospheric environment can be reduced. In this paper, point cloud data by triangular functions and Line Regression model are used to implement measurement algorithm, that has improved detecting objects in real time and reduce the error of measuring separation distances based on improved reliability of raw data from LiDAR sensor. It has verified that the range of object detection errors can be improved by using the Python imaging library.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2015.04a
• /
• pp.766-769
• /
• 2015

본 논문에서는 컬러 영상과 깊이 영상으로부터 영상 전체의 정보를 활용하는 형판 정합 방법으로 특징을 추출하여, 사물의 속성을 인식하는 시스템을 제안한다. 본 시스템은 입력 영상으로부터 더 많은 정보를 얻기 위해 컬러 영상과 깊이 영상을 함께 사용하였다. 그리고 영상의 부분적인 정보가 아닌 전체 정보를 활용하는 형판 정합 방법을 사용하여 속성 인식률을 향상 시켰다. 본 시스템의 성능을 확인하기 위해 워싱턴 대학에서 제공하는 RGB-D 데이터 집합을 이용하여 다른 특징들 및 분류기와 비교실험을 진행하였고, 본 논문에서 제안하는 시스템의 높은 성능을 확인할 수 있었다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2007.06a
• /
• pp.171-174
• /
• 2007

A realization for FF-PID(Feed-Forward PID) controlling system with backward propagation algorithm and image pattern recognition is presented in this paper. The pattern recognition used backward propagation of nervous network is teaming. FF-PID is enhanced the response characteristic of moving image by using the controlling value which is output error for the target value of nervous system. In conclusion of experiment, the system is shown that the response is worked as 2.7sec that is enhanced round 15% in comparison with general difference image algorithm. The system is able to control a moving object with effect.

• The KIPS Transactions:PartB
• /
• v.17B no.4
• /
• pp.287-294
• /
• 2010

TOF technique is one of the skills that can obtain the object's 3D depth information. But depth image has low resolution and fingertip occupy very small region, so, it is difficult to find the precise fingertip's 3D information by only using depth image from TOF camera. In this paper, we estimate fingertip's 3D location using Arm Model and reliable hand's 3D location information that is modified by hexahedron as hand model. Using proposed method we can obtain more precise fingertip's 3D information than using only depth image.

• KIPS Transactions on Software and Data Engineering
• /
• v.5 no.2
• /
• pp.89-94
• /
• 2016

This paper proposed a facial expression recognition approach using SIFT feature and SVM classifier. The SIFT was generally employed as feature descriptor at key-points in object recognition fields. However, this paper applied the SIFT descriptor as feature vector for facial expression recognition. In this paper, the facial feature was extracted by applying SIFT descriptor at each sub-block image without key-point detection procedure, and the facial expression recognition was performed using SVM classifier. The performance evaluation was carried out through comparison with binary pattern feature-based approaches such as LBP and LDP, and the CK facial expression database and the JAFFE facial expression database were used in the experiments. From the experimental results, the proposed method using SIFT descriptor showed performance improvements of 6.06% and 3.87% compared to previous approaches for CK database and JAFFE database, respectively.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.16 no.10
• /
• pp.2280-2286
• /
• 2012

In this paper, we propose a novel nonlinear 3D image correlator using a fast computational integral imaging reconstruction (CIIR) method. In order to implement the fast CIIR method, the magnification process was eliminated. In the proposed correlator, elemental images of the reference and target objects are picked up by lenslet arrays. Using these elemental images, reference and target plane images are reconstructed on the output plane by means of the proposed fast CIIR method. Then, through nonlinear cross-correlations between the reconstructed reference and the target plane images, the pattern recognition can be performed from the correlation outputs. Nonlinear correlation operation can improve the recognition of 3D objects. To show the feasibility of the proposed method, some preliminary experiments are carried out and the results are presented by comparing the conventional method.

• Proceedings of the Korea Institutes of Information Security and Cryptology Conference
• /
• 2006.06a
• /
• pp.703-706
• /
• 2006

RFID 시스템은 전자태그를 이용한 자동 무선 식별 시스템으로써 RFID 전자 태그를 물체나 사람 또는 동물에게 부착하여 무선 주파수를 통해 태그의 정보를 인식할 수 있도록 해주는 시스템이다. 이는 동시에 다량의 정보를 인식할 수 있다는 장점을 무기로 현재 접촉식 판독 기법의 바코드 시스템을 대처할 수 있을 것이다. 반면 이러한 장점에도 불고하고 RFID 시스템이 사용되는데 걸림돌이 되는 가장 큰 단점은 RFID 태그 정보에 대한 접근이 자유롭다는 점에서 프라이버시 문제를 야기하기 때문이다. 현재 이러한 문제를 해결하기 위해 많은 연구가 진행되고 있으며, 그 중 Ari Juels는 두 개의 RFID 태그가 동시에 있다는 것을 증명하기 위한 프로토콜인 yoking proof 프로토콜을 제안하였다. 하지만 이는 재생(replay) 공격이 가능하다는 취약점을 가지고 있으며, 이를 보안하기 위해 제안된 여러 프로토콜 들에서도 역시 재생 공격에 대한 취약점이 발견되고 있다. 따라서 본 논문에서는 이러한 yoking proof 프로토콜의 취약점을 보안하기 위하여 공격에 대한 복잡도를 높여 공격자로 하여금 재생 공격이 어렵게 하는 프로토콜을 제안한다.