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Adaptive Real-Time Ship Detection and Tracking Using Morphological Operations
Arshad, Nasim,Moon, Kwang-Seok,Kim, Jong-Nam The Korea Institute of Information and Commucation 2014 Journal of information and communication convergen Vol.12 No.3
In this paper, we propose an algorithm that can efficiently detect and monitor multiple ships in real-time. The proposed algorithm uses morphological operations and edge information for detecting and tracking ships. We used smoothing filter with a $3{\times}3$ Gaussian window and luminance component instead of RGB components in the captured image. Additionally, we applied Sobel operator for edge detection and a threshold for binary images. Finally, object labeling with connectivity and morphological operation with open and erosion were used for ship detection. Compared with conventional methods, the proposed method is meant to be used mainly in coastal surveillance systems and monitoring systems of harbors. A system based on this method was tested for both stationary and non-stationary backgrounds, and the results of the detection and tracking rates were more than 97% on average. Thousands of image frames and 20 different video sequences in both online and offline modes were tested, and an overall detection rate of 97.6% was achieved.
Nasim Arshad,Won hee Kim,Kwang-Seok Moon,Jong Nam Kim 대한전자공학회 2010 ICEIC:International Conference on Electronics, Inf Vol.1 No.1
In this paper, the wheel detection with distance estimation algorithm is proposed using one camera mounted beside the vehicle of our experimental car, Figure 1. The Canny edge detection and gray intensity are applied for the tire marking and vehicle detection in our work. The side of the vehicles can be easily detected by comparing the gray intensity with the road surface color. The image coordinate model is then utilized for the distance estimation with the still images of the vehicle in different distances. The distances used for this study ranged from 30 ㎝ to 100㎝. This system will be extended in the future for estimation of distance in moving vehicle sequences to give us more efficient result.
A Secure Face Cryptogr aphy for Identity Document Based on Distance Measures
Arshad, Nasim,Moon, Kwang-Seok,Kim, Jong-Nam Korea Multimedia Society 2013 멀티미디어학회논문지 Vol.16 No.10
Face verification has been widely studied during the past two decades. One of the challenges is the rising concern about the security and privacy of the template database. In this paper, we propose a secure face verification system which generates a unique secure cryptographic key from a face template. The face images are processed to produce face templates or codes to be utilized for the encryption and decryption tasks. The result identity data is encrypted using Advanced Encryption Standard (AES). Distance metric naming hamming distance and Euclidean distance are used for template matching identification process, where template matching is a process used in pattern recognition. The proposed system is tested on the ORL, YALEs, and PKNU face databases, which contain 360, 135, and 54 training images respectively. We employ Principle Component Analysis (PCA) to determine the most discriminating features among face images. The experimental results showed that the proposed distance measure was one the promising best measures with respect to different characteristics of the biometric systems. Using the proposed method we needed to extract fewer images in order to achieve 100% cumulative recognition than using any other tested distance measure.
Eccentricity를 이용한 차선 검출에 관한 연구
정태일,나심 아샤드,문광석,김종남,Jeong, Tae-Il,Arshad, Nasim,Moon, Kwang-Seok,Kim, Jong-Nam 한국정보통신학회 2012 한국정보통신학회논문지 Vol.16 No.12
본 논문에서는 Eccentricity를 이용한 차선 검출 알고리듬을 제안한다. 차선 검출 알고리듬은 자동차 운전자의 안정성을 증가시키는 차선 이탈 경보 시스템 등에 활용될 수 있다. 차선 검출율을 개선하기 위하여 그래프 이론에서 소개되는 Eccentricity를 정의하고, 이를 차선 검출 알고리듬에 이용하여 Eccentricity를 계산하였다. 직선도로인 경우 Eccentricity는 1이고 1차 함수로 구현이 가능하다. 그래서 시간 복잡도와 공간 복잡도를 개선하였고, 아울러 기존의 방법들보다 차선 검출율이 향상됨을 확인하였다. In this paper, a lane detection algorithm using Eccentricity calculation is proposed. Lane detection is used for lane departure warning which can support safe driving to prevent accidents. In other to enhance the detection rate, we define the Eccentricity calculation which is introduced in graph theory, and evaluate the Eccentricity. The Eccentricity for any straight line is equal to 1, hence computing the Eccentricity allows the implementation of a first order equation. As a results of simulation, we confirmed that the proposed algorithm was enhanced by time and space complexity, and superior to the performance of the conventional lane detections.