• The Journal of the Korea institute of electronic communication sciences
• /
• v.6 no.2
• /
• pp.206-211
• /
• 2011

In this paper, a fire detection algorithm based on video processing is proposed. At the first stage, background image extracted from CCTV video input signal, and then foreground image were separated by differencing CCTV input signal from background image. At the second stage, candidated area were extracted by using color information from foreground image. At the final stage, smoke or flame characteristic area were separated by using Gaussian mixture modeling applied to candidated area, and then fire can be detected. Through real experiments at the inner room, it is shown that the proposed system works well.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.13 no.4
• /
• pp.195-204
• /
• 2018

Lane keeping assist and departure prevention system are the key functions of ADAS. In this paper, we propose lane detection method which uses Gaussian function based RANSAC. The proposed method consists mainly of IPM (inverse perspective mapping), Canny edge detector, and Gaussian function based RANSAC (Random Sample Consensus). The RANSAC uses Gaussian function to extract the parameters of straight or curved lane. The proposed RANSAC is different from the conventional one, in the following two aspects. One is the selection of sample with different probability depending on the distance between sample and camera. Another is the inlier sample score that assigns higher weights to samples near to camera. Through simulations, we show that the proposed method can achieve good performance in various of environments.

• Current Optics and Photonics
• /
• v.7 no.1
• /
• pp.73-82
• /
• 2023

In this study, we demonstrate ultrahigh-resolution spectral-domain optical coherence tomography with a 200-kHz line rate using a superluminescent diode with a -3-dB bandwidth of 100 nm at 849 nm. To increase the line rate, a subset of the total number of camera pixels is used. In addition, a partial-spectrum detection method is used to obtain OCT images within an imaging depth of 2.1 mm while maintaining ultrahigh axial resolution. The partially detected spectrum has a flat-topped intensity profile, and side lobes occur after fast Fourier transformation. Consequently, we propose and apply the super-Gaussian window function as a new window function, to reduce the side lobes and obtain a result that is close to that of the axial-resolution condition with no window function applied. Upon application of the super-Gaussian window function, the result is close to the ultrahigh axial resolution of 4.2 ㎛ in air, corresponding to 3.1 ㎛ in tissue (n = 1.35).

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.18 no.3
• /
• pp.735-741
• /
• 2014

Edge in images appears when a great difference shows up in light and shade between pixels and includes data of the subject's size, location direction and etc. The edge is generally detected by the methods such as Sobel, Roberts, Laplacian, LoG(Laplacian of Gaussian) and etc. However, in AWGN(additive white Gaussian noise) added images, quality of the edge becomes slightly uncertain. Therefore, this paper proposed edge detection algorithm using modified mask of weighting to improve the quality of the existing methods. And in order to verify the performance efficiency of the proposed method, processed image and PFOM(Pratt's figure of merit) has been used as valuation standard for a comparison with the existing methods.

• International Journal of Advanced Culture Technology
• /
• v.10 no.2
• /
• pp.252-259
• /
• 2022

Artificial intelligence is used in fusion with image processing techniques using cameras. Image processing technology is a technology that processes objects in an image received from a camera in real time, and is used in various fields such as security monitoring and medical image analysis. If such image processing reduces the accuracy of recognition, providing incorrect information to medical image analysis, security monitoring, etc. may cause serious problems. Therefore, this paper uses a mixture of YOLOv4-tiny model and image processing algorithm and uses the COCO dataset for learning. The image processing algorithm performs five image processing methods such as normalization, Gaussian distribution, Otsu algorithm, equalization, and gradient operation. For RGB images, three image processing methods are performed: equalization, Gaussian blur, and gamma correction proceed. Among the nine algorithms applied in this paper, the Equalization and Gaussian Blur model showed the highest object detection accuracy of 96%, and the gamma correction (RGB environment) model showed the highest object detection rate of 89% outdoors (daytime). The image binarization model showed the highest object detection rate at 89% outdoors (night).

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2013.05a
• /
• pp.892-894
• /
• 2013

Edge has been utilized in various application fields with development of technique of digital image processing. In conventional edge detection methods, there are some methods using mask including Sobel, Prewitt, Roberts and Laplacian operator. Those methods are that implement is simple but generates errors of edge detection in images added AWGN(additive white Gaussian noise). Therefore, to compensate the defect of those methods, in this paper, an edge detection algorithm using modified mask is proposed, and it showed superior edge detection property in AWGN.

• ETRI Journal
• /
• v.35 no.4
• /
• pp.644-654
• /
• 2013

In this paper, a signal detection scheme for cognitive radio (CR) based on the Bussgang theorem is proposed. The proposed scheme calculates the statistical difference between Gaussian noise and the primary user signal by applying the Bussgang theorem to the received signal. Therefore, the proposed scheme overcomes the noise uncertainty and gives scalable complexity according to the zero-memory nonlinear function for a mobile device. We also present the theoretical analysis on the detection threshold and the detection performance in the additive white Gaussian noise channel. The proposed detection scheme is evaluated by computer simulations based on the IEEE 802.22 standard for the wireless regional area network. Our results show that the proposed scheme is robust to the noise uncertainty and works well in a very low signal-to-noise ratio.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2014.05a
• /
• pp.801-803
• /
• 2014

In the modern society, image processing is utilized in various fields. Edge detection used for image processing as such is essential for most of the applications. Accordingly, there are studies conducted both in and out of Korea in order to detect edge. Representative edge detection methods include Sobel, Prewitt and Roberts. However, these methods are rather limited when it comes to the edge detection characteristics when used for the image with damaged AWGN(additive white Gaussian noise). Thus, this paper presented edge detection method utilizing local mask in order to overcome the shortcomings of the existing methods.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2022.05a
• /
• pp.437-440
• /
• 2022

In modern society, the use of the image processing field is increasing rapidly due to the 4th industrial revolution and the development of IoT technology. In particular, edge detection is widely used in various fields as an essential preprocessing process in image processing applications such as image classification and object detection. Conventional methods for detecting an edge include a Sobel edge detection filter, a Roberts edge detection filter, a Prewitt edge detection filter, Laplacian of Gaussian (LoG), and the like. However, existing methods have the disadvantage of showing somewhat insufficient performance of edge detection characteristics in a low-light level environment with low contrast. Therefore, this paper proposes an edge detection algorithm based on contrast analysis to increase edge detection characteristics even in low-light level environments.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2022.10a
• /
• pp.136-138
• /
• 2022

Edge detection includes information such as the shape, position, size, and material of an object with respect to an image, and is a very important factor in analyzing the characteristics of the image. Existing edge detection methods include Sobel edge detection filter, Roberts edge detection filter, Prewitt edge detection filter, and LoG (Lapacian of Gaussian) using secondary differentials. However, these methods have a disadvantage in that the edge detection results are somewhat insufficient because a fixed weight mask is applied to the entire image area. Therefore, in this paper, we propose an edge detection algorithm that increases edge detection characteristics by considering the center pixel in the mask. In addition, in order to confirm the proposed edge detection performance, it was compared through simulation result images.