• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.7
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• pp.641-648
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• 2001

A face detection in color images is important for many multimedia applications. It is first step for face recognition and can be used for classifying specific shorts. This paper describes a new method to detect faces in color images based on the skin color and hair color. This paper presents a fuzzy-based method for classifying skin color region in a complex background under varying illumination. The Fuzzy rule bases of the fuzzy system are generated using training method like a genetic algorithm(GA). We find the skin color region and hair color region using the fuzzy system and apply the convex-hull to each region and find the face from their intersection relationship. To validity the effectiveness of the proposed method, we make experiment with various cases.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.185-189
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• 2004

For face recognition system, a face detector which can find exact face region from complex image is needed. Many face detection algorithms have been developed under the assumption that background of the source image is quite simple . this means that face region occupy more than a quarter of the area of the source image or the background is one-colored. Color-based face detection is fast but can't be applicable to the images of which the background color is similar to face color. And the algorithm using neural network needs so many non-face data for training and doesn't guarantee general performance. In this paper, A multi-scale, multi-face detection algorithm using PCA is suggested. This algorithm can find most multi-scaled faces contained in static images with small number of training data in reasonable time.

• Journal of Information Processing Systems
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• v.13 no.3
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• pp.590-598
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• 2017

Recently, there has been an increase in the number of hazardous events, such as fire accidents. Monitoring systems that rely on human resources depend on people; hence, the performance of the system can be degraded when human operators are fatigued or tensed. It is easy to use fire alarm boxes; however, these are frequently activated by external factors such as temperature and humidity. We propose an approach to fire detection using an image processing technique. In this paper, we propose a fire detection method using multichannel information and gray level co-occurrence matrix (GLCM) image features. Multi-channels consist of RGB, YCbCr, and HSV color spaces. The flame color and smoke texture information are used to detect the flames and smoke, respectively. The experimental results show that the proposed method performs better than the previous method in terms of accuracy of fire detection.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06a
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• pp.491-493
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• 2011

Traffic sign detection is the domain of automatic driver assistant systems. There are literatures for traffic sign detection using color information, however, color-based method contains ill-posed condition and to extract the region of interest is difficult. In our work, we propose a method for traffic sign detection using k-means clustering method, back-propagation neural network, and projection histogram features that yields the robustness for ill-posed condition. Using the color information of traffic signs enables k-means algorithm to cluster the region of interest for the detection efficiently. In each step of clustering, a cluster is verified by the neural network so that the cluster exactly represents the location of a traffic sign. Proposed method is practical, and yields robustness for the unexpected region of interest or for multiple detections.

• Journal of Advanced Navigation Technology
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• v.13 no.6
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• pp.1011-1016
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• 2009

In this paper, we propose the method of fire detection. A wide distribution of CCTV cameras (Closed Circuit Television) in many public areas can be used not only for video surveillance systems but also for detecting fire occurrence. A proposed approach is based on visual information through a static camera. Video sequences are analyzed to find fire candidates and then spatial analyses procedure for detected fire-like color foreground is carried out. From the simulation results, our method showed the best performance when spatial and temporal fire candidates changes rapidly and close to fire motion.

• Journal of the Korea Society of Computer and Information
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• v.20 no.7
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• pp.25-32
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• 2015

In this paper, we propose a license plate detection method of running cars in various road images. The proposed method first classifies the road image into day and night images to improve detection accuracy, and then the tail-light regions are detected by finding red color areas in RGB color space. The candidate regions of the license plate areas are detected by using symmetrical property, size, width and variance of the tail-light regions, and to find the license plate areas of the various sizes the morphological operations with adaptive size structuring elements are applied. Finally, the plate area is verified and confirmed with the geometrical and image features of the license plate areas. The proposed method was tested with the various road images and the detection rates (precisions) of 84.2% of day images and 87.4% of night images were achieved.

• Proceedings of the IEEK Conference
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• 2007.07a
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• pp.339-340
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• 2007

Face detection plays an important role in HCI and face recognition. In this paper, we propose a rotation-invariant real-time face detection algorithm for color images in complex background. It consists of four processing step: (1) motion detection, (2) skin color region filler, (3) Eyemap detector for rotated face, and (4) Adaboost face classifier. This system has been tested in in-door environments, such as office and achieves over 95% detection rate.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.24 no.2
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• pp.282-297
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• 2019

Detection of mesoscale oceanic eddies using satellite data can utilize various ocean parameters such as sea surface temperature (SST), chlorophyll-a pigment concentration in phytoplankton, and sea level altimetry measurements. Observation methods vary for each satellite dataset, as it is obtained using different temporal and spatial resolution, and optimized data processing. Different detection results can be derived for the same oceanic eddies; therefore, fundamental research on eddy detection using satellite data is required. In this study, we used ocean color satellite data, sea level altimetry data, and infrared SST data to detect mesoscale eddies in the East Sea and compared results from different detection methods. The sea surface current field derived from the consecutive ocean color chlorophyll-a concentration images using the maximum cross correlation coefficient and the geostrophic current field obtained from the sea level altimetry data were used to detect the mesoscale eddies in the East Sea. In order to compare the eddy detection from satellite data, the results were divided into three cases as follows: 1) the eddy was detected in both the ocean color and altimeter images simultaneously; 2) the eddy was detected from ocean color and SST images, but no eddy was detected in the altimeter data; 3) the eddy was not detected in ocean color image, while the altimeter data detected the eddy. Through these three cases, we described the difficulties with satellite altimetry data and the limitations of ocean color and infrared SST data for eddy detection. It was also emphasized that study on eddy detection and related research required an in-depth understanding of the mesoscale oceanic phenomenon and the principles of satellite observation.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.3
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• pp.107-115
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• 2009

Skin color detection is used to classify input pixels into skin and non skin area, and it requires the classifier to have a high classification rate. In previous work, most classifiers used single color model for skin color detection. However the classification rate can be increased by using more than one color model due to the various characteristics of skin color distribution in different color models, and the MLP is also invested as a more efficient classifier with less parameters than other classifiers. But the input dimension and required parameters of MLP will be increased when using two color models in skin color detection, as a result, the increased parameters will cause the huge teaming time in MLP. In this paper, we propose a MLP based classifier with less parameters in two color models. The proposed partially connected MLP based on two color models can reduce the number of weights and improve the classification rate. Because the characteristic of different color model can be learned in different partial networks. As the experimental results, we obtained 91.8% classification rate when testing various images in RGB and CbCr models.

• Journal of Digital Convergence
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• v.13 no.9
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• pp.183-189
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• 2015

In this paper, we propose an object tracking method using the color information of the image in surveillance network. This method perform a object detection using of adaptive color model. Object contour detection plays an important role in application such as object recognition. Experimental results demonstrate successful object detection over a wide range of object's variation in color and scale. In applications to detect an object in real time, when transmitting a large amount of image data it is possible to find the mode of a color distribution. The specific color of an object is modified at dynamically changing color in image. So, this algorithm detects the tracking area information of object within relevant tracking area and only tracking the movement of that object.Through experiments, we show that proposed method is more robust than other methods under certain ideal situations.