• Journal of Digital Contents Society
• /
• v.18 no.8
• /
• pp.1635-1640
• /
• 2017

In this paper, a hierarchical image tree model for weather classification is defined in a single outdoor image, and a weather classification algorithm using image intensity and k-mean segmentation image is proposed. In the first level of the hierarchical image tree model, the indoor and outdoor images are distinguished. Whether the outdoor image is daytime, night, or sunrise/sunset image is judged using the intensity and the k-means segmentation image at the second level. In the last level, if it is classified as daytime image at the second level, it is finally estimated whether it is sunny or foggy image based on edge map and fog rate. Some experiments are conducted so as to verify the weather classification, and as a result, the proposed method shows that weather features are effectively detected in a given image.

• Journal of the Semiconductor & Display Technology
• /
• v.16 no.3
• /
• pp.78-81
• /
• 2017

Augmented Reality(AR) is the core technology of the future knowledge service industry. It is expected to be used in various fields such as medical, education, entertainment etc. Briefly, augmented reality technology is a technique in which a mapped virtual object is augmented when a real-world object is viewed through a device after mapping a real-world object and a virtual object. In this paper, we implemented object detection and tracking system, which is a key technology of augmented reality. To speed up the object tracking, the ORB algorithm, which is a lightweight algorithm compared to the detection algorithm, is applied. In addition, KNN classifier, which is a machine learning algorithm, was applied to detect a single object by learning multiple images.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.2
• /
• pp.771-789
• /
• 2019

Text detection has been a popular research topic in the field of computer vision. It is difficult for prevalent text detection algorithms to avoid the dependence on datasets. To overcome this problem, we proposed a novel unsupervised text detection algorithm inspired by bootstrap learning. Firstly, the text candidate in a novel form of superpixel is proposed to improve the text recall rate by image segmentation. Secondly, we propose a unique text sample selection model (TSSM) to extract text samples from the current image and eliminate database dependency. Specifically, to improve the precision of samples, we combine maximally stable extremal regions (MSERs) and the saliency map to generate sample reference maps with a double threshold scheme. Finally, a multiple kernel boosting method is developed to generate a strong text classifier by combining multiple single kernel SVMs based on the samples selected from TSSM. Experimental results on standard datasets demonstrate that our text detection method is robust to complex backgrounds and multilingual text and shows stable performance on different standard datasets.

• Journal of information and communication convergence engineering
• /
• v.18 no.4
• /
• pp.267-277
• /
• 2020

It is important to understand the exact habitat distribution of endangered species because of their decreasing numbers. In this study, we build a system with a deep learning module that collects the image data of endangered animals, processes the data, and saves the data automatically. The system provides a more efficient way than human effort for classifying images and addresses two problems faced in previous studies. First, specious answers were suggested in those studies because the probability distributions of answer candidates were calculated even if the actual answer did not exist within the group. Second, when there were more than two entities in an image, only a single entity was focused on. We applied an object detection algorithm (YOLO) to resolve these problems. Our system has an average precision of 86.79%, a mean recall rate of 93.23%, and a processing speed of 13 frames per second.

• Journal of the Korean Society for Marine Environment & Energy
• /
• v.15 no.4
• /
• pp.323-329
• /
• 2012

In this study, a new optical method for oil detection using an analysis the light-absorption image of separate oil-water mixture with a LED illumination is described. To obtain an information about the presence of oil on water and the thickness of oil, the intensity of light-absorption images acquired through CCD is analyzed. To select the optimal wavelength of the light source, the experiment is conducted using several LEDs having four different wavelength. In the case of using a blue LED having 465 nm wavelength, an intensity decreasing tendency of light-absorption image is obvious and clear. To identify the applicability of sensing system at the real sea condition, experiments are conducted as varying the brightness and water surface angle. Through this research, new optical oil detection methodology is proposed using the absorption difference between water and oil with single-wavelength LED and CCD.

• Journal of the Korea Society of Computer and Information
• /
• v.13 no.6
• /
• pp.1-12
• /
• 2008

This paper presents automatic moving object detection method using the rotating camera covering larger area with a single camera. The proposed method is based on the edge segment matching which robust to the dynamic environment with illumination change and background movement. The proposed algorithm presents an edge segment based background panorama image generation method minimizing the distortion due to image stitching, the background image generation method using Generalized Hough Transformation which can reliably register the current image to the panorama image overcoming the stitching distortions, the moving edge segment extraction method that overcome viewpoint difference and distortion. The experimental results show that the proposed method can detect correctly moving object under illumination change and camera vibration.

• International Journal of Advanced Culture Technology
• /
• v.9 no.4
• /
• pp.302-306
• /
• 2021

Indoor localization is one of the basic elements of Location-Based Service, such as indoor navigation, location-based precision marketing, spatial recognition of robotics, augmented reality, and mixed reality. We propose a Voxel Labeling-based visual positioning system using object simultaneous localization and mapping (SLAM). Our method is a method of determining a location through single image 3D cuboid object detection and object SLAM for indoor navigation, then mapping to create an indoor map, addressing it with voxels, and matching with a defined space. First, high-quality cuboids are created from sampling 2D bounding boxes and vanishing points for single image object detection. And after jointly optimizing the poses of cameras, objects, and points, it is a Visual Positioning System (VPS) through matching with the pose information of the object in the voxel database. Our method provided the spatial information needed to the user with improved location accuracy and direction estimation.

• The Journal of the Korea Contents Association
• /
• v.13 no.6
• /
• pp.40-47
• /
• 2013

In case of wild fire, early detection of wild fire is the most important factor in minimizing the damages. In this paper, we suggest an effective system that detects wild fire using a panoramic image from a single camera with PAN/TILT head. This enables the system to detect the size and the location of the fire in the early stages. After converting RGB image input to color YCrCb image, the differential image is used to detect changes in movement of the smoke to determine the regions which may be prone to forest fire. Histogram analysis of fire flame is used to determine the possibility of fire in the predetermined regions. In addition, image matching and SURF were used to create the panoramic image. There are many advantages in this system. First of all, it is very economical because this system needs only a single camera and a monitor. Second, it shows the live image of wide view through panoramic image. Third, this system can reduce the quantity of saved data by storing panoramic images.

• Journal of Institute of Control, Robotics and Systems
• /
• v.13 no.11
• /
• pp.1074-1081
• /
• 2007

This paper presents a method for real-time face detection and tracking which combined Adaboost and Camshift algorithm. Adaboost algorithm is a method which selects an important feature called weak classifier among many possible image features by tuning weight of each feature from learning candidates. Even though excellent performance extracting the object, computing time of the algorithm is very high with window size of multi-scale to search image region. So direct application of the method is not easy for real-time tasks such as multi-task OS, robot, and mobile environment. But CAMshift method is an improvement of Mean-shift algorithm for the video streaming environment and track the interesting object at high speed based on hue value of the target region. The detection efficiency of the method is not good for environment of dynamic illumination. We propose a combined method of Adaboost and CAMshift to improve the computing speed with good face detection performance. The method was proved for real image sequences including single and more faces.

• JSTS:Journal of Semiconductor Technology and Science
• /
• v.13 no.1
• /
• pp.34-42
• /
• 2013

Human faces have been broadly studied in digital image and video processing fields. An appearance-based method, the adaptive boosting learning algorithm using integral image representations has been successfully employed for face detection, taking advantage of the feature extraction's low computational complexity. In this paper, we propose a face-detection postprocessing method that equalizes instantaneous facial regions in an efficient hardware architecture for use in real-time multimedia applications. The proposed system requires low hardware resources and exhibits robust performance in terms of the movements, zooming, and classification of faces. A series of experimental results obtained using video sequences collected under dynamic conditions are discussed.