• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.7
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• pp.1377-1382
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• 2012

In this paper, we propose a novel recognition method that extract source data from encoded signal that are displayed on FND mounted on home appliances. First of all, it find a candidate FND region from sequential difference images taken by smartphone and extract segment image using clustering RGB value. After that, it normalize segment images to correct a slant error and recognize each segments using a relative distance. Experiments show the robustness of the recognition algorithm on smartphone.

• International Journal of Contents
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• v.9 no.2
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• pp.8-13
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• 2013

Locating text data in images automatically has been a challenging task. In this approach, we build a three stage system for text detection purpose. This system utilizes tensor voting and Completed Local Binary Pattern (CLBP) to classify text and non-text regions. While tensor voting generates the text line information, which is very useful for localizing candidate text regions, the Nearest Neighbor classifier trained on discriminative features obtained by the CLBP-based operator is used to refine the results. The whole algorithm is implemented in MATLAB and applied to all images of ICDAR 2011 Robust Reading Competition data set. Experiments show the promising performance of this method.

• Journal of Korea Multimedia Society
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• v.8 no.5
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• pp.633-640
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• 2005

In this paper, we propose an adaptive face region extraction algorithm based on skin color information. It consists oi the extraction of face candidate region and projection step. In the step of face candidate region extraction, we extract the pixels which are regarded as the candidate skin color pixels by using the given range. Then, the ratio between the total pixels and the extracted pixels is calculated. According to the ratio, we adaptively decide the range of the skin color and extract face candidate region. In the projection step, we project the extracted face candidate region into vertical direction to estimate the width of the face. Then the redundant parts are efficiently removed by using the estimated face width. And the extracted face width information is used at the horizontal projection step to extract the height of the face. From the experiment results for the various images, the proposed algorithm shows more accurate results than the conventional algorithm.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.3
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• pp.847-852
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• 2010

Text in natural images has a various and important feature of image. Therefore, to detect text and extraction of text, recognizing it is a studied as an important research area. Lately, many applications of various fields is being developed based on mobile phone camera technology. Detecting edge component form gray-scale image and detect an boundary of text regions by local standard deviation and get an connected components using Euclidean distance of RGB color space. Labeling the detected edges and connected component and get bounding boxes each regions. Candidate of text achieved with heuristic rule of text. Detected candidate text regions was merged for generation for one candidate text region, then text region detected with verifying candidate text region using ectilarity characterization of adjacency and ectilarity between candidate text regions. Experctental results, We improved text region detection rate using completentary of edge and color connected component.

• Korean Journal of Remote Sensing
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• v.28 no.6
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• pp.683-691
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• 2012

High resolution satellite images are now widely used for a variety of mapping applications including photogrammetry, GIS data acquisition and visualization. As the spectral and spatial data size of satellite images increases, a greater processing power is needed to process the images. The solution of these problems is parallel systems. Parallel processing techniques have been developed for improving the performance of image processing along with the development of the computational power. However, conventional CPU-based parallel computing is often not good enough for the demand for computational speed to process the images. The GPU is a good candidate to achieve this goal. Recently GPUs are used in the field of highly complex processing including many loop operations such as mathematical transforms, ray tracing. In this study we proposed a technique for parallel processing of high resolution satellite images using GPU. We implemented a spectral radiometric processing algorithm on Landsat-7 ETM+ imagery using CUDA, a parallel computing architecture developed by NVIDIA for GPU. Also performance of the algorithm on GPU and CPU is compared.

• The Journal of Korea Robotics Society
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• v.10 no.4
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• pp.223-229
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• 2015

This paper proposes a novel method for detection of hand raising poses from images acquired from a single camera attached to a mobile robot that navigates unknown dynamic environments. Due to unconstrained illumination, a high level of variance in human appearances and unpredictable backgrounds, detecting hand raising gestures from an image acquired from a camera attached to a mobile robot is very challenging. The proposed method first detects faces to determine the region of interest (ROI), and in this ROI, we detect hands by using a HOG-based hand detector. By using the color distribution of the face region, we evaluate each candidate in the detected hand region. To deal with cases of failure in face detection, we also use a HOG-based hand raising pose detector. Unlike other hand raising pose detector systems, we evaluate our algorithm with images acquired from the camera and images obtained from the Internet that contain unknown backgrounds and unconstrained illumination. The level of variance in hand raising poses in these images is very high. Our experiment results show that the proposed method robustly detects hand raising poses in complex backgrounds and unknown lighting conditions.

• Journal of Korea Multimedia Society
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• v.16 no.2
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• pp.169-179
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• 2013

Change detection algorithms take two image frames and return the locations of newly introduced objects which cause differences between the images. This paper presents a new change detection method, which classifies intensity changes due to introduced objects, reflected light and shadow from the objects to their neighborhood, and the noise, and exactly localizes the introduced objects. For classification and localization, first we analyze the histogram of the intensity difference between two images, and estimate multiple threshold values. Second we estimate candidate object boundaries using the gradient difference between two images. Using those threshold values and candidate object boundaries, we segment the frame difference image into multiple regions. Finally we classify whether each region belongs to the introduced objects or not using textures in the region. Experiments show that the proposed method exactly localizes the objects in various scenes with different lighting.

• Journal of the Korea Convergence Society
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• v.8 no.8
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• pp.33-40
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• 2017

In this paper, we propose a text extraction method by pattern analysis on contour for effective text detection in image. Text extraction algorithms using edge based methods show good performance in images with simple backgrounds, The images of complex background has a poor performance shortcomings. The proposed method simplifies the color of the image by using K-means clustering in the preprocessing process to detect the character region in the image. Enhance the boundaries of the object through the High pass filter to improve the inaccuracy of the boundary of the object in the color simplification process. Then, by using the difference between the expansion and erosion of the morphology technique, the edges of the object is detected, and the character candidate region is discriminated by analyzing the pattern of the contour portion of the acquired region to remove the unnecessary region (picture, background). As a final result, we have shown that the characters included in the candidate character region are extracted by removing unnecessary regions.

• Journal of Korea Multimedia Society
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• v.17 no.2
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• pp.143-151
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• 2014

In images degraded by bad weather, edges representing object boundaries become weak and faint. In this paper we present an image enhancement method, which increases image visibility by making edges as clear as possible. First, we choose edge candidate regions by finding local maxima and minima in an image intensity field, and then build a histogram using image intensities of pixels located at the two sides of candidate edges. Second, we decompose this histogram into multiple modes, which are determined by local minima in the histogram. Once modes are computed, we find modes connected by edges in the image intensity field and build link chains of connected modes. Finally we choose the longest link chain of modes and make the distances between every connected modes as large as possible. The darkest mode and the brightest mode should be within the image intensity range. This stretch makes edges clear and increases image visibility. Experiments show that the proposed method real-time enhances images degraded by bad weather as good as well known time-consuming methods.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.2
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• pp.358-363
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• 2009

Text region detection from the natural scene images used in a variety of applications, many research are needed in this field. Recent research methods is to detect the text region using various algorithm which it is combination of edge based and connected component based. Therefore, this paper proposes an text region detection using edge and regional minima/maxima transformation algorithm from natural scene images, and then detect the connected components of edge and regional minima/maxima, labeling edge and regional minima/maxima connected components. Analysis the labeled regions and then detect a text candidate regions, each of detected text candidates combined and create a single text candidate image, Final text region validated by comparing the similarity and adjacency of individual characters, and then as the final text regions are detected. As the results of experiments, proposed algorithm improved the correctness of text regions detection using combined edge and regional minima/maxima connected components detection methods.