• Korean journal of communication and information
• /
• v.44
• /
• pp.108-143
• /
• 2008

Candidate images refer to a holistic impression of a candidate which is composed of various dimensions of attributes. This study investigated how online and offline news media, campaign media such as political ads, televised debates, and candidate web-sites, and interpersonal political talk influenced voters' images of a candidate in such dimensions as personal traits, job-performance abilities, and policy capabilities, and further their political decision making in the 17th presidential election in Korea. The analysis focused on President Lee, Myung Bak who won the election by obtaining nearly 50% of the effective votes. According to the data analyses, first, uses of offline newspapers positively influenced voters' images of candidate Lee's personal traits such as his morality, integrity, trustworthiness, and compassion, yet online news uses had an opposite effect on voters' impression of his job-performance and economic policy capabilities. Secondly, among various campaign media, television ads and candidate web-sites positively contributed to the formation of candidate Lee's images, yet showed little direct effect on vote choice, indicating that campaign media mainly indirectly influenced voters' political decision making. Each of the first, second, and third televised candidate debates revealed unique effects on image formation and vote choice. Thirdly, the network size and frequency of political talk negatively influenced image formation regarding candidate Lee's personal traits and economic policy capabilities, yet the discussion network size positively contributed to the Lee votes. Forth, among various dimensions of candidate images, voters' perceptions of candidate Lee's personal traits appeared to be the most significant predictor of the support for him.

• Transactions on Electrical and Electronic Materials
• /
• v.14 no.4
• /
• pp.187-192
• /
• 2013

Because of the importance of small target detection in infrared images, many studies have been carried out in this area. Using a Kalman filter and mean shift algorithm, this study proposes an algorithm to track multiple small moving targets even in cases of target disappearance and appearance in serial infrared images in an environment with many noises. Difference images, which highlight the background images estimated with a background estimation filter from the original images, have a relatively very bright value, which becomes a candidate target area. Multiple target tracking consists of a Kalman filter section (target position prediction) and candidate target classification section (target selection). The system removes error detection from the detection results of candidate targets in still images and associates targets in serial images. The final target detection locations were revised with the mean shift algorithm to have comparatively low tracking location errors and allow for continuous tracking with standard model updating. In the experiment with actual marine infrared serial images, the proposed system was compared with the Kalman filter method and mean shift algorithm. As a result, the proposed system recorded the lowest tracking location errors and ensured stable tracking with no tracking location diffusion.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2009.01a
• /
• pp.588-591
• /
• 2009

This paper proposes a reversible information hiding method for binary images. A half of pixels in noisy blocks on cover images is candidate for embeddable pixels. Among the candidate pixels, we select compressive pixels by bit patterns of its neighborhood to compress the pixels effectively. Thus, embeddable pixels in the proposed method are compressive pixels in noisy blocks. We provide experimental results using several binary images binarized by the different methods.

• International journal of advanced smart convergence
• /
• v.13 no.2
• /
• pp.88-93
• /
• 2024

The main problem with existing edge detection techniques is that they have many limitations in detecting edges for complex and diverse images that exist in the real world. This is because only edges of a defined shape are discovered based on an accurate definition of the edge. One of the methods to solve this problem is the cost minimization method. In the cost minimization method, cost elements and cost functions are defined and used. The cost function calculates the cost for the candidate edge model generated according to the candidate edge generation strategy, and if the cost is found to be satisfactory, the candidate edge model becomes the edge for the image. In this study, we proposed an enhanced candidate edge generation strategy to discover edges for more diverse types of images in order to improve the shortcoming of the cost minimization method, which is that it only discovers edges of a defined type. As a result, improved edge detection results were confirmed.

• Journal of information and communication convergence engineering
• /
• v.11 no.1
• /
• pp.62-68
• /
• 2013

This paper proposes a method for identification of harmful video images based on the degree of harmfulness in the video content. To extract harmful candidate frames from the video effectively, we used a video color extraction method applying a projection map. The procedure for identifying the harmful video has five steps, first, extract the I-frames from the video and map them onto projection map. Next, calculate the similarity and select the potentially harmful, then identify the harmful images by comparing the similarity measurement value. The method estimates similarity between the extracted frames and normative images using the critical value of the projection map. Based on our experimental test, we propose how the harmful candidate frames are extracted and compared with normative images. The various experimental data proved that the image identification method based on the 2-dimensional projection map is superior to using the color histogram technique in harmful image detection performance.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.12 no.6
• /
• pp.2807-2814
• /
• 2011

This paper proposes an identification algorithm that detects detrimental digital video contents based on the color space features. In this paper, discrimination algorithm based on a 2-Dimensional Projection Maps is suggested to find targeted video images. First, 2-Dimensional Projection Maps which is extracting the color characteristics of the video images is applied to extract effectively detrimental candidate frames from the videos, and next estimates similarity between the extracted frames and normative images using the suggested algorithm. Then the detrimental candidate frames are selected from the result of similarity evaluation test which uses critical value. In our experimental test, it is suggested that the results of the comparison between the Color Histogram and the 2-Dimensional Projection Maps technique to detect detrimental candidate frames. Through the various experimental data to test the suggested method and the similarity algorithm, detecting method based on the 2-Dimensional Projection Maps show more superior performance than using the Color Histogram technique in calculation speed and identification abilities searching target video images.

• Proceedings of the IEEK Conference
• /
• 2003.07d
• /
• pp.1545-1548
• /
• 2003

This paper presents a method for detecting skin tumors on chicken carcasses using hyperspectral images. It utilizes both fluorescence and reflectance image information in hyperspectral images. A detection system that is built on this concept can increase detection rate and reduce processing time. Chicken carcasses are examined first using band ratio FCM information of fluorescence image and it results in candidate regions for skin tumor. Next classifier selects the real tumor spots using PCA components information of reflectance image from the candidate regions.

• Proceedings of the KSRS Conference
• /
• 2005.10a
• /
• pp.571-574
• /
• 2005

In this paper, we propose an efficient image segmentation technique for large-sized monochrome images using a hybrid approach which combines threshold and region-based techniques. First, an image is partitioned into fixed-size blocks and for each block the representative intensity is determined by averaging pixel intensities within the block. Next, the neighborhood blocks that have similar characteristics with respect to a specific threshold are merged in order to form candidate regions. Finally, those candidate regions are refined to get final target object regions by merging regions considering the spatial locality and certain criteria. We have performed experiments on images selected from various domains and showed that our technique was able to extract target object regions appropriately from most images.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2005.05a
• /
• pp.521-524
• /
• 2005

This paper proposes a fusion algorithm which merges multiple vein pattern images into a single image, larger than those images. As a preprocessing step of template matching, during the verification of biometric data such as fingerprint image, vein pattern image of hand, etc., the fusion technique is used to make reference image larger than the candidate images in order to enhance the matching performance. In this paper, a new algorithm, called BSA (Boundary Stitching Algorithm) is proposed, in which the boundary rectilinear parts extracted from the candidate images are stitched to the reference image in order to enlarge its matching space. By applying BSA to practical vein pattern verification system, its verification rate was increased by about 10%.

• Journal of Information Processing Systems
• /
• v.11 no.4
• /
• pp.528-537
• /
• 2015

Text in images is one of the most important cues for understanding a scene. In this paper, we propose a novel approach based on interest points to localize text in natural scene images. The main ideas of this approach are as follows: first we used interest point detection techniques, which extract the corner points of characters and center points of edge connected components, to select candidate regions. Second, these candidate regions were verified by using tensor voting, which is capable of extracting perceptual structures from noisy data. Finally, area, orientation, and aspect ratio were used to filter out non-text regions. The proposed method was tested on the ICDAR 2003 dataset and images of wine labels. The experiment results show the validity of this approach.