• The KIPS Transactions:PartB
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• v.15B no.1
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• pp.25-36
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• 2008

In this paper, we present a robust hand recognition approach to sudden illumination changes. The proposed approach constructs a background model with respect to hue and hue gradient in HSI color space and extracts a foreground hand region from an input image using the background subtraction method. Eighteen features are defined for a hand pose and multi-class SVM(Support Vector Machine) approach is applied to learn and classify hand poses based on eighteen features. The proposed approach robustly extracts the contour of a hand with variations in illumination by applying the hue gradient into the background subtraction. A hand pose is defined by two Eigen values which are normalized by the size of OBB(Object-Oriented Bounding Box), and sixteen feature values which represent the number of hand contour points included in each subrange of OBB. We compared the RGB-based background subtraction, hue-based background subtraction and the proposed approach with sudden illumination changes and proved the robustness of the proposed approach. In the experiment, we built a hand pose training model from 2,700 sample hand images of six subjects which represent nine numerical numbers from one to nine. Our implementation result shows 92.6% of successful recognition rate for 1,620 hand images with various lighting condition using the training model.

• Korean Journal of Remote Sensing
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• v.34 no.6_3
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• pp.1415-1425
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• 2018

Kompsat-5 is the first Earth Observation Satellite which is equipped with an SAR in Korea. SAR images are generated by receiving signals reflected from an object by microwaves emitted from a SAR antenna. Because the wavelengths of microwaves are longer than the size of particles in the atmosphere, it can penetrate clouds and fog, and high-resolution images can be obtained without distinction between day and night. However, there is no color information in SAR images. To overcome these limitations of SAR images, colorization of SAR images using Cycle GAN, a deep learning model developed for domain translation, was conducted. Training of Cycle GAN is unstable due to the unsupervised learning based on unpaired dataset. Therefore, we proposed MS Cycle GAN applying multi-scale discriminator to solve the training instability of Cycle GAN and to improve the performance of colorization in this paper. To compare colorization performance of MS Cycle GAN and Cycle GAN, generated images by both models were compared qualitatively and quantitatively. Training Cycle GAN with multi-scale discriminator shows the losses of generators and discriminators are significantly reduced compared to the conventional Cycle GAN, and we identified that generated images by MS Cycle GAN are well-matched with the characteristics of regions such as leaves, rivers, and land.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.2
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• pp.101-115
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• 2022

This study compared the performance of LiDAR in detecting objects in rough weather with that in clear weather. An experiment that reproduced rough weather divided the fog visibility into four stages from 200 m to 50 m and controlled the rainfall by dividing it into 20 mm/h and 50 mm/h. The number of points cloud and intensity were used as the performance indicators. The difference in performance was statistically investigated by a T-Test. The result of the study indicates that the performance of LiDAR decreased in the order in situations of 20 mm/h rainfall, fog visibility less than 200 m, 50 mm/h rainfall, fog visibility less than 150 m, fog visibility less than 100 m, and fog visibility less than 50 m. The decreased performance was greater when the measurement distance was greater and when the color was black rather than white. However, in the case of white, there was no difference in performance at a measurement distance of 10 m even at 50 m fog visibility, which is considered the worst situation in this experiment. This no difference in performance was also statistically significant. These performance verification results are expected to be utilized in the manufacture of road facilities in the future that improve the visibility of sensors.

• Journal of Broadcast Engineering
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• v.26 no.6
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• pp.692-703
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• 2021

Recently, with the development of computer graphics technology, research on technology for expressing real objects as more realistic virtual graphics is being actively conducted. Point cloud is a technology that uses numerous points, including 2D spatial coordinates and color information, to represent 3D objects, and they require huge data storage and high-performance computing devices to provide various services. Video-based Point Cloud Compression (V-PCC) technology is currently being studied by the international standard organization MPEG, which is a projection based method that projects point cloud into 2D plane, and then compresses them using 2D video codecs. V-PCC technology compresses point cloud objects using 2D images such as Occupancy map, Geometry image, Attribute image, and other auxiliary information that includes the relationship between 2D plane and 3D space. When increasing the density of point cloud or expanding an object, 3D calculation is generally used, but there are limitations in that the calculation method is complicated, requires a lot of time, and it is difficult to determine the correct location of a new point. This paper proposes a method to generate additional points at more accurate locations with less computation by applying 2D interpolation to the image on which the point cloud is projected, in the V-PCC technology.

• Journal of the Korea Society of Computer and Information
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• v.28 no.4
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• pp.105-112
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• 2023

In this paper, we propose a new method for displaying colored defects by measuring the relative density with the wide-area and local densities of X-ray. The relative density of one pixel represents a relative difference from the surrounding pixels, and we also suggest a colorization of X-ray images representing these pixels as normal and defective. The traditional method mainly inspects materials such as plastics and metals, which have large differences in transmittance to the object. Our proposed method can be used to detect defects such as sprouts or holes in images obtained by an inspection machine that detects X-rays. In the experiment, the products that could not be seen with the naked eye were colored with pests or sprouts in a specific color so that they could be used in the agricultural product selection system. Products that are uniformly filled with a single ingredient inside, such as potatoes, carrots, and apples, can be detected effectively. However, it does not work well with bumpy products, such as peppers and paprika. The advantage of this method is that, unlike machine learning, it doesn't require large amounts of data. The proposed method could be applied to a screening system using X-rays and used not only in agricultural product screening systems but also in manufacturing processes such as processed food and parts manufacturing, so that it can be actively used to select defective products.

• Korean Journal of Cognitive Science
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• v.16 no.4
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• pp.305-322
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• 2005

Watermarking is a widely employed method tn protecting copyright of a digital image, the owner's unique image is embedded into the original image. Strengthened level of watermark insertion would help enhance its resilience in the process of extraction even from various distortions of transformation on the image size or resolution. However, its level, at the same time, should be moderated enough not to reach human visibility. Finding a balance between these two is crucial in watermarking. For the algorithm for watermarking, the predefined strength of a watermark, computed from the physical difference between the original and embedded images, is applied to all images uniformal. The mean brightness or contrast of the surrounding images, other than the absolute brightness of an object, could affect human sensitivity for object detection. In the present study, we examined whether the detectability for watermark noise might be attired by image statistics: mean brightness and contrast of the image. As the first step to examine their effect, we made rune fundamental images with varied brightness and control of the original image. For each fundamental image, detectability for watermark noise was measured. The results showed that the strength ot watermark node for detection increased as tile brightness and contrast of the fundamental image were increased. We have fitted the data to a regression line which can be used to estimate the strength of watermark of a given image with a certain brightness and contrast. Although we need to take other required factors into consideration in directly applying this formula to actual watermarking algorithm, an adaptive watermarking algorithm could be built on this formula with image statistics, such as brightness and contrast.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.193-205
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• 2023

A large amount of floating debris from land-based sources during heavy rainfall has negative social, economic, and environmental impacts, but there is a lack of monitoring systems for floating debris accumulation areas and amounts. With the recent development of artificial intelligence technology, there is a need to quickly and efficiently study large areas of water systems using drone imagery and deep learning-based object detection models. In this study, we acquired various images as well as drone images and trained with You Only Look Once (YOLO)v5s and the recently developed YOLO7 and YOLOv8s to compare the performance of each model to propose an efficient detection technique for land-based floating debris. The qualitative performance evaluation of each model showed that all three models are good at detecting floating debris under normal circumstances, but the YOLOv8s model missed or duplicated objects when the image was overexposed or the water surface was highly reflective of sunlight. The quantitative performance evaluation showed that YOLOv7 had the best performance with a mean Average Precision (intersection over union, IoU 0.5) of 0.940, which was better than YOLOv5s (0.922) and YOLOv8s (0.922). As a result of generating distortion in the color and high-frequency components to compare the performance of models according to data quality, the performance degradation of the YOLOv8s model was the most obvious, and the YOLOv7 model showed the lowest performance degradation. This study confirms that the YOLOv7 model is more robust than the YOLOv5s and YOLOv8s models in detecting land-based floating debris. The deep learning-based floating debris detection technique proposed in this study can identify the spatial distribution of floating debris by category, which can contribute to the planning of future cleanup work.

• Archives of design research
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• v.19 no.5 s.67
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• pp.119-128
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• 2006

The scale of visual expression has expanded from freeze frame to motion picture as media have developed. Moving pictures such as animation, movies, TV CM and GUI become formative elements whose movement is necessary compared to freeze frame as apparent movement phenomenon and unit structure such as short and scene appear. Therefore, of formative elements such as a shape, color, space, size and movement, movement is importantly distinguished in the moving image. The expression and form of image as a relationship between the signified and signifier explained by Saussure are accepted as a sign by mutual complement even though they limit the content. This makes it possible to infer that the formal feature of movement participates in the message content. To verify this, the result of moving picture visual perception experiment based on the gestalt grouping principle result shows that 70-80 percent of subjects think that 'movement' is the important grouping clue in perception. Movement affects the maintenance of the context of message content in the communication process when the meaning structure of moving picture is analyzed based on the structural feature. The identity can be maintained with if there is a movement with similar directive point even if the color and shape of people, things and background are changed. Second, the clarity of the content is elevated by a distinguished object as a figure by movement. Third, it acts as a knowledge representation which can predict similar movement process of next information processing. Forth, movement gives the content consistency even though more than two scenes have fast switch and complicated editing structure like cross-cutting. Movement becomes a clue which can make grouping information input by visual perception reaction. Also, it gives the order to the visual expression which can be used improperly by formation of structural frame of image message and has the effectiveness which elevates the clarity of signification. Moving picture has discourse with several mixed unit structures because it fundamentally contains time and the common and distinguished expression is needed by media-mix circumstances. Therefore, by the application of gestalt grouping principle to moving picture field, movement becomes the more distinguished than other formative elements and affects the formation of meaning structure. This study propose a viewpoint that develops structural formative beauty and new image expression in the media image field.

• Korean Journal of Remote Sensing
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• v.35 no.6_4
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• pp.1319-1326
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• 2019

The KARI(Korea Aerospace Research Institute) operates the government satellite information application consultation to cope with ever-increasing demand for satellite images in the public sector, and carries out various support projects including the generation and provision of mosaic images on the Korean Peninsula every year to enhance user convenience and promote the use of satellite images. In particular, the government has wanted to increase the utilization of mosaic images on the Korean Peninsula and seek to classify and update mosaic images so that users can use them in their businesses easily. However, it is necessary to test and verify whether the classification results of the mosaic images can be utilized in the field since the original spectral information is distorted during pan-sharpening and color balancing, and there is a limitation that only R, G, and B bands are provided. Therefore, in this study, the reliability of the classification result of the mosaic image was compared to the result of KOMPSAT-3 image. The study found that the accuracy of the classification result of KOMPSAT-3 image was between 81~86% (overall accuracy is about 85%), while the accuracy of the classification result of mosaic image was between 69~72% (overall accuracy is about 72%). This phenomenon is interpreted not only because of the distortion of the original spectral information through pan-sharpening and mosaic processes, but also because NDVI and NDWI information were extracted from KOMPSAT-3 image rather than from the mosaic image, as only three color bands(R, G, B) were provided. Although it is deemed inadequate to distribute classification results extracted from mosaic images at present, it is believed that it will be necessary to explore ways to minimize the distortion of spectral information when making mosaic images and to develop classification techniques suitable for mosaic images as well as the provision of NIR band information. In addition, it is expected that the utilization of images with limited spectral information could be increased in the future if related research continues, such as the comparative analysis of classification results by geomorphological characteristics and the development of machine learning methods for image classification by objects of interest.

• Cartoon and Animation Studies
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• s.35
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• pp.329-346
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• 2014

Art is the product from the combination of politics, economy, and social and cultural aspects. Recent development of digital media has affected on the expansion of visual expression in art. Digital media allow artists to use sound and physical interaction as well as image as an plastic element for making a work of art. Also, digital media help artists create an interactive, synaesthetic and visual perceptive environment by combining viewers' physical interaction with the reconstruction of image, sound, light, and among other plastic elements. This research was focused on the analysis of the relationship between images in art work and the viewer and data visualization using sound from the perspective of visual perception. This research also aimed to develop an interactive art by visualizing physical data with sound generating from outer stimulus or the viewer. Physical data generating from outer sound can be analyzed in various aspects. For example, Sound data can be analyzed and sampled within pitch, volume, frequency, and etc. This researcher implemented a new form of media art through the visual experiment of LED light triggered by sound frequency generating from viewers' voice or outer physical stimulus. Also, this researcher explored the possibility of various visual image expression generating from the viewer's reaction to illusionary characteristics of light(LED), which can be transformed within external physical data in real time. As the result, this researcher used a motif from Piet Mondrian's Broadway Boogie Woogie in order to implement a visual perceptive interactive work reacting with sound. Mondrian tried to approach at the essence of visual object by eliminating unnecessary representation elements and simplifying them in painting and making them into abstraction consisting of color, vertical and horizontal lines. This researcher utilized Modrian's simplified visual composition as a representation metaphor in oder to transform external sound stimulus into the element of light(LED), and implemented an environment inducing viewers' participation, which is a dynamic composition maximizing a synaesthetic expression, differing from Modrian's static composition.