• Journal of information and communication convergence engineering
• /
• v.13 no.3
• /
• pp.205-214
• /
• 2015

In this paper, we propose a new approach to learning a discriminative model for figure/ground segmentation by incorporating the bag-of-features and conditional random field (CRF) techniques. We advocate the use of image patches instead of superpixels as the basic processing unit. The latter has a homogeneous appearance and adheres to object boundaries, while an image patch often contains more discriminative information (e.g., local image structure) to distinguish its categories. We use pixel-level sparse coding to represent an image patch. With the proposed feature representation, the unary classifier achieves a considerable binary segmentation performance. Further, we integrate unary and pairwise potentials into the CRF model to refine the segmentation results. The pairwise potentials include color and texture potentials with neighborhood interactions, and an edge potential. High segmentation accuracy is demonstrated on three benchmark datasets: the Weizmann horse dataset, the VOC2006 cow dataset, and the MSRC multiclass dataset. Extensive experiments show that the proposed approach performs favorably against the state-of-the-art approaches.

• Journal of Digital Convergence
• /
• v.14 no.11
• /
• pp.489-494
• /
• 2016

Stop-Motion Animation is the work that gives lives to the objects. In order to give lives to objects and to form story line to persuade the audience, it is important that the reason of choice of color, material, and texture has something to do with the narratives. For this study, among the Garri Bardin's animation, the ones with good representation of the objects have been chosen: Conflict (1983), Fioritures (1987), and Adagio (2000). With these animations, I would like to look for the meaning of existence of the objects, and to consider the importance of the relationship between the types of objects that are represented and the narratives.

• Journal of the Korea Computer Graphics Society
• /
• v.16 no.4
• /
• pp.11-16
• /
• 2010

We present an efficient algorithm for rendering sweep surfaces using programmable graphics hardware. A sweep surface can be represented by a cross-section curve undergoing a spline motion. This representation has a simple matrix-vector multiplication structure that can easily be adapted to programmable graphics hardware. The data for the motion and cross-section curves are stored in texture memory. The vertex processor considers a pair of surface parameters as a vertex and evaluates its coordinates and normal vector with a single matrix multiplication. Using the GPU in this way is between 10 and 40 times as fast as CPU-based rendering.

• Journal of Korea Game Society
• /
• v.19 no.3
• /
• pp.33-42
• /
• 2019

This paper is the result of research on how to represent 'Landscape' of Oriental painting in 3D space within virtual reality. Based on the similarities between the characteristics of Oriental painting and virtual reality put together three features of painting and put them into the digital content. For the expression of painting style, we produced texture, represented spaces and shadows as a content development environment, and the view point with helper storytelling.

• Journal of KIISE:Computer Systems and Theory
• /
• v.37 no.3
• /
• pp.184-193
• /
• 2010

This paper proposes a biological iridescence BRDF(Bidirectional Reflectance Distribution Function) compression and rendering method. In the graphics technology, iridescence sometimes is named structure colors. The main features of these symptoms are shown transform of color and brightness by varying viewpoint. Graphics technology to render this is the BRDF technology. The BRDF methods enable realistic representation of varying view direction, but it requires a lot of computing power because of large data. In this paper, we obtain reflection map from iridescence BRDF, analyze color of reflection map and propose representation method by several colorfully concentric circle. The one concentric circle represents beam width of reflection ray by one normal vector. In this paper, we synthesize rough concentric by using several virtually optical normal vectors. And we obtain spectrum information from concentric circles passing through the center point. The proposed method enables IBR(image based rendering) technique which results is realistic illuminance and spectrum distribution by one texture from reduced BRDF data within spectrum.

• (The) Research of the performance art and culture
• /
• no.40
• /
• pp.5-35
• /
• 2020

Affect is intensity and quality that are generated as the physical body senses the outside world. Of experienced affect, notions that are granted meaning and interpretation are emotions. Affect theory distinguishes emotion and affect and by focusing on affect, it provides methods with which to analyze physical body responses and changes and it presents new possibilities to performing arts research that uses the physical body as a medium. Nong-ak is art that concentrates mainly on the occurrence of affect rather than 'representation'. Nong-ak is a performance type in which sound, color, texture, and physical movement overlap and exist in a synesthetic way. Here, physical things such as instruments, props, costumes, and stage devices are gathered together with non-physical things such as rhythm, mood, and atmosphere around human bodies. The physical body is stimulated by these things, displays tendencies that suit performances, and becomes 'the body without an image' as it immerses itself into the performance, acting while displaying 'quasi-corporeality'. The body, which moves automatically as if without consciousness, appears more easily within groups. To transition individuals of everyday life to 'the body without an image', Nong-ak executes the group physical exercise of 'Jinpuri'. Such physical exercise builds up affect by increasing nonverbal communion and communication and brings out the creativity of individuals within mutual trust and a sense of belonging. Affect and emotion stirred up by Nong-ak act as confirmation and affirmation of the existence, vitality, and ability of one's self and groups. Such affirmation recalls Nong-ak as a meaningful and important value from group dimensions and perceives it as a performance form that should be preserved and passed on.

• KIPS Transactions on Software and Data Engineering
• /
• v.12 no.12
• /
• pp.519-524
• /
• 2023

In hashing-based image retrieval, the hash code of a manipulated image is different from the original image, making it difficult to search for the same image. This paper proposes and evaluates a self-supervised deephashing model that generates perceptual hash codes from feature information such as texture, shape, and color of images. The comparison models are autoencoder-based variational inference models, but the encoder is designed with a fully connected layer, convolutional neural network, and transformer modules. The proposed model is a variational inference model that includes a SimAM module of extracting geometric patterns and positional relationships within images. The SimAM module can learn latent vectors highlighting objects or local regions through an energy function using the activation values of neurons and surrounding neurons. The proposed method is a representation learning model that can generate low-dimensional latent vectors from high-dimensional input images, and the latent vectors are binarized into distinguishable hash code. From the experimental results on public datasets such as CIFAR-10, ImageNet, and NUS-WIDE, the proposed model is superior to the comparative model and analyzed to have equivalent performance to the supervised learning-based deephashing model. The proposed model can be used in application systems that require low-dimensional representation of images, such as image search or copyright image determination.

• The KIPS Transactions:PartA
• /
• v.16A no.3
• /
• pp.159-168
• /
• 2009

In this paper, we present an efficient way of implementing OpenGL ES 1.1 standard for the environments with hardware-supported OpenGL API, such as desktop PCs. Although OpenGL ES was started from the existing OpenGL features, it becomes a new three-dimensional graphics library customized for embedded systems through introducing fixed-point arithmetic operations, buffer management with fixed-point data type supports, completely new texture mapping functionalities and others. Currently, it is the official three dimensional graphics library for Google Android, Apple iPhone, PlayStation3, etc. In this paper, we achieved improvements on the arithmetic operations for the fixed-point number representation, which is the most characteristic data type for OpenGL ES. For the conversion of fixed-point data types to the floating-point number representations for the underlying OpenGL, we show the way of efficient conversion processes even with satisfying OpenGL ES standard requirements. We also introduced a simple memory management scheme to mange the converted data for the buffer containing fixed-point numbers. In the case of texture processing, the requirements in both standards are quite different and thus we used completely new software-implementations. Our final implementation result of OpenGL ES library provides all of over than 200 functions in OpenGL ES 1.1 standard and completely passed its conformance test, to show its compliance with the standard. From the efficiency viewpoint, we measured its execution times for several OpenGL ES-specific application programs and achieved at most 33.147 times improvements, to become the fastest one among the OpenGL ES implementations in the same category.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.49 no.4
• /
• pp.49-57
• /
• 2012

This paper presents a fast multiple-image-based deblurring method that decreases the computation loads in the image deblurring, enhancing the sharpness of the textures or edges of the restored images. First, two blurred images with some blurring artifacts and one noisy image including severe noises are consecutively captured under a relatively long and short exposures, respectively. To improve the processing speeds, the captured multiple images are downsampled at the ratio of two, and then a way of estimating the point spread function(PSF) based on the image or edge patches extracted from the whole images, is introduced. The method enables to effectively reduce the computation time taken in the PSF prediction. Next, the texture-enhanced image deblurring method of supplementing the ability of the texture representation degraded by the downsampling of the input images, is developed and then applied. Finally, to get the same image size as the original input images, an upsampling method of utilizing the sharp edges of the captured noisy image is applied. By using the proposed method, the processing times taken in the image deblurring, which is the main obstacle of its application to the digital cameras, can be shortened, while recovering the fine details of the textures or edge components.

• Journal of Internet Computing and Services
• /
• v.21 no.6
• /
• pp.23-31
• /
• 2020

This study was carried out to generate various images of railroad surfaces with random defects as training data to be better at the detection of defects. Defects on the surface of railroads are caused by various factors such as friction between track binding devices and adjacent tracks and can cause accidents such as broken rails, so railroad maintenance for defects is necessary. Therefore, various researches on defect detection and inspection using image processing or machine learning on railway surface images have been conducted to automate railroad inspection and to reduce railroad maintenance costs. In general, the performance of the image processing analysis method and machine learning technology is affected by the quantity and quality of data. For this reason, some researches require specific devices or vehicles to acquire images of the track surface at regular intervals to obtain a database of various railway surface images. On the contrary, in this study, in order to reduce and improve the operating cost of image acquisition, we constructed the 'Defective Railroad Surface Regeneration Model' by applying the methods presented in the related studies of the Generative Adversarial Network (GAN). Thus, we aimed to detect defects on railroad surface even without a dedicated database. This constructed model is designed to learn to generate the railroad surface combining the different railroad surface textures and the original surface, considering the ground truth of the railroad defects. The generated images of the railroad surface were used as training data in defect detection network, which is based on Fully Convolutional Network (FCN). To validate its performance, we clustered and divided the railroad data into three subsets, one subset as original railroad texture images and the remaining two subsets as another railroad surface texture images. In the first experiment, we used only original texture images for training sets in the defect detection model. And in the second experiment, we trained the generated images that were generated by combining the original images with a few railroad textures of the other images. Each defect detection model was evaluated in terms of 'intersection of union(IoU)' and F1-score measures with ground truths. As a result, the scores increased by about 10~15% when the generated images were used, compared to the case that only the original images were used. This proves that it is possible to detect defects by using the existing data and a few different texture images, even for the railroad surface images in which dedicated training database is not constructed.