• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.825-834
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• 2023

The demands of people who purchase fashion products on Internet shopping are gradually increasing, and attempts are being made to provide user-friendly images with 3D contents and web 3D software instead of pictures and videos of products provided. As a reason for this issue, which has emerged as the most important aspect in the fashion web shopping industry, complaints that the product is different when the product is received and the image at the time of purchase has been heightened. As a way to solve this problem, various image processing technologies have been introduced, but there is a limit to the quality of 2D images. In this study, we proposed an automatic conversion technology that converts 2D images into 3D and grafts them to web 3D technology that allows customers to identify products in various locations and reduces the cost and calculation time required for conversion. We developed a system that shoots a mannequin by placing it on a rotating turntable using only 8 cameras. In order to extract only the clothing part from the image taken by this system, markers are removed using U-net, and an algorithm that extracts only the clothing area by identifying the color feature information of the background area and mannequin area is proposed. Using this algorithm, the time taken to extract only the clothes area after taking an image is 2.25 seconds per image, and it takes a total of 144 seconds (2 minutes and 4 seconds) when taking 64 images of one piece of clothing. It can extract 3D objects with very good performance compared to the system.

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

• Journal of the Korea Society of Computer and Information
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• v.18 no.4
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• pp.35-43
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• 2013

As texture images have become prevalent throughout a variety of industrial applications, copyright protection of these images has become important issues. For this reason, this paper proposes a color-texture image watermarking algorithm utilizing texture properties inherent in the image. The proposed algorithm selects suitable blocks to embed a watermark using the energy and homogeneity properties of the grey level co-occurrence matrices as inputs for the fuzzy c-means clustering algorithm. To embed the watermark, we first perform a discrete wavelet transform (DWT) on the selected blocks and choose one of DWT subbands. Then, we embed the watermark into discrete cosine transformed blocks with a gain factor. In this study, we also explore the effects of the DWT subbands and gain factors with respect to the imperceptibility and robustness against various watermarking attacks. Experimental results show that the proposed algorithm achieves higher peak signal-to-noise ratio values (47.66 dB to 48.04 dB) and lower M-SVD values (8.84 to 15.6) when we embedded a watermark into the HH band with a gain factor of 42, which means the proposed algorithm is good enough in terms of imperceptibility. In addition, the proposed algorithm guarantees robustness against various image processing attacks, such as noise addition, filtering, cropping, and JPEG compression yielding higher normalized correlation values (0.7193 to 1).

• Journal of KIISE:Computer Systems and Theory
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• v.33 no.6
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• pp.354-362
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• 2006

This paper suggests a method for generating high qualify stereo video avatar to support visual communication in a CAVE$^{TM}$-like system. In such a system because of frequent change of light projected onto screens around user, it is not easy to extract user silhouette robustly, which is an essential step to generate a video avatar. In this study, we use an infrared reflective image acquired by a grayscale camera with a longpass filter so that the change of visible light on a screen is blocked to extract robust user silhouette. In addition, using two color cameras positioned at a distance of a binocular disparity of human eyes, we acquire two stereo images of the user for fast generation and stereoscopic display of a high quality video avatar without 3D reconstruction. We also suggest a fitting algorithm of a silhouette mask on an infrared reflective image into an acquired color image to remove background. Generated stereo images of a video avatar are texture mapped into a plane in virtual world and can be displayed in stereoscopic using frame sequential stereo method. Suggested method have advantages that it generates high quality video avatar taster than 3D approach and it gives stereoscopic feeling to a user 2D based approach can not provide.

• Journal of Korea Game Society
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• v.10 no.2
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• pp.49-56
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• 2010

In this paper, we propose a control method of virtual hand by the recognition of a user's hand in the virtual reality game environment. We display virtual hand on the game screen after getting the information of the user's hand movement and the direction thru input images by camera. We can utilize the movement of a user's hand as an input interface for virtual hand to select and move the object. As a hand recognition method based on the vision technology, the proposed method transforms input image from RGB color space to HSV color space, then segments the hand area using double threshold of H, S value and connected component analysis. Next, The center of gravity of the hand area can be calculated by 0 and 1 moment implementation of the segmented area. Since the center of gravity is positioned onto the center of the hand, the further apart pixels from the center of the gravity among the pixels in the segmented image can be recognized as fingertips. Finally, the axis of the hand is obtained as the vector of the center of gravity and the fingertips. In order to increase recognition stability and performance the method using a history buffer and a bounding box is also shown. The experiments on various input images show that our hand recognition method provides high level of accuracy and relatively fast stable results.

• The KIPS Transactions:PartB
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• v.9B no.6
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• pp.853-862
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• 2002

Face Detection can be defined as follows : Given a digitalized arbitrary or image sequence, the goal of face detection is to determine whether or not there is any human face in the image, and if present, return its location, direction, size, and so on. This technique is based on many applications such face recognition facial expression, head gesture and so on, and is one of important qualify factors. But face in an given image is considerably difficult because facial expression, pose, facial size, light conditions and so on change the overall appearance of faces, thereby making it difficult to detect them rapidly and exactly. Therefore, this paper proposes fast and exact face detection which overcomes some restrictions by using neural network. The proposed system can be face detection irrelevant to facial expression, background and pose rapidily. For this. face detection is performed by neural network and detection response time is shortened by reducing search region and decreasing calculation time of neural network. Reduced search region is accomplished by using skin color segment and frame difference. And neural network calculation time is decreased by reducing input vector sire of neural network. Principle Component Analysis (PCA) can reduce the dimension of data. Also, pose estimates in extracted facial image and eye region is located. This result enables to us more informations about face. The experiment measured success rate and process time using the Squared Mahalanobis distance. Both of still images and sequence images was experimented and in case of skin color segment, the result shows different success rate whether or not camera setting. Pose estimation experiments was carried out under same conditions and existence or nonexistence glasses shows different result in eye region detection. The experiment results show satisfactory detection rate and process time for real time system.

• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.1
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• pp.1-14
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• 2017

A land cover map provides basic information to help understand the current state of a region, but its utilization in the ecological research field has deteriorated due to limited temporal and spatial resolutions. The purpose of this study was to investigate the possibility of using a land cover map with data based on high resolution images acquired by UAV. Using the UAV, 10.5 cm orthoimages were obtained from the $2.5km^2$ study area, and land cover maps were obtained from object-based and pixel-based classification for comparison and analysis. From accuracy verification, classification accuracy was shown to be high, with a Kappa of 0.77 for the pixel-based classification and a Kappa of 0.82 for the object-based classification. The overall area ratios were similar, and good classification results were found in grasslands and wetlands. The optimal image segmentation weights for object-based classification were Scale=150, Shape=0.5, Compactness=0.5, and Color=1. Scale was the most influential factor in the weight selection process. Compared with the pixel-based classification, the object-based classification provides results that are easy to read because there is a clear boundary between objects. Compared with the land cover map from the Ministry of Environment (subdivision), it was effective for natural areas (forests, grasslands, wetlands, etc.) but not developed areas (roads, buildings, etc.). The application of an object-based classification method for land cover using UAV images can contribute to the field of ecological research with its advantages of rapidly updated data, good accuracy, and economical efficiency.

• Industry Promotion Research
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• v.2 no.1
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• pp.31-38
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• 2017

This study is an exploratory study on the package design research strategy for the activation of the energy drink market. We analyzed the case study of energy drinks and the preference of energy beverage package design which are being sold in Korea and overseas from Oct. 24 to 28, 2016. The results of this study are as follows: First, preference is given to two colors, blue and red, regarding the color of package design of energy drinks. In particular, blue was the most preferred at 62.8%, and the reason for choosing color was the image that reminds me of energy drinks, and it was highly visible. Second, most preference of energy drinks for packaging material was selected from can and glass materials, and most preferred for can. Third, preference for calligraphy was found to be due to the dynamic images of energy drink preferring rough images. Fourth, package design awareness focused on overall image rather than one factor. The limitations of the study are the research subjects only to university students in Daejeon and Chungnam area. In the future, it will be necessary to classify the overall elements of package design and various research subjects, and to study the major consumption and consumption areas of energy drinks. In this study, it is implied that the design of the package needs a package design strategy in accordance with the overall product image, rather than focusing on one factor.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.3
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• pp.271-278
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• 2014

In spite of that stop line and crosswalk should be aware of the most basic objects in transportation system, its features extracted are very limited. In addition to image-based recognition technology, laser and RF, GPS/INS recognition technology, it is difficult to recognize. For this reason, the limited research in this area has been done. In this paper, the algorithm to recognize the stop line and crosswalk is designed and implemented using image-based recognition technology with the images input through a vision sensor. This algorithm consists of three functions.; One is to select the area, in advance, needed for feature extraction in order to speed up the data processing, 'Region of Interest', another is to process the images only that white color is detected more than a certain proportion in order to remove the unnecessary operation, 'Color Pattern Inspection', the other is 'Feature Extraction and Recognition', which is to extract the edge features and compare this to the previously-modeled one to identify the stop line and crosswalk. For this, especially by using case based feature comparison algorithm, it can identify either both stop line and crosswalk exist or just one exists. Also the proposed algorithm is to develop existing researches by comparing and analysing effect of in-vehicle camera installation and changes in recognition rate of distance estimation and various constraints such as backlight and shadow.

• Cartoon and Animation Studies
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• s.45
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• pp.215-233
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• 2016

Timelapse which was appeared Georges Mellieoseu in 1897 was not familiar to people and was not popular in film or broadcast industry in spite of long history. But about 5 years ago, timelapse has become distinguished in all around image art, because it show us aesthetic extraction of time and variation of color in Vimeo and Youtube which neve been seen before. So nowadays latest camera has come out with default menu which can shoot timelapse. But popularity of timelapse seem not development of cinematography technique or appearance of new function but production of image which reflects ages of present with fully accept digital media property. This study is approached from two formative characteristics. First it is strengthen of time which is showed timelapse and analog media from a comparison angle. Second, it is how to extend their sense and imitate images through resolution which can be possible high quality image and color especially with Raw data Through these methodology, this study defines correlation of character of digital media and artistic value of images in timelapse. And also it cleary assures the era of penetrating art has not only visual amusement as known but, also representative value of our ages and technology of media.