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

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.9 no.5
• /
• pp.1938-1962
• /
• 2015

Security of information during transmission is a major issue in this modern era. All of the communicating bodies want confidentiality, integrity, and authenticity of their secret information. Researchers have presented various schemes to cope with these Internet security issues. In this context, both steganography and cryptography can be used effectively. However, major limitation in the existing steganographic methods is the low-quality output stego images, which consequently results in the lack of security. To cope with these issues, we present an efficient method for RGB images based on gray level modification (GLM) and multi-level encryption (MLE). The secret key and secret data is encrypted using MLE algorithm before mapping it to the grey-levels of the cover image. Then, a transposition function is applied on cover image prior to data hiding. The usage of transpose, secret key, MLE, and GLM adds four different levels of security to the proposed algorithm, making it very difficult for a malicious user to extract the original secret information. The proposed method is evaluated both quantitatively and qualitatively. The experimental results, compared with several state-of-the-art algorithms, show that the proposed algorithm not only enhances the quality of stego images but also provides multiple levels of security, which can significantly misguide image steganalysis and makes the attack on this algorithm more challenging.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.12 no.4
• /
• pp.373-379
• /
• 2019

In this study, image-based flock monitoring and density evaluation were conducted for broiler chicks welfare. Image data were captured by using a mono camera and region of broiler chicks in the image was detected using converting to HSV color model, thresholding, and clustering with filtering. The results show that region detection was performed with 5% relative error and 0.81 IoU on average. The detected region was corrected to the actual region by projection into ground using coordinate transformation between camera and real-world. The flock density of broiler chicks was estimated using the corrected actual region, and it was observed with an average of 80%. The developed algorithm can be applied to the broiler chicks house through enhancing accuracy of region detection and low-cost system configuration.

• Composites Research
• /
• v.27 no.5
• /
• pp.183-189
• /
• 2014

Experimental considerations have been performed to obtain the clear optical microscopic images of graphene oxide which are useful to probe its quality and morphological information such as a shape, a size, and a thickness. In this study, we investigated the contrast enhancement of the optical images of graphene oxide after hydrazine vapor reduction on a Si substrate coated with a 300 nm-thick $SiO_2$ dielectric layer. Also, a green-filtered light source gave higher contrast images comparing to optical images under standard white light. Furthermore, it was found that a image channel separation technique can be an alternative to simply identify the morphological information of graphene oxide, where red, green, and blue color values are separated at each pixels of the optical image. The approaches performed in this study can be helpful to set up a simple and easy protocol for the morphological identification of graphene oxide using a conventional optical microscope instead of a scanning electron microscopy or an atomic force microscopy.

• Korean Journal of Remote Sensing
• /
• v.39 no.2
• /
• pp.223-232
• /
• 2023

A widely used method for monitoring land cover using high-resolution satellite images is to classify the images based on the colors of the objects of interest. In urban areas, not only major objects such as buildings and roads but also vegetation such as trees frequently appear in high-resolution satellite images. However, the colors of vegetation objects often resemble those of other objects such as buildings, roads, and shadows, making it difficult to accurately classify objects based solely on color information. In this study, we propose a method that can accurately classify not only objects with various colors such as buildings but also vegetation objects. The proposed method uses the normalized difference vegetation index (NDVI) image, which is useful for detecting vegetation objects, along with the RGB image and classifies objects into subclasses. The subclass classification results are fused, and the final classification result is generated by combining them with the image segmentation results. In experiments using Compact Advanced Satellite 500-1 imagery, the proposed method, which applies the NDVI and subclass classification together, showed an overall accuracy of 87.42%, while the overall accuracy of the subchannel classification technique without using the NDVI and the subclass classification technique alone were 73.18% and 81.79%, respectively.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.9 no.4
• /
• pp.897-904
• /
• 2005

Face Recognition has been an active research area because it is not difficult to acquire face image data and it is applicable in wide range area in real world. Due to the high dimensionality of a face image space, however, it is not easy to process the face images. In this paper, we propose a method to reduce the dimension of the facial data and extract the features from them. It will be solved using the method which extracts the features from holistic face images. The proposed algorithm consists of two parts. The first is the using of principal component analysis (PCA) to transform three dimensional color facial images to one dimensional gray facial images. The second is integrated linear discriminant analusis (PCA+LDA) to prevent the loss of informations in case of performing separated steps. Integrated LDA is integrated algorithm of PCA for reduction of dimension and LDA for discrimination of facial vectors. First, in case of transformation from color image to gray image, PCA(Principal Component Analysis) is performed to enhance the image contrast to raise the recognition rate. Second, integrated LDA(Linear Discriminant Analysis) combines the two steps, namely PCA for dimensionality reduction and LDA for discrimination. It makes possible to describe concise algorithm expression and to prevent the information loss in separate steps. To validate the proposed method, the algorithm is implemented and tested on well controlled face databases.

• Journal of the Ergonomics Society of Korea
• /
• v.36 no.2
• /
• pp.123-144
• /
• 2017

Objective: The objective of this study is to evaluate affection on how users perceive colors viewed from an automotive visual display according to cultural and radical differences including North America, Europe, and Southeast Asia. This study especially aims to identify effects of the variation of tone and chroma of representative color groups by analyzing affection differences depending on cultural and racial differences targeting the colors constituted through variation of tone and chroma, centered on representative colors. Background: The colors of the menu, information display or background viewed through an automotive visual display are an important factor stimulating consumer's affection, and therefore an effort to express the vehicle's brand and product image through colors is made. The studies on colors focus only on the research on unique characteristics of colors, but an affective approach lacks according to cultural and racial differences on colors considering tone and chroma variation within a color from the currently used automotive visual displays. Method: To grasp the visual affection felt by users, this study extracted affective adjectives related with colors through existing literature and a dictionary for adjectives, and presented human affection dimensions on colors through evaluation of various colors. Prior to carrying out affection evaluation, the basic light sources, red (R), green (G), and blue (B) constituting the colors used for automotive visual displays were defined as a representative color group, respectively. When colors in a color group are constituted, the evaluation target of each color group consisted of the colors considering the variation of tone and chroma by changing color sense through RGB values of the remaining two light sources. And then, this study carried out affection evaluation on the constituted colors targeting the subjects with cultural and racial differences. Results: As a result of evaluating the constituted colors with representative affections, there were statistically significant differences between the groups having cultural and racial differences. As a result of S-N-K post-hoc analysis on the colors showing significant differences, North America and Europe were classified as heterogeneous groups. In some cases, Korea was classified as the homogeneous group with North America, but Korea was mainly classified as the homogenous group with Europe. Conclusion: The representative affections on colors from an automotive visual display was drawn as three affective dimensions: passionate, neat, and masculine. Based on these, the affection of Korea and Europe on the constituted colors showed significant differences from that of North America, as a result of affection evaluation on the constituted colors viewed through the visual display by reflecting cultural and racial factors. Regarding representative color groups, bigger cultural and racial differences were revealed in terms of affection on red and green colors than on blue color, and variation of affection was the biggest in the red color. Application: This study analyzed correlations of affection considering the colors constituted through variation of tone and chroma, and the culture and race in the representative color groups constituting a visual display. The results of this study are predicted to be utilized in coordination and selection of colors viewed from an automotive visual display taking into account culture and race.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.42 no.6
• /
• pp.9-18
• /
• 2005

This paper proposes the color decomposition method for multi-primary display (MPD) using a 3-dimensional look-up-table (3D-LUT) in a linearized LAB space. The proposed method decomposes conventional three-primary colors into the multi-primary control values of a display device under constraints of tristimulus match. To reproduce images on the MPD, the color signals should be estimated from a device-independent color space, such as CIEXYZ and CIELAB. In this paper, the linearized LAB space is used due to its linearity and additivity in color conversion. The proposed method constructs the 3-D LUT, which contain gamut boundary information to calculate color signals of the MPD. For the image reproduction, standard RGB or CIEXYZ is transformed to the linearized LAB and then hue and chroma are computed to refer to the 3D-LUT. In the linearlized LAB space, the color signals of a gamut boundary point with the same lightness and hue of an input point are calculated. Also, color signals of a point on gray axis are calculated with the same lightness of an input. With gamut boundary points and input point, color signals of the input points are obtained with the chroma ratio divided by the chroma of the gamut boundary point. Specially, for the hue change, neighboring boundary points are employed. As a result the proposed method guarantees the continuity of color signals and computational efficiency, and requires less amount of memory.

• Journal of Animal Science and Technology
• /
• v.65 no.3
• /
• pp.638-651
• /
• 2023

The objective of this study was to quantitatively estimate the level of grazing area damage in outdoor free-range pig production using a Unmanned Aerial Vehicles (UAV) with an RGB image sensor. Ten corn field images were captured by a UAV over approximately two weeks, during which gestating sows were allowed to graze freely on the corn field measuring 100 × 50 m². The images were corrected to a bird's-eye view, and then divided into 32 segments and sequentially inputted into the YOLOv4 detector to detect the corn images according to their condition. The 43 raw training images selected randomly out of 320 segmented images were flipped to create 86 images, and then these images were further augmented by rotating them in 5-degree increments to create a total of 6,192 images. The increased 6,192 images are further augmented by applying three random color transformations to each image, resulting in 24,768 datasets. The occupancy rate of corn in the field was estimated efficiently using You Only Look Once (YOLO). As of the first day of observation (day 2), it was evident that almost all the corn had disappeared by the ninth day. When grazing 20 sows in a 50 × 100 m² cornfield (250 m²/sow), it appears that the animals should be rotated to other grazing areas to protect the cover crop after at least five days. In agricultural technology, most of the research using machine and deep learning is related to the detection of fruits and pests, and research on other application fields is needed. In addition, large-scale image data collected by experts in the field are required as training data to apply deep learning. If the data required for deep learning is insufficient, a large number of data augmentation is required.

• ETRI Journal
• /
• v.46 no.2
• /
• pp.290-306
• /
• 2024

To treat the novel COronaVIrus Disease (COVID), comparatively fewer medicines have been approved. Due to the global pandemic status of COVID, several medicines are being developed to treat patients. The modern COVID medicines development process has various challenges, including predicting and detecting hazardous COVID medicine responses. Moreover, correctly predicting harmful COVID medicine reactions is essential for health safety. Significant developments in computational models in medicine development can make it possible to identify adverse COVID medicine reactions. Since the beginning of the COVID pandemic, there has been significant demand for developing COVID medicines. Therefore, this paper presents the transferlearning methodology and a multilabel convolutional neural network for COVID (MLCNN-COV) medicines development model to identify negative responses of COVID medicines. For analysis, a framework is proposed with five multilabel transfer-learning models, namely, MobileNetv2, ResNet50, VGG19, DenseNet201, and Inceptionv3, and an MLCNN-COV model is designed with an image augmentation (IA) technique and validated through experiments on the image of three-dimensional chemical conformer of 17 number of COVID medicines. The RGB color channel is utilized to represent the feature of the image, and image features are extracted by employing the Convolution2D and MaxPooling2D layer. The findings of the current MLCNN-COV are promising, and it can identify individual adverse reactions of medicines, with the accuracy ranging from 88.24% to 100%, which outperformed the transfer-learning model's performance. It shows that three-dimensional conformers adequately identify negative COVID medicine responses.