• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.22 no.2
• /
• pp.127-133
• /
• 2021

A recent study of people physically falling focused on analyzing the motions of the falls using a recurrent neural network (RNN) and a deep learning approach to get good results from detecting 2D human poses from a single color image. In this paper, we investigate a detection method for estimating the position of the head and shoulder keypoints and the acceleration of positional change using the skeletal keypoints information extracted using PoseNet from an image obtained with a low-cost 2D RGB camera, increasing the accuracy of judgments about the falls. In particular, we propose a fall detection method based on the characteristics of post-fall posture in the fall motion-analysis method. A public data set was used to extract human skeletal features, and as a result of an experiment to find a feature extraction method that can achieve high classification accuracy, the proposed method showed a 99.8% success rate in detecting falls more effectively than a conventional, primitive skeletal data-use method.

• Clean Technology
• /
• v.28 no.4
• /
• pp.323-330
• /
• 2022

Microfluidic paper-based analytical devices (μPADs) have recently been in the spotlight for their applicability in point-of-care diagnostics and environmental material detection. This study presents a double-sided printing method for fabricating 3D-μPADs, providing simple and cost effective metal ion detection. The design of the 3D-μPAD was made into an acryl stamp by laser cutting and then coating it with a thin layer of PDMS using the spin-coating method. This fabricated stamp was used to form the 3D structure of the hydrophobic barrier through a double-sided contact printing method. The fabrication of the 3D hydrophobic barrier within a single sheet was optimized by controlling the spin-coating rate, reagent ratio and contacting time. The optimal conditions were found by analyzing the area change of the PDMS hydrophobic barrier and hydrophilic channel using ink with chromatography paper. Using the fabricated 3D-μPAD under optimized conditions, Ni²⁺, Cu²⁺, Hg²⁺, and pH were detected at different concentrations and displayed with color intensity in grayscale for quantitative analysis using ImageJ. This study demonstrated that a 3D-μPAD biosensor can be applied to detect metal ions without special analysis equipment. This 3D-μPAD provides a highly portable and rapid on-site monitoring platform for detecting multiple heavy metal ions with extremely high repeatability, which is useful for resource-limited areas and developing countries.

• Imaging Science in Dentistry
• /
• v.54 no.1
• /
• pp.43-48
• /
• 2024

Purpose: Oral hygiene, maintained through plaque control, helps prevent periodontal disease and dental caries. This study was conducted to examine the accuracy of plaque detection with an intraoral scanner(IOS) compared to images captured with an optical camera. Materials and Methods: To examine the effect of color tone, artificial tooth resin samples were stained red, blue, and green, after which images were acquired with a digital single-lens reflex (DSLR) camera and an IOS device. Stained surface ratios were then determined and compared. Additionally, the deviation rate of the IOS relative to the DSLR camera was computed for each color. In the clinical study, following plaque staining with red disclosing solution, the staining was captured by the DSLR and IOS devices, and the stained area on each image was measured. Results: The stained surface ratios did not differ significantly between DSLR and IOS images for any color group. Additionally, the deviation rate did not vary significantly across colors. In the clinical test, the stained plaque appeared slightly lighter in color, and the delineation of the stained areas less distinct, on the IOS compared to the DSLR images. However, the stained surface ratio was significantly higher in the IOS than in the DSLR group. Conclusion: When employing IOS with dental plaque staining, the impact of color was minimal, suggesting that the traditional red stain remains suitable for plaque detection. IOS images appeared relatively blurred and enlarged relative to the true state of the teeth, due to inferior sharpness compared to camera images.

• Clinical and Experimental Reproductive Medicine
• /
• v.24 no.3
• /
• pp.335-340
• /
• 1997

Fluorescence in situ hybridization (FISH) techniques allow the enumeration of chromosome abnormalities and from a great potential for many clinical applications. In order to produce quantitative and reproducible results, expensive tools such as a cooled CCD camera and a computer software are required. We have developed a Chromosome Image Processing System (Chips) using FISH that allows the detection and mapping of the genetic aberrations. The aim of our study, therefore, is to evaluate the capabilities of our original system using a black-and-white video camera. As a model system, three repetitive DNA probes (D18Z1, DXZ1, and DYZ3) were hybridized to variety different clinical samples such as human metaphase spreads and interphase nuclei obtained from uncultured peripheral blood lymphocytes, uncultured amniocytes, and germ cells. The visualization of the FISH signals was performed using our system for image acquisition and pseudocoloring. FISH images were obtained by combining images from each of probes and DAPI counterstain captured separately. Using our original system, the aberrations of single or multiple chromosomes in a single hybridization experiment using chromosomes and interphase nuclei from a variety of cell types, including lymphocytes, amniocytes, sperm, and biopsied blastomeres, were enabled to evaluate. There were no differences in the image quality in accordance with FISH method, fluorochrome types, or different clinical samples. Always bright signals were detected using our system. Our system also yielded constant results. Our Chips would permit a level of performance of FISH analysis on metaphase chromosomes and interphase nuclei with unparalleled capabilities. Thus, it would be useful for clinical purposes.

• Korean Journal of Remote Sensing
• /
• v.34 no.6_1
• /
• pp.1025-1032
• /
• 2018

Snow Cover is a form of precipitation that is defined by snow on the surface and is the single largest component of the cryosphere that plays an important role in maintaining the energy balance between the earth's surface and the atmosphere. It affects the regulation of the Earth's surface temperature. However, since snow cover is mainly distributed in area where human access is difficult, snow cover detection using satellites is actively performed, and snow cover detection in forest area is an important process as well as distinguishing between cloud and snow. In this study, we applied the Normalized Difference Snow Index (NDSI) and the Normalized Difference Vegetation Index (NDVI) to the geostationary satellites for the snow detection of forest area in existing polar orbit satellites. On the rest of the forest area, the snow cover detection using $R_{1.61{\mu}m}$ anomaly technique and NDSI was performed. As a result of the indirect validation using the snow cover data and the Visible Infrared Imaging Radiometer (VIIRS) snow cover data, the probability of detection (POD) was 99.95 % and the False Alarm Ratio (FAR) was 16.63 %. We also performed qualitative validation using the Himawari-8 Advanced Himawari Imager (AHI) RGB image. The result showed that the areas detected by the VIIRS Snow Cover miss pixel are mixed with the area detected by the research false pixel.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.34 no.12C
• /
• pp.1138-1146
• /
• 2009

Digital cameras adopting a single CCD detector collect image color by subsampling in three color planes and successively interpolating the information to reconstruct full-resolution color images. Therefore, to recovery of a full-resolution color image from a color filter array (CFA) like the Bayer pattern is generally considered as an interpolation issue for the unknown color components. In this paper, we first calculate luminance component value by combining R, G, B channel component information which is quite different from the conventional demosaicing algorithm. Because conventional system calculates G channel component followed by computing R and B channel components. Integrating the obtained gradient edge information and the improved weighting function in luminance component, a new edge sensitive demosaicing technique is presented. Based on 24 well known testing images, simulation results proved that our presented high-quality demosaicing technique shows the best image quality performance when compared with several recently presented techniques.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.15 no.4
• /
• pp.851-859
• /
• 2011

In this paper, we propose the method that automatically extracts the silhouette and the joints of consecutive input image, and track joints to trace object for interaction between human and computer. Also the proposed method presents the action of human being to map human body using joints. To implement the algorithm, we model human body using 14 joints to refer to body size. The proposed method converts RGB color image acquired through a single camera to hue, saturation, value images and extracts body's silhouette using the difference between the background and input. Then we automatically extracts joints using the corner points of the extracted silhouette and the data of body's model. The motion of object is tracted by applying block-matching method to areas around joints among all image and the human's motion is mapped using positions of joints. The proposed method is applied to the test videos and the result shows that the proposed method automatically extracts joints and effectively maps human body by the detected joints. Also the human's action is aptly expressed to reflect locations of the joints

• Proceedings of the Plant Resources Society of Korea Conference
• /
• 2019.10a
• /
• pp.87-87
• /
• 2019

The consumption of herbal medicine and related products (herbal products) have increased in South Korea. At the same time the quality, safety, and efficacy of herbal products is being raised. Currently, the herbal products are standardized and controlled according to the requirements of the Korean Pharmacopoeia, the National Institute of Health and the Ministry of Public Health and Social Affairs. The validation of herbal products and their medicinal component is important, since many of these herbal products are composed of two or more medicinal plants. However, there are no tools to support the validation process. Interest in deep learning has exploded over the past decade, for herbal medicine using algorithms to achieve herb recognition, symptom related target prediction, and drug repositioning have been reported. In this study, individual images of four herbs (Panax ginseng C.A. Meyer, Atractylodes macrocephala Koidz, Poria cocos Wolf, Glycyrrhiza uralensis Fischer), actually sold in the market, were achieved. Certain image preprocessing steps such as noise reduction and resize were formatted. After the features are optimized, we applied GoogLeNet_Inception v4 model for herb image recognition. Experimental results show that our method achieved test accuracy of 95%. However, there are two limitations in the current study. Firstly, due to the relatively small data collection (100 images), the training loss is much lower than validation loss which possess overfitting problem. Secondly, herbal products are mostly in a mixture, the applied method cannot be reliable to detect a single herb from a mixture. Thus, further large data collection and improved object detection is needed for better classification.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2022.10a
• /
• pp.256-257
• /
• 2022

In this paper, we introduce a method of extracting three-dimensional feature points through the movement of a single mobile device. Using a monocular camera, a 2D image is acquired according to the camera movement and a baseline is estimated. Perform stereo matching based on feature points. A feature point and a descriptor are acquired, and the feature point is matched. Using the matched feature points, the disparity is calculated and a depth value is generated. The 3D feature point is updated according to the camera movement. Finally, the feature point is reset at the time of scene change by using scene change detection. Through the above process, an average of 73.5% of additional storage space can be secured in the key point database. By applying the algorithm proposed to the depth ground truth value of the TUM Dataset and the RGB image, it was confirmed that the\re was an average distance difference of 26.88mm compared with the 3D feature point result.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.24 no.2
• /
• pp.52-63
• /
• 2021

In previous research, the coastal wetlands were detected by using the vegetation indices or land cover classification maps derived from the multispectral bands of the satellite or aerial imagery, and this approach caused the various limitations for detecting the coastal wetlands with high accuracy due to the difficulty of acquiring both land cover and topographic information by using the single remote sensing data. This research suggested the efficient methodology for detecting the coastal wetlands using the sentinel-2 satellite image and SRTM(Shuttle Radar Topography Mission) DEM (Digital Elevation Model) acquired in Gomsoman Bay, west coasts of South Korea through the following steps. First, the NDWI(Normalized Difference Water Index) image was generated using the green and near-infrared bands of the given Sentinel-2 satellite image. Then, the binary image that separating lands and waters was generated from the NDWI image based on the pixel intensity value 0.2 as the threshold and the other binary image that separating the upper sea level areas and the under sea level areas was generated from the SRTM DEM based on the pixel intensity value 0 as the threshold. Finally, the coastal wetland map was generated by overlaying analysis of these binary images. The generated coastal wetland map had the 94% overall accuracy. In addition, the other types of wetlands such as inland wetlands or mountain wetlands were not detected in the generated coastal wetland map, which means that the generated coastal wetland map can be used for the coastal wetland management tasks.