• Title/Summary/Keyword: hidden image

Search Result 340, Processing Time 0.025 seconds

The Influence of the Eyebrow Make-up on Facial Image (눈썹화장이 얼굴이미지에 미치는 영향)

  • Gang, Eun-Ju
    • Journal of the Korean Society of Fashion and Beauty
    • /
    • v.3 no.2 s.2
    • /
    • pp.31-38
    • /
    • 2005
  • Make-up changes facial images. In particular, eyebrow make-up is a part of changing expression most easily and effectively. While color make-up is helpful to produce women's desired image with their favorite colors, eyebrow make-up is hidden actor to give a clear impression to others. Therefore, this study connected facial type which is an important factor deciding facial image with eyebrow, examined image of eyebrow make-up and that changed by facial types and aimed to be helpful in producing more effective facial image with eyebrow make-up considering one's facial type. Consequently, it was found that eyebrow make-up was a great factor in making better facial impression and image and complementing the weakness of facial type. h strong impression of facial type can be changed into soft shape or foolish shape in worse case depending on the types of eyebrow make-up. Eyebrow make-up shows charming image as angle of eyebrow is steep, heavy image as eyebrow is horizontal, cold image as eyebrow tail rises and simple and dull image as it lowers. Therefore, it is known that image of eyebrow make-up can be governed by several factors including angle and direction of eyebrow. Consequently, it is thought that most effective eyebrow make-up considers individual facial types, images of their eyes, noses and mouths and factors deciding angle, direction and colors of eyebrow.

  • PDF

Human Activity Recognition Using Spatiotemporal 3-D Body Joint Features with Hidden Markov Models

  • Uddin, Md. Zia;Kim, Jaehyoun
    • KSII Transactions on Internet and Information Systems (TIIS)
    • /
    • v.10 no.6
    • /
    • pp.2767-2780
    • /
    • 2016
  • Video-based human-activity recognition has become increasingly popular due to the prominent corresponding applications in a variety of fields such as computer vision, image processing, smart-home healthcare, and human-computer interactions. The essential goals of a video-based activity-recognition system include the provision of behavior-based information to enable functionality that proactively assists a person with his/her tasks. The target of this work is the development of a novel approach for human-activity recognition, whereby human-body-joint features that are extracted from depth videos are used. From silhouette images taken at every depth, the direction and magnitude features are first obtained from each connected body-joint pair so that they can be augmented later with motion direction, as well as with the magnitude features of each joint in the next frame. A generalized discriminant analysis (GDA) is applied to make the spatiotemporal features more robust, followed by the feeding of the time-sequence features into a Hidden Markov Model (HMM) for the training of each activity. Lastly, all of the trained-activity HMMs are used for depth-video activity recognition.

Visualization and classification of hidden defects in triplex composites used in LNG carriers by active thermography

  • Hwang, Soonkyu;Jeon, Ikgeun;Han, Gayoung;Sohn, Hoon;Yun, Wonjun
    • Smart Structures and Systems
    • /
    • v.24 no.6
    • /
    • pp.803-812
    • /
    • 2019
  • Triplex composite is an epoxy-bonded joint structure, which constitutes the secondary barrier in a liquefied natural gas (LNG) carrier. Defects in the triplex composite weaken its shear strength and may cause leakage of the LNG, thus compromising the structural integrity of the LNG carrier. This paper proposes an autonomous triplex composite inspection (ATCI) system for visualizing and classifying hidden defects in the triplex composite installed inside an LNG carrier. First, heat energy is generated on the surface of the triplex composite using halogen lamps, and the corresponding heat response is measured by an infrared (IR) camera. Next, the region of interest (ROI) is traced and noise components are removed to minimize false indications of defects. After a defect is identified, it is classified as internal void or uncured adhesive and its size and shape are quantified and visualized, respectively. The proposed ATCI system allows the fully automated and contactless detection, classification, and quantification of hidden defects inside the triplex composite. The effectiveness of the proposed ATCI system is validated using the data obtained from actual triplex composite installed in an LNG carrier membrane system.

Application of Image Super-Resolution to SDO/HMI magnetograms using Deep Learning

  • Rahman, Sumiaya;Moon, Yong-Jae;Park, Eunsu;Cho, Il-Hyun;Lim, Daye
    • The Bulletin of The Korean Astronomical Society
    • /
    • v.44 no.2
    • /
    • pp.70.4-70.4
    • /
    • 2019
  • Image super-resolution (SR) is a technique that enhances the resolution of a low resolution image. In this study, we use three SR models (RCAN, ProSRGAN and Bicubic) for enhancing solar SDO/HMI magnetograms using deep learning. Each model generates a high resolution HMI image from a low resolution HMI image (4 by 4 binning). The pixel resolution of HMI is about 0.504 arcsec. Deep learning networks try to find the hidden equation between low resolution image and high resolution image from given input and the corresponding output image. In this study, we trained three models with HMI images in 2014 and test them with HMI images in 2015. We find that the RCAN model achieves higher quality results than the other two methods in view of both visual aspects and metrics: 31.40 peak signal-to-noise ratio(PSNR), Correlation Coefficient (0.96), Root mean square error (RMSE) is 0.004. This result is also much better than the conventional bi-cubic interpolation. We apply this model to a full-resolution SDO/HMI image and compare the generated image with the corresponding Hinode NFI magnetogram. As a result, we get a very high correlation (0.92) between the generated SR magnetogram and the Hinode one.

  • PDF

Classification of Insulation Fault Signals for High Voltage Motors Stator Winding using Image Signal Process Technique (영상신호처리 기법을 이용한 고압전동기 고정자권선 절연결함신호 분류)

  • Park, Jae-Jun;Kim, Hee-Dong
    • Journal of the Korean Institute of Electrical and Electronic Material Engineers
    • /
    • v.20 no.1
    • /
    • pp.65-73
    • /
    • 2007
  • Pattern classification of single and multiple discharge sources was applied using a wavelet image signal method in which a feature extraction was applied using a hidden sub-image. A feature extracting method that used vertical and horizontal images using an MSD method was applied to an averaging process for the scale of pulses for the phase. A feature extracting process for the preprocessing of the input of a neural network was performed using an inverse transformation of the horizontal, vertical, and diagonal sub-images. A back propagation algorithm in a neural network was used to classify defective signals. An algorithm for wavelet image processing was developed. In addition, the defective signal was classified using the extracted value that was quantified for the input of a neural network.

Neural Network Image Reconstruction for Magnetic Particle Imaging

  • Chae, Byung Gyu
    • ETRI Journal
    • /
    • v.39 no.6
    • /
    • pp.841-850
    • /
    • 2017
  • We investigate neural network image reconstruction for magnetic particle imaging. The network performance strongly depends on the convolution effects of the spectrum input data. The larger convolution effect appearing at a relatively smaller nanoparticle size obstructs the network training. The trained single-layer network reveals the weighting matrix consisting of a basis vector in the form of Chebyshev polynomials of the second kind. The weighting matrix corresponds to an inverse system matrix, where an incoherency of basis vectors due to low convolution effects, as well as a nonlinear activation function, plays a key role in retrieving the matrix elements. Test images are well reconstructed through trained networks having an inverse kernel matrix. We also confirm that a multi-layer network with one hidden layer improves the performance. Based on the results, a neural network architecture overcoming the low incoherence of the inverse kernel through the classification property is expected to become a better tool for image reconstruction.

High Capacity Information Hiding Method Based on Pixel-value Adjustment with Modulus Operation

  • Li, Teng;Zhang, Yu;Wang, Sha;Sun, Jun-jie
    • KSII Transactions on Internet and Information Systems (TIIS)
    • /
    • v.15 no.4
    • /
    • pp.1521-1537
    • /
    • 2021
  • Through information hiding technique, secret message can be hidden in pictures. Stego-image quality and hiding capacity are two important metrics for information hiding. To enhance these metrics, many schemes were proposed by scholars in recent years. Some of them are effective and successful, but there is still a room for further improvement. A high capacity information hiding scheme (PAMO, Pixel-value Adjustment with Modulus Operation Algorithm) is introduced in this paper. PAMO scheme uses pixel value adjustment with modulus operation to hide confidential data in cover-image. PAMO scheme and some referenced schemes are implemented in Python and experiments are carried out to evaluate their performance. In the experiments, PAMO scheme shows better performance than other methods do. When secret message length is less than 72000 bits, the highest hiding capacity of PAMO can reach 7 bits per pixel, at the same time the PSNR of stego-images is greater than 30 dB.

Image Matching Based on Robust Feature Extraction for Remote Sensing Haze Images (위성 안개 영상을 위한 강인한 특징점 검출 기반의 영상 정합)

  • Kwon, Oh-Seol
    • Journal of Broadcast Engineering
    • /
    • v.21 no.2
    • /
    • pp.272-275
    • /
    • 2016
  • This paper presents a method of single image dehazing and surface-based feature detection for remote sensing images. In the conventional dark channel prior (DCP) algorithm, the resulting transmission map invariably includes some block artifacts because of patch-based processing. This also causes image blur. Therefore, a refined transmission map based on a hidden Markov random field and expectation-maximization algorithm can reduce the block artifacts and also increase the image clarity. Also, the proposed algorithm enhances the accuracy of image matching surface-based features in an remote sensing image. Experimental results confirm that the proposed algorithm is superior to conventional algorithms in image haze removal. Moreover, the proposed algorithm is suitable for the problem of image matching based on feature extraction.

A Human Activity Recognition System Using ICA and HMM

  • Uddin, Zia;Lee, J.J.;Kim, T.S.
    • 한국HCI학회:학술대회논문집
    • /
    • 2008.02a
    • /
    • pp.499-503
    • /
    • 2008
  • In this paper, a novel human activity recognition method is proposed which utilizes independent components of activity shape information from image sequences and Hidden Markov Model (HMM) for recognition. Activities are represented by feature vectors from Independent Component Analysis (ICA) on video images, and based on these features; recognition is achieved by trained HMMs of activities. Our recognition performance has been compared to the conventional method where Principle Component Analysis (PCA) is typically used to derive activity shape features. Our results show that superior recognition is achieved with our proposed method especially for activities (e.g., skipping) that cannot be easily recognized by the conventional method.

  • PDF

Emotional Human Body Recognition by Using Extraction of Human Body from Image (인간의 움직임 추출을 이용한 감정적인 행동 인식 시스템 개발)

  • Song, Min-Kook;Joo, Young-Hoon;Park, Jin-Bae
    • Proceedings of the KIEE Conference
    • /
    • 2006.10c
    • /
    • pp.214-216
    • /
    • 2006
  • Expressive face and human body gestures are among the main non-verbal communication channels in human-human interaction. Understanding human emotions through body gesture is one of the necessary skills both for humans and also for the computers to interact with their human counterparts. Gesture analysis is consisted of several processes such as detecting of hand, extracting feature, and recognizing emotions. Skin color information for tracking hand gesture is obtained from face detection region. We have revealed relationships between paricular body movements and specific emotions by using HMM(Hidden Markov Model) classifier. Performance evaluation of emotional human body recognition has experimented.

  • PDF