• Title/Summary/Keyword: Image model

Search Result 6,488, Processing Time 0.032 seconds

CREATION OF DIGITAL CITY MODEL FROM A SINGLE KOMPSAT-2 IMAGE

  • Kim, Hye-Jin;Choi, Jae-Wan;Han, You-Kyung;Kim, Yong-II
    • Proceedings of the KSRS Conference
    • /
    • 2008.10a
    • /
    • pp.365-367
    • /
    • 2008
  • A digital city model represents a 3D environment of a city with various city object information such as 3D building model, road, and land cover. Usually, at least two satellite images with some image overlap are necessary and a complex satellite-related computation needs to be carried out to create a city model. This is an expensive technique, because it requires many resources and excessive computational cost. The authors propose a methodology to create a digital city model including 3D building model and land cover information from a single high resolution satellite image. The approach consists of image pan-sharpening, shadow recovery, building occlusion restoration, building model extraction, and land cover classification. We create a digital city model using a single KOMPSAT-2 image and review the result.

  • PDF

Digital Image Quality Assessment Based on Standard Normal Deviation

  • Park, Hyung-Ju;Har, Dong-Hwan
    • International Journal of Contents
    • /
    • v.11 no.2
    • /
    • pp.20-30
    • /
    • 2015
  • We propose a new method that specifies objective image quality factors by evaluating an image quality measurement model using random images. In other words, No-Reference variables are used to evaluate the quality of an original image without using any reference for comparison. 1000 portrait images were collected from a web gallery with votes constituting over 30 recommendation values. The bottom-up data collecting process was used to calculate the following image quality factors: total range, average, standard deviation, normalized distribution, z-score, preference percentage. A final grade is awarded out of 100 points, and this method ranks and grades the final estimated image quality preference in terms of total image quality factors. The results of the proposed image quality evaluation model consist of the specific dynamic range, skin tone R, G, B, L, A, B, and RSC contrast. We can present the total for the expected preference points as the average of the objective image qualities. Our proposed image quality evaluation model can measure the preferences for an actual image using a statistical analysis. The results indicate that this is a practical image quality measurement model that can extract a subject's preferred image quality.

Variational Bayesian inference for binary image restoration using Ising model

  • Jang, Moonsoo;Chung, Younshik
    • Communications for Statistical Applications and Methods
    • /
    • v.29 no.1
    • /
    • pp.27-40
    • /
    • 2022
  • In this paper, the focus on the removal noise in the binary image based on the variational Bayesian method with the Ising model. The observation and the latent variable are the degraded image and the original image, respectively. The posterior distribution is built using the Markov random field and the Ising model. Estimating the posterior distribution is the same as reconstructing a degraded image. MCMC and variational Bayesian inference are two methods for estimating the posterior distribution. However, for the sake of computing efficiency, we adapt the variational technique. When the image is restored, the iterative method is used to solve the recursive problem. Since there are three model parameters in this paper, restoration is implemented using the VECM algorithm to find appropriate parameters in the current state. Finally, the restoration results are shown which have maximum peak signal-to-noise ratio (PSNR) and evidence lower bound (ELBO).

A Study on Quantitative Space Analysis Model - Focused on a Visual Analysis and Image Analysis by Digital Image Processing - (정량적 공간분석 모델에 관한 연구 - 시각 분석과 영상처리에 의한 이미지 분석 모델을 중심으로 -)

  • 이혁준;이종석
    • Korean Institute of Interior Design Journal
    • /
    • no.37
    • /
    • pp.136-143
    • /
    • 2003
  • Users' demands on the space are changing in variety. These demands include reasonable space and form, harmonious composition with surroundings and esthetic satisfaction that could be brought by personal tastes and preferences. In addition, models that are introduced from designing process and from various forms tend to lack objective decision making standard. Accordingly it is difficult to find a clear alternative plan and process. In an effort to solve these problems, the objects of this study are; to propose an analysis model of image and space by using image process techniques that are on study in the field of artificial intelligence based on acquisition of digital image and to verify the application possibilities of such analysis model, 'Isovist' on quantitative analysis. The model can be applied with variable analysis model, as digital image process and other analysis model such as 'Isovist' It is possible that further study can complement problems from this study.

Increasing Spatial Resolution of Remotely Sensed Image using HNN Super-resolution Mapping Combined with a Forward Model

  • Minh, Nguyen Quang;Huong, Nguyen Thi Thu
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
    • /
    • v.31 no.6_2
    • /
    • pp.559-565
    • /
    • 2013
  • Spatial resolution of land covers from remotely sensed images can be increased using super-resolution mapping techniques for soft-classified land cover proportions. A further development of super-resolution mapping technique is downscaling the original remotely sensed image using super-resolution mapping techniques with a forward model. In this paper, the model for increasing spatial resolution of remote sensing multispectral image is tested with real SPOT 5 imagery at 10m spatial resolution for an area in Bac Giang Province, Vietnam in order to evaluate the feasibility of application of this model to the real imagery. The soft-classified land cover proportions obtained using a fuzzy c-means classification are then used as input data for a Hopfield neural network (HNN) to predict the multispectral images at sub-pixel spatial resolution. The 10m SPOT multispectral image was improved to 5m, 3,3m and 2.5m and compared with SPOT Panchromatic image at 2.5m resolution for assessment.Visually, the resulted image is compared with a SPOT 5 panchromatic image acquired at the same time with the multispectral data. The predicted image is apparently sharper than the original coarse spatial resolution image.

Side Information Extrapolation Using Motion-aligned Auto Regressive Model for Compressed Sensing based Wyner-Ziv Codec

  • Li, Ran;Gan, Zongliang;Cui, Ziguan;Wu, Minghu;Zhu, Xiuchang
    • KSII Transactions on Internet and Information Systems (TIIS)
    • /
    • v.7 no.2
    • /
    • pp.366-385
    • /
    • 2013
  • In this paper, we propose a compressed sensing (CS) based Wyner-Ziv (WZ) codec using motion-aligned auto regressive model (MAAR) based side information (SI) extrapolation to improve the compression performance of low-delay distributed video coding (DVC). In the CS based WZ codec, the WZ frame is divided into small blocks and CS measurements of each block are acquired at the encoder, and a specific CS reconstruction algorithm is proposed to correct errors in the SI using CS measurements at the decoder. In order to generate high quality SI, a MAAR model is introduced to improve the inaccurate motion field in auto regressive (AR) model, and the Tikhonov regularization on MAAR coefficients and overlapped block based interpolation are performed to reduce block effects and errors from over-fitting. Simulation experiments show that our proposed CS based WZ codec associated with MAAR based SI generation achieves better results compared to other SI extrapolation methods.

Membership Inference Attack against Text-to-Image Model Based on Generating Adversarial Prompt Using Textual Inversion (Textual Inversion을 활용한 Adversarial Prompt 생성 기반 Text-to-Image 모델에 대한 멤버십 추론 공격)

  • Yoonju Oh;Sohee Park;Daeseon Choi
    • Journal of the Korea Institute of Information Security & Cryptology
    • /
    • v.33 no.6
    • /
    • pp.1111-1123
    • /
    • 2023
  • In recent years, as generative models have developed, research that threatens them has also been actively conducted. We propose a new membership inference attack against text-to-image model. Existing membership inference attacks on Text-to-Image models produced a single image as captions of query images. On the other hand, this paper uses personalized embedding in query images through Textual Inversion. And we propose a membership inference attack that effectively generates multiple images as a method of generating Adversarial Prompt. In addition, the membership inference attack is tested for the first time on the Stable Diffusion model, which is attracting attention among the Text-to-Image models, and achieve an accuracy of up to 1.00.

A Model to Predict the Strength of Watermark in DWT-Based Image Watermarking

  • Moon, Ho-Seok;Park, Suk-Bong;Bae, Hyun-Wung
    • Journal of the Korean Data and Information Science Society
    • /
    • v.19 no.2
    • /
    • pp.475-485
    • /
    • 2008
  • One of main issues in watermarking is to resolve the strength of watermark for solving the problem of trade-off between fidelity and robustness of watermarking. In the previous research, the strength of watermark has been resolved fixed value generally without considering local image characteristics such as image brightness, contrast, and edge. This paper proposes a new model to predict the strength of watermark considering local image characteristics such as image brightness, contrast, and edge for digital wavelet transform(DWT)-based image watermarking. For the study, psychological experiment was fulfilled to measure the human image perception and regression analysis showed the proposed model was statistically significant at the level of ${\alpha}\;=\;0.01$. Also the model is practically validated on fidelity and robustness of watermarking.

  • PDF

Content-based Image Retrieval using an Improved Chain Code and Hidden Markov Model (개선된 chain code와 HMM을 이용한 내용기반 영상검색)

  • 조완현;이승희;박순영;박종현
    • Proceedings of the IEEK Conference
    • /
    • 2000.09a
    • /
    • pp.375-378
    • /
    • 2000
  • In this paper, we propose a novo] content-based image retrieval system using both Hidden Markov Model(HMM) and an improved chain code. The Gaussian Mixture Model(GMM) is applied to statistically model a color information of the image, and Deterministic Annealing EM(DAEM) algorithm is employed to estimate the parameters of GMM. This result is used to segment the given image. We use an improved chain code, which is invariant to rotation, translation and scale, to extract the feature vectors of the shape for each image in the database. These are stored together in the database with each HMM whose parameters (A, B, $\pi$) are estimated by Baum-Welch algorithm. With respect to feature vector obtained in the same way from the query image, a occurring probability of each image is computed by using the forward algorithm of HMM. We use these probabilities for the image retrieval and present the highest similarity images based on these probabilities.

  • PDF

Weather Classification and Image Restoration Algorithm Attentive to Weather Conditions in Autonomous Vehicles (자율주행 상황에서의 날씨 조건에 집중한 날씨 분류 및 영상 화질 개선 알고리듬)

  • Kim, Jaihoon;Lee, Chunghwan;Kim, Sangmin;Jeong, Jechang
    • Proceedings of the Korean Society of Broadcast Engineers Conference
    • /
    • 2020.11a
    • /
    • pp.60-63
    • /
    • 2020
  • With the advent of deep learning, a lot of attempts have been made in computer vision to substitute deep learning models for conventional algorithms. Among them, image classification, object detection, and image restoration have received a lot of attention from researchers. However, most of the contributions were refined in one of the fields only. We propose a new paradigm of model structure. End-to-end model which we will introduce classifies noise of an image and restores accordingly. Through this, the model enhances universality and efficiency. Our proposed model is an 'One-For-All' model which classifies weather condition in an image and returns clean image accordingly. By separating weather conditions, restoration model became more compact as well as effective in reducing raindrops, snowflakes, or haze in an image which degrade the quality of the image.

  • PDF