Deep Sampling Networks
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Sensitive Text Icon Classification for Android Apps
SENSITIVE TEXT ICON CLASSIFICATION FOR ANDROID APPS by ZHIHAO CAO Submitted in partial fulfillment of the requirements for the degree of Master of Science Thesis Advisor: Dr. Xusheng Xiao Department of Electrical Engineering and Computer Science CASE WESTERN RESERVE UNIVERSITY January, 2018 CASE WESTERN RESERVE UNIVERSITY SCHOOL OF GRADUATE STUDIES We hereby approve the thesis/dissertation of Zhihao Cao candidate for the degree of Master of Science. Committee Chair Xusheng Xiao Committee Member Andy Podgurski Committee Member Ming-Chun Huang Date of Defense Nov. 30 2017 *We also certify that written approval has been obtained for any proprietary material contained therein. Contents List of Tables 3 List of Figures 4 List of Abbreviations 7 Abstract 8 1 Introduction 9 2 Background 16 2.1 Permission System in Android…………………………………………………..16 2.2 Sensitive UI Detection in Android……………………………………………….17 2.3 Pixel and Color Model…………………………………………………………...19 2.4 Optical Character Recognition…………………………………………………...21 3 Design of DroidIcon 23 3.1 Overview…………………………………………………………..……………..23 3.2 Image Mutation…………………………………………………………………..24 3.2.1 Image Scaling………………………………………………………………24 3.2.2 Color Inversion…………………………………………………………….31 1 3.2.3 Opacity Conversion………………………………………………………..33 3.2.4 Grayscale Conversion………..…………………………………………….37 3.2.5 Contrast Adjustment……………………………………………………….42 3.3 Text Icon Classification 48 3.3.1 Text Cleaning……………………...……………………………………….48 3.3.2 Keyword Dataset Construction…………………………………………….49 3.3.3 Classification Algorithm…………………………………………………...50 -
Deep Learning for Image Super-Resolution: a Survey
1 Deep Learning for Image Super-resolution: A Survey Zhihao Wang, Jian Chen, Steven C.H. Hoi, Fellow, IEEE Abstract—Image Super-Resolution (SR) is an important class of image processing techniques to enhance the resolution of images and videos in computer vision. Recent years have witnessed remarkable progress of image super-resolution using deep learning techniques. This article aims to provide a comprehensive survey on recent advances of image super-resolution using deep learning approaches. In general, we can roughly group the existing studies of SR techniques into three major categories: supervised SR, unsupervised SR, and domain-specific SR. In addition, we also cover some other important issues, such as publicly available benchmark datasets and performance evaluation metrics. Finally, we conclude this survey by highlighting several future directions and open issues which should be further addressed by the community in the future. Index Terms—Image Super-resolution, Deep Learning, Convolutional Neural Networks (CNN), Generative Adversarial Nets (GAN) F 1 INTRODUCTION MAGE super-resolution (SR), which refers to the process and strategies [8], [31], [32], etc. I of recovering high-resolution (HR) images from low- In this paper, we give a comprehensive overview of re- resolution (LR) images, is an important class of image cent advances in image super-resolution with deep learning. processing techniques in computer vision and image pro- Although there are some existing SR surveys in literature, cessing. It enjoys a wide range of real-world applications, our work differs in that we are focused in deep learning such as medical imaging [1], [2], [3], surveillance and secu- based SR techniques, while most of the earlier works [33], rity [4], [5]), amongst others. -
Movimenc(1) the Movim Video Codec Movimenc(1)
movimenc(1) The MovIm video codec movimenc(1) NAME movimenc - MovIm encoder SYNOPSIS movimenc [input_options] -i input_file [encoding_options] [output_options] -o output_file movimenc -h DESCRIPTION MovIm is a video codec specifically designed for both conservation and restoration of moving images. The MovIm package includes the libmovim Clibrary implementing MovIm and its associated movimenc, movimdec and movimplay utilities, as well as the openmovim Bash command-line interface allowing to encode, decode, play and analyse virtually anymoving images. movimenc is a MovIm encoder. OPTIONS GENERAL OPTIONS -i input_file, --input=input_file All container formats and video codecs supported by FFmpegshould work. -o output_file, --output=output_file The uncompressed or lossless compressed MovIm data can be used directly as a file (.movim). This format is directly inspired from FFmpeg’sNUT container. INPUT AND OUTPUT OPTIONS --flip=(vertical|horizontal) flip the image on the vertical or horizontal axis This option may be repeated. --rotate=angle angle of counterclockwise rotation in degrees, expressed as an integer or a real number This option may be repeated. --lut[:channel]=path path to an 1D LUT or to a 3D LUT to apply LUTs can be applied to the input file and/or the output file. MoreoveraLUT can be applied to the whole file (default) or only to a single channel. This option may be repeated. For1DLUT,which transforms e.g. from floating-point scene linear into camera log or a display- referred space, the maximum allowed size is currently 16’777’216, that is 24-bit precision. ENCODING OPTIONS The following list is not exhaustive. --bit-depth[:channel]=bit_depth bit_depth can be anypositive integer We hav e tested mainly with 10, 12, 16 (default) or 24 per channel. -
3D LANCZOS INTERPOLATION for MEDICAL VOLUMES Thiago
15th International Symposium on Computer Methods in Biomechanics and Biomedical Engineering and 3rd Conference on Imaging and Visualization CMBBE 2018 P. R. Fernandes and J. M. Tavares (Editors) 3D LANCZOS INTERPOLATION FOR MEDICAL VOLUMES Thiago Moraes1, Paulo Amorim1, Jorge Silva1 and Helio Pedrini2 1Division of 3D Technologies, Center for Information Technology Renato Archer Campinas-SP, Brazil, 13069-901 2Institute of Computing, University of Campinas Campinas, SP, Brazil, 13083-852 Keywords: Image Interpolation, Resampling, Medical Imaging. Abstract: Medical imaging techniques have been widely used in the diagnosis and treatment of many diseases, such as magnetic resonance imaging, computed tomography, mammography, ultrasonography, and photon emission tomography. Their main purpose is to enable the visu- alization of internal anatomic structures, such as organs and tissues, for clinical procedures. Resampling is required for certain medical tasks, for instance, registration and reconstruction, which occur when images or volumes are scaled, translated or rotated. The result of the re- sampling depends on the interpolation filter. In this paper, we develop and analyze a novel three-dimensional Lanczos resampling method in the context of medical imaging. 1. INTRODUCTION In the context of numerical analysis, interpolation techniques [12–15, 19, 21–23] aim to con- struct new data points from a set of known samples. In the field of image analysis, resampling techniques [9, 21, 23] transform a sampled image from one coordinate system to another. Resampling is typically employed to create a new version of an image with a different dimension. The increase and reduction of an image are called upsampling and downsampling, respectively. Image interpolation and resampling techniques play an important role in several biomed- ical applications, such as data reslicing, image registration, tomographic reconstruction, and volumetric rendering [23]. -
SATELLITE IMAGE ENHANCEMENT TECHNIQUE BASED on LANCZOS INTERPOLATION and NLFMT FILTERING Jino AR 1* , Jayaraj V 2 1M.E Student, Dept
Jino and Jayaraj. UJEAS 2014, 02 (02): Page 103-107 ISSN 2348 -375X Unique Journal of Engineering and Advanced Sciences Available online: www.ujconline.net Research Article SATELLITE IMAGE ENHANCEMENT TECHNIQUE BASED ON LANCZOS INTERPOLATION AND NLFMT FILTERING Jino AR 1* , Jayaraj V 2 1M.E Student, Dept. of ECE, Nehru Institute of Engineering & Technology, Coimbatore, India 2Professor, Dept. of ECE, Nehru Institute of Engineering & Technology, Coimbatore, India Received: 28-02-2014; Revised: 26-03-2014; Accepted: 25-04-2014 *Corresponding Author : A. R. Jino M.E Student, 2Professor, Dept. of ECE, Nehru Institute of Engineering & Technology, Coimbatore [email protected], [email protected] ABSTRACT Satellite images are being used now a days in various fields such as weather forecasting, remote sensing, cartography, oceanography etc. Obtaining high quality images are of prime importance in such cases. But it may not be always possible due to the sensor limitations, atmospheric conditions and due to transmission. Thus the images suffer from the drawback of losing high frequency contents (which results in blurring). A wavelet domain approach based on method noise thresholding and non local means (NLM) is proposed for the enhancement of the images. An input image is passed through a NLM filter, then its method noise is thresholded. The NLM filtered image is summed with the thresholded method noise to cater for the image details removed by the NLM filter. The denoised image is interpolated using the Lanczos interpolator for resolution enhancement. Objective and subjective analyses reveal superiority of the proposed technique over the conventional and state-of-the-art image enhancement techniques. -
United States Patent (10) Patent No.: US 8,094.208 B2 Myhrvold (45) Date of Patent: Jan
USOO8094208B2 (12) United States Patent (10) Patent No.: US 8,094.208 B2 Myhrvold (45) Date of Patent: Jan. 10, 2012 (54) COLOR FILTERS AND DEMOSAICING 6,025,213 A * 2/2000 Nemoto et al. ............... 438,122 TECHNIQUES FOR DIGITAL IMAGING 2002fOO97492 A1* 7, 2002 Cobb et al. .................... 359,577 2004/00325 17 A1 2/2004 Walmsley et al. (75) Inventor: Nathan P. Myhrvold, Bellevue, WA 2004/0095531 A1 5/2004 Jiang et al. (US) 2004/0263994 Al 12/2004 Sayag 2007/O145273 A1 6/2007 Chang (73) Assignee: The Invention Sciennce Fund I, LLC 2008.0137215 A1 6/2008 Nurishi ......................... 359/698 (*) Notice: Subject to any disclaimer, the term of this 2009/017.4638 A1 7, 2009 Elliott et al. patent is extended or adjusted under 35 OTHER PUBLICATIONS U.S.C. 154(b) by 273 days. -- “Tutorials: Diffraction & Photography': Diffraction Limited Photog (21) Appl. No.: 12/6.53,172 raphy: Pixel Size, Aperture and Airy Disks;printed on Oct. 29, 2009; (22) Filed: Dec. 8, 2009 pp. 1-5, located at: http://www.cambridgeincolour.com/tutorials, dif O O fraction-photography.htm. (65) Prior Publication Data PCT International Search Report; International App. No. PCT/US US 2011 FOO 13054A1 Jan. 20, 2011 10/02018; Nov. 10, 2010; pp. 1-4. Related U.S. Application Data * cited by examiner (62) Division of application No. 12/590,041, filed on Oct. 30, 2009. Primary Examiner —Yogesh Aggarwal (60) Provisional application No. 61/271,195, filed on Jul. (57) ABSTRACT 17, 2009. Color filter arrays or mosaics are provided for imaging a (51) Int.