A new digital steganography system based on hiding online signature within document image data in YUV color space

2021 ◽  
Author(s):  
Anissa Zenati ◽  
Wael Ouarda ◽  
Adel M. Alimi
Keyword(s):  
Color Space ◽  
Image Data ◽  
Document Image ◽  
Online Signature ◽  
Digital Steganography

Pencarian File Gambar Berdasarkan Dominasi Warna

2011 ◽  
Vol 2 (1) ◽  
Author(s):  
Vina Chovan Epifania ◽  
Eko Sediyono
Keyword(s):  
Digital Image ◽  
Retrieval System ◽  
Color Image ◽  
Color Space ◽  
Image Data ◽  
Image Search ◽  
Test Results ◽  
Application Form ◽  
Space Model ◽  
Image File

Abstract. Image File Searching Based on Color Domination. One characteristic of an image that can be used in image searching process is the composition of the colors. Color is a trait that is easily seen by man in the picture. The use of color as a searching parameter can provide a solution in an easier searching for images stored in computer memory. Color images have RGB values that can be computed and converted into HSL color space model. Use of HSL images model is very easy because it can be calculated using a percent, so that in each pixel of the image can be grouped and named, this can give a dominant values of the colors contained in one image. By obtaining these values, the image search can be done quickly just by using these values to a retrieval system image file. This article discusses the use of the HSL color space model to facilitate the searching for a digital image in the digital image data warehouse. From the test results of the application form, a searching is faster by using the colors specified by the user. Obstacles encountered were still searching with a choice of 15 basic colors available, with a limit of 33% dominance of the color image search was not found. This is due to the dominant color in each image has the most dominant value below 33%.   Keywords: RGB, HSL, image searching Abstrak. Salah satu ciri gambar yang dapat dipergunakan dalam proses pencarian gambar adalah komposisi warna. Warna adalah ciri yang mudah dilihat oleh manusia dalam citra gambar. Penggunaan warna sebagai parameter pencarian dapat memberikan solusi dalam memudahkan pencarian gambar yang tersimpan dalam memori komputer. Warna gambar memiliki nilai RGB yang dapat dihitung dan dikonversi ke dalam model HSL color space. Penggunaan model gambar HSL sangat mudah karena dapat dihitung dengan menggunakan persen, sehingga dalam setiap piksel gambar dapat dikelompokan dan diberi nama, hal ini dapat memberikan suatu nilai dominan dari warna yang terdapat dalam satu gambar. Dengan diperolehnya nilai tersebut, pencarian gambar dapat dilakukan dengan cepat hanya dengan menggunakan nilai tersebut pada sistem pencarian file gambar. Artikel ini membahas tentang penggunaan model HSL color space untuk mempermudah pencarian suatu gambar digital didalam gudang data gambar digital. Dari hasil uji aplikasi yang sudah dibuat, diperoleh pencarian yang lebih cepat dengan menggunakan pilihan warna yang ditentukan sendiri oleh pengguna. Kendala yang masih dijumpai adalah pencarian dengan pilihan 15 warna dasar yang tersedia, dengan batas dominasi warna 33% tidak ditemukan gambar yang dicari. Hal ini disebabkan warna dominan disetiap gambar kebanyakan memiliki nilai dominan di bawah 33%. Kata Kunci: RGB, HSL, pencarian gambar

scholarly journals IMPLEMENTASI JARINGAN SYARAF TIRUAN (JST) DAN PENGOLAHAN CITRA UNTUK KlASIFIKASI KEMATANGAN TBS KELAPA SAWIT

2018 ◽  
Vol 15 (1) ◽  
pp. 36
Author(s):  
Minarni Minarni ◽  
Roni Salumbae ◽  
Zilhan Hasbi
Keyword(s):  
Oil Palm ◽  
Color Space ◽  
Human Perception ◽  
Image Data ◽  
Cost Effective ◽  
Fresh Fruit ◽  
Human Vision ◽  
Palm Fruit ◽  
Color Parameters ◽  
Fresh Fruit Bunches

The clasification of ripeness stages of oil palm fresh fruit bunches (FFBs) can be done using color parameters. These parameters are often evaluated by human vision, whose degree of accuracy is subjective which can cause doubt in judgement. Automatic clasifications offreshfruit bunches (FFBs) based on color parameters can be done using computer vision. This method is known as a nondestructive, fast and cost effective method. In this research, a MATLAB computer program has been developed which consists of RGB and HSV GUI which is used to record, display, and process FFB image data. The backpropagation artificial neural network (ANN) program is also developed which is used to classify the oil palm fruit fresh bunches (FFBs). Samples are fresh fruit bunches (FFB) of oil palm varieties of Tenera which comprise of Topaz, Marihat, and Lonsum clones. Each clone composed of three levels of ripeness represented by five fractions. The measurements were started by capturing images of oil palm, extracting RGB and HSV values, calculating weight values from the image database to make anANN program, preparing grid programs for oil palm FFBs, and comparing grading levels of oil palm FFBs using program and by harvester. This program successfully classified oil palm (FFBs) into three categories of ripeness which are unripe (F0 and F1), ripe (F1 and F1) and over ripe (F4 and F5). The RGB and HSV programs successfully classified 79 out of 216 FFBs or 36.57% and 106 out of 216 TBS or 49.07%. Respectively the HSV program is better than RGB program because the representation of HSV color space are more understood by human perception hence can be used in calibration and color comparison.

Digital Camera Color Resolution Test Based on Knife-Edge Method

2013 ◽  
Vol 469 ◽  
pp. 246-250 ◽  
Author(s):  
Yue Hong Song ◽  
Jun Qing Xu
Keyword(s):  
Transfer Function ◽  
Modulation Transfer Function ◽  
Color Space ◽  
Digital Camera ◽  
Image Data ◽  
Knife Edge ◽  
Modulation Transfer ◽  
Lab Color Space ◽  
Edge Method

A resolution test-chart for digital camera has been developed based on the knife-edge method. RAW images have been acquired with NikonD7000 DSLR and Canon G12. Set the device color-space to Adobe RGB, and convert the image data to LAB color-space using Adobe RGB profile. The Fourier transfer of the LAB tonal value were computed under MATLAB environment. Then by analyzing the L/C/H (Derived from LAB space ) and the Modulation Transfer Function (MTF), we can get the resolution characteristics of the Nikon D7000 and Canon G12 camera.

DocBrowse: a system for information retrieval from document image data

1996 ◽  
Author(s):  
Mysore Y. Jaisimha ◽  
Andrew G. Bruce ◽  
Thien Nguyen
Keyword(s):  
Information Retrieval ◽  
Image Data ◽  
Document Image

Camera Captured Document De-Warping and De-Skewing

2020 ◽  
Vol 17 (9) ◽  
pp. 4398-4403
Author(s):  
H. C. Vinod ◽  
S. K. Niranjan
Keyword(s):  
Color Space ◽  
Threshold Value ◽  
Document Image ◽  
Geometric Distortion ◽  
Document Images ◽  
Data Set ◽  
Handwritten Documents ◽  
Perspective Distortion ◽  
Bounding Box ◽  
Analysis Technique

De-warping is the elementary step in the analysis of document images which are camera based. Processing of warped image is a challenging task. Therefore, to make the document images OCR understandable de-warping has become a major task. In this paper, we have presented an effective pre-processing, de-warping and de-skewing techniques for camera captured document image processing. In pre-processing, we have divided input color document image into R, G and B-band, further convert R, G, B-band to C, M and Y-color space respectively, convert the average CMY grey scale image to binary by determining threshold value automatically. In de-warping and de-skewing, we have presented an effective techniques to remove geometrical and perspective distortion in camera captured document by combination of centroid and mid-point of bounding box height, bounding box is plotted on text blocks using connected component analysis technique. The introduced work is robust in correcting geometric distortion and skew correction for standard printed data set and also for Kannada handwritten documents.

scholarly journals An Efficient Two-Level Dictionary-Based Technique for Segmentation and Compression Compound Images

2020 ◽  
Vol 14 (4) ◽  
pp. 52
Author(s):  
Nidhal Kamel Taha El-Omari
Keyword(s):  
Data Storage ◽  
Image Data ◽  
Data File ◽  
Document Image ◽  
Relative Reduction ◽  
Storage Space ◽  
Space Reduction ◽  
Long Run ◽  
Block Based ◽  
Real Essence

Image data compression algorithms are essential for getting storage space reduction and, perhaps more importantly, to increase their transfer rates, in terms of space-time complexity. Considering that there isn't any encoder that gives good results across all image types and contents, this paper proposed an evolvable lossless statistical block-based technique for segmentation and compression compound or mixed documents that have different content types, such as pictures, graphics, and/or texts. Derived from the number of detected colors and to achieve better compression ratios, a new well-defined representation of the image is created which nonetheless retains the same image components. With the effort of reducing noise or other variations inside the scanned image, some primary operations are implemented. Thereafter, the proposed algorithm breaks down the compound document image into equal-size-square blocks. Next, inspired by the number of colors detected in each block, these blocks are categorized into a set of six-image objects, called classes, where each one contains a set of closely interrelated pixels that share the same common relevant attributes like color gamut and number, color occurrence, grey level, and others. After that, a new representation of these coherent classes is formed using the Lookup Dictionary Table (LUD), which is the real essence of this proposed algorithm. In order to form distinguishable labeled regions sharing the same attributes, adjacent blocks of similar color features are consolidated together into a single coherent whole entity, called segments or regions. After each region is encoded by one of the most off-the-shelf applicable compression techniques, these regions are eventually fused together into a single data file which then subjects to another compression stage to ensure better compression ratios. After the proposed algorithm has been applied and tested on a database containing 3151 24-bit-RGB-bitmap document images, the empirically-based results prove that the overall algorithm is efficient in the long run and has superior storage space reduction when compared with other existing algorithms. As for the empirical findings, the proposed algorithm has achieved (71.039 %) relative reduction in the data storage space.

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