The Interpretation of Digital Image Data

1986 ◽  
pp. 69-82 ◽  
Author(s):  
John A. Richards
Keyword(s):  
Digital Image ◽  
Image Data

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

Edge and Region Analysis for Digital Image Data

1981 ◽  
pp. 171-184
Author(s):  
ROBERT M. HARALICK
Keyword(s):  
Digital Image ◽  
Image Data ◽  
Region Analysis

Content-Based Image Retrieval Query Paradigms

2005 ◽  
pp. 556-563 ◽  
Author(s):  
Colin C. Venters ◽  
Richard J. Hartley ◽  
William T. Hewitt
Keyword(s):  
Image Retrieval ◽  
Digital Image ◽  
Image Data ◽  
Data Capture ◽  
Content Based Image Retrieval ◽  
Spatial Relationships ◽  
Classification Schemes ◽  
Technological Advances ◽  
Retrieval Problem ◽  
And Storage

The proliferation in volume of digital image data has exacerbated the general image retrieval problem, creating a need for efficient storage and flexible retrieval of vast amounts of image data (Chang, 1989). Whilst there have been significant technological advances with image data capture and storage, developments in effective image retrieval have not kept pace. Research in image retrieval has been divided into two areas: concept-based image retrieval and content-based image retrieval. The former focuses on the use of classification schemes or indexing terms to retrieve images while the latter focuses on the visual features of the image, such as colour, shape, texture, and spatial relationships.

Texture analysis of digital image data using spatial cooccurrence

1987 ◽  
Vol 13 (3) ◽  
pp. 293-311 ◽  
Author(s):  
Steven E. Franklin ◽  
Derek R. Peddle
Keyword(s):  
Texture Analysis ◽  
Digital Image ◽  
Image Data

Base of Digital Image Data and Physical Measurement

2009 ◽  
Vol 65 (8) ◽  
pp. 1085-1093 ◽  
Author(s):  
Hiroshi Kunitomo
Keyword(s):  
Digital Image ◽  
Image Data ◽  
Physical Measurement
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