Enhancement of color image data captured using single chip CCD arrays

1996 ◽  
Author(s):  
Barbara E. Schmitz ◽  
Robert L. Stevenson
Keyword(s):  
Color Image ◽  
Image Data ◽  
Single Chip ◽  
Ccd Arrays

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

Color image analysis for vehicle speed measurement

1993 ◽  
Vol 20 (2) ◽  
pp. 228-235 ◽  
Author(s):  
Yean-Jye Lu ◽  
Xidong Yuan
Keyword(s):  
Image Analysis ◽  
Traffic Control ◽  
Road Traffic ◽  
Color Image ◽  
Image Data ◽  
Difficult Problem ◽  
Vehicle Speed ◽  
Color Image Analysis ◽  
Speed Measurement ◽  
Monochrome Image

Image analysis for traffic data collection has been studied throughout the world for more than a decade. A survey of existing systems shows that research was focused mainly on the monochrome image analysis and that the field of color image analysis was rarely studied. With the application of color image analysis in mind, this paper proposes a new algorithm for vehicle speed measurement in daytime. The new algorithm consists of four steps: (i) image input, (ii) pixel analysis, (iii) single image analysis, and (iv) image sequence analysis. It has three significant advantages. First, the algorithm can distinguish the shadows caused by moving vehicles outside the detection area from the actual vehicles passing through the area, which is a difficult problem for the monochrome image analysis technique to handle. Second, the algorithm significantly reduces the image data to be processed; thus only a personal computer is required without the addition of any special hardware. The third advantage is the flexible placement of detection spots at any position in the camera's field of view. The accuracy of the algorithm is also discussed. Key words: speed measurement, vehicle detection, image analysis, image processing, traffic control, traffic measurement and road traffic.

A single chip 1/2" frame transfer CCD color image sensor

1984 ◽  
Author(s):  
N. Mitani ◽  
T. Furusawa ◽  
Y. Tsuchihashi ◽  
Y. Kitamura ◽  
Y. Kiriyama ◽  
...  
Keyword(s):  
Color Image ◽  
Image Sensor ◽  
Single Chip

Fusion on Citrus Image Data from Cold Mirror Acquisition System

2012 ◽  
Vol 2 (4) ◽  
pp. 11-24 ◽  
Author(s):  
Peilin Li ◽  
Sang-Heon Lee ◽  
Hung-Yao Hsu
Keyword(s):  
Near Infrared ◽  
Color Image ◽  
Infrared Image ◽  
Image Data ◽  
Acquisition System ◽  
Support Vector ◽  
Self Organizing Map ◽  
Visible Image ◽  
Low Pass ◽  
Low Dimensional

In this paper, an image fusion is presented to improve the citrus identification by filtering the incoming data from two cameras. The citrus image data has been photographed by using a portable bi-camera cold mirror acquisition system. The prototype of the customized fixture has been manufactured to position and align a classical cold mirror with two CCD cameras in relative kinematic position. The algorithmic registration on the pairwise images has been bypassed by both the spatial alignment of two cameras with recourse of software calibration and the triggering synchronization in temporal during the photographing. The pairwise frames have been fused by using the Daubechies wavelets decomposition filters. The pixel level fusion index rule is proposed to combine the low pass coefficients of the visible image and the low pass coefficients of the near-infrared image convoluted by the complementary of entropy filter from the visible low pass coefficients. In the study, the fused artifact color image and the non-fused color image have been processed and compared by some classification methods such as low dimensional projection, self-organizing map and the support vector machine.

Development of a 3-Dimensional Internal Structure Microscope (3D-ISM) for the Observation to Biological Organisms.

1997 ◽  
Vol 3 (S2) ◽  
pp. 243-244 ◽  
Author(s):  
Hideo Yokota ◽  
Ken-ichi Kudoh ◽  
Kahei Sato ◽  
Toshiro Higuchi
Keyword(s):  
Internal Structure ◽  
Color Image ◽  
Block Diagram ◽  
Three Dimensional ◽  
Image Data ◽  
3 Dimensional ◽  
True Color Image ◽  
Sample Position ◽  
Type 3 ◽  
Biological Organisms

We developed a new type 3-Dimensional Internal Structure Microscope (3D-ISM) for the observation of internal structures of samples. The internal structure of a sample is obtained by cutting it into thin slices and observing the cutting side continuously while cutting. The position of the camera, as well as the sample position are fixed. Therefore there is no shift between the sections, and this system can obtain a true color image of the sample, which is a high resolution and a high-quality three-dimensional image compared with X-ray CT and MRI. After repeatedly slicing a sample, the digital image data from the sectional views is transferred to a computer, where 3-dimensional images of the internal structure of the sample are reconstructed. Using this system we analyzed many biological organisms. In this paper, a mouse specimen has been cut and the 3-dimensional images are shown.This article presents the outline of the device and the principle of the observation. FIG.l shows a corresponding block diagram and Fig. 2 a schematic view of the 3D-ISM.

scholarly journals PENERAPAN REKAYASA MESIN SORTIR SEBAGAI PENENTU KEMATANGAN BUAH JERUK DAN TOMAT MERAH BERBASIS IMAGE PROCESSING

2020 ◽  
Vol 9 (3) ◽  
pp. 222
Author(s):  
Ridwan Siskandar ◽  
Noer A Indrawan ◽  
Billi Rifa Kusumah ◽  
Sesar Husen Santosa ◽  
Irmansyah Irmansyah ◽  
...  
Keyword(s):  
Image Processing ◽  
Light Intensity ◽  
Color Image ◽  
Image Data ◽  
Post Harvest ◽  
Automation Systems ◽  
Efficient Alternative ◽  
Sorting System ◽  
Production Automation ◽  
Fruit Sorting

The embedded systems in the industrial, especially image processing, is increasingly leading to the study of production automation systems such as fruit sorting. Post-harvest sorting system implemented by the industry is manual, so it’s not effective. The solution was to conduct research aimed at modifying post-harvest sorting tools by engineering tomato and orange sorting machines based on their color. The method uses image processing. It’s the most efficient alternative in terms of cost and complexity of hardware design, does not require many sensors, but produces an accurate output. The camera is placed on the mechanical sorting machine system, taking images to determine the sorting execution after the fruit color type are recognized. The results of the research were carried out through several tests, namely: light intensity, color image data, and organoleptics. Light intensity test showed that the position of the tool had a value of 0.78% of the outside light disturbance. Color image shows the range of ripeness values (R/G) for raw tomatoes 0<=1.04; half ripe tomatoes 1.04<=1.39; ripe tomatoes 1.39<=3.59; raw orange 0<=0.92; undercooked oranges 0.92<=0.98; and ripe oranges 0.98<=1.66. Organoleptic test from five observers had the same results as the reading on the fruit sorting tool. Keywords : engineering, fruit maturity, oranges, sorting machines, tomatoes

scholarly journals Improving the Quality of Digital Images Using the Median Filter Technique to Reduce Noise

SinkrOn ◽  
2019 ◽  
Vol 4 (1) ◽  
pp. 143 ◽  
Author(s):  
Annas Prasetio ◽  
Paska Marto Hasugian
Keyword(s):  
Digital Images ◽  
Color Image ◽  
Median Filter ◽  
Image Data ◽  
Original Image ◽  
Matrix Kernel ◽  
Filter Technique ◽  
Image Histogram ◽  
Point Line

The combination of point, line, shape and color elements combined to create a physical imitation of an object is called an image. The arrangement of the box elements in the image forms pixels or matrices. each image experiences degradation or loss of quality called noise. The effect of gaussian noise is the number of colored dots that are equal to the percentage of noise. This study raises the topic of improving the quality of digital images using median filter techniques to reduce noise. In this study using color image data (Red Green Blue) as test data and then converted into grayscale images to determine the gray degree of the image. The grayscale image is stored in the database. Then noise is generated by using random numbers. Noise in the form of impulse can be positive or negative in the form of adding pixel values to the original image, or it can reduce the value of the original image. The noise type used is salt & pepper. Gray degrees 0-255 spread. Can be calculated through image histograms. To reduce noise the median filter technique is used. Image histogram as a measure of the spread of numbers from the median filter. The result is a median filter can reduce noise salt and pepper by using a matrix kernel.

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