Developing a new color model for image analysis and processing

Rasras, Rashad J.; Emary, Ibrahiem M. M. El; Skopin, Dimitri E.

doi:10.2298/csis0701043r

Cited by 11 publications

(6 citation statements)

References 3 publications

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“…Then, it is difficult to carry out some image processing algorithms. Many processing techniques, such as histogram equalization, only work on the intensity component of an image [7] .…”

Section: Color Image Conversionsmentioning

confidence: 99%

Vision enhancement of line following robot using Wiener filter and morphology

2011

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Line following robot (LFR) plays an important rule in the recent industries, where it can carriers the products from one manufacturing plant to another which are usually in different buildings or separate blocks. This paper presents preliminary development of the LFR which use a normal laptop with a web cam to guide itself through a track. The designed LFR is insensitive to environmental factors such as darkness, lighting, camera distortion or line color. This development is based on computer vision enhancement using digital image processing. It is accomplished through the following stages: Firstly, the acquired RGB image using the web cam is converted to another color coordinates for testing and comparing to choose the best color space. After that, the image contrast is enhanced using adaptive histogram equalization, and then the image is filtered using wiener filter. Finally morphological operations are carried out. The results are evaluated qualitatively and quantitatively from the points of peak signal-to-noise ratio (PSNR), entropy, and image smoothness. The results show that the proposed method is promising for vision enhancement of LFR.

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“…Then, it is difficult to carry out some image processing algorithms. Many processing techniques, such as histogram equalization, only work on the intensity component of an image [7] .…”

Section: Color Image Conversionsmentioning

confidence: 99%

Vision enhancement of line following robot using Wiener filter and morphology

2011

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“…The red, green, and blue color components are highly correlated. Due to the interaction between the color components (R, G, B), it is difficult to discriminate between objects of interest (mines) and the background in the color image [11]. As a result, the RGB image is converted to a grayscale image prior to performing the object detection operations.…”

Section: Grayscale Based Automatic Object Detectionmentioning

confidence: 99%

False Alarm Rate Reduction in the Interpretation of Acoustic to Seismic Landmine Data using Mathematical Morphology and the Wavelet Transform

et al. 2010

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This paper presents a new technique for false alarm rate reduction to improve the performance of automatic object detection systems that operate on digital images. The proposed technique is applied to the images obtained by a Laser Doppler Vibrometer (LDV) based acoustic to seismic (A/S) system. This technique is based on morphological image processing and the discrete wavelet transform (DWT) of the image data. It begins with a color image transformation to obtain a grayscale image and then a closing operation that enhances the larger objects in the image. After that, the DWT is applied to the closed image. The decimation effect of this DWT helps in the removal of small clutter objects the image. The approximation component resulting from the DWT is used in the detection process. Results from the proposed technique are compared to those obtained using traditional detection techniques.

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“…So, we convert the RGB data into another color space. The HSI space is often a better way to represent color images [6,7]. The hue component (H) is a measure of the angle that represents the type of the color.…”

Section: Color Image Transformationsmentioning

confidence: 99%

Efficient Detection of Landmines From Acoustic Images

Kasban¹,

Zahran²,

El-Kordy³

et al. 2009

PIER C

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Abstract-The Laser Doppler Vibrometer (LDV)-based Acoustic toSeismic (A/S) landmine detection system is one of the reliable and powerful landmine detection systems. The interpretation of the LDVbased A/S data is performed off-line, manually, depending heavily on the skills, experience, alertness and consistency of a trained operator. This takes a long time. The manually obtained results suffer from errors, particularly when dealing with large volumes of data. This paper proposes some techniques for the automatic detection of objects from the acoustic images which are obtained from the LDV-based A/S landmine detection system. These techniques are based on Corresponding author: F. E. Abd El-Samie (fathi sayed@yahoo.com). 80Kasban et al.color image transformations, morphological image processing and the wavelet transform. The proposed techniques are compared considering the probability of detection, the false alarm rate, the accuracy and the processing speed.

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Developing a new color model for image analysis and processing

Cited by 11 publications

References 3 publications

Vision enhancement of line following robot using Wiener filter and morphology

Vision enhancement of line following robot using Wiener filter and morphology

False Alarm Rate Reduction in the Interpretation of Acoustic to Seismic Landmine Data using Mathematical Morphology and the Wavelet Transform

Efficient Detection of Landmines From Acoustic Images

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