Image acquisition cropping background removal

Image acquisition cropping background removal

Image acquisition cropping background removal Capturing the digital image is very first step in image processing. A controlled light source is required to get a better image. Moreover, the background, distance from camera to the object should also be controlled in order to get better picture and consistent results. The factors have a great effect on image segmentation. If a fixed background is used, no pre-processing is required (Jhawar, December 2015). Images was captured by a CCD camera, which resulted colour images in RGB colour space. Exposure value and AWB gains for camera were set manually to get images with consistent parameters. Automatic metering was turned off and images was captured as frames from video stream.

An imaging chamber was used to capture the image, which had the physical arrangement such that fruit could only appear at a fixed central region in the camera’s field of view. The probability of finding the fruit outside that region was zero. Therefore, it was practical to crop only the central region and perform further operations on that region only. It could save the memory and the processing power resulting in speeding up the real-time operations.

Background needs to be removed so that the algorithm only performs calculations on the object of interest only. Background removal can save a lot of processing power later and reduces the complexities in the later algorithm. Since we are using a fixed black background, only a little or no effort is required to remove the background. The algorithm checks for pixel values for all three channels (R, G, and B). Knowing the pixel intensity range for the fruit, all other values would be set to zero. The process was simple and robust and could isolate image region accurately.Since the background used was black and under different lighting conditions, it could only produce levels of grey. Using the fact that grey levels always have all three pixels (R, G, and B) nearly equal, green channel values were subtracted from red channel and whenever the absolute difference was below a threshold, the pixel was set to black.

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