Preprocessing with Image Processing Techniques

In order for many of the projects that we aim for to be realized, it is necessary to undergo image processing steps. In this article, we will reveal the pre-processing stages with Gauss, average filter, threshold filters, and Canny Edge Sensor. As a Platform, you can work in Colab like me! In this way, you can perform your projects both very quickly and without taking up space.

Image processing techniques are systems for obtaining various information by analyzing existing images. Image processing techniques work through different mathematical expressions, from the simplest algorithms to the most complex algorithms, depending on their place of use.

In order to use image processing methods, real-world data captured using a previously obtained camera will be processed. Operations were determined as reading data through OpenCV, controlling pixel values in terms of color channels, eliminating noise contained in the image, and using existing filters.

It would be better for our working principle to prepare the data set that we will use for our projects in advance. Images will be extracted from the file using the imread( ) method from your dataset. Let’s get to work by loading the necessary libraries for this process.

📍NOTE: After performing the preprocessing step, I usually use the imshow() function to check the image. But let’s not forget that since our imshow function does not work in Colab, we must install the cv2_imshow module!

The image you see below contains code that allows you to obtain the image and examine the pixel values.

After installing our libraries, we must create a class and copy the path to the PATH variable for the data set that we will use. Because our image processing techniques will work on the images contained in this folder.

As you can see in the imread function, I put 0 next to the file, since the images I use are gray-level images. In this way, even if the hue is possible in the images, this image will turn gray. We can print our first image on the screen using the RGB imshow method.

When we put 0 next to our image, we will have reached the image that you see below. it is possible to print the image that you see on the screen with the cv2_imshow(image) command. After this step, we can move on to the image processing steps.

Image Processing Steps

If you want to see the pixel values of your RGB or gray scale image, you can print it to the screen using the print command. In this way, you will also check which channel you are working on. Since I use an RGB rose image in this image, the pixel values show the following numbers.

📌 RGB Color Channel: RGB is the most commonly used color space. In this color model, each color sees red, green, and blue as its main spectral components. The infrastructure of this model includes a Cartesian coordinate system.

The code required to convert our image to RGB and examine the pixel values is given as follows.

📌 HSV Color Channel: The name of the HSV space comes from the initials hue, saturation and intensity, the English equivalent of the words hue, saturation and brightness. The HSV color space defines color with the terms Hue, Saturation, and Value. Although RGB also uses a mixture of colors, HSV also uses color, saturation, and brightness values. Saturation determines the vitality of the color, while brightness refers to the brightness of the color.

📌 CIE-LAB Color Channel: CIE, 1931 color spaces are the first defined quantitative links between the distribution of wavelengths in the electromagnetic visible spectrum and physiologically perceived colors in human color vision. The mathematical relationships that define these color spaces are essential tools for Color Management, which is important when dealing with recording devices such as color inks, illuminated displays, and digital cameras.

LAB Color Channel

Elimination of Noise In The Image

Because images are real-world data from the camera, they often contain Gaussian noise due to current changes on a camera’s sensor. Noisy images can lead to worse performance in edge detection, which we use for Element detection. Therefore, it is important to reduce this noise.

🖇 There are many methods available in the literature to reduce noise. Today we will discuss 2 methods with you.

  1. Adaptive Threshold Gaussian
  2. Adaptive Threshold Mean
➡️ Adaptive Threshold Gaussian

I have included the Python code in which the Gaussian method is applied to make our images become gaussian noise removed images as follows. It is possible to reach the desired result by manipulating the parameters in the adaptiveThreshold method here.

When Gaussian and average threshold filters, which are frequently used in the literature, are applied on these images, it is seen that almost the same blur (softening) level is approached. These methods are adaptive gaussian filter and average filter application, respectively.

➡️ Adaptive Threshold Mean

Adaptive thresholding is the method by which the threshold value is calculated for smaller regions and therefore there will be different threshold values for different regions.

You will appreciate that there are very minor nuances between Gaussian and Mean filters. You can continue with the filter you want by changing the parameter values yourself.

➡️ Edge Detection

Edge detection is an important technique used to detect features. Canny edge detection algorithm, which is one of the edge detection techniques in the figure, has been run on the images.

Canny Code

Canny Image

REFERENCES

  1. Medium, Cerebro, Using Artificial Intelligence in Image Processing Techniques, April 2018.
  2. Wikipedia, The Free Encyclopedia, ‘Image Processing’, September 2020.
  3. C. Gonzalez, Rafael, E. Woods, Richard, Digital Image Processing, Palme Publishing, (Ankara, 2014).
  4. S. Singh and B. Singh. “Effects of noise on various edge detection techniques”. In: 2015 2nd International Conference on Computing for Sustainable Global Development (INDIACom). Mar. 2015, pp. 827–830.
  5. https://www.tutorialspoint.com/opencv/opencv_adaptive_threshold.htm.
  6. Ajay Kumar Boyat and Brijendra Kumar Joshi. “A Review Paper: Noise Models in Digital Image Processing”. In: CoRR abs/1505.03489 (2015). arXiv: 1505.03489. url: http:// arxiv.org/abs/1505.03489.
  7. T. Silva da Silva et al. “User-Centered Design and Agile Methods: A Systematic Review”. In: 2011 Agile Conference. Aug. 2011, pp. 77–86. doi: 10.1109/AGILE.2011.24.

 

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