When it comes to computer vision, normalizing images is one of the most important steps. This article will go into great detail about what normalization does, how different types work, and why you should care!
Normalization removes distortions or changing elements from an image so that you can use the image more effectively later. It also makes pre-processing images easier as there are less parameters to manage.
This article will focus only on color normalizations. There are many ways to do this, but we will choose between using RGB channels, HSV (hue, saturation, value), Lab (luminance, alpha, beta) and CIEXYZ (chroma in–axis, hue, and axis). We will also look at some examples of each!
Color normalizations can be done either before or after other stages such as feature extraction or classification. For example, if you were doing object recognition then you would normally perform color normalization on the images first and then carry out additional processes.
There are several reasons why performing color normalization early on is a good idea. One is efficiency – by performing these transformations earlier, you have already computed part of the algorithm which saves time later on when those computations have been included in the model. Another is consistency – having similar colors across all your images helps make analysis faster and clearer. A third is accuracy – some algorithms depend on specific colors being present and missing them can affect results.
Calculate the correct resolution
When starting your experiment, one of the first things you will need to do is determine what size of image you want to use in your model. This is determined by two values: file size and resolution.
The first thing you should do is make sure that your images are not too small or too large. We recommend using an average picture length of around 100 pixels (for example, looking at https://www.pexels.com/). A good rule of thumb is to take half of the height of the image as your new width value. For instance, if your image has a vertical dimension of 200 px, then its new width would be 100px!
This way, your image does not get unnecessarily scaled up or down, and your calculation of the normalization factor is more accurate. Make sure to test this out with some examples!
After calculating these two values, we calculated our third important variable: total number of pixels in your image. This can easily be done by taking the product of both dimensions mentioned before. In our example above, this would look like this: (height x width) = 2000px. Subtracting this from 1 gives us our final value: 0.2 or 2% of all pixels in the image!
This ratio comes directly after the normalization value we picked earlier, so they work together to scale down the intensity of colors in your pictures.
Use dark backgrounds
When normalizing images, you want to make sure that there is not very much contrast in the image. If the image has a very bright background or very dark foreground, it may not work well when using computer vision applications like face detection or object recognition.
Images with lots of white space are also more easily normalized because you can just add some other colors into the mix. By including many shades of color, the system will be able to find an average value for each pixel which helps reduce noise.
By having a darker background, your image will look good no matter what colors are in the rest of the photo. The software will automatically adjust so that the important parts (like the person’s face) do not get distorted due to low contrast.
Try experimenting by taking several pictures of the same thing with different backgrounds.
Use bright backgrounds
When it comes down to it, neural networks work best when they have lots of examples of different features in the data. A very important feature is the background or “noise” in an image.
Neural nets are built off of how well neurons connect with other nearby neurons. If there are not many instances of similar looking background features, then the network has a hard time figuring out what parts of the picture are actually part of the object being identified.
By having large amounts of diverse noise, the net can learn more about the space and how to use that information to identify objects.
General purpose software will try to remove all shades of gray from images, which may distort fine details. You do not need to be as careful here because you will re-compress the file later.
Instead, add some variety to your image files by using plain white, black, and pastel colored backgrounds.
Use the correct color setting
There are two main settings that affect how colors look in an image. These are color space and intensity (or level) of color.
Color spaces refer to what qualities the digital numbers representing colors have. Different color spaces mean different things, such as whether shades of gray count as part of the color or not.
Intensity is a way to control the amount of white, black, and other contrast in an image. Settings with higher intensities result in very bright images and low ones that become dark.
Both of these settings depend on the original source material of the image you are working with. For example, if the picture was taken using natural light, then it has its own internal color balance that does not need any intervention.
When editing pictures digitally, there are tools available that can help you fix bad color balances or poor overall quality. Many software packages include basic color correction features, but advanced users may want to try their hand at more complex settings.
Use the same color setting
Choosing your image’s color settings is an important step in getting good results from any computer vision task, such as object detection or segmentation.
When it comes down to it, colors influence how well AI algorithms perform their tasks. For instance, red tends to activate areas of the brain that perceive strong emotions, while blue makes people feel calm.
So when pre-processing images for training neural networks, you should make sure they are balanced in both hue and intensity (or brightness). This way, the algorithm can use either information about the color palette or the shape of the item in the picture, not just one factor.
There are several ways to do this. You could use Photoshop or other software to find out what values of RGB give pleasant looking pictures.
Use the same intensity
This is an important topic because it applies not only to images, but also to colors.
When we talk about normalizing images, what we mean by that term is changing the image’s overall intensity or color balance.
By this, we mean altering the way the pixels in the picture light up. Some people call this desaturating the image, which is just making everything grayish-colorful.
Another option is overexposing the image, which makes everything very bright and white. Or maybe you can think of it as increasing the brightness level.
Either one of these options removes some of the rich detail in the image, creating a smoother look. That’s why it’s important to be aware of how different types of intensities affect neural networks when performing image classification or other tasks like object detection.
General recommendations are to use either black and white pictures or extremely dark pictures with no colors.
Use the same contrast
When normalizing images, you should make sure that all of your pictures have similar levels of intensity or “contrast”. This is typically done through Photoshop by adjusting the brightness and/or darkness of an image.
By having equal amounts of dark and light in an image, it becomes difficult to determine which parts are lighter and which ones are darker unless they look identical before being adjusted.
This makes it more difficult to use computer algorithms such as neural networks (NNs) to recognize patterns in the picture. For example, if there is not enough contrast in an image, then NNs will think that part of the image looks like another one!
There are many ways to do this, but most people choose to adjust either the exposure or the color balance (clarity) of an image first. Then, depending on whether you want the image to be brighter or darker, they either increase or decrease the amount of gain (brightness) or loss (darkness).
General tips: remember that too much gain can result in overexposed photos while too much loss can create blurry or very soft images. Also, avoid completely black and white photographs as these may lose detail
Practice: Try experimenting with different types of images and see what changes you can make to improve the quality. In addition, you can also try taking some funny or weird pictures to see how changing the settings affects the overall feel of the photograph.
Use the same scale
There is some controversy over whether or not it’s important to use normalized images when performing image classification with neural networks. Some argue that using un-normalized raw pixels can sometimes improve performance, while others say that it doesn’t matter either way!
The reason many people think normalizing images is unnecessary comes down to one thing: the number of possible values an integer pixel can take. An example would be considering every color in the rainbow as meaningful and equally significant.
By this reasoning, there are only so many colors there can be, and therefore integers 0 through 255 don’t really represent all the possibilities. The more dimensions you have, the more information you contain.
When working with natural imagery (e.g., pictures of houses, landscapes), there are often several similar looking buildings, trees, or other features present. This article will talk about how to fix this problem by developing your own internal norm for images.
If you ever find yourself arguing about whether or not normalization is necessary, then let us help you choose.
