Kruithof Curve

mainThis is more of a continuation of my blog post on color temperature. Back when fluorescent light sources first came up, they changed the way humans thought about light and color rendering. A scientist named Arie Andries Kruithof performed some experiments on how the human eye relates the amount of light in a given time of day to the color temperature of the light source, and came up with a theory. As we move through the day, the amount of light we get keeps varying. Typically, human beings like higher color temperature light sources during the daytime hours, and lower color temperature sources once the sun goes down. People in warmer climates tend to favor cooler color temperature sources, and people in colder climates like warmer light. So what does this have to do with the Kruithof curve?   Continue reading

What Is Color Temperature?

mainWait a minute, isn’t “temperature” associated with weather? How can color have temperature? The thing is that color temperature is actually a characteristic of visible light that has several important applications in photography, publishing, and many other fields. We actually see and feel it all the time, it’s just that we don’t realize that we like certain color temperatures more than others. The concept of color is more easily apparent to us. We can see what’s red and what’s blue. There are a lot of characteristics of color that we feel, but don’t realize. So what exactly is color temperature?   Continue reading

Pixelation

mainPixelation is the display of a digitized image where the individual pixels are easily visible to a viewer. This can happen unintentionally when a low-resolution image designed for an ordinary computer display is projected on a large screen. In this situation, each pixel becomes separately viewable. It’s not pretty! Pixelation is also sometimes used to describe the act of turning a printed image into a digitized image file. As the image is captured, it is processed into a vectorized or rasterized file that can be used to illuminate color units (called pixels) on a display surface. So why does it happen? What exactly happens in there?   Continue reading

The Intuition Behind Image Watermarking

mainThis is a continuation of my blog post on intro to digital watermarking. In that post, we discussed what digital watermarking is and how it can be achieved. Here, we will discuss the intuition behind image watermarking and a few techniques that can be used. If you look at enough number of images, you will realize that not all of them are equally suited for watermarking. At least, we cannot use the same criteria to watermark all the images. How do we know where to watermark an image? Are there any rules or do we just place some watermark randomly in an image? Does it make a difference?   Continue reading

Digital Watermarking

mainLet’s say you want to verify the authenticity of a signal. The signal can take any form like an image, audio, video, or any other kind of bit stream. By now, everybody would have heard the term “watermark” being used in the general sense. The most common example would be currency notes. Watermarks are embedded to verify the authenticity of the notes. But how do we achieve that with more complicated signals? As things move into the virtual world, where the threats are elevated to a much higher and abstract level, we need a way to verify the authenticity of different forms of digital signals. How do we do it?   Continue reading

Image Steganography

mainAs discussed in my previous post, steganography is the art of hiding the fact that communication is taking place. We achieve this by hiding original information inside other information known as carrier files. Many different carrier file formats can be used, but digital images are the most popular because of their frequency of occurrence on the internet. For hiding secret information in images, there exists a large variety of steganographic techniques, some are more complex than others, and all of them have respective strong and weak points. Different applications have different requirements of the steganography technique used. For example, some applications may require absolute invisibility of the secret information, while others require a larger secret message to be hidden. How do we achieve this? How robust is it?   Continue reading

What Are Conditional Random Fields?

main nodesThis is a continuation of my previous blog post. In that post, we discussed about why we need conditional random fields in the first place. We have graphical models in machine learning that are widely used to solve many different problems. But Conditional Random Fields (CRFs) address a critical problem faced by these graphical models. A popular example for graphical models is Hidden Markov Models (HMMs). HMMs have gained a lot of popularity in recent years due to their robustness and accuracy. They are used in computer vision, speech recognition and other time-series related data analysis. CRFs outperform HMMs in many different tasks. How is that? What are these CRFs and how are they formulated?   Continue reading