A18 - Basic Video Processing

For all my past blog posts, I have done numerous tasks such as classification, playing musical notes, measuring areas, cleaning by Fourier transform, etc.

But what's the common thing about them? Yes, all of them are based on extracting features using an image only. So the question is, how about videos? Can the same techniques be employed?

In this blog post, I will show you a simple experiment on extracting features from a kinematic event captured using a video camera.

A17 - Neural Networks

Artificial neural network or simply neural network is a mathematical model that aims to mimic the structural/functional aspect of biological neural networks. So what is the difference between the two? Biological networks aim to explain a complex phenomena using real neurons, forming a circuit, releasing physical signals (voltage action potential)to one another. Artificial neural networks, on the other hand, are purely mathematical and uses artificial neurons instead.

A16 - Probabilistic Classification

Pattern recognition has been an interest of many people in the previous years. Many classification tools have been developed to perform this complex task.

In relation to my previous blog post about pattern recognition using the minimum distance classification, I will show you another tool using Linear Discriminant Analysis which is the process of finding a linear combination of features enabling separation of two or more classes of objects [2].

A15 - Pattern Recognition

Humans have an intrinsic capability to differentiate things from each other. That is to recognize an unknown object and classify the group it belongs by just looking at patterns such as color, shape, size, texture, etc. The amazing thing is, we do this complex task in just a short period of time. Not so long ago, computers arrived in the playing field to at least mimic this impressive feat of humans. Computers' do this by doing an inspection on an object's characteristics repeatedly. But for a computer to be able to do this, humans must teach it first. The question is how?

The first important thing a computer must define is a set of object features or pattern. These features are quantifiable properties such as color and size. These features are then used to create classifiers to conveniently group together objects into a class sharing common properties.

In this blog post, I'll show you how to extract features from objects and finally use them to create a function that will do pattern recognition.

A14 - Color Image Segmentation

In today's era, a wide selection of "high-technology" imaging tools have come to life. One can choose from the sleek point-and-shoot cameras, to the high resolution DSLR's and the compact built-in high megapixel cellphone cameras. With all these complicated and expensive tools, we only have one simple ultimate goal. It is just to capture a scene or an important moment in our lives. A pretty simple idea right?

The images we capture can always be separated into two parts, the scene or object of our interest and the background. Sometimes the background is not as interesting as the object/scene but we have no choice but to take them also. That's where image segmentation enters. We can use this method to separate the object from the not-so-interesting background. If you have been following my blog, separating a region of interest (ROI) from the background is not a new idea. And it was always done using grayscale images. But many objects have intrinsic colors similar to the background, thus doing segmentation in grayscale world will definitely fail. In this blog post, I'll show you two techniques, the parametric and non-parametric segmentation, of separating a colored image from a background.

A13 - Image Compression

Remember the old school floppy disk? The unattractive bulky square-like storage device. Imagine this, the device only stores up to a maximum of 1.5 MB only! But it was ubiquitous many years ago that everyone was so contented with it until the arrival of new storage devices with storage size ranging from GB to TB.

With such small storage space, what can we do to maximize its usage??
--> Compression is the answer!

Compressing a file such as a document or an image means reducing its storage size for convenience purposes.

In this blog post, we will use the Principal Component Analysis to represent an image as a superposition of weighted basis images and minimize the number of features to be used to compress the image. A possible useful discussion of Principal Component Analysis method can be found in this wiki page.

A12 - Preprocessing Text

Just some random thoughts... One of the very first things we learned when we started going to school was to write. Writing is a representation of a language through the use of a set of symbols (in our case, alphabet). Before computers became popular, most people hand-write texts, letters, etc.; and the nature of the hand-written text is unique for each individual.

With this concept, how do people understand other people's handwritten text especially if it's too "ugly"?
--> I guess it's our innate ability to read words not letter by letter but by the first and last letters only and decipher the exact word instantly.

In relation to the handwritten text I was talking about above, I'll show in this blog post how to extract handwritten text from an imaged document with lines.