What is Pattern Recognition?

Pattern recognition is, in a nutshell, grouping, description, recognition, and classification of perceptual structure. In other words, I have some data (images, sounds, inputs, etc.), what information can I get out of it?

Dr. Hamed Sari-Sarraf, my image processing professor at Texas Tech, described pattern recognition and image processing as the two sub-disciplines that make up computer vision. He said, that image processing converts images into data that is useful e.g. remove noise, highlight regions of interest, etc.; whereas, pattern recognition is using those processed images to make a judgment about the data e.g. identify a face, perform a classification of an object, etc.

According to a former co-worker, Don Waagen, pattern recognition is broken down into four basic parts from lowest level processing to highest: sensing, segmentation, feature extraction, and classification. Sensing converts images, sounds, x-rays, etc. to a signal. Segmentation isolates sensed objects from uninteresting signal i.e. noise. Feature extraction measures useful properties of objects e.g. width of a face image or the length of a bridge; basically, a feature is any information from an object that can be unique to that object. Classification assigns objects to a category.

Standing on the shoulders of Hamed and Don, I view pattern recognition as feature extraction and classification of data. I consider sensing and segmentation as more image processing. But, like anything, the lines between the topics are blurry, and I will discuss some image processing topics; however this blog will mainly focus on pattern recognition.