Digital image processing allows the use of much more complex algorithms, and hence, can offer both more sophisticated performance at simple tasks, and the implementation of methods which would be impossible by analog means.
In particular, digital image processing is the only practical technology for: Classification, Feature extraction, Multi-scale signal analysis, Pattern recognition, Projection
Some techniques which are used in digital image processing include: Anisotropic diffusion, Hidden Markov models, Image editing, Image restoration, Independent component analysis, Linear filtering, Neural networks, Partial differential equations, Pixelation, Principal components analysis, Self-organizing maps and Wavelets
Computer vision deals with how computers can be made for gaining high-level understanding from digital images or videos. Computer vision tasks include methods for acquiring, processing, analyzing and understanding digital images, and extraction of high-dimensional data from the real world in order to produce numerical or symbolic information, e.g., in the forms of decisions. Understanding in this context means the transformation of visual images (the input of the retina) into descriptions of the world that can interface with other thought processes and elicit appropriate action. This image understanding can be seen as the disentangling of symbolic information from image data using models constructed with the aid of geometry, physics, statistics, and learning theory.
Sub-domains of computer vision include scene reconstruction, event detection, video tracking, object recognition, 3D pose estimation, learning, indexing, motion estimation, and image restoration.
Evolved from the study of pattern recognition and computational learning theory in artificial intelligence, machine learning explores the study and construction of algorithms that can learn from and make predictions on data – such algorithms overcome following strictly static program instructions by making data-driven predictions or decisions, through building a model from sample inputs.
Machine Learning Algorithms include Naïve Bayes Classifier, K Means Clustering, Support Vector Machine Algorithm, Apriori Algorithm, Linear Regression, Logistic Regression, Artificial Neural Networks, Random Forests Decision Trees and Nearest Neighbours.
Natural Language Processing
Natural language processing (NLP) is a field of computer science, artificial intelligence and computational linguistics concerned with the interactions between computers and human (natural) languages, and, in particular, concerned with programming computers to fruitfully process large natural language corpora. Challenges in natural language processing frequently involve speech recognition, natural language understanding, natural language generation, connecting language and machine perception, dialog systems, or some combination thereof.
NLP techniques include Tokenization, Acronym normalization and tagging, Lemmatization/Stemming, Decompounding, Entity extraction, Regex extraction, Dictionary extraction, Complex pattern-based extraction, Statistical extraction, Phrase extraction, Part of speech tagging, Statistical phrase extraction, etc.
Data science is a “concept to unify statistics, data analysis and their related methods” in order to “understand and analyze actual phenomena” with data. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science, in particular from the subdomains of machine learning, classification, cluster analysis, data mining, databases, and visualization.