Image processing usually refers to digital image processing, but optical and analog image processing also are possible. This article is about general techniques that apply to all of them. The acquisition of images (producing the input image in the first place) is referred to as imaging.
Closely related to image processing are computer graphics and computer vision. In computer graphics, images are manually made from physical models of objects, environments, and lighting, instead of being acquired (via imaging devices such as cameras) from natural scenes, as in most animated movies. Computer vision, on the other hand, is often considered high-level image processing out of which a machine/computer/software intends to decipher the physical contents of an image or a sequence of images (e.g., videos or 3D full-body magnetic resonance scans).
In modern sciences and technologies, images also gain much broader scopes due to the ever growing importance of scientific visualization (of often large-scale complex scientific/experimental data). Examples include microarray data in genetic research, or real-time multi-asset portfolio trading in finance.
Hybrid intelligent system denotes a software system which employs, in parallel, a combination of methods and techniques from artificial intelligence subfields as:
- Neuro-fuzzy systems
- hybrid connectionist-symbolic models
- Fuzzy expert systems
- Connectionist expert systems
- Evolutionary neural networks
- Genetic fuzzy systems
- Rough fuzzy hybridization
- Reinforcement learning with fuzzy, neural, or evolutionary methods as well as symbolic reasoning methods.
From the cognitive science perspective, every natural intelligent system is hybrid because it performs mental operations on both the symbolic and subsymbolic levels. For the past few years there has been an increasing discussion of the importance of A.I. Systems Integration. Based on notions that there have already been created simple and specific AI systems (such as systems for computer vision, speech synthesis, etc., or software that employs some of the models mentioned above) and now is the time for integration to create broad AI systems.
Intelligent systems usually rely on hybrid reasoning systems, which include induction, deduction, abduction and reasoning by analogy.
Intelligent Transport Systems(ITS)
Active Traffic Management
Technology is used to smooth traffic flows by coordinating ramp signals and introducing lane-use management systems such as variable speed limits and variable message signs.
Current GPS systems can provide information to drivers on traffic and road conditions as well as their primary purpose of giving directions. In some cases manufacturers have combined to share information to build a real-time model of traffic flows from the data provided by individual vehicles.
This is an ITS technology that allows monitoring of an individual vehicle's movements and can record the speed, location and mass of a vehicle. This technology is already being used by transport companies and can be used as a regulatory tool, for purposes such as road user charging, compliance and enforcement.
By using ITS technology, controllers will be able to run more efficient schedules due to better information on the location, speed and length of trains using the network.
Cooperative ITS (C-ITS) technology
It will provide a real time communication between individual vehicles and between vehicles and roadside infrastructure. For example, C-ITS can warn a driver that a collision is likely, or alert a driver to a vehicle that is braking hard but is hidden from view.
Artificial Immune Systems(AIS)
AIS concerns the usage of abstract structure and function of the immune system to computational systems, and investigating the application of these systems towards solving computational problems from mathematics, engineering, and information technology. AIS is a sub-field of Biologically inspired computing, and natural computation, with interests in Machine Learning and belonging to the broader field of Artificial Intelligence.
Swarm intelligence is the collective behavior of decentralized, self-organized systems, natural or artificial.
SI systems consist typically of a population of simple agents or boids interacting locally with one another and with their environment. The inspiration often comes from nature, especially biological systems. The agents follow very simple rules, and although there is no centralized control structure dictating how individual agents should behave, local, and to a certain degree random, interactions between such agents lead to the emergence of "intelligent" global behavior, unknown to the individual agents. Examples in natural systems of SI include ant colonies, bird flocking, animal herding, bacterial growth, fish schooling and microbial intelligence.
Development of algorithms to partially simplify or automate architectural design and urban planning tasks.
Definition of computable models of architectural knowledge, as general and flexible as possible.
Automatic generation of variations on architectural features, structures and objects.