Pursuing the task of intelligent machines design, thinking about their possible architectures, we always look first at the prototypes that the nature has prepared for us so plentiful and generously. Most commonly, the human brain is chosen to be the source of our inspiration. This way of thinking - considering the human brain as the source of our inspiration - was established more than 50 years ago at the Dartmouth college meeting (in summer of 1956), where the concept of Artificial Intelligence has been proclaimed and inaugurated [1]. What is "intelligence" was not defined at the meeting, but it was self understood that human intelligence is what's being meant and the human brain is supposed to be its most probable location. Soon after that, the Artificial Neural Network (ANN) toolkit was devised and put in use for AI studies and investigations. The ANN was contrived as a collection of small interconnected computational units (called artificial neurons), which are supposed to imitate the biological neurons of the human brain, and in a greatly simplified form simulate the way in which the brain supposedly performs its duties.

Video Surveillance is a fast evolving field of research and development (R&D) driven by the urgent need for public security and safety (due to the growing threats of terrorism, vandalism, and anti-social behavior). Traditionally, surveillance systems are comprised of two components - video cameras distributed over the guarded area and human observer watching and analyzing the incoming video. Explosive growth of installed cameras and limited human operator's ability to process the delivered video content raise an urgent demand for developing surveillance systems with human like cognitive capabilities, that is - Cognitive surveillance systems. The growing interest in this issue is testified by the tens of workshops, symposiums and conferences held over the world each year. The IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS) is certainly one of them. However, for unknown reasons, the term Cognitive Surveillance does never appear among its topics. As to me, the explanation for this is simple - the complexity and the indefinable nature of the term "Cognition". In this paper, I am trying to resolve the problem providing a novel definition of cognition equally suitable for biological as well as technological applications. I hope my humble efforts will be helpful.

"Information Processing" is a recently launched buzzword whose meaning is vague and obscure even for the majority of its users. The reason for this is the lack of a suitable definition for the term "information". In my attempt to amend this bizarre situation, I have realized that, following the insights of Kolmogorov's Complexity theory, information can be defined as a description of structures observable in a given data set. Two types of structures could be easily distinguished in every data set - in this regard, two types of information (information descriptions) should be designated: physical information and semantic information. Kolmogorov's theory also posits that the information descriptions should be provided as a linguistic text structure. This inevitably leads us to an assertion that information processing has to be seen as a kind of text processing. The idea is not new - inspired by the observation that human information processing is deeply rooted in natural language handling customs, Lotfi Zadeh and his followers have introduced the so-called "Computing With Words" paradigm. Despite of promotional efforts, the idea is not taking off yet. The reason - a lack of a coherent understanding of what should be called "information", and, as a result, misleading research roadmaps and objectives. I hope my humble attempt to clarify these issues would be helpful in avoiding common traps and pitfalls.

Traditionally, semantics has been seen as a feature of human language. The advent of the information era has led to its widespread redefinition as an information feature. Contrary to this praxis, I define semantics as a special kind of information. Revitalizing the ideas of Bar-Hillel and Carnap I have recreated and re-established the notion of semantics as the notion of Semantic Information. I have proposed a new definition of information (as a description, a linguistic text, a piece of a story or a tale) and a clear segregation between two different types of information - physical and semantic information. I hope, I have clearly explained the (usually obscured and mysterious) interrelations between data and physical information as well as the relation between physical information and semantic information. Consequently, usually indefinable notions of "information", "knowledge", "memory", "learning" and "semantics" have also received their suitable illumination and explanation.

Machine Consciousness and Machine Intelligence are not simply new buzzwords that occupy our imagination. Over the last decades, we witness an unprecedented rise in attempts to create machines with human-like features and capabilities. However, despite widespread sympathy and abundant funding, progress in these enterprises is far from being satisfactory. The reasons for this are twofold: First, the notions of cognition and intelligence (usually borrowed from human behavior studies) are notoriously blurred and ill-defined, and second, the basic concepts underpinning the whole discourse are by themselves either undefined or defined very vaguely. That leads to improper and inadequate research goals determination, which I will illustrate with some examples drawn from recent documents issued by DARPA and the European Commission. On the other hand, I would like to propose some remedies that, I hope, would improve the current state-of-the-art disgrace.

For the most of my life, I have earned my living as a computer vision professional busy with image processing tasks and problems. In the computer vision community there is a widespread belief that artificial vision systems faithfully replicate human vision abilities or at least very closely mimic them. It was a great surprise to me when one day I have realized that computer and human vision have next to nothing in common. The former is occupied with extensive data processing, carrying out massive pixel-based calculations, while the latter is busy with meaningful information processing, concerned with smart objects-based manipulations. And the gap between the two is insurmountable. To resolve this confusion, I had had to return and revaluate first the vision phenomenon itself, define more carefully what visual information is and how to treat it properly. In this work I have not been, as it is usually accepted, biologically inspired . On the contrary, I have drawn my inspirations from a pure mathematical theory, the Kolmogorov s complexity theory. The results of my work have been already published elsewhere. So the objective of this paper is to try and apply the insights gained in course of this my enterprise to a more general case of information processing in human brain and the challenging issue of human intelligence.

The ongoing discussion whether modern vision systems have to be viewed as visually-enabled cognitive systems or cognitively-enabled vision systems is groundless, because perceptual and cognitive faculties of vision are separate components of human (and consequently, artificial) information processing system modeling.

It is generally accepted that human vision is an extremely powerful information processing system that facilitates our interaction with the surrounding world. However, despite extended and extensive research efforts, which encompass many exploration fields, the underlying fundamentals and operational principles of visual information processing in human brain remain unknown. We still are unable to figure out where and how along the path from eyes to the cortex the sensory input perceived by the retina is converted into a meaningful object representation, which can be consciously manipulated by the brain. Studying the vast literature considering the various aspects of brain information processing, I was surprised to learn that the respected scholarly discussion is totally indifferent to the basic keynote question: "What is information?" in general or "What is visual information?" in particular. In the old days, it was assumed that any scientific research approach has first to define its basic departure points. Why was it overlooked in brain information processing research remains a conundrum. In this paper, I am trying to find a remedy for this bizarre situation. I propose an uncommon definition of "information", which can be derived from Kolmogorov's Complexity Theory and Chaitin's notion of Algorithmic Information. Embracing this new definition leads to an inevitable revision of traditional dogmas that shape the state of the art of brain information processing research. I hope this revision would better serve the challenging goal of human visual information processing modeling.

We live in the Information Age, and information has become a critically important component of our life. The success of the Internet made huge amounts of it easily available and accessible to everyone. To keep the flow of this information manageable, means for its faultless circulation and effective handling have become urgently required. Considerable research efforts are dedicated today to address this necessity, but they are seriously hampered by the lack of a common agreement about "What is information?" In particular, what is "visual information" - human's primary input from the surrounding world. The problem is further aggravated by a long-lasting stance borrowed from the biological vision research that assumes human-like information processing as an enigmatic mix of perceptual and cognitive vision faculties. I am trying to find a remedy for this bizarre situation. Relying on a new definition of "information", which can be derived from Kolmogorov's compexity theory and Chaitin's notion of algorithmic information, I propose a unifying framework for visual information processing, which explicitly accounts for the perceptual and cognitive image processing peculiarities. I believe that this framework will be useful to overcome the difficulties that are impeding our attempts to develop the right model of human-like intelligent image processing.