10 research outputs found
Searching for image information content, its discovery, extraction, and representation
Image information content is known to be a complicated and controvercial
problem. This paper posits a new image information content definition.
Following the theory of Solomonoff-Kolmogorov-Chaitin's complexity, we define
image information content as a set of descriptions of imafe data structures.
Three levels of such description can be generally distinguished: 1)the global
level, where the coarse structure of the entire scene is initially outlined; 2)
the intermediate level, where structures of separate, non-overlapping image
regions usually associated with individual scene objects are deliniated; and 3)
the low-level description, where local image structures observed in a limited
and restricted field of view are resolved. A technique for creating such image
information content descriptors is developed. Its algorithm is presented and
elucidated with some examples, which demonstrate the effectiveness of the
proposed approach
I'm sorry to say, but your understanding of image processing fundamentals is absolutely wrong
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.Comment: To be published as chapter 5 in "Frontiers in Brain, Vision and AI",
I-TECH Publisher, Viena, 200
Let us first agree on what the term "semantics" means: An unorthodox approach to an age-old debate
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 Learning: When and Where the Horses Went Astray?
Machine Learning is usually defined as a subfield of AI, which is busy with
information extraction from raw data sets. Despite of its common acceptance and
widespread recognition, this definition is wrong and groundless. Meaningful
information does not belong to the data that bear it. It belongs to the
observers of the data and it is a shared agreement and a convention among them.
Therefore, this private information cannot be extracted from the data by any
means. Therefore, all further attempts of Machine Learning apologists to
justify their funny business are inappropriate.Comment: The paper is accepted to be published in the Machine Learning serie
of the InTec