Conference Paper

# Space-color quantization of multispectral images in hierarchy of scales

Univ. of Montenegro, Podgorica

DOI: 10.1109/ICIP.2001.959195 Conference: Image Processing, 2001. Proceedings. 2001 International Conference on, Volume: 1 Source: IEEE Xplore

- [Show abstract] [Hide abstract]

**ABSTRACT:**SCIENTIFIC AND TECHNOLOGICAL OBJECTIVES Stochastic Resonance Synergetics is a generalized description of an intelligent agent, with a memory and means of action, and its interactions with the environment. It studies coding, control, and propagation of information. This paradigm of computation recognizes an internal state of networked neural system and its interaction with the environment via coupled information propagation. It combines spatial and a dimension of scale in a dynamical system of computation, in the scale-space. The SRS hypothesis aims for building a study discipline of intelligent agents that uses a quantitative analysis of attention, memory and behavioral data. It requires a cross psucho-physics, bio-imaging and computer science, machine learning approaches to achieve: A proof of the concept in multi-dimensional, information-driven situations: unpredictable and/or evolutions in a systemic or environmental nature; both, a system of intelligent agents and environments may entrain each other at different spatio-temporal and dynamic scales. Diagnosis of disordered states and self-adjustment to those aligned with the environment. Coordination of motion of the self-adjusted intelligent agent systems for different (structural as well as functional) morphodynamic and evolutionary purposes. The main goal of the project is to further develop and validate scale-space approaches using renormalization techniques that yield hierarchies of robust regimes of synergetics07/2012; - [Show abstract] [Hide abstract]

**ABSTRACT:**We consider issues of computability and complexity in statistical physics from the perspective of information theory. It assumes information coupling by a mass conservation. Finally, we explain here our view on the 'mass phenomenon' in the clusters of information.http://milanjovovic.wordpress.com/. 01/2010; - [Show abstract] [Hide abstract]

**ABSTRACT:**In this report a multi-dimensional data scaling approach is proposed in data mining and knowledge discovery applications. We derive the method based on an analogy to the physical computation of signal distortion. A dynamical cascade computation diagrams result from the statistical physics model computation in the free energy decomposition. We assess the scale invariance of various data sets, such as with the image motion sequences, and with the high dimensional chemical data sets. Theoretical model of error propagation is given by the numerical computational schemes. Statistical mapping of the data is analyzed through dynamical cascades, as a way of approaching its coding and control data structure. We show how it correlates by segmenting set of chemical compounds observations in a high dimensional property space. The proposed algorithm, also, is suitable for the implementation in parallel computer architectures. An example implementation on the multicore processors is given in the end of this report. A multifractal model formalism is derived in the "Thalweg ARC." project report (12), to explain the decomposition of image sequences into the singular data sets. The partition function describes the probabilistic model of data clusters and is analyzed as a multifractal measure in the method. Singularity analysis of computational maps of clustering vectors is derived to describe the computational means of decomposing the image information into different singular sets. We show also that the propagation of information in image sequences is governed by the scale-space wave equation, therefore enabling us to treat singular frequencies of data clusters in an unified way, both in space and in time. Contextual information of the spatial coherency of data is used in the segmentation process in the hierarchical scale computation of feature vectors. The spatial segmentation of images is performed while using the Green's function, parameterized with the scale parameter, as the integration function in the segmentation process. The scale information is evaluated by conjoining the two parameters: the scale parameter β of the signal distortion, and the spatial scale parameter r. A larger extent of spatial integration of the motion information is used on a larger scale, while it becomes effectively more local in space as we decrease the scale of segmentation. Distinct singular features are segmented on a certain scale and the least singular feature become segmented in two spatial windows with the Laplacian system regularity constraints, in the hierarchical scale computation. Accordingly, the reconstruction formula is derived based on the Laplacian system of the diffusion of the residual information from the most singular sets. This gives us an effective way of compressing and progressive coding of the information in image sequences. The binary tree data structure of the clustering parameters is suitable in the coding schemes that use the hierarchical structure of the binary images of the spatial distribution of cluster windows, along with the feature vectors and residual image information that make up for the point feature vector estimation. We give here a derivation of the computational scheme for a 2- dimensional case, like in image sequences. We then consider a dynamical coupling and the energy exchange between 3 clusters computed. Corresponding statistical maps are analyzed w.r.t. the dimensionality of the eigenvalue decomposition of the clusters" covariances. The results are shown for the chemical compounds in the 155 properties dimensional data set. Projections along the most singular components are computed in 1 and dimensional statistical maps. 2. METHODIU, report. 01/2007;

Data provided are for informational purposes only. Although carefully collected, accuracy cannot be guaranteed. The impact factor represents a rough estimation of the journal's impact factor and does not reflect the actual current impact factor. Publisher conditions are provided by RoMEO. Differing provisions from the publisher's actual policy or licence agreement may be applicable.