Pixel clustering for color image segmentation

Programming and Computer Software - Image segmentation using a hierarchical sequence of piecewise constant approximations that minimally differ from the original image in terms of the total squared...     

Adaptive dichotomous image segmentation toolkit

The article deals with the adaptive hierarchical segmentation of the digital image divided into segments of the calculated form. The dichotomous segmentation is examined, in which case the vast majority of segments are divided into two nested segments. A method is described for the rapid construction of a dichotomous hierarchy by a given criterion for the proximity of segments by the iterative merging of adjacent segments of an initial image decomposition. The numerical characteristic of the regularity of the dichotomous hierarchy of segments is introduced. Variants are constructed of the hierarchical approximation of the image by nested partitions (levels of the hierarchy) formed from the segments with repetitions. Three basic types of transformations are determined on the set of hierarchical decompositions. In order to optimize the approximation of visible objects by image segments, 22 algorithms are studied of its partition to successively increase the number of segments. Depending on the number of segments, the required standard deviation is estimated. A comparison with similar solutions is given.     

Image segmentation method by merging and correction of sets of pixels

The Otsu method (1979) and the Mumford-Shah model (1985) for image segmentation described by image approximations by a step (piecewise constant) function are described and developed to formalize automatic object detection. Segmentation here means image preprocessing performed without tuning parameters, assumptions about image content, or training data from the user. A sequence of partitions of image pixels into sets that consist of either pixels of specific intensity ranges or pixels of connected image segments is considered a result of segmentation. The resulting partitions of image pixels need to generate a sequence of optimal approximations of an image averaged over the sets according to intensity with the lowest standard deviation of the approximation from the image. In general, the sequence of optimal approximations of an image is nonhierarchical, and the conventionally used merging of sets is insufficient to calculate it. Therefore, the merging of sets is complemented by a correction operation. The paper presents an analytical validation for correction of sets by reclassification of pixels and discusses the stability of optimal approximations during the reclassification operation of image pixels. Experimental results are demonstrated, and a comparative analysis of image approximations obtained in different algorithms is given. The prospects for improving segmentation with respect to standard deviation are analyzed.     

Invariant representation of information and information quantity in image-processing tasks

A model of a signal (image) containing the information codes in its own digital virtual memory independent of stretching, packing according to intensity, and other prescribed image transformations is proposed. Virtual memory cells contain pixel values of information representation. Storage elements of virtual memory cells are classified as either fixed or modifiable so that arbitrary variation of the latter does not cause image destruction. The number of modifiable storage elements juxtaposed with image pixels is regarded as the integer-valued information quantity. A comparison of image information quantity estimations is curried out. The applications of the model to image recognition, document protection, and other tasks are discussed. The text was submitted by the author in English. Mikhail V. Kharinov. Born 1953. Graduated from Leningrad State University in 1978. Received candidateís degree in 1993. Senior researcher at and St. Petersburg Institute of Informatics and Automation, Russian, Academy of Sciences. Scientific interests: analysis of numerical information, system of numerical representation, hierarchical data structures, idempotent transformations, unified algorithms for processing of images and audio signals, color transformation of images. Author of 60 papers, including the patents.     

Image segmentation using optimal and hierarchical piecewise-constant approximations

The problem of approaching optimal image approximations using quasi-optimal hierarchical approximations, which are close to the optimal ones in terms of the total squared error, is considered. The problem is solved using algorithms for merging and dividing pixel clusters. The algorithms are based on elementary formulas. Information encoded in the image is proposed to be described, based on quasi-optimal approximations, using the invariant isomorphic image representation. The definition of the integer quantity of information is given, and the comparison of the integer estimate with the classical estimates in terms of R. Hartley and K. Shannon is performed. The experimental study of the properties of quasi-optimal hierarchical approximations, which are characteristic and uncharacteristic of nonhierarchical optimal image approximations, is carried out.     

Interpretation of image information for computer calculations

A model of a digital image as a sequence of its mappings to a sum of invariant and variable representations is proposed. The invariant representation is independent of the changes in the variable representation in certain calculated limits and is interpreted as a virtual carrier of the variable representation (message). The information quantity (natural, intrinsic, and noise) is interpreted sensu stricto with respect to the quantity of the arbitrary digital data of the message transferred by the image. A characteristic application involves the solution of the problem on the embedding of digital information into the image via two independent communication channels. Mikhail V. Kharinov. Born 1953. Graduated from Leningrad State University in 1978. Received candidate’s degree in 1993. Senior researcher at the St. Petersburg Institute of Information Science and Automation, Russian Academy of Sciences. Scientific interests: analysis of numerical information, system of numerical representation, hierarchical data structures, idempotent transformations, unified algorithms for processing of images and audio signals, color transformation of images. Author of 60 papers, including a patent.     

Adaptive hierarchical image segmentation technique

In the report a technique of multiple adaptive image segmentation is outlined. Three hierarchy-to-hierarchy transforms for primary digital data ordering and filtration are introduced. To optimize the approximation of observed object with image segments the 22 algorithms of sequential segmentation are tested. The required standard deviation value depending on segment number is estimated. The prototypes are reviewed.