Color image segmentation using false colors and its applications to geo-images treatment: alphanumeric character recognition

Levachkine, Serguei; Velázquez, Aurelio; Alexandrov, V. V.

doi:10.1109/igarss.2001.977952

Cited by 3 publications

(8 citation statements)

References 7 publications

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“…We have found that in addition to natural decomposition (e.g., R, G or B -component splitting) of color images, artificial representations can also be useful for objects of interest detection-recognition [6], [7], [8], and [9]. Our approach provides composite representations of the source image by means of reduced number of color or tone components and segments.…”

Section: Composite Image Representationmentioning

confidence: 99%

“…The method requires significant operative memory space. To overcome this disadvantage, we used special data organization in the form of irregular dynamic trees (Section 2 and [6], [7], [8], [9]) that provides optimal in memory space computing for the successive scanning of image scales. Due to data organization, a practical use of our program package does not require further algorithmic development.…”

Section: Color Compositesmentioning

confidence: 99%

“…In the following sections, we build up composite image representations based on object-fitting compact hierarchical segmentation. See also [6], [7], [8], and [9].…”

Section: Introductionmentioning

confidence: 99%

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Simultaneous Segmentation-Recognition-Vectorization of Meaningful Geographical Objects in Geo-Images

Levachkine

Torres

Moreno

et al. 2003

Lecture Notes in Computer Science

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We present an approach to color image segmentation by applying it to recognition and vectorization of geo-images (satellite, cartographic). This is a simultaneous segmentation-recognition system when segmented geographical objects of interest (alphanumeric, punctual, linear, and area) are labeled by the system in same, but are different for each type of objects, gray-level values. We exchange the source image by a number of simplified images. These images are called composites. Every composite image is associated with certain image feature. Some of the composite images that contain the objects of interest are used in the following object detection-recognition by means of association to the segmented objects corresponding "names" from the user-defined subject domain. The specification of features and object names associated with perspective composite representations is regarded as a type of knowledge domain, which allows automatic or interactive system's learning. The results of gray-level and color image segmentation-recognition and vectoriztion are shown.

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Section: Composite Image Representationmentioning

confidence: 99%

Section: Color Compositesmentioning

confidence: 99%

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Simultaneous Segmentation-Recognition-Vectorization of Meaningful Geographical Objects in Geo-Images

Levachkine

Torres

Moreno

et al. 2003

Lecture Notes in Computer Science

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show abstract

“…We have found that in addition to natural decomposition (e.g., R, G or B -component splitting) of color images, artificial representations can also be useful for objects of interest detection-recognition [2,3]. Our approach provides composite representations of the source image by means of reduced number of color or tone components and segments.…”

Section: Composite Image Representationmentioning

confidence: 98%

“…In the frameworks of the composite image technique, we obtained a few solutions for this task [2,3]. By applying composites, we are able to extract cartographic data using R, G, and Bcomponents of full-size color raster-scanned image (Fig.…”

Section: Applications Of Compositesmentioning

confidence: 99%

Intelligent segmentation of color geo-images

Levachkine¹,

Torres²,

Moreno³

IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477)

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We present an approach to a segmentation of color cartographic images. This is simultaneous segmentationrecognition-interpretation system when successfully segmented objects of interest (alphanumeric, punctual, linear, and area) are labeled by the system in the same, but are different for each type of objects, gray-level values. We call it the composite image technique. We exchange the source image by a number of composite image representations. Every composite image representation is associated with certain feature. Some of the composite images that contain the objects of interest serve to be used in the following object detection-recognition-interpretation. The specification of features associated with perspective composite representations is regarded as a type of knowledge domain. The results of gray-level color image segmentation are presented.

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Shannon Entropy Loss in Mixed-Radix Conversions

Vennos

Michaels

2021

Entropy

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This paper models a translation for base-2 pseudorandom number generators (PRNGs) to mixed-radix uses such as card shuffling. In particular, we explore a shuffler algorithm that relies on a sequence of uniformly distributed random inputs from a mixed-radix domain to implement a Fisher–Yates shuffle that calls for inputs from a base-2 PRNG. Entropy is lost through this mixed-radix conversion, which is assumed to be surjective mapping from a relatively large domain of size 2J to a set of arbitrary size n. Previous research evaluated the Shannon entropy loss of a similar mapping process, but this previous bound ignored the mixed-radix component of the original formulation, focusing only on a fixed n value. In this paper, we calculate a more precise formula that takes into account a variable target domain radix, n, and further derives a tighter bound on the Shannon entropy loss of the surjective map, while demonstrating monotonicity in a decrease in entropy loss based on increased size J of the source domain 2J. Lastly, this formulation is used to specify the optimal parameters to simulate a card-shuffling algorithm with different test PRNGs, validating a concrete use case with quantifiable deviations from maximal entropy, making it suitable to low-power implementation in a casino.

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Color image segmentation using false colors and its applications to geo-images treatment: alphanumeric character recognition

Cited by 3 publications

References 7 publications

Simultaneous Segmentation-Recognition-Vectorization of Meaningful Geographical Objects in Geo-Images

Simultaneous Segmentation-Recognition-Vectorization of Meaningful Geographical Objects in Geo-Images

Intelligent segmentation of color geo-images

Shannon Entropy Loss in Mixed-Radix Conversions

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