The Experts below are selected from a list of 246 Experts worldwide ranked by ideXlab platform
Paulina Mitrea - One of the best experts on this subject based on the ideXlab platform.
-
IWCC - DIM - A Distributed Image Processing System, Based on ISO IEC 12087 Image Processing Standard
Lecture Notes in Computer Science, 2002Co-Authors: Paulina MitreaAbstract:The amount of computing in the vast majority of Image Processing applications is high, leading to a high interest in reducing execution time. Following our line in developing complete and general purpose Image Processing tools, succeeding to a Multithreaded Image Processing System conceived for single processor systems, we develop now a Distributed Image Processing System, which will implement the ISO IEC 12087 Image Processing Standard with Cluster Computing methods. The final aim of this work is to obtain a complete Image-Processing library for Cluster Computing. Some basic notions, followed by the presentation of the most important methods implemented in order to generate the distributed algorithms, will be presented in this paper.
-
DIM: A Distributed Image Processing System, based on ISO IEC 12087 Image Processing standard
Lecture Notes in Computer Science, 2002Co-Authors: Paulina MitreaAbstract:The amount of computing in the vast majority of Image Processing applications is high, leading to a high interest in reducing execution time. Following our line in developing complete and general purpose Image Processing tools, succeeding to a Multithreaded Image Processing System conceived for single processor systems, we develop now a Distributed Image Processing System, which will implement the ISO IEC 12087 Image Processing Standard with Cluster Computing methods. The final aim of this work is to obtain a complete Image-Processing library for Cluster Computing. Some basic notions, followed by the presentation of the most important methods implemented in order to generate the distributed algorithms, will be presented in this paper.
Mario Mastriani - One of the best experts on this subject based on the ideXlab platform.
-
Quantum Image Processing?
Quantum Information Processing, 2016Co-Authors: Mario MastrianiAbstract:This paper presents a number of problems concerning the practical (real) implementation of the techniques known as quantum Image Processing. The most serious problem is the recovery of the outcomes after the quantum measurement, which will be demonstrated in this work that is equivalent to a noise measurement, and it is not considered in the literature on the subject. It is noteworthy that this is due to several factors: (1) a classical algorithm that uses Dirac’s notation and then it is coded in MATLAB does not constitute a quantum algorithm, (2) the literature emphasizes the internal representation of the Image but says nothing about the classical-to-quantum and quantum-to-classical interfaces and how these are affected by decoherence, (3) the literature does not mention how to implement in a practical way (at the laboratory) these proposals internal representations, (4) given that quantum Image Processing works with generic qubits, this requires measurements in all axes of the Bloch sphere, logically, and (5) among others. In return, the technique known as quantum Boolean Image Processing is mentioned, which works with computational basis states (CBS), exclusively. This methodology allows us to avoid the problem of quantum measurement, which alters the results of the measured except in the case of CBS. Said so far is extended to quantum algorithms outside Image Processing too.
Mastriani Mario - One of the best experts on this subject based on the ideXlab platform.
-
Quantum Image Processing?
2016Co-Authors: Mastriani MarioAbstract:This paper presents a number of problems concerning the practical (real) implementation of the techniques known as Quantum Image Processing. The most serious problem is the recovery of the outcomes after the quantum measurement, which will be demonstrated in this work that is equivalent to a noise measurement, and it is not considered in the literature on the subject. It is noteworthy that this is due to several factors: 1) a classical algorithm that uses Dirac's notation and then it is coded in MATLAB does not constitute a quantum algorithm, 2) the literature emphasizes the internal representation of the Image but says nothing about the classical-to-quantum and quantum-to-classical interfaces and how these are affected by decoherence, 3) the literature does not mention how to implement in a practical way (at the laboratory) these proposals internal representations, 4) given that Quantum Image Processing works with generic qubits this requires measurements in all axes of the Bloch sphere, logically, and 5) among others. In return, the technique known as Quantum Boolean Image Processing is mentioned, which works with computational basis states (CBS), exclusively. This methodology allows us to avoid the problem of quantum measurement, which alters the results of the measured except in the case of CBS. Said so far is extended to quantum algorithms outside Image Processing too.Comment: 28 pages, 11 figures, 1 table. arXiv admin note: text overlap with arXiv:1409.2918, arXiv:1406.5121, arXiv:1408.2427; text overlap with arXiv:quant-ph/0402085 by other author
Danny Crookes - One of the best experts on this subject based on the ideXlab platform.
-
Parallel architectures for Image Processing
Electronics & Communication Engineering Journal, 1998Co-Authors: A. Downton, Danny CrookesAbstract:Image Processing is often considered a good candidate for the application of parallel Processing because of the large volumes of data and the complex algorithms commonly encountered. This paper presents a tutorial introduction to the field of parallel Image Processing. After introducing the classes of parallel Processing a brief review of architectures for parallel Image Processing is presented. Software design for low-level Image Processing and parallelism in high-level Image Processing are discussed and an application of parallel Processing to handwritten postcode recognition is described. The paper concludes with a look at future technology and market trends.
Tom Hintz - One of the best experts on this subject based on the ideXlab platform.
-
Distributed Image Processing on spiral architecture
Fifth International Conference on Algorithms and Architectures for Parallel Processing 2002. Proceedings., 1Co-Authors: Tom HintzAbstract:Improving computation efficiency is a key issue in Image Processing, especially in edge detection, which is very computationally intensive. With the development of real-time applications of Image Processing, fast Processing response is becoming more critical. In this paper, a technique for distributed Image Processing on a spiral architecture is proposed, which provides a platform for speeding up Image Processing based on clusters.
