349 research outputs found
ΠΠΈΠ½Π΅ΡΠ°Π»ΠΎΠ³ΠΎ-ΠΏΠ΅ΡΡΠΎΡ ΠΈΠΌΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΈ Π³Π΅ΠΎΡ ΠΈΠΌΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΡΠ΅ΡΡΡ ΠΎΠΊΠΎΠ»ΠΎΡΡΠ΄Π½ΠΎΠ³ΠΎ ΠΌΠ΅ΡΠ°ΡΠΎΠΌΠ°ΡΠΈΠ·ΠΌΠ° Π² ΠΠ°ΠΏΠ°Π΄Π½ΠΎΠΌ Π·ΠΎΠ»ΠΎΡΠΎΡΡΠ΄Π½ΠΎΠΌ ΠΌΠ΅ΡΡΠΎΡΠΎΠΆΠ΄Π΅Π½ΠΈΠΈ (Π‘Π΅Π²Π΅ΡΠ½ΠΎΠ΅ ΠΠ°Π±Π°ΠΉΠΊΠ°Π»ΡΠ΅)
ΠΡΠΈΠ²Π΅Π΄Π΅Π½Ρ Π΄Π°Π½Π½ΡΠ΅ ΠΎΠ± ΡΡΠ»ΠΎΠ²ΠΈΡΡ
Π·Π°Π»Π΅Π³Π°Π½ΠΈΡ, ΠΌΠΎΡΡΠΎΠ»ΠΎΠ³ΠΈΠΈ ΡΡΠ΄Π½ΡΡ
ΡΠ΅Π», ΠΌΠΈΠ½Π΅ΡΠ°Π»ΡΠ½ΠΎΠΌ ΡΠΎΡΡΠ°Π²Π΅, ΡΠΈΠ·ΠΈΠΊΠΎ-Ρ
ΠΈΠΌΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΈ ΡΠ΅ΡΠΌΠΎΠ΄ΠΈΠ½Π°ΠΌΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠ΅ΠΆΠΈΠΌΠ°Ρ
ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΡ ΡΡΠ΄ ΠΠ°ΠΏΠ°Π΄Π½ΠΎΠ³ΠΎ Π·ΠΎΠ»ΠΎΡΠΎΡΡΠ΄Π½ΠΎΠ³ΠΎ ΠΌΠ΅ΡΡΠΎΡΠΎΠΆΠ΄Π΅Π½ΠΈΡ Π‘Π΅Π²Π΅ΡΠ½ΠΎΠ³ΠΎ ΠΠ°Π±Π°ΠΉΠΊΠ°Π»ΡΡ. ΠΠΏΠ΅ΡΠ²ΡΠ΅ ΠΏΠΎΠΊΠ°Π·Π°Π½Ρ ΠΏΠΎΡΡΠ΄ΠΎΠΊ ΠΌΠΈΠ½Π΅ΡΠ°Π»ΡΠ½ΠΎΠΉ Π·ΠΎΠ½Π°Π»ΡΠ½ΠΎΡΡΠΈ (ΡΡΡΡΠΊΡΡΡΠ°) ΠΈ ΠΌΠΈΠ½Π΅ΡΠ°Π»ΠΎΠ³ΠΎ-ΠΏΠ΅ΡΡΠΎΡ
ΠΈΠΌΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΡΠ΅ΡΡΡ Π°ΠΏΠΎΠ΄ΠΎΠ»Π΅ΡΠΈΡΠΎΠ²ΡΡ
ΠΎΠΊΠΎΠ»ΠΎΠΆΠΈΠ»ΡΠ½ΡΡ
ΠΌΠ΅ΡΠ°ΡΠΎΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΎΡΠ΅ΠΎΠ»ΠΎΠ². ΠΠΎΠΊΠ°Π·ΡΠ²Π°Π΅ΡΡΡ ΠΏΡΠΈΠ½Π°Π΄Π»Π΅ΠΆΠ½ΠΎΡΡΡ ΠΏΠΎΡΠ»Π΅Π΄Π½ΠΈΡ
ΠΊ Π±Π΅ΡΠ΅Π·ΠΈΡΠΎΠ²ΠΎΠΉ ΠΌΠ΅ΡΠ°ΡΠΎΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΠΎΡΠΌΠ°ΡΠΈΠΈ, Π° ΠΌΠ΅ΡΡΠΎΡΠΎΠΆΠ΄Π΅Π½ΠΈΡ - ΠΊ Π·ΠΎΠ»ΠΎΡΠΎΠΉ ΡΡΠ±ΡΠΎΡΠΌΠ°ΡΠΈΠΈ Π·ΠΎΠ»ΠΎΡΠΎ-ΡΡΠ°Π½-ΠΏΠΎΠ»ΠΈΠΌΠ΅ΡΠ°Π»Π»ΠΈΡΠ΅ΡΠΊΠΎΠΉ Π±Π΅ΡΠ΅Π·ΠΈΡΠΎΠ²ΠΎΠΉ ΡΡΠ΄Π½ΠΎΠΉ ΡΠΎΡΠΌΠ°ΡΠΈΠΈ. Π‘Π»Π°Π±ΠΎ ΠΊΠΎΠ½ΡΡΠ°ΡΡΠ½ΡΠ΅ Π°Π½ΠΎΠΌΠ°Π»ΠΈΠΈ Π·ΠΎΠ»ΠΎΡΠ°, ΡΠ΅ΡΠ΅Π±ΡΠ°, ΡΡΡΡΠΈ ΠΏΡΠΈΡΡΠΎΡΠ΅Π½Ρ ΠΊ ΡΡΠ»ΠΎΠ²ΡΠΌ Π·ΠΎΠ½Π°ΠΌ ΠΎΠΊΠΎΠ»ΠΎΠΆΠΈΠ»ΡΠ½ΡΡ
ΠΌΠ΅ΡΠ°ΡΠΎΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΎΡΠ΅ΠΎΠ»ΠΎΠ² Π² Π½Π΅ΠΏΠΎΡΡΠ΅Π΄ΡΡΠ²Π΅Π½Π½ΠΎΠΌ ΠΎΠ±ΡΠ°ΠΌΠ»Π΅Π½ΠΈΠΈ ΡΠ»Π°Π±ΠΎΠ·ΠΎΠ»ΠΎΡΠΎΠ½ΠΎΡΠ½ΡΡ
(ΠΏΠ΅ΡΠ²ΡΠ΅ Π³/Ρ) ΠΈΠ½ΡΠ΅ΡΠ²Π°Π»ΠΎΠ² ΠΊΠ²Π°ΡΡΠ΅Π²ΡΡ
ΠΆΠΈΠ». ΠΡΠ³ΡΠΌΠ΅Π½ΡΠΈΡΡΡΡΡΡ Π³Π΅Π½Π΅ΡΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΡΠ²ΡΠ·ΠΈ ΠΎΠΊΠΎΠ»ΠΎΠΆΠΈΠ»ΡΠ½ΡΡ
ΠΌΠ΅ΡΠ°ΡΠΎΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΈ Π³Π΅ΠΎΡ
ΠΈΠΌΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΎΡΠ΅ΠΎΠ»ΠΎΠ² Ρ ΡΡΠ΄Π°ΠΌΠΈ ΠΈ ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΠ΅ ΠΈΡ
Π² ΡΡΠ΄ΠΎΠΎΠ±ΡΠ°Π·ΡΡΡΠ΅ΠΌ ΠΏΡΠΎΡΠ΅ΡΡΠ΅ ΠΏΠΎΠ·Π΄Π½Π΅ΠΏΠ°Π»Π΅ΠΎΠ·ΠΎΠΉΡΠΊΠΎΠΉ ΠΌΠ΅ΡΠ°Π»Π»ΠΎΠ³Π΅Π½ΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΠΏΠΎΡ
ΠΈ. ΠΡΠ½ΠΎΠ²Π½ΡΠ΅ ΠΌΠΈΠ½Π΅ΡΠ°Π»ΠΎΠ³ΠΎ-ΠΏΠ΅ΡΡΠΎΡ
ΠΈΠΌΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΈ Π³Π΅ΠΎΡ
ΠΈΠΌΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΡΠ΅ΡΡΡ ΠΎΠΊΠΎΠ»ΠΎΠΆΠΈΠ»ΡΠ½ΠΎΠ³ΠΎ ΠΌΠ΅ΡΠ°ΡΠΎΠΌΠ°ΡΠΈΠ·ΠΌΠ° ΡΠ°ΡΡΠΌΠΎΡΡΠ΅Π½Ρ Π² ΡΡΠ°Π²Π½Π΅Π½ΠΈΠΈ Ρ ΡΠ°ΠΊΠΎΠ²ΡΠΌΠΈ Π΄ΡΡΠ³ΠΈΡ
ΠΌΠ΅ΡΡΠΎΡΠΎΠΆΠ΄Π΅Π½ΠΈΠΉ Π‘Π΅Π²Π΅ΡΠΎ-ΠΠ°Π±Π°ΠΉΠΊΠ°Π»ΡΡΠΊΠΎΠ³ΠΎ Π·ΠΎΠ»ΠΎΡΠΎΡΡΠ΄Π½ΠΎΠ³ΠΎ ΡΠ°ΠΉΠΎΠ½Π°
Π Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡΠΈ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΡ ΠΈΠ½Π΄ΡΠΊΡΠΈΠ²Π½ΠΎΠ³ΠΎ ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠΎΠ½Π° Π΄Π»Ρ Π·Π°ΡΠΈΡΡ ΠΎΡ Π·Π°ΠΌΡΠΊΠ°Π½ΠΈΠΉ Π½Π° Π·Π΅ΠΌΠ»Ρ Π² ΡΠ΅ΡΡΡ Ρ ΠΈΠ·ΠΎΠ»ΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠΉ Π½Π΅ΠΉΡΡΠ°Π»ΡΡ
ΠΠΎΠΊΠ°Π·Π°Π½Π° Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡΡ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΡ ΠΈΠ½Π΄ΡΠΊΡΠΈΠ²Π½ΠΎΠ³ΠΎ ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠΎΠ½Π° ΠΊΠ°ΠΊ ΡΠ΅Π°Π³ΠΈΡΡΡΡΠ΅Π³ΠΎ ΠΎΡΠ³Π°Π½Π° Π² Π·Π°ΡΠΈΡΠ°Ρ
ΠΎΡ Π·Π°ΠΌΡΠΊΠ°Π½ΠΈΠΉ Π½Π° Π·Π΅ΠΌΠ»Ρ Π² ΡΠ΅ΡΡΡ
Ρ ΠΈΠ·ΠΎΠ»ΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠΉ Π½Π΅ΠΉΡΡΠ°Π»ΡΡ. ΠΠ°ΡΠΈΡΠ° Ρ ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠΎΠ½ΠΎΠΌ ΡΠ²Π»ΡΠ΅ΡΡΡ ΡΠ΅Π»Π΅ΠΊΡΠΈΠ²Π½ΠΎΠΉ ΠΈ ΡΠ°Π²Π½ΠΎΡΡΠ²ΡΡΠ²ΠΈΡΠ΅Π»ΡΠ½ΠΎΠΉ, ΠΎΠ±Π»Π°Π΄Π°Π΅Ρ Π΄ΠΎΡΡΠ°ΡΠΎΡΠ½ΠΎΠΉ ΡΡΠ²ΡΡΠ²ΠΈΡΠ΅Π»ΡΠ½ΠΎΡΡΡΡ
Compression methods for graph algorithms
Two compression methods for representing graphs are presented, in conjunction with algorithms applying these methods. A decomposition technique for networks that can be generated in O(m) time is presented. The components of the decomposition and the shortest path matrix of the compressed network can be used to find the shortest path between any pair of vertices in the original network in linear time.
A compression method for boolean matrices and a method for applying the compression to boolean matrix multiplication is developed. The algorithms have an expected running time of O(nΒ²*log βn). From this compression method a simple heuristic that may be applied to any algorithm for boolean matrix multiplication has been developed. This heuristic will improve the average running time of boolean matrix multiplication algorithms.
An order of magnitude analysis of the results published by Loukakis and Tsouris [1981], on the efficiency of algorithms for finding all maximal independent sets of a graph has been performed. This analysis showed that their conclusions, which are based on a direct comparison of the running times of the algorithms, do not take into account implementation factors.
An average constant factor improvement is developed for the algorithm of Tsukiyama, Ide, Ariyoshi and Shirakawa [1977] for finding all maximal independent sets of a graph.
Analysis of the running time results from the algorithm comparisons presented in this thesis show that the Bron-Kerbosch algorithm has the smallest rate of increase in running time as the size of the graphs increase
ΠΠ»ΠΈΡΠ½ΠΈΠ΅ Π»ΠΎΠΌΠ±Π°ΡΠ΄Π½ΠΎΠΉ ΠΈΠ½Π΄ΡΡΡΡΠΈΠΈ Π½Π° ΡΡΠ½ΠΎΠΊ ΠΏΠΎΡΡΠ΅Π±ΠΈΡΠ΅Π»ΡΡΠΊΠΎΠ³ΠΎ ΠΊΡΠ΅Π΄ΠΈΡΠΎΠ²Π°Π½ΠΈΡ Π² ΡΡΠ»ΠΎΠ²ΠΈΡΡ ΡΠΈΠ½Π°Π½ΡΠΎΠ²ΠΎΠ³ΠΎ ΠΊΡΠΈΠ·ΠΈΡΠ°
ΠΠ΅ΡΠ°Π»ΡΠ½ΠΎ ΠΈΠ·ΡΡΠ΅Π½ΠΎ ΡΠΎΡΡΠΎΡΠ½ΠΈΠ΅ Π»ΠΎΠΌΠ±Π°ΡΠ΄Π½ΠΎΠΉ ΠΈΠ½Π΄ΡΡΡΡΠΈΠΈ Π² Π ΠΎΡΡΠΈΠΈ. ΠΡΠΎΠ²Π΅Π΄Π΅Π½Π° ΡΡΠ°Π²Π½ΠΈΡΠ΅Π»ΡΠ½Π°Ρ Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊΠ° Π»ΠΎΠΌΠ±Π°ΡΠ΄Π½ΠΎΠ³ΠΎ ΠΈ Π±Π°Π½ΠΊΠΎΠ²ΡΠΊΠΎΠ³ΠΎ ΠΏΠΎΡΡΠ΅Π±ΠΈΡΠ΅Π»ΡΡΠΊΠΎΠ³ΠΎ ΠΊΡΠ΅Π΄ΠΈΡΠ° Π² ΠΏΠ΅ΡΠΈΠΎΠ΄ ΡΠΈΠ½Π°Π½ΡΠΎΠ²ΠΎΠ³ΠΎ ΠΊΡΠΈΠ·ΠΈΡΠ°, Π²ΡΡΠ²Π»Π΅Π½Ρ ΠΏΡΠ΅ΠΈΠΌΡΡΠ΅ΡΡΠ²Π° ΠΈ Π½Π΅Π΄ΠΎΡΡΠ°ΡΠΊΠΈ Π»ΠΎΠΌΠ±Π°ΡΠ΄Π½ΠΎΠ³ΠΎ ΠΊΡΠ΅Π΄ΠΈΡΠΎΠ²Π°Π½ΠΈΡ. ΠΠΎΠΊΠ°Π·Π°Π½ΠΎ, ΡΡΠΎ Π»ΠΎΠΌΠ±Π°ΡΠ΄Ρ Π·Π½Π°ΡΠΈΡΠ΅Π»ΡΠ½ΠΎ Π²Π»ΠΈΡΡΡ Π½Π° ΡΡΠ½ΠΎΠΊ ΠΏΠΎΡΡΠ΅Π±ΠΈΡΠ΅Π»ΡΡΠΊΠΎΠ³ΠΎ ΠΊΡΠ΅Π΄ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΈ ΡΠΎΡΡΠ°Π²Π»ΡΡΡ ΡΡΡΠ΅ΡΡΠ²Π΅Π½Π½ΡΡ ΠΊΠΎΠ½ΠΊΡΡΠ΅Π½ΡΠΈΡ ΠΊΠΎΠΌΠΌΠ΅ΡΡΠ΅ΡΠΊΠΈΠΌ Π±Π°Π½ΠΊΠ°ΠΌ. ΠΠ±ΠΎΡΠ½ΠΎΠ²Π°Π½Π° Π²ΠΎΠ·ΡΠΎΡΡΠ°Ρ ΡΠΎΠ»Ρ Π»ΠΎΠΌΠ±Π°ΡΠ΄ΠΎΠ² ΠΊΠ°ΠΊ ΡΠΎΡΠΈΠ°Π»ΡΠ½ΡΡ
ΡΡΠ°Π±ΠΈΠ»ΠΈΠ·Π°ΡΠΎΡΠΎΠ²
Estimation Of Multiple Local Orientations In Image Signals
Local orientation estimation can be posed as the problem of finding the minimum grey level variance axis within a local neighbourhood. In 2D image signals, this corresponds to the eigensystem analysis of a 22-tensor, which yields valid results for single orientations. We describe extensions to multiple overlaid orientations, which may be caused by transparent objects, crossings, bifurcations, corners etc. Multiple orientation detection is based on the eigensystem analysis of an appropriately extended tensor, yielding so-called mixed orientation parameters. These mixed orientation parameters can be regarded as another tensor built from the sought individual orientation parameters. We show how the mixed orientation tensor can be decomposed into the individual orientations by finding the roots of a polynomial. Applications are, e.g., in directional filtering and interpolation, feature extraction for corners or crossings, and signal separation
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