| 000 | 03424nlm1a2200445 4500 | ||
|---|---|---|---|
| 001 | 666504 | ||
| 005 | 20231030042039.0 | ||
| 035 | _a(RuTPU)RU\TPU\network\37708 | ||
| 035 | _aRU\TPU\network\35020 | ||
| 090 | _a666504 | ||
| 100 | _a20211228a2021 k y0engy50 ba | ||
| 101 | 0 | _aeng | |
| 135 | _adrcn ---uucaa | ||
| 181 | 0 | _ai | |
| 182 | 0 | _ab | |
| 200 | 1 |
_aNature and Biologically Inspired Image Segmentation Techniques _fS. Singh, N. Mittal, D. Thakur [et al.] |
|
| 203 |
_aText _celectronic |
||
| 300 | _aTitle screen | ||
| 320 | _a[References: 144 tit.] | ||
| 330 | _aImage processing is among the signifcant areas of growth in the current scenario. It consist of a set of techniques typically used to enhance the raw image obtained from diferent scenes. Segmentation of images is an essential step in image analysis and pre-processing. During the course of the work, standard multilevel thresholding methods are very efective due to their low computational cost, reliability, reduced convergence time, and precision. Nature-inspired methods of optimization play an essential role in the processing of images. Several optimization procedures have been proposed for diferent image processing applications. These optimization techniques can improve the performance of image segmentation, image restoration, edge detection, image enhancement, pattern recognition, image generation, image thresholding, and image fusion algorithms. This paper includes an overview of several metaheuristic frefy algorithm (FA), diferential evolution (DE), particle swarm optimization (PSO), genetic algorithm (GA), artifcial bee colony optimization (ABC), etc. Moreover, artifcial neural networks (ANN) and other machine learning techniques (nature or biological inspired) are discussed in context with image segmentation application and their algorithms. | ||
| 461 | _tArchives of Computational Methods in Engineering | ||
| 463 |
_tVol. XX, iss. X _v[28 p.] _d2021 |
||
| 610 | 1 | _aэлектронный ресурс | |
| 610 | 1 | _aтруды учёных ТПУ | |
| 610 | 1 | _aбиологические методы | |
| 610 | 1 | _aсегментация | |
| 610 | 1 | _aизображения | |
| 610 | 1 | _aобработка изображений | |
| 610 | 1 | _aискусственные нейронные сети | |
| 610 | 1 | _aмашинное обучение | |
| 701 | 1 |
_aSingh _bS. _gSimrandeep |
|
| 701 | 1 |
_aMittal _bN. _gNitin |
|
| 701 | 1 |
_aThakur _bD. _gDiksha |
|
| 701 | 1 |
_aSingh _bH. _gHarbinder |
|
| 701 | 1 |
_aOliva Navarro _bD. A. _cspecialist in the field of informatics and computer technology _cProfessor of Tomsk Polytechnic University _f1983- _gDiego Alberto _2stltpush _3(RuTPU)RU\TPU\pers\37366 |
|
| 701 | 1 |
_aDemin _bA. Yu. _cspecialist in the field of Informatics and computer engineering _cAssociate Professor of Tomsk Polytechnic University, candidate of technical sciences _f1973- _gAnton Yurievich _2stltpush _3(RuTPU)RU\TPU\pers\33696 |
|
| 712 | 0 | 2 |
_aНациональный исследовательский Томский политехнический университет _bИнженерная школа информационных технологий и робототехники _bОтделение информационных технологий _h7951 _2stltpush _3(RuTPU)RU\TPU\col\23515 |
| 801 | 2 |
_aRU _b63413507 _c20211228 _gRCR |
|
| 856 | 4 | _uhttps://doi.org/10.1007/s11831-021-09619-1 | |
| 942 | _cCF | ||