Matches in SemOpenAlex for { <https://semopenalex.org/work/W2999607073> ?p ?o ?g. }
- W2999607073 abstract "Image segmentation is a key topic in image processing and computer vision with applications such as scene understanding, medical image analysis, robotic perception, video surveillance, augmented reality, and image compression, among many others. Various algorithms for image segmentation have been developed in the literature. Recently, due to the success of deep learning models in a wide range of vision applications, there has been a substantial amount of works aimed at developing image segmentation approaches using deep learning models. In this survey, we provide a comprehensive review of the literature at the time of this writing, covering a broad spectrum of pioneering works for semantic and instance-level segmentation, including fully convolutional pixel-labeling networks, encoder-decoder architectures, multi-scale and pyramid based approaches, recurrent networks, visual attention models, and generative models in adversarial settings. We investigate the similarity, strengths and challenges of these deep learning models, examine the most widely used datasets, report performances, and discuss promising future research directions in this area." @default.
- W2999607073 created "2020-01-23" @default.
- W2999607073 creator A5017101039 @default.
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- W2999607073 creator A5038550641 @default.
- W2999607073 creator A5054292278 @default.
- W2999607073 creator A5076371436 @default.
- W2999607073 date "2020-01-15" @default.
- W2999607073 modified "2023-09-27" @default.
- W2999607073 title "Image Segmentation Using Deep Learning: A Survey" @default.
- W2999607073 cites W1159302035 @default.
- W2999607073 cites W125693051 @default.
- W2999607073 cites W1495267108 @default.
- W2999607073 cites W1498436455 @default.
- W2999607073 cites W1508960934 @default.
- W2999607073 cites W1664573881 @default.
- W2999607073 cites W1686810756 @default.
- W2999607073 cites W1745334888 @default.
- W2999607073 cites W1817277359 @default.
- W2999607073 cites W1861492603 @default.
- W2999607073 cites W1901129140 @default.
- W2999607073 cites W1903029394 @default.
- W2999607073 cites W1905829557 @default.
- W2999607073 cites W1909234690 @default.
- W2999607073 cites W1923184257 @default.
- W2999607073 cites W1938976761 @default.
- W2999607073 cites W1954128991 @default.
- W2999607073 cites W1973054923 @default.
- W2999607073 cites W1985238052 @default.
- W2999607073 cites W1997709480 @default.
- W2999607073 cites W2031489346 @default.
- W2999607073 cites W2064675550 @default.
- W2999607073 cites W2082765213 @default.
- W2999607073 cites W2097117768 @default.
- W2999607073 cites W2099471712 @default.
- W2999607073 cites W2101926813 @default.
- W2999607073 cites W2102492119 @default.
- W2999607073 cites W2103328396 @default.
- W2999607073 cites W2104095591 @default.
- W2999607073 cites W2104408738 @default.
- W2999607073 cites W2112546694 @default.
- W2999607073 cites W2112796928 @default.
- W2999607073 cites W2115579991 @default.
- W2999607073 cites W2116040950 @default.
- W2999607073 cites W2117671523 @default.
- W2999607073 cites W2121927366 @default.
- W2999607073 cites W2124592697 @default.
- W2999607073 cites W2125186487 @default.
- W2999607073 cites W2125215748 @default.
- W2999607073 cites W2125389028 @default.
- W2999607073 cites W2125849446 @default.
- W2999607073 cites W2133059825 @default.
- W2999607073 cites W2143516773 @default.
- W2999607073 cites W2144794286 @default.
- W2999607073 cites W2145094598 @default.
- W2999607073 cites W2152632881 @default.
- W2999607073 cites W2154996879 @default.
- W2999607073 cites W2163605009 @default.
- W2999607073 cites W2173520492 @default.
- W2999607073 cites W2190691619 @default.
- W2999607073 cites W2194775991 @default.
- W2999607073 cites W2216125271 @default.
- W2999607073 cites W2221101993 @default.
- W2999607073 cites W2302548814 @default.
- W2999607073 cites W2309415944 @default.
- W2999607073 cites W2322480645 @default.
- W2999607073 cites W2340897893 @default.
- W2999607073 cites W2342591535 @default.
- W2999607073 cites W2400000673 @default.
- W2999607073 cites W2407521645 @default.
- W2999607073 cites W2412782625 @default.
- W2999607073 cites W2419448466 @default.
- W2999607073 cites W2431874326 @default.
- W2999607073 cites W2464708700 @default.
- W2999607073 cites W2508741746 @default.
- W2999607073 cites W2508829638 @default.
- W2999607073 cites W2513586865 @default.
- W2999607073 cites W2536208356 @default.
- W2999607073 cites W2557283755 @default.
- W2999607073 cites W2557889580 @default.
- W2999607073 cites W2558580397 @default.
- W2999607073 cites W2560023338 @default.
- W2999607073 cites W2563705555 @default.
- W2999607073 cites W2565639579 @default.
- W2999607073 cites W2586114507 @default.
- W2999607073 cites W2592939477 @default.
- W2999607073 cites W2594519801 @default.
- W2999607073 cites W2598666589 @default.
- W2999607073 cites W2604176797 @default.
- W2999607073 cites W2605161420 @default.
- W2999607073 cites W2605482930 @default.
- W2999607073 cites W2605570189 @default.
- W2999607073 cites W2605929543 @default.
- W2999607073 cites W2607363228 @default.
- W2999607073 cites W2609822318 @default.
- W2999607073 cites W2612445135 @default.
- W2999607073 cites W2613718673 @default.
- W2999607073 cites W2630837129 @default.
- W2999607073 cites W2737258237 @default.