Matches in SemOpenAlex for { <https://semopenalex.org/work/W4378575997> ?p ?o ?g. }
- W4378575997 endingPage "6512" @default.
- W4378575997 startingPage "6512" @default.
- W4378575997 abstract "Image matting is a fundamental technique used to extract a fine foreground image from a given image by estimating the opacity values of each pixel. It is one of the key techniques in image processing and has a wide range of applications in practical scenarios, such as in image and video editing. Deep learning has demonstrated outstanding performance in various image processing tasks, making it a popular research topic. In recent years, image matting methods based on deep learning have gained significant attention due to their superior performance. Therefore, this article presents a comprehensive overview of the deep learning-based image matting algorithms that have been proposed in recent years. This paper initially introduces frequently used datasets and their production methods, along with the basic principles of traditional image matting techniques. We then analyze deep learning-based matting algorithms in detail and introduce commonly used image matting evaluation metrics. Additionally, this paper discusses the application scenarios of image matting, conducts experiments to illustrate the limitations of current image matting methods, and outlines potential future research directions in this field. Overall, this paper can serve as a valuable reference for researchers that are interested in image matting." @default.
- W4378575997 created "2023-05-28" @default.
- W4378575997 creator A5002820727 @default.
- W4378575997 creator A5019871897 @default.
- W4378575997 creator A5030439103 @default.
- W4378575997 creator A5035820547 @default.
- W4378575997 creator A5057320518 @default.
- W4378575997 date "2023-05-26" @default.
- W4378575997 modified "2023-10-18" @default.
- W4378575997 title "Deep Learning Methods in Image Matting: A Survey" @default.
- W4378575997 cites W1861492603 @default.
- W4378575997 cites W1903029394 @default.
- W4378575997 cites W1978508694 @default.
- W4378575997 cites W2007640671 @default.
- W4378575997 cites W2035773017 @default.
- W4378575997 cites W2077604257 @default.
- W4378575997 cites W2098554565 @default.
- W4378575997 cites W2108598243 @default.
- W4378575997 cites W2109815552 @default.
- W4378575997 cites W2114378523 @default.
- W4378575997 cites W2125637308 @default.
- W4378575997 cites W2142771691 @default.
- W4378575997 cites W2157887643 @default.
- W4378575997 cites W2169040970 @default.
- W4378575997 cites W2194775991 @default.
- W4378575997 cites W2293194425 @default.
- W4378575997 cites W2295475768 @default.
- W4378575997 cites W2296349909 @default.
- W4378575997 cites W2412782625 @default.
- W4378575997 cites W2518810941 @default.
- W4378575997 cites W2571232531 @default.
- W4378575997 cites W2604469346 @default.
- W4378575997 cites W2752782242 @default.
- W4378575997 cites W2771360532 @default.
- W4378575997 cites W2793939440 @default.
- W4378575997 cites W2891201469 @default.
- W4378575997 cites W2901374586 @default.
- W4378575997 cites W2948070565 @default.
- W4378575997 cites W2955340181 @default.
- W4378575997 cites W2963222130 @default.
- W4378575997 cites W2963304750 @default.
- W4378575997 cites W2963887671 @default.
- W4378575997 cites W2970820338 @default.
- W4378575997 cites W2974480317 @default.
- W4378575997 cites W2997597456 @default.
- W4378575997 cites W3016693803 @default.
- W4378575997 cites W3022935549 @default.
- W4378575997 cites W3025800305 @default.
- W4378575997 cites W3034368090 @default.
- W4378575997 cites W3034522851 @default.
- W4378575997 cites W3035254352 @default.
- W4378575997 cites W3084905931 @default.
- W4378575997 cites W3120238727 @default.
- W4378575997 cites W3174887243 @default.
- W4378575997 cites W3176288137 @default.
- W4378575997 cites W3179698659 @default.
- W4378575997 cites W3191969711 @default.
- W4378575997 cites W3193427204 @default.
- W4378575997 cites W3206445121 @default.
- W4378575997 cites W3207351435 @default.
- W4378575997 cites W4205174848 @default.
- W4378575997 cites W4213152761 @default.
- W4378575997 cites W4225787400 @default.
- W4378575997 cites W4312524025 @default.
- W4378575997 cites W4319440659 @default.
- W4378575997 cites W4322621399 @default.
- W4378575997 doi "https://doi.org/10.3390/app13116512" @default.
- W4378575997 hasPublicationYear "2023" @default.
- W4378575997 type Work @default.
- W4378575997 citedByCount "0" @default.
- W4378575997 crossrefType "journal-article" @default.
- W4378575997 hasAuthorship W4378575997A5002820727 @default.
- W4378575997 hasAuthorship W4378575997A5019871897 @default.
- W4378575997 hasAuthorship W4378575997A5030439103 @default.
- W4378575997 hasAuthorship W4378575997A5035820547 @default.
- W4378575997 hasAuthorship W4378575997A5057320518 @default.
- W4378575997 hasBestOaLocation W43785759971 @default.
- W4378575997 hasConcept C108583219 @default.
- W4378575997 hasConcept C115961682 @default.
- W4378575997 hasConcept C119857082 @default.
- W4378575997 hasConcept C126422989 @default.
- W4378575997 hasConcept C127413603 @default.
- W4378575997 hasConcept C146978453 @default.
- W4378575997 hasConcept C154945302 @default.
- W4378575997 hasConcept C202444582 @default.
- W4378575997 hasConcept C204323151 @default.
- W4378575997 hasConcept C26517878 @default.
- W4378575997 hasConcept C2776674983 @default.
- W4378575997 hasConcept C31972630 @default.
- W4378575997 hasConcept C33923547 @default.
- W4378575997 hasConcept C38652104 @default.
- W4378575997 hasConcept C41008148 @default.
- W4378575997 hasConcept C9417928 @default.
- W4378575997 hasConcept C9652623 @default.
- W4378575997 hasConceptScore W4378575997C108583219 @default.
- W4378575997 hasConceptScore W4378575997C115961682 @default.
- W4378575997 hasConceptScore W4378575997C119857082 @default.
- W4378575997 hasConceptScore W4378575997C126422989 @default.