| 1. |
- Ayyalasomayajula, Kalyan Ram, et al.
(författare)
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Historical document binarization combining semantic labeling and graph cuts
- 2017
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Ingår i: Image Analysis. - Cham : Springer. - 9783319591254 ; , s. 386-396
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Konferensbidrag (refereegranskat)abstract
- Most data mining applications on collections of historical documents require binarization of the digitized images as a pre-processing step. Historical documents are often subjected to degradations such as parchment aging, smudges and bleed through from the other side. The text is sometimes printed, but more often handwritten. Mathematical modeling of appearance of the text, background and all kinds of degradations, is challenging. In the current work we try to tackle binarization as pixel classification problem. We first apply semantic segmentation, using fully convolutional neural networks. In order to improve the sharpness of the result, we then apply a graph cut algorithm. The labels from the semantic segmentation are used as approximate estimates of the text and background, with the probability map of background used for pruning the edges in the graph cut. The results obtained show significant improvement over the state of the art approach.
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| 2. |
- Bengtsson, Ewert, 1948-
(författare)
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Image processing and its hardware support : Analysis vs synthesis - historical trends
- 2017
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Ingår i: Image Analysis, SCIA 2017, Pt I. - Switzerland : Springer International Publishing. - 9783319591261 - 9783319591254 ; , s. 3-14
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Konferensbidrag (refereegranskat)abstract
- Computers can be used to handle images in two fundamen-tally dierent ways. They can be used to analyse images to obtain quan-titative data or some image understanding. And they can be used tocreate images that can be displayed through computer graphics and vi-sualization. For both of these purposes it is of interest to develop ecientways of representing, compressing and storing the images. While SCIA,the Scandinavia Conference of Image Analysis, according to its name ismainly concerned with the former aspect of images, it is interesting tonote that image analysis throughout its history has been strongly in u-enced also by developments on the visualization side. When the confer-ence series now has reached its 20th milestone it may be worth re ectingon what factors have been important in forming the development of theeld. To understand where you are it is good to know where you comefrom and it may even help you understand where you are going.
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| 3. |
- Eilertsen, Gabriel, 1984-, et al.
(författare)
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BriefMatch: Dense binary feature matching for real-time optical flow estimation
- 2017
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Ingår i: Proceedings of the Scandinavian Conference on Image Analysis (SCIA17). - Cham : Springer. - 9783319591254 ; , s. 221-233
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Konferensbidrag (refereegranskat)abstract
- Research in optical flow estimation has to a large extent focused on achieving the best possible quality with no regards to running time. Nevertheless, in a number of important applications the speed is crucial. To address this problem we present BriefMatch, a real-time optical flow method that is suitable for live applications. The method combines binary features with the search strategy from PatchMatch in order to efficiently find a dense correspondence field between images. We show that the BRIEF descriptor provides better candidates (less outlier-prone) in shorter time, when compared to direct pixel comparisons and the Census transform. This allows us to achieve high quality results from a simple filtering of the initially matched candidates. Currently, BriefMatch has the fastest running time on the Middlebury benchmark, while placing highest of all the methods that run in shorter than 0.5 seconds.
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| 4. |
- Gupta, Anindya, et al.
(författare)
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Convolutional neural networks for false positive reduction of automatically detected cilia in low magnification TEM images
- 2017
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Ingår i: Image Analysis. - Cham : Springer. - 9783319591254 ; , s. 407-418
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Konferensbidrag (refereegranskat)abstract
- Automated detection of cilia in low magnification transmission electron microscopy images is a central task in the quest to relieve the pathologists in the manual, time consuming and subjective diagnostic procedure. However, automation of the process, specifically in low magnification, is challenging due to the similar characteristics of non-cilia candidates. In this paper, a convolutional neural network classifier is proposed to further reduce the false positives detected by a previously presented template matching method. Adding the proposed convolutional neural network increases the area under Precision-Recall curve from 0.42 to 0.71, and significantly reduces the number of false positive objects.
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