| 1. |
- Chanda, Sukalpa, et al.
(författare)
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Face Recognition - A One-Shot Learning Perspective
- 2019
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Ingår i: 15th IEEE Conference on Signal Image Technology and Internet based Systems. - 9781728156866 ; , s. 113-119
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Konferensbidrag (refereegranskat)abstract
- Ability to learn from a single instance is something unique to the human species and One-shot learning algorithms try to mimic this special capability. On the other hand, despite the fantastic performance of Deep Learning-based methods on various image classification problems, performance often depends having on a huge number of annotated training samples per class. This fact is certainly a hindrance in deploying deep neural network-based systems in many real-life applications like face recognition. Furthermore, an addition of a new class to the system will require the need to re-train the whole system from scratch. Nevertheless, the prowess of deep learned features could also not be ignored. This research aims to combine the best of deep learned features with a traditional One-Shot learning framework. Results obtained on 2 publicly available datasets are very encouraging achieving over 90% accuracy on 5-way One-Shot tasks, and 84% on 50-way One-Shot problems.
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| 2. |
- Chanda, Sukalpa, et al.
(författare)
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Finding Logo and Seal in Historical Document Images - : An Object Detection based Approach
- 2019
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Ingår i: The 5th Asian Conference on Pattern Recognition (ACPR 2019). ; , s. 821-834
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Konferensbidrag (refereegranskat)abstract
- Logo and Seal serves the purpose of authenticating and referring to the source of a document. This strategy was also prevalent in the medieval period. Different algorithm exists for detection of logo and seal in document images. A close look into the present state-of-the-art methods reveals that those methods were focused toward detection of logo and seal in contemporary document images. However, such methods are likely to underperform while dealing with historical documents. This is due to the fact that historical documents are attributed with additional challenges like extra noise, bleed-through effect, blurred foreground elements and low contrast. The proposed method frames the problem of the logo and seals detection in an object detection framework. Using a deep-learning technique it counters earlier mentioned problems and evades the need for any pre-processing stage like layout analysis and/or binarization in the system pipeline. The experiments were conducted on historical images from 12th to the 16th century and the results obtained were very encouraging for detecting logo in historical document images. To the best of our knowledge, this is the first attempt on logo detection in historical document images using an object-detection based approach.
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| 3. |
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| 4. |
- Olsson, Björn, 1975-
(författare)
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Image Based Visualization Methods for Meteorological Data
- 2004
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Visualization is the process of constructing methods, which are able to synthesize interesting and informative images from data sets, to simplify the process of interpreting the data. In this thesis a new approach to construct meteorological visualization methods using neural network technology is described. The methods are trained with examples instead of explicitely designing the appearance of the visualization.This approach is exemplified using two applications. In the fist the problem to compute an image of the sky for dynamic weather, that is taking account of the current weather state, is addressed. It is a complicated problem to tie the appearance of the sky to a weather state. The method is trained with weather data sets and images of the sky to be able to synthesize a sky image for arbitrary weather conditions. The method has been trained with various kinds of weather and images data. The results show that this is a possible method to construct weather visaualizations, but more work remains in characterizing the weather state and further refinement is required before the full potential of the method can be explored. This approach would make it possible to synthesize sky images of dynamic weather using a fast and efficient empirical method.In the second application the problem of computing synthetic satellite images form numerical forecast data sets is addressed. In this case a mode is trained with preclassified satellite images and forecast data sets to be able to synthesize a satellite image representing arbitrary conditions. The resulting method makes it possible to visualize data sets from numerical weather simulations using synthetic satellite images, but could also be the basis for algorithms based on a preliminary cloud classification.
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| 5. |
- Pihlström, Max, et al.
(författare)
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Triangulation Painting
- 2015
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Ingår i: SIGRAD. ; , s. 1-4
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Konferensbidrag (refereegranskat)
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| 6. |
- Abrate, Matteo, et al.
(författare)
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Geomemories - A Platform for Visualizing Historical, Environmental and Geospatial Changes of the Italian Landscape
- 2013
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Ingår i: ISPRS International Journal of Geo-Information. Special issue: Geospatial Monitoring and Modelling of Environmental Change. - : MDPI - Open Access Publishing. - 2220-9964. ; 2:2, s. 432-455
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Tidskriftsartikel (refereegranskat)abstract
- The GeoMemories project aims at publishing on the Web and digitally preserving historical aerial photographs that are currently stored in physical form within the archives of the Aerofototeca Nazionale in Rome. We describe a system, available at http://www.geomemories.org, that lets users visualize the evolution of the Italian landscape throughout the last century. The Web portal allows comparison of recent satellite imagery with several layers of historical maps, obtained from the aerial photos through a complex workflow that merges them together. We present several case studies carried out in collaboration with geologists, historians and archaeologists, that illustrate the great potential of our system in different research fields. Experiments and advances in image processing technologies are envisaged as a key factor in solving the inherent issue of vast amounts of manual work, from georeferencing to mosaicking to analysis.
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| 7. |
- Azar, Jimmy, et al.
(författare)
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Automated Classification of Glandular Tissue by Statistical Proximity Sampling
- 2015
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Ingår i: International Journal of Biomedical Imaging. - : Hindawi Limited. - 1687-4188 .- 1687-4196.
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Tidskriftsartikel (refereegranskat)abstract
- Due to the complexity of biological tissue and variations in staining procedures, features that are based on the explicit extraction of properties from subglandular structures in tissue images may have difficulty generalizing well over an unrestricted set of images and staining variations. We circumvent this problem by an implicit representation that is both robust and highly descriptive, especially when combined with a multiple instance learning approach to image classification. The new feature method is able to describe tissue architecture based on glandular structure. It is based on statistically representing the relative distribution of tissue components around lumen regions, while preserving spatial and quantitative information, as a basis for diagnosing and analyzing different areas within an image. We demonstrate the efficacy of the method in extracting discriminative features for obtaining high classification rates for tubular formation in both healthy and cancerous tissue, which is an important component in Gleason and tubule-based Elston grading. The proposed method may be used for glandular classification, also in other tissue types, in addition to general applicability as a region-based feature descriptor in image analysis where the image represents a bag with a certain label (or grade) and the region-based feature vectors represent instances.
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| 8. |
- Azar, Jimmy, 1984-
(författare)
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Automated Tissue Image Analysis Using Pattern Recognition
- 2014
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Automated tissue image analysis aims to develop algorithms for a variety of histological applications. This has important implications in the diagnostic grading of cancer such as in breast and prostate tissue, as well as in the quantification of prognostic and predictive biomarkers that may help assess the risk of recurrence and the responsiveness of tumors to endocrine therapy.In this thesis, we use pattern recognition and image analysis techniques to solve several problems relating to histopathology and immunohistochemistry applications. In particular, we present a new method for the detection and localization of tissue microarray cores in an automated manner and compare it against conventional approaches.We also present an unsupervised method for color decomposition based on modeling the image formation process while taking into account acquisition noise. The method is unsupervised and is able to overcome the limitation of specifying absorption spectra for the stains that require separation. This is done by estimating reference colors through fitting a Gaussian mixture model trained using expectation-maximization.Another important factor in histopathology is the choice of stain, though it often goes unnoticed. Stain color combinations determine the extent of overlap between chromaticity clusters in color space, and this intrinsic overlap sets a main limitation on the performance of classification methods, regardless of their nature or complexity. In this thesis, we present a framework for optimizing the selection of histological stains in a manner that is aligned with the final objective of automation, rather than visual analysis.Immunohistochemistry can facilitate the quantification of biomarkers such as estrogen, progesterone, and the human epidermal growth factor 2 receptors, in addition to Ki-67 proteins that are associated with cell growth and proliferation. As an application, we propose a method for the identification of paired antibodies based on correlating probability maps of immunostaining patterns across adjacent tissue sections.Finally, we present a new feature descriptor for characterizing glandular structure and tissue architecture, which form an important component of Gleason and tubule-based Elston grading. The method is based on defining shape-preserving, neighborhood annuli around lumen regions and gathering quantitative and spatial data concerning the various tissue-types.
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| 9. |
- Azar, Jimmy C., et al.
(författare)
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Image segmentation and identification of paired antibodies in breast tissue
- 2014
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Ingår i: Computational & Mathematical Methods in Medicine. - : Hindawi Limited. - 1748-670X .- 1748-6718. ; , s. 647273:1-11
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Tidskriftsartikel (refereegranskat)abstract
- Comparing staining patterns of paired antibodies designed towards a specific protein but toward different epitopes of the protein provides quality control over the binding and the antibodies' ability to identify the target protein correctly and exclusively. We present a method for automated quantification of immunostaining patterns for antibodies in breast tissue using the Human Protein Atlas database. In such tissue, dark brown dye 3,3'-diaminobenzidine is used as an antibody-specific stain whereas the blue dye hematoxylin is used as a counterstain. The proposed method is based on clustering and relative scaling of features following principal component analysis. Our method is able (1) to accurately segment and identify staining patterns and quantify the amount of staining and (2) to detect paired antibodies by correlating the segmentation results among different cases. Moreover, the method is simple, operating in a low-dimensional feature space, and computationally efficient which makes it suitable for high-throughput processing of tissue microarrays.
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| 10. |
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