| 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Finding Logo and Seal in Historical Document Images - : An Object Detection based Approach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 However, such methods are likely to underperform while dealing with historical documents. This is due to the fact that historical documents are attributed with additional challenges like extra noise, bleed-through effect, blurred foreground elements and low contrast.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. |
- Ohlsson, Henrik, 1981-, et al.
(författare)
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Enabling Bio-Feedback using Real-Time fMRI
- 2008
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Ingår i: 47th IEEE Conference on Decision and Control, 2008, CDC 2008. - Linköping : IEEE. - 9781424431236 ; , s. 3336-3341
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Konferensbidrag (refereegranskat)abstract
- Despite the enormous complexity of the human mind, fMRI techniques are able to partially observe the state of a brain in action. In this paper we describe an experimental setup for real-time fMRI in a bio-feedback loop. One of the main challenges in the project is to reach a detection speed, accuracy and spatial resolution necessary to attain sufficient bandwidth of communication to close the bio-feedback loop. To this end we have banked on our previous work on real-time filtering for fMRI and system identification, which has been tailored for use in the experiment setup. In the experiments presented the system is trained to estimate where a person in the MRI scanner is looking from signals derived from the visual cortex only. We have been able to demonstrate that the user can induce an action and perform simple tasks with her mind sensed using real-time fMRI. The technique may have several clinical applications, for instance to allow paralyzed and "locked in" people to communicate with the outside world. In the meanwhile, the need for improved fMRI performance and brain state detection poses a challenge to the signal processing community. We also expect that the setup will serve as an invaluable tool for neuro science research in general.
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| 4. |
- Ayyalasomayajula, Kalyan Ram, 1980-, et al.
(författare)
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CalligraphyNet: Augmenting handwriting generation with quill based stroke width
- 2019
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Realistic handwritten document generation garners a lot ofinterest from the document research community for its abilityto generate annotated data. In the current approach we haveused GAN-based stroke width enrichment and style transferbased refinement over generated data which result in realisticlooking handwritten document images. The GAN part of dataaugmentation transfers the stroke variation introduced by awriting instrument onto images rendered from trajectories cre-ated by tracking coordinates along the stylus movement. Thecoordinates from stylus movement are augmented with thelearned stroke width variations during the data augmentationblock. An RNN model is then trained to learn the variationalong the movement of the stylus along with the stroke varia-tions corresponding to an input sequence of characters. Thismodel is then used to generate images of words or sentencesgiven an input character string. A document image thus cre-ated is used as a mask to transfer the style variations of the inkand the parchment. The generated image can capture the colorcontent of the ink and parchment useful for creating annotated data.
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| 5. |
- Ayyalasomayajula, Kalyan Ram, 1980-
(författare)
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Learning based segmentation and generation methods for handwritten document images
- 2019
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Computerized analysis of handwritten documents is an active research area in image analysis and computer vision. The goal is to create tools that can be available for use at university libraries and for researchers in the humanities. Working with large collections of handwritten documents is very time consuming and many old books and letters remain unread for centuries. Efficient computerized methods could help researchers in history, philology and computer linguistics to cost-effectively conduct a whole new type of research based on large collections of documents. The thesis makes a contribution to this area through the development of methods based on machine learning. The passage of time degrades historical documents. Humidity, stains, heat, mold and natural aging of the materials for hundreds of years make the documents increasingly difficult to interpret. The first half of the dissertation is therefore focused on cleaning the visual information in these documents by image segmentation methods based on energy minimization and machine learning. However, machine learning algorithms learn by imitating what is expected of them. One prerequisite for these methods to work is that ground truth is available. This causes a problem for historical documents because there is a shortage of experts who can help to interpret and interpret them. The second part of the thesis is therefore about automatically creating synthetic documents that are similar to handwritten historical documents. Because they are generated from a known text, they have a given facet. The visual content of the generated historical documents includes variation in the writing style and also imitates degradation factors to make the images realistic. When machine learning is trained on synthetic images of handwritten text, with a known facet, in many cases they can even give an even better result for real historical documents.
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| 6. |
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| 7. |
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| 8. |
- Brun, Anders, 1976-, et al.
(författare)
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A tensor-like representation for averaging, filtering and interpolation of 3D object orientation data
- 2005
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Ingår i: Image Processing, 2005. ICIP 2005. IEEE International Conference on (Volume:3 ). - 0780391349 ; , s. 1092-1095
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Konferensbidrag (refereegranskat)abstract
- Averaging, filtering and interpolation of 3-D object orientation data is important in both computer vision and computer graphics, for instance to smooth estimates of object orientation and interpolate between keyframes in computer animation. In this paper we present a novel framework in which the non-linear nature of these problems is avoided by embedding the manifold of 3-D orientations into a 16-dimensional Euclidean space. Linear operations performed in the new representation can be shown to be rotation invariant, and defining a projection back to the orientation manifold results in optimal estimates with respect to the Euclidean metric. In other words, standard linear filters, interpolators and estimators may be applied to orientation data, without the need for an additional machinery to handle the non-linear nature of the problems. This novel representation also provides a way to express uncertainty in 3-D orientation, analogous to the well known tensor representation for lines and hyperplanes.
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| 9. |
- Brun, Anders, 1976-, et al.
(författare)
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Clustering Fiber Traces Using Normalized Cuts
- 2004
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Ingår i: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2004. - Berlin, Heidelberg : Springer Berlin/Heidelberg. - 9783540229766 - 9783540301356 ; , s. 368-375
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Konferensbidrag (refereegranskat)abstract
- In this paper we present a framework for unsupervised segmentation of white matter fiber traces obtained from diffusion weighted MRI data. Fiber traces are compared pairwise to create a weighted undirected graph which is partitioned into coherent sets using the normalized cut (Ncut) criterion. A simple and yet effective method for pairwise comparison of fiber traces is presented which in combination with the Ncut criterion is shown to produce plausible segmentations of both synthetic and real fiber trace data. Segmentations are visualized as colored stream-tubes or transformed to a segmentation of voxel space, revealing structures in a way that looks promising for future explorative studies of diffusion weighted MRI data.
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| 10. |
- Brun, Anders, 1976-, et al.
(författare)
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Coloring of DT-MRI fiber traces using Laplacian Eigenmaps
- 2003
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Ingår i: Computer Aided Systems Theory - EUROCAST 2003. - Berlin, Heidelberg : Springer Berlin/Heidelberg. - 9783540202219 - 9783540452102 ; , s. 518-529
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Konferensbidrag (refereegranskat)abstract
- We propose a novel post processing method for visualization of fiber traces from DT-MRI data. Using a recently proposed non-linear dimensionality reduction technique, Laplacian eigenmaps [3], we create a mapping from a set of fiber traces to a low dimensional Euclidean space. Laplacian eigenmaps constructs this mapping so that similar traces are mapped to similar points, given a custom made pairwise similarity measure for fiber traces. We demonstrate that when the low-dimensional space is the RGB color space, this can be used to visualize fiber traces in a way which enhances the perception of fiber bundles and connectivity in the human brain.
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