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WP4: MI prototypes for image processing and analysis PDF Print E-mail

The objective of this WP is the prototype specification, development and evaluation in different applications where the proposed framework is relevant, and more specifically in applications related to image processing and analysis problems. This is useful for validatingand showing the viability of the proposed approach, as well as for dissemination activities of WP6 aimed the international community, both scientific and non-scientific audience.


Selected Publications

Relevance: In this work we propose and analyze a globally and locally adaptive super-resolution Bayesian methodology for pansharpening of multispectral images. The methodology incorporates prior knowledge on the expected characteristics of the multispectral images uses the sensor characteristics to model the observation process of both panchromatic and multispectral images and includes information on the unknown parameters in the model in the form of hyperprior distributions

  • [Ipa-med] M. Drozdzal, S. Segui, J. Vitria and P. Radeva. Interactive labeling of WCE images. Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2011, Las Palmas de Gran Canaria (Spain), June 8-10, 2011 (In press, to be published by Springer).
Relevance: Labeling of the data can be a very expensive process, specially while dealing with data of high variability and complexity. A good example of such data are the videos from Wireless Capsule Endoscopy. Building a representative WCE data set means many videos to be labeled by an expert. In this approach an interactive labeling scheme that allows reducing expert effort in the labeling process is presented. The proposed system allows the annotation of informative/non-informative frames of the WCE video with less than 100 clicks.

  • [Ipa-dar] Rodríguez -Serrano, J.A., Perronnin, F., Sánchez, G. and Lladós, J. Unsupervised writer adaptation of whole-word HMMs with application to word-spotting. Pattern Recognition Letters, Volume 31, Issue 8, 1 June 2010, Pages 742-749.
Relevance: In this work we propose a novel approach for writer adaptation in a handwritten word-spotting task. The method exploits the fact that the semi-continuous hidden Markov model separates the word model parameters into (i) a codebook of shapes and (ii) a set of word-specific parameters. The main contribution is to employ this property to derive writer-specific word models by statistically adapting an initial universal codebook to each document. This process is unsupervised and does not even require the appearance of the keyword(s) in the searched document.

Relevance: To date, automatic handwriting recognition systems are far from being perfect and heavy human intervention is often required to check and correct the results of such systems. This “post-editing” process is both inefficient and uncomfortable to the user. As an alternative to fully manual transcription and post-editing, a multimodal interactive approach is proposed here where user feedback is provided by means of touchscreen pen strokes and/or more traditional keyboard and mouse operation. Empirical tests on three cursive handwritten tasks suggest that, using this approach, considerable amounts of user effort can be saved with respect to both pure manual work and non-interactive, post-editing processing.

Relevance: In two previous experiments, the boundaries of basic colour categories were measured. In the first experiment, samples were presented in isolation (ie on a dark background) and boundaries were measured using a yes/no paradigm. In the second, subjects adjusted the chromaticity of a sample presented on a random Mondrian background to find the boundary between pairs of adjacent colours. Results from these experiments showed significant differences but it was not possible to conclude whether this discrepancy was due to the absence/presence of a colourful background or to the differences in the paradigms used. In this work, we settle this question by repeating the first experiment (ie samples presented on a dark background) using the second paradigm. A comparison of results shows that although boundary locations are very similar, boundaries measured in context are significantly different (more diffuse) than those measured in isolation (confirmed by a Student’s t-test analysis on the subject’s answers statistical distributions).

Relevance: A fundamental problem in image retrieval is how to improve the text-based retrieval systems, which is known as “bridging the semantic gap”. One way to overcome this problem and increase thus retrieval performance is to consider user feedback in an interactive scenario. In this approach, a user starts a query and is then presented with a set of (hopefully) relevant images; selecting from these images those which are more relevant to her. Then the system refines its results after each iteration, using late fusion methods, and allowing the user to dynamically tune the amount of textual and visual information that will be used to retrieve similar images.
Last Updated ( Wednesday, 30 May 2012 )