Grounding semantics in robots for Visual Question Answering

In this thesis I describe an operational implementation of an object detection and description system that incorporates in an end-to-end Visual Question Answering system and evaluated it on two visual question answering datasets for compositional language and elementary visual reasoning

Using contour information and segmentation for object registration, modeling and retrieval

This thesis considers different aspects of the utilization of contour information and syntactic and semantic image segmentation for object registration, modeling and retrieval in the context of content-based indexing and retrieval in large collections of images. Target applications include retrieval in collections of closed silhouettes, holistic w ord recognition in handwritten historical manuscripts and shape registration. Also, the thesis explores the feasibility of contour-based syntactic features for improving the correspondence of the output of bottom-up segmentation to semantic objects present in the scene and discusses the feasibility of different strategies for image analysis utilizing contour information, e.g. segmentation driven by visual features versus segmentation driven by shape models or semi-automatic in selected application scenarios. There are three contributions in this thesis. The first contribution considers structure analysis based on the shape and spatial configuration of image regions (socalled syntactic visual features) and their utilization for automatic image segmentation. The second contribution is the study of novel shape features, matching algorithms and similarity measures. Various applications of the proposed solutions are presented throughout the thesis providing the basis for the third contribution which is a discussion of the feasibility of different recognition strategies utilizing contour information. In each case, the performance and generality of the proposed approach has been analyzed based on extensive rigorous experimentation using as large as possible test collections

A survey of fingerprint classification Part I: taxonomies on feature extraction methods and learning models

This paper reviews the fingerprint classification literature looking at the problem from a double perspective. We first deal with feature extraction methods, including the different models considered for singular point detection and for orientation map extraction. Then, we focus on the different learning models considered to build the classifiers used to label new fingerprints. Taxonomies and classifications for the feature extraction, singular point detection, orientation extraction and learning methods are presented. A critical view of the existing literature have led us to present a discussion on the existing methods and their drawbacks such as difficulty in their reimplementation, lack of details or major differences in their evaluations procedures. On this account, an experimental analysis of the most relevant methods is carried out in the second part of this paper, and a new method based on their combination is presented.This work was supported by the Research Projects CAB(CDTI), TIN2011-28488, and TIN2013-40765-P.

A Survey of Fingerprint Classification Part I: Taxonomies on Feature Extraction Methods and Learning Models

This paper reviews the fingerprint classification literature looking at the problem from a double perspective. We first deal with feature extraction methods, including the different models considered for singular point detection and for orientation map extraction. Then, we focus on the different learning models considered to build the classifiers used to label new fingerprints. Taxonomies and classifications for the feature extraction, singular point detection, orientation extraction and learning methods are presented. A critical view of the existing literature have led us to present a discussion on the existing methods and their drawbacks such as difficulty in their reimplementation, lack of details or major differences in their evaluations procedures. On this account, an experimental analysis of the most relevant methods is carried out in the second part of this paper, and a new method based on their combination is presented.Research Projects CAB(CDTI) TIN2011-28488 TIN2013-40765Spanish Government FPU12/0490

An Overview of Advances of Pattern Recognition Systems in Computer Vision

26 pagesFirst of all, let's give a tentative answer to the following question: what is pattern recognition (PR)? Among all the possible existing answers, that which we consider being the best adapted to the situation and to the concern of this chapter is: "pattern recognition is the scientific discipline of machine learning (or artificial intelligence) that aims at classifying data (patterns) into a number of categories or classes". But what is a pattern? A pattern recognition system (PRS) is an automatic system that aims at classifying the input pattern into a specific class. It proceeds into two successive tasks: (1) the analysis (or description) that extracts the characteristics from the pattern being studied and (2) the classification (or recognition) that enables us to recognise an object (or a pattern) by using some characteristics derived from the first task

Image segmentation, evaluation, and applications

This thesis aims to advance research in image segmentation by developing robust techniques for evaluating image segmentation algorithms. The key contributions of this work are as follows. First, we investigate the characteristics of existing measures for supervised evaluation of automatic image segmentation algorithms. We show which of these measures is most effective at distinguishing perceptually accurate image segmentation from inaccurate segmentation. We then apply these measures to evaluating four state-of-the-art automatic image segmentation algorithms, and establish which best emulates human perceptual grouping. Second, we develop a complete framework for evaluating interactive segmentation algorithms by means of user experiments. Our system comprises evaluation measures, ground truth data, and implementation software. We validate our proposed measures by showing their correlation with perceived accuracy. We then use our framework to evaluate four popular interactive segmentation algorithms, and demonstrate their performance. Finally, acknowledging that user experiments are sometimes prohibitive in practice, we propose a method of evaluating interactive segmentation by algorithmically simulating the user interactions. We explore four strategies for this simulation, and demonstrate that the best of these produces results very similar to those from the user experiments