Abstract: This paper proposes a new framework for real-time estimation of the onboard stereo head's position and orientation relative to the road surface, which is required for any advanced driver-assistance application. This framework can be used with all road types: highways, urban, etc. Unlike existing works that rely on feature extraction in either the image domain or 3-D space, we propose a framework that directly estimates the unknown parameters from the stream of stereo pairs' brightness. The proposed approach consists of two stages that are invoked for every stereo frame. The first stage segments the road region in one monocular view. The second stage estimates the camera pose using a featureless registration between the segmented monocular road region and the other view in the stereo pair. This paper has two main contributions. The first contribution combines a road segmentation algorithm with a registration technique to estimate the online stereo camera pose. The second contribution solves the registration using a featureless method, which is carried out using two different optimization techniques: 1) the differential evolution algorithm and 2) the Levenberg-Marquardt (LM) algorithm. We provide experiments and evaluations of performance. The results presented show the validity of our proposed framework.

Abstract: In the early twenty first century Given Imaging Ltd. presented wireless capsule endoscopy (WCE) as a new technological breakthrough that allowed the visualization of
the intestine by using a small, swallowed camera. This small size device was received
with a high enthusiasm within the medical community, and until now, it is still one
of the medical devices with the highest use growth rate. WCE can be used as a novel
diagnostic tool that presents several clinical advantages, since it is non-invasive and
at the same time it provides, for the first time, a full picture of the small bowel morphology, contents and dynamics. Since its appearance, the WCE has been used to
detect several intestinal dysfunctions such as: polyps, ulcers and bleeding. However,
the visual analysis of WCE videos presents an important drawback: the long time
required by the physicians for proper video visualization. In this sense and regarding
to this limitation, the development of computer aided systems is required for the extensive use of WCE in the medical community.
The work presented in this thesis is a set of contributions for the automatic image
analysis and computer-aided diagnosis of intestinal motility disorders using WCE.
Until now, the diagnosis of small bowel motility dysfunctions was basically performed
by invasive techniques such as the manometry test, which can only be conducted at
some referral centers around the world owing to the complexity of the procedure and
the medial expertise required in the interpretation of the results.
Our contributions are divided in three main blocks:
1. Image analysis by computer vision techniques to detect events in the endoluminal WCE scene. Several methods have been proposed to detect visual events
such as: intestinal contractions, intestinal content, tunnel and wrinkles;
2. Machine learning techniques for the analysis and the manipulation of the data
from WCE. These methods have been proposed in order to overcome the problems that the analysis of WCE presents such as: video acquisition cost, unlabeled data and large number of data;
3. Two different systems for the computer-aided diagnosis of intestinal motility
disorders using WCE. The first system presents a fully automatic method that
aids at discriminating healthy subjects from patients with severe intestinal motor disorders like pseudo-obstruction or food intolerance. The second system presents another automatic method that models healthy subjects and discriminate them from mild intestinal motility patients.

Abstract: Put succinctly, the bag-of-words based image representation is the most successful approach for object and scene recognition. Within the bag-of-words framework the optimal fusion of multiple cues, such as shape, texture and color, still remains an active research domain. There exist two main approaches to combine color and shape information within the bag-of-words framework. The first approach called, early fusion, fuses color and shape at the feature level as a result of which a joint colorshape vocabulary is produced. The second approach, called late fusion, concatenates histogram representation of both color and shape, obtained independently. In the first part of this thesis, we analyze the theoretical implications of both early and late feature fusion. We demonstrate that both these approaches are suboptimal for a subset of object categories. Consequently, we propose a novel method for recognizing object categories when using multiple cues by separately processing the shape and color cues and combining them by modulating the shape features by category specific color attention. Color is used to compute bottom-up and top-down attention maps. Subsequently, the color attention maps are used to modulate the weights of the shape features. Shape features are given more weight in regions with higher attention and vice versa. The approach is tested on several benchmark object recognition data sets and the results clearly demonstrate the effectiveness of our proposed method. In the second part of the thesis, we investigate the problem of obtaining compact spatial pyramid representations for object and scene recognition. Spatial pyramids have been successfully applied to incorporate spatial information into bag-of-words based image representation. However, a major drawback of spatial pyramids is that it leads to high dimensional image representations. We present a novel framework for obtaining compact pyramid representation. The approach reduces the size of a high dimensional pyramid representation upto an order of magnitude without any significant reduction in accuracy. Moreover, we also investigate the optimal combination of multiple features such as color and shape within the context of our compact pyramid representation. Finally, we describe a novel technique to build discriminative visual words from multiple cues learned independently from training images. To this end, we use an information theoretic vocabulary compression technique to find discriminative combinations of visual cues and the resulting visual vocabulary is compact, has the cue binding property, and supports individual weighting of cues in the final image representation. The approach is tested on standard object recognition data sets. The results obtained clearly demonstrate the effectiveness of our approach.

Abstract: We present a generic framework for object segmentation using depth maps based on Random Forest and Graph-cuts theory, and apply it to the segmentation of human limbs in depth maps. First, from a set of random depth features, Random Forest is used to infer a set of label probabilities for each data sample. This vector of probabilities is used as unary term in α-β swap Graph-cuts algorithm. Moreover, depth of spatio-temporal neighboring data points are used as boundary potentials. Results on a new multi-label human depth data set show high performance in terms of segmentation overlapping of the novel methodology compared to classical approaches.

Abstract: Optical flow has got a major role in making advanced driver assistance systems (ADAS) a reality. ADAS applications are expected to perform efficiently in all kinds of environments, those are highly probable, that one can drive the vehicle in different kinds of roads, times and seasons. In this work, we study the relationship of optical flow with different roads, that is by analyzing optical flow accuracy on different road textures. Texture measures such as TeX , TeX and TeX are evaluated for this purpose. Further, the relation of regularization weight to the flow accuracy in the presence of different textures is also analyzed. Additionally, we present a framework to generate synthetic sequences of different textures in ADAS scenarios with ground-truth optical flow.

Abstract: This paper defines an intelligent and interactive framework to classify multiple regions of interest from the original data on demand, without requiring any preprocessing or previous segmentation. The proposed intelligent and interactive approach is divided in three stages: visualize, training and testing. First, users visualize and label some samples directly on slices of the volume. Training and testing are based on a framework of Error Correcting Output Codes and Adaboost classifiers that learn to classify each region the user has painted. Later, at the testing stage, each classifier is directly applied on the rest of samples and combined to perform multi-class labeling, being used in the final rendering. We also parallelized the training stage using a GPU-based implementation for
obtaining a rapid interaction and classification.

Abstract: Most document analysis applications rely on the extraction of shape descriptors, which may be grouped into different categories, each category having its own advantages and drawbacks (O.R. Terrades et al. in Proceedings of ICDAR’07, pp. 227–231, 2007). In order to improve the richness of their description, many authors choose to combine multiple descriptors. Yet, most of the authors who propose a new descriptor content themselves with comparing its performance to the performance of a set of single state-of-the-art descriptors in a specific applicative context (e.g. symbol recognition, symbol spotting...). This results in a proliferation of the shape descriptors proposed in the literature. In this article, we propose an innovative protocol, the originality of which is to be as independent of the final application as possible and which relies on new quantitative and qualitative measures. We introduce two types of measures: while the measures of the first type are intended to characterize the descriptive power (in terms of uniqueness, distinctiveness and robustness towards noise) of a descriptor, the second type of measures characterizes the complementarity between multiple descriptors. Characterizing upstream the complementarity of shape descriptors is an alternative to the usual approach where the descriptors to be combined are selected by trial and error, considering the performance characteristics of the overall system. To illustrate the contribution of this protocol, we performed experimental studies using a set of descriptors and a set of symbols which are widely used by the community namely ART and SC descriptors and the GREC 2003 database.