Humans live in physical world and perform activities that are physical, natural and biological. But humans are forced to shift explicitly from physical world to virtual world and vice-versa in performing computer aided physical activities. The research reported here is investigating: How implicit human-computer interaction can be used as a means to bridge the gap between physical world and virtual world. An agent-centric approach is introduced to extend ubiquitous computing to unlimited geographical space and a framework for implicit human-computer interaction is also discussed. The benefits of standardized ontologies are used as a base upon which this framework is built. This semantic approach together with agent-centric approach is discussed to visualize the visions of implicit Human-Computer Interaction (i-HCI). / PHYVIR project

In many applications, the dataset for classification may be highly imbalanced where most of the instances in the training set may belong to some of the classes (majority classes), while only a few instances are from the other classes (minority classes). Conventional classifiers will strongly favor the majority class and ignore the minority instances. The imbalance problem can occur in both binary data classification and also in ordinal regression. Ordinal regression is a supervised approach for learning the ordinal relationship between classes. Extensive research has been performed for addressing imbalanced datasets for binary classification; however, current methods do not address within-class imbalance and between-class imbalance at the same time. Similarly, there has been very little research work on addressing imbalanced datasets for ordinal regression. Although current standard oversampling methods can be used to improve the dataset class distribution, they do not consider the ordinal relationship between the classes. The class imbalance problem is a big challenge in classification problems. Most of the clinical datasets are highly imbalanced, which can weaken the performance of classifiers significantly. In this research, the imbalanced dataset classification problem is also examined in the context of a clinical application, particularly pelvic organ prolapse diagnosis. Pelvic organ prolapse (POP) is a major health problem that affects between 30-50% of women in the U.S. Although clinical examination is currently used to diagnose POP, there is still little evidence on specific risk factors that are directly related to particular types of POP and their severity or stages (Stage 0-IV). Data from dynamic MRI related to the movement of pelvic organs has the potential to improve POP prediction but it is currently analyzed manually limiting its exploration and use to small datasets. Moreover, POP is a disorder with multiple stages that are ordinal and whose distribution is highly imbalanced. The main goal of this research is two-fold. The first goal is to design new oversampling methods for imbalanced datasets for both binary classification and ordinal regression. The second goal is to automatically track, segment, and classify the trajectory of multiple organs on dynamic MRI to quantitatively describe pelvic organ movement. The extracted image-based data along with the designed oversampling methods will be used to improve the diagnosis of POP. The proposed research consists of three major objectives: 1) to design a new oversampling technique for binary imbalanced dataset classification; 2) to design a novel oversampling technique for ordinal regression with imbalanced datasets; and 3) to design a two-stage method to automatically track and segment multiple pelvic organs on dynamic MRI for improving the prediction of multi-stage POP with imbalanced datasets. The proposed research aims to provide robust oversampling techniques and image processing models that can (1) effectively handle highly imbalanced datasets for both binary classification and ordinal regression, and (2) automatically track and segment multiple deformable structures for feature extraction from low contrast and nonhomogeneous images and classify them using the resulted trajectories. This research will set the foundation towards a computer-aided decision support system that can automatically extract and analyze image and clinical data to improve the prediction of disorders where the dataset is highly imbalanced through personalized and evidence-based assessment.

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Feature detection and matching is an important step in many object tracking and detection algorithms. This paper discusses methods to improve upon previous work on the SYnthetic BAsis feature descriptor (SYBA) algorithm, which describes and compares image features in an efficient and discreet manner. SYBA utilizes synthetic basis images overlaid on a feature region of interest (FRI) to generate binary numbers that uniquely describe the feature contained within the FRI. These binary numbers are then used to compare against feature values in subsequent images for matching. However, in a non-ideal environment the accuracy of the feature matching suffers due to variations in image scale, and rotation. This paper introduces a new version of SYBA which processes FRI’s such that the descriptions developed by SYBA are rotation and scale invariant. To demonstrate the improvements of this robust implementation of SYBA called rSYBA, included in this paper are applications that have to cope with high amounts of image variation. The first detects objects along an oil pipeline by transforming and comparing frame-by-frame two surveillance videos recorded at two different times. The second shows camera pose plotting for a ground based vehicle using monocular visual odometry. The third generates panoramic images through image stitching and image transforms. All applications contain large amounts of image variation between image frames and therefore require a significant amount of correct feature matches to generate acceptable results.

This master's thesis deals with automatic detection and tracking of a soccer ball in sports videos. Based on the introduced techniques focusing on tracking of small objects in high-resolution videos, effective convolutional neural networks are designed and used by a modified version of tracking algorithm SORT for automatic object detection. A set of experiments with the processing of images in different resolutions and with various frequencies of detection extraction is carried out in order to examine the trade-off between processing speed and tracking accuracy. The obtained results of experiments are presented and used to form proposals for future work, which could lead to improvements in tracking accuracy while maintaining reasonable processing speed.

The master thesis maps the state of the art of visual object tracking in panoramic 360° video. The thesis aims to reveal the main problems related to visual object tracking and moreover focuses on their solution in panoramic videos. In the study of the existing approaches was found that very few solutions of visual object tracking in equirectangular projection of panoramic video have been implemented so far. This thesis therefore presents two improvements of object tracking methods that are based on the adaptation of equirectangular frames. In addition, this thesis brings the manually created dataset of panoramic videos with more than 9900 annotations. Finally the detailed evaluation of 12 well known and state of the art trackers has been performed for this new dataset.

This master thesis is focused on parametrization of image point neighborhood. Some methods for point localization and point descriptors are described and summarized. Gabor filter is described in detail. The practical part of thesis is chiefly concerned with particle filter tracking system. The weight of each particle is determined by the Gabor filter.

Master's thesis presents a possible application of augmented reality in domain of commerci- als. It presents designed architecture of a mobile application that is able to detect and track specific objects (e.g. printed commercials, logos) in mobile';s phone camera in real time and provide some extra information about the detected object towards the user. Thesis also provides a review of contemporary used techniques in object recognition, object tracking and image retrieval from image databases.

Le suivi multi-objets, malgré les avancées récentes en détection d'objets, présente encore plusieurs difficultés spécifiques et reste ainsi une problématique difficile. Au cours de cette thèse nous proposons d'examiner l'emploi de représentations parcimonieuses au sein de méthodes de suivi multi-objets, dans le but d'améliorer les performances de ces dernières. La première contribution de cette thèse consiste à employer des représentations parcimonieuses collaboratives dans un système de suivi en ligne pour distinguer au mieux les cibles. Des représentations parcimonieuses structurées sont ensuite considérées pour s'adapter plus spécifiquement aux approches de suivi à fenêtre glissante. Une dernière contribution consiste à employer des dictionnaires denses, prenant en considération un grand nombre de positions non détectées au sein des images, de manière à être plus robuste vis-à-vis de la performance du détecteur d'objets employé. / Despite recent advances in object detection, multi-object tracking still raises some specific issues and therefore remains a challenging problem. In this thesis, we propose to investigate the use of sparse representations within multi-object tracking approaches in order to gain in performances. The first contribution of this thesis consists in designing an online tracking approach that takes advantage of collaborative sparse representations to better distinguish between the targets. Then, structured sparse representations are considered in order to be more suited to traking approaches based on a sliding window. In order to rely less on the object detector quality, we consider for the last contribution of this thesis to use dense dictionaries that are taking into account a large number of undetected locations inside each frame.

abstract: The problem of multiple object tracking seeks to jointly estimate the time-varying cardinality and trajectory of each object. There are numerous challenges that are encountered in tracking multiple objects including a time-varying number of measurements, under varying constraints, and environmental conditions. In this thesis, the proposed statistical methods integrate the use of physical-based models with Bayesian nonparametric methods to address the main challenges in a tracking problem. In particular, Bayesian nonparametric methods are exploited to efficiently and robustly infer object identity and learn time-dependent cardinality; together with Bayesian inference methods, they are also used to associate measurements to objects and estimate the trajectory of objects. These methods differ from the current methods to the core as the existing methods are mainly based on random finite set theory. The first contribution proposes dependent nonparametric models such as the dependent Dirichlet process and the dependent Pitman-Yor process to capture the inherent time-dependency in the problem at hand. These processes are used as priors for object state distributions to learn dependent information between previous and current time steps. Markov chain Monte Carlo sampling methods exploit the learned information to sample from posterior distributions and update the estimated object parameters. The second contribution proposes a novel, robust, and fast nonparametric approach based on a diffusion process over infinite random trees to infer information on object cardinality and trajectory. This method follows the hierarchy induced by objects entering and leaving a scene and the time-dependency between unknown object parameters. Markov chain Monte Carlo sampling methods integrate the prior distributions over the infinite random trees with time-dependent diffusion processes to update object states. The third contribution develops the use of hierarchical models to form a prior for statistically dependent measurements in a single object tracking setup. Dependency among the sensor measurements provides extra information which is incorporated to achieve the optimal tracking performance. The hierarchical Dirichlet process as a prior provides the required flexibility to do inference. Bayesian tracker is integrated with the hierarchical Dirichlet process prior to accurately estimate the object trajectory. The fourth contribution proposes an approach to model both the multiple dependent objects and multiple dependent measurements. This approach integrates the dependent Dirichlet process modeling over the dependent object with the hierarchical Dirichlet process modeling of the measurements to fully capture the dependency among both object and measurements. Bayesian nonparametric models can successfully associate each measurement to the corresponding object and exploit dependency among them to more accurately infer the trajectory of objects. Markov chain Monte Carlo methods amalgamate the dependent Dirichlet process with the hierarchical Dirichlet process to infer the object identity and object cardinality. Simulations are exploited to demonstrate the improvement in multiple object tracking performance when compared to approaches that are developed based on random finite set theory. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2019