Variability provides the ability to adapt and customize a software system's artifacts for a particular context or circumstance. Variability enables code reuse, but its mechanisms are often tangled within a software artifact or scattered over multiple artifacts. This makes the system harder to maintain for developers, and harder to understand for users that configure the software. Feature models provide a centralized source for describing the variability in a software system. A feature model consists of a hierarchy of features—the common and variable system characteristics—with constraints between features. Constructing a feature model, however, is a arduous and time-consuming manual process. We developed two techniques for feature model synthesis. The first, Feature-Graph-Extraction, is an automated algorithm for extracting a feature graph from a propositional formula in either conjunctive normal form (CNF), or disjunctive normal form (DNF). A feature graph describes all feature diagrams that are complete with respect to the input. We evaluated our algorithms against related synthesis algorithms and found that our CNF variant was significantly faster than the previous comparable technique, and the DNF algorithm performed similarly to a comparable, but newer technique, with the exception of several models where our algorithm was faster. The second, Feature-Tree-Synthesis, is a semi-automated technique for building a feature model given a feature graph. This technique uses both logical constraints and text to address the most challenging part of feature model synthesis—constructing the feature hierarchy—by ranking potential parents of a feature with a textual similarity heuristic. We found that the procedure effectively reduced a modeler's choices from thousands, to five or less when synthesizing the Linux and eCos variability models. Our third contribution is the analysis of Kconfig—a language similar to feature modeling used to specify the variability model of the Linux kernel. While large feature models are reportedly used in industry, these models have not been available to the research community for benchmarking feature model analysis and synthesis techniques. We compare Kconfig to feature modeling, reverse engineer formal semantics, and translate 12 open-source Kconfig models—including the Linux model with over 6000 features—to propositional logic.

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With the proliferation of social networks, people are likely to share their opinions about news, social events and products on the Web. There is an increasing interest in understanding users’ attitude or sentiment from the large repository of opinion-rich data on the Web. This can benefit many commercial and political applications. Primarily, the researchers concentrated on the documents such as users’ comments on the purchased products. Recent works show that visual appearance also conveys rich human affection that can be predicted. While great efforts have been devoted on the single media, either text or image, little attempts are paid for the joint analysis of multi-view data which is becoming a prevalent form in the social media. For example, paired with the posted textual messages on Twitter, users are likely to upload images and videos which may carry their affective states. One common obstacle is the lack of sufficient manually annotated instances for model learning and performance evaluation. To prompt the researches on this problem, we introduce a multi-view sentiment analysis dataset (MVSA) including a set of manually annotated image-text pairs collected from Twitter. The dataset can be utilized as a valuable benchmark for both single-view and multi-view sentiment analysis. In this thesis, we further conduct a comprehensive study on computational analysis of sentiment from the multi-view data. The state-of-the-art approaches on single view (image or text) or multi view (image and text) data are introduced, and compared through extensive experiments conducted on our constructed dataset and other public datasets. More importantly, the effectiveness of the correlation between different views is also studied using the widely used fusion strategies and advanced multi-view feature extraction methods.

The aim of this thesis is to familiarize with the issue of registration of medical image sequences. The main objective was to focus on the method of feature tracking in the image and various options of its implementation. The theoretical part describes various methods for detection of feature points and future point matching methods. In the practical part these methods were implemented in Matlab programming environment and a simple graphical user interface was created.

The aims of this research work are to develop a feature detection, description, and matching system for low-resource applications. This work was motivated by the need for a vision sensor to assist the flight of a quad-rotor UAV. This application presented a real-world challenge of autonomous drift stabilization using vision sensors. The initial solution implemented a basic feature detector and matching system on an FPGA. The research then pursued ways to improve the vision system. Research began with color feature detection, and the Color Difference of Gaussians feature detector was developed. CDoG provides better results than gray scale DoG and does not require any additional processing than gray scale if implemented in a parallel architecture. The CDoG Scale-Invariant Feature Transform modification was developed which provided color feature detection and description to the gray scale SIFT descriptor. To demonstrate the benefits of color information, the CDSIFT algorithm was applied to a real application: library book inventory. While color provides added benefit to the CDSIFT descriptor, CDSIFT descriptors are still computationally intractable for a low-resource hardware implementation. Because of these shortcomings, this research focused on developing a new feature descriptor. The BAsis Sparse-coding Inspired Similarity (BASIS) descriptor was developed with low-resource systems in mind. BASIS utilizes sparse coding to provide a generic description of feature characterstics. The BASIS descriptor provided improved accuracy over SIFT, and similar accuracy to SURF on the task of aerial UAV frame-to-frame feature matching. However, basis dictionaries are non-orthogonal and can contain redundant information. In addition to a feature descriptor, an FPGA-based feature correlation (or matching) system needed to be developed. TreeBASIS was developed to answer this need and address the redundancy issues of BASIS. TreeBASIS utilizes a vocabulary tree to drastically reduce descriptor computation time and descriptor size. TreeBASIS also obtains a higher level of accuracy than SIFT, SURF, and BASIS on the UAV aerial imagery task. Both BASIS and TreeBASIS were implemented in VHDL and are well suited for low-resource FPGA applications. TreeBASIS provides a complete feature detection, description, and correlation system-on-a-chip for low-resource FPGA vision systems.

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Les adultes peuvent éprouver des difficultés à discriminer des phonèmes d’une langue seconde (L2) qui ne servent pas à distinguer des items lexicaux dans leur langue maternelle (L1). Le Feature Model (FM) de Brown (1998) propose que les adultes peuvent réussir à créer des nouvelles catégories de sons seulement si celles-ci peuvent être construites à partir de traits distinctifs existant dans la L1 des auditeurs. Cette hypothèse a été testée sur plusieurs contrastes consonantiques dans différentes langues; cependant, il semble que les traits qui s’appliquent sur les voyelles n’aient jamais été examinés dans cette perspective et encore moins les traits qui opèrent à la fois dans les systèmes vocalique et consonantique et qui peuvent avoir un statut distinctif ou non-distinctif. Le principal objectif de la présente étude était de tester la validité du FM concernant le contraste vocalique oral-nasal du portugais brésilien (PB). La perception naïve du contraste /i/-/ĩ/ par des locuteurs du français, de l’anglais, de l’espagnol caribéen et de l’espagnol conservateur a été examinée, étant donné que ces quatre langues diffèrent en ce qui a trait au statut de la nasalité. De plus, la perception du contraste non-naïf /e/-/ẽ/ a été inclus afin de comparer les performances dans la perception naïve et non-naïve. Les résultats obtenus pour la discrimination naïve de /i/-/ĩ/ a permis de tirer les conclusions suivantes pour la première exposition à un contraste non natif : (1) le trait [nasal] qui opère de façon distinctive dans la grammaire d’une certaine L1 peut être redéployé au sein du système vocalique, (2) le trait [nasal] qui opère de façon distinctive dans la grammaire d’une certaine L1 ne peut pas être redéployé à travers les systèmes (consonne à voyelle) et (3) le trait [nasal] qui opère de façon non-distinctive dans la grammaire d’une certaine L1 peut être ou ne pas être redéployé au statut distinctif. En dernier lieu, la discrimination non-naïve de /e/-/ẽ/ a été réussie par tous les groupes, suggérant que les trois types de redéploiement s’avèrent possibles avec plus d’expérience dans la L2. / Adults may experience difficulties discriminating phonemes of a second language (L2) that do not serve to distinguish lexical items in their native language (L1). Brown’s (1998) Feature Model (FM) advances that adults may be able to create new sound categories only if these can be built from contrastive features existing in their L1. This hypothesis has been tested on various consonant contrasts in a number of languages; however, it appears that features applying on vowels have never been examined from this perspective and neither have features that operate both in the vowel and the consonant systems and that may have a contrastive or a non-contrastive status. The main purpose of the present study was to test the validity of the FM with respect to the oral-nasal vowel contrast of Brazilian Portuguese. The naïve perception of the contrast /i/-/ĩ/ by French, English, Caribbean Spanish, and conservative Spanish speakers was examined, given that these four languages differ with respect to the status of nasality. Moreover, the perception of the non-naïve contrast /e/-/ẽ/ was included to compare naïve and non-naïve perception performances. The obtained data for the naïve discrimination of /i/-/ĩ/ allowed to draw the following conclusions for the first exposure to a non-native contrast: (1) the feature [nasal] operating contrastively in the grammar of a given L1 can be redeployed within the vowel system, (2) the feature [nasal] operating contrastively in the grammar of a given L1 may not be redeployed across systems (consonant to vowel), and (3) the feature [nasal] operating non-contrastively in the grammar of a given L1 might or might not be redeployed to contrastive status. Lastly, the non-naïve perception of /e/-/ẽ/ was successful for all groups, suggesting that the three types of redeployment are possible with more experience in the L2.

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 thesis deals with the Featured Driven Development (FDD) agile methodics. To support this methodics, an information system has been created, providing all team-members with instruments to follow the methodics. This multi-user system is implemented as a web-based application, enabling creation of a feature list,  plan a project, support the cooperation among the feature team members and watch the project progress in an illustrative way. To improve the visualization possibilities, a wide range of reports aimed at the company management and the client is provided.

Software product line engineering is a common method for designing complex software systems. Feature modeling is the most common approach to specify product lines. A feature model is a feature diagram (a special tree of features) plus some crosscutting constraints. Feature modeling languages are grouped into basic and cardinality-based models. The common understanding of the semantics of feature models is a Boolean semantics. We discuss a major deficiency of this semantics and fix it by applying, in turn, modal logic, the theory of multisets, and formal language theory. In order to adequately represent the semantics of basic models, we propose a Kripke semantics and show that basic feature modeling needs a modal rather than Boolean logic. We propose two multiset based theories for cardinality-based feature diagrams, called flat and hierarchical semantics. We show that the hierarchical semantics of a given cardinality-based diagram captures all information in the diagram. We also charac- terize sets of multisets, which can provide a hierarchical semantics of some diagrams. We provide three different reduction processes going from a cardinality-based diagram to an appropriate regular expression. As for crosscutting constraints, we propose a formal language interpretation of them. We also characterize some existing analysis operations over feature models in terms of operations on the corresponding languages and discuss the relevant decidability problems. / Thesis / Doctor of Philosophy (PhD)