Improving Bags-of-Words model for object categorization

In the past decade, Bags-of-Words (BOW) models have become popular for the task of object recognition, owing to their good performance and simplicity. Some of the most effective recent methods for computer-based object recognition work by detecting and extracting local image features, before quantizing them according to a codebook rule such as k-means clustering, and classifying these with conventional classifiers such as Support Vector Machines and Naive Bayes. In this thesis, a Spatial Object Recognition Framework is presented that consists of the four main contributions of the research. The first contribution, frequent keypoint pattern discovery, works by combining pairs and triplets of frequent keypoints in order to discover intermediate representations for object classes. Based on the same frequent keypoints principle, algorithms for locating the region-of-interest in training images is then discussed. Extensions to the successful Spatial Pyramid Matching scheme, in order to better capture spatial relationships, are then proposed. The pairs frequency histogram and shapes frequency histogram work by capturing more redefined spatial information between local image features. Finally, alternative techniques to Spatial Pyramid Matching for capturing spatial information are presented. The proposed techniques, variations of binned log-polar histograms, divides the image into grids of different scale and different orientation. Thus captures the distribution of image features both in distance and orientation explicitly. Evaluations on the framework are focused on several recent and popular datasets, including image retrieval, object recognition, and object categorization. Overall, while the effectiveness of the framework is limited in some of the datasets, the proposed contributions are nevertheless powerful improvements of the BOW model

Deep Learning Pipeline for Automated Visual Moth Monitoring: Insect Localization and Species Classification

Biodiversity monitoring is crucial for tracking and counteracting adverse trends in population fluctuations. However, automatic recognition systems are rarely applied so far, and experts evaluate the generated data masses manually. Especially the support of deep learning methods for visual monitoring is not yet established in biodiversity research, compared to other areas like advertising or entertainment. In this paper, we present a deep learning pipeline for analyzing images captured by a moth scanner, an automated visual monitoring system of moth species developed within the AMMOD project. We first localize individuals with a moth detector and afterward determine the species of detected insects with a classifier. Our detector achieves up to 99.01% mean average precision and our classifier distinguishes 200 moth species with an accuracy of 93.13% on image cutouts depicting single insects. Combining both in our pipeline improves the accuracy for species identification in images of the moth scanner from 79.62% to 88.05%

Identification of Pecan Weevils Through Image Processing

The Pecan Weevil attacks the pecan nut, causes significant financial loss and can cause total crop failure. A traditional way of controlling this insect is by setting traps in the pecan orchard and regularly checking them for weevils. The objective of this study is to develop a recognition system that can serve in a wireless imaging network for monitoring pecan weevils. Recognition methods used in this study are based on template matching. The training set consisted of 205 pecan weevils and the testing set included 30 randomly selected pecan weevils and 75 other insects which typically exist in a pecan habitat. Five recognition methods, namely, Zernike moments, Region properties, Normalized cross-correlation, String matching, and Fourier descriptors methods were used in this recognition system. It was found that no single method was sufficiently robust to yield the desired recognition rate, especially in varying data sets. It was also found that region-based shape representation methods were better suited inBiosystems and Agricultural Engineerin

Miscegenation in the Marvelous: Race and Hybridity in the Fantasy Novels of Neil Gaiman and China Miéville

Fantasy literature in the twentieth and twenty-first centuries uses the construction of new races as a mirror through which to see the human race more clearly. Categorizations of fantasy have tended to avoid discussions of race, in part because it is an uncomfortable gray area since fantasy literature does not yet have a clear taxonomy. Nevertheless, race is often an unavoidable component of fantasy literature. This thesis considers J.R.R. Tolkien’s The Lord of the Rings as a taproot text for fantasy literature before moving on to Neil Gaiman’s American Gods and China Miéville’s Perdido Street Station, both newer fantasy novels which include interesting constructions of race and raise issues of miscegenation and hybridity. This thesis moves towards an understanding of what purpose creating and utilizing races serves, and how fantasy literature allows for the identification and potential resolution of a number of human anxieties regarding race

The role of science in contemporary art conservation : a study into the conservation and presentation of food-based art

The conservation of contemporary food-based art appeared to be problematic. Due to the perishable nature, long-term preservation and short-term exposition periods can be problematic in terms of food deterioration and loss of sensorial properties (visual appearance, smell, texture). For instance, for contemporary food-based art the decay processes of the food materials are often intrinsic to the meaning of the artwork. Because of the perishable nature of the food, sometimes, they need to be recreated. In both cases (preservation and recreation), food preservation methods, applied in the food industry, and knowledge on food science offer art conservators more possibilities to develop a suitable conservation strategy. Throughout this PhD research the role of science in the context of conservation of contemporary food-based art was investigated, with a primary emphasis on applicability and sustainability in a museum context. By introducing the knowledge of food science, the deterioration mechanisms of food in art became clear and compatible treatment options were given

An exploration and validation of computer modeling of evolution, natural selection, and evolutionary biology with cellular automata for secondary students.

The Evolutionary Tool Kit, a new software package, is the prototype of a concept simulator providing an environment for students to create microworlds of populations of artificial organisms. Its function is to model processes, concepts and arguments in natural selection and evolutionary biology, using either Mendelian asexual or sexual reproduction, or counterfactual systems such as \u27paint pot\u27 or blending inheritance. In this environment students can explore a conceptual What if? in evolutionary biology, test misconceptions and deepen understanding of inheritance and changes in populations. Populations can be defined either with typological, or with populational thinking, to inquire into the role and necessity of variation in natural selection. The approach is generative not tutorial. The interface is highly graphic with twenty traits set as icons that are moved onto the \u27phenotypes\u27. Activities include investigations of evolutionary theory of aging, reproductive advantage, sexual selection and mimicry. Design of the activities incorporates Howard Gardner\u27s Theory of Multiple Intelligences. Draft of a teacher and student manual are included

Engineering derivatives from biological systems for advanced aerospace applications

The present study consisted of a literature survey, a survey of researchers, and a workshop on bionics. These tasks produced an extensive annotated bibliography of bionics research (282 citations), a directory of bionics researchers, and a workshop report on specific bionics research topics applicable to space technology. These deliverables are included as Appendix A, Appendix B, and Section 5.0, respectively. To provide organization to this highly interdisciplinary field and to serve as a guide for interested researchers, we have also prepared a taxonomy or classification of the various subelements of natural engineering systems. Finally, we have synthesized the results of the various components of this study into a discussion of the most promising opportunities for accelerated research, seeking solutions which apply engineering principles from natural systems to advanced aerospace problems. A discussion of opportunities within the areas of materials, structures, sensors, information processing, robotics, autonomous systems, life support systems, and aeronautics is given. Following the conclusions are six discipline summaries that highlight the potential benefits of research in these areas for NASA's space technology programs

CHARACTERIZING FOREST STANDS USING UNMANNED AERIAL SYSTEMS (UAS) DIGITAL PHOTOGRAMMETRY: ADVANCEMENTS AND CHALLENGES IN MONITORING LOCAL SCALE FOREST COMPOSITION, STRUCTURE, AND HEALTH

Present-day forests provide a wide variety of ecosystem services to the communities that rely on them. At the same time, these environments face routine and substantial disturbances that direct the need for site-specific, timely, and accurate monitoring/management (i.e., precision forestry). Unmanned Aerial Systems (UAS or UAV) and their associated technologies offer a promising tool for conducting such precision forestry. Now, even with only natural color, uncalibrated, UAS imagery, software workflows involving Structure from Motion (SfM) (i.e., digital photogrammetry) modelling and segmentation can be used to characterize the features of individual trees or forest communities. In this research, we tested the effectiveness of UAS-SfM for mapping local scale forest composition, structure, and health. Our first study showed that digital (automated) methods for classifying forest composition that utilized UAS imagery produced a higher overall accuracy than those involving other high-spatial-resolution imagery (7.44% - 16.04%). The second study demonstrated that natural color sensors could provide a highly efficient estimate of individual tree diameter at breast height (dbh) (± 13.15 cm) as well as forest stand basal area, tree density, and stand density. In the final study, we join a growing number of researchers examining precision applications in forest health monitoring. Here, we demonstrate that UAS, equipped with both natural color and multispectral sensors, are more capable of distinguishing forest health classes than freely available high-resolution airborne imagery. For five health classes, these UAS data produced a 14.93% higher overall accuracy in comparison to the airborne imagery. Together, these three chapters present a wholistic approach to enhancing and enriching precision forest management, which remains a critical requirement for effectively managing diverse forested landscapes

The contribution of fashion design to the development of alternative medical clothing

The present research work envisage the development of paediatric, antimicrobial and sustainable clothing for use in hospital environment. This innovative piece of cloth was devised to offer thermoregulation, physiological comfort and psychological well being for children undergoing chemotherapy. Simultaneously, it is intended to contribute for the prevention of nosocomial infections, particularly, cross-contamination with Staphylococcus aureus. To accomplish these goals we sought to create an aesthetically pleasing and appealing imaging combined with an ergonomic shape; well adapted to infants; with an easy facility of dressing / undressing; open and close; flexibility; freedom of movement; temperature and moisture management and, especially, antimicrobial protection. For this purpose we built a single knit structure (jersey) with two different raw material compositions: 100% cotton and 100% hemp, which were put to test. The aforementioned knits were submitted to an antimicrobial finishing treatment provided by three different agents: Agiene®, Bionyl® and Chitosan, each one individually applied by three different techniques: pad batch, print screen and spray/exhaustion. Antimicrobial activity was analysed by ISO 20743:2007 standard wheather major thermo physical properties were studied with alambeta (considering the dry and the wet state). The physiological behaviour was characterised by the permetest apparatus. Results have shown that the best optimized performance was attained by the sample 100% hemp with chitosan applied by pad batch. Taking into account this information and all the previous considerations, a prototype of the gown was produced

Integrated visual perception architecture for robotic clothes perception and manipulation

This thesis proposes a generic visual perception architecture for robotic clothes perception and manipulation. This proposed architecture is fully integrated with a stereo vision system and a dual-arm robot and is able to perform a number of autonomous laundering tasks. Clothes perception and manipulation is a novel research topic in robotics and has experienced rapid development in recent years. Compared to the task of perceiving and manipulating rigid objects, clothes perception and manipulation poses a greater challenge. This can be attributed to two reasons: firstly, deformable clothing requires precise (high-acuity) visual perception and dexterous manipulation; secondly, as clothing approximates a non-rigid 2-manifold in 3-space, that can adopt a quasi-infinite configuration space, the potential variability in the appearance of clothing items makes them difficult to understand, identify uniquely, and interact with by machine. From an applications perspective, and as part of EU CloPeMa project, the integrated visual perception architecture refines a pre-existing clothing manipulation pipeline by completing pre-wash clothes (category) sorting (using single-shot or interactive perception for garment categorisation and manipulation) and post-wash dual-arm flattening. To the best of the author’s knowledge, as investigated in this thesis, the autonomous clothing perception and manipulation solutions presented here were first proposed and reported by the author. All of the reported robot demonstrations in this work follow a perception-manipulation method- ology where visual and tactile feedback (in the form of surface wrinkledness captured by the high accuracy depth sensor i.e. CloPeMa stereo head or the predictive confidence modelled by Gaussian Processing) serve as the halting criteria in the flattening and sorting tasks, respectively. From scientific perspective, the proposed visual perception architecture addresses the above challenges by parsing and grouping 3D clothing configurations hierarchically from low-level curvatures, through mid-level surface shape representations (providing topological descriptions and 3D texture representations), to high-level semantic structures and statistical descriptions. A range of visual features such as Shape Index, Surface Topologies Analysis and Local Binary Patterns have been adapted within this work to parse clothing surfaces and textures and several novel features have been devised, including B-Spline Patches with Locality-Constrained Linear coding, and Topology Spatial Distance to describe and quantify generic landmarks (wrinkles and folds). The essence of this proposed architecture comprises 3D generic surface parsing and interpretation, which is critical to underpinning a number of laundering tasks and has the potential to be extended to other rigid and non-rigid object perception and manipulation tasks. The experimental results presented in this thesis demonstrate that: firstly, the proposed grasp- ing approach achieves on-average 84.7% accuracy; secondly, the proposed flattening approach is able to flatten towels, t-shirts and pants (shorts) within 9 iterations on-average; thirdly, the proposed clothes recognition pipeline can recognise clothes categories from highly wrinkled configurations and advances the state-of-the-art by 36% in terms of classification accuracy, achieving an 83.2% true-positive classification rate when discriminating between five categories of clothes; finally the Gaussian Process based interactive perception approach exhibits a substantial improvement over single-shot perception. Accordingly, this thesis has advanced the state-of-the-art of robot clothes perception and manipulation