Character Shape Restoration of Binarized Historica l Documents by Smoothing via Geodesic Morphology

2013年度～2017年度科学研究費補助金基盤研究(S) 研究成果報告書（課題番号25220401

Extracting Maya Glyphs from Degraded Ancient Documents via Image Segmentation

We present a system for automatically extracting hieroglyph strokes from images of degraded ancient Maya codices. Our system adopts a region-based image segmentation framework. Multi-resolution super-pixels are first extracted to represent each image. A Support Vector Machine (SVM) classifier is used to label each super-pixel region with a probability to belong to foreground glyph strokes. Pixelwise probability maps from multiple super-pixel resolution scales are then aggregated to cope with various stroke widths and background noise. A fully connected Conditional Random Field model is then applied to improve the labeling consistency. Segmentation results show that our system preserves delicate local details of the historic Maya glyphs with various stroke widths and also reduces background noise. As an application, we conduct retrieval experiments using the extracted binary images. Experimental results show that our automatically extracted glyph strokes achieve comparable retrieval results to those obtained using glyphs manually segmented by epigraphers in our team

Taking shape: The data science of elastic shape analysis with practical applications

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London.A mathematical curve can represent many different objects, both physical and abstract, from the outline curve of an artefact in an image to the weight of growing animal to the set of frequencies used in a sound. Regardless of these variations, the curves can almost always vary non-linearly. One way to study shapes and their potential variations is elastic shape analysis, a rich theory of which has developed over the past twenty years. However, methods of elastic shape analysis are seldom utilized in practical applications on real-world data, especially outside of the mathematical shape analysis community. Our aim in this thesis is to explore some practical applications of elastic shape analysis. To do this, we work with various types of shape data, the majority of which are based on image datasets. As our focus is on two-dimensional curves, it is important to be able to robustly extract contours from images, before we can apply elastic shape analysis tools. In order to analyse the shapes in a dataset, we turn to methods of machine learning, to investigate the applications of elastic shape analysis in classification. In this thesis, we introduce an anthology of projects, in order to emphasise and under- stand the potential of elastic shape analysis in practical applications. There are four main projects in this thesis: (i) Classification of objects using outlines and the comparisons between methods of elastic shape analysis, geometric morphometrics, and human experts, with a focus on ancient Greek vases, (ii) Mussel species identification and a demonstra- tion that shape may not be enough in some applications, (iii) A novel tool to monitor the development of k ̄ak ̄ap ̄o chicks, and (iv) Classifying individual kiwi based on acoustic data from their calls. By combining tools from computer vision and machine learning with methods of elastic shape analysis, we introduce a practical framework for the application of elastic shape analysis, through a data science lens

15 Years and Challenges of Wooden Tablets Image Database

2013年度～2017年度科学研究費補助金基盤研究(S) 研究成果報告書（課題番号25220401

Exploiting Spatio-Temporal Coherence for Video Object Detection in Robotics

This paper proposes a method to enhance video object detection for indoor environments in robotics. Concretely, it exploits knowledge about the camera motion between frames to propagate previously detected objects to successive frames. The proposal is rooted in the concepts of planar homography to propose regions of interest where to find objects, and recursive Bayesian filtering to integrate observations over time. The proposal is evaluated on six virtual, indoor environments, accounting for the detection of nine object classes over a total of ∼ 7k frames. Results show that our proposal improves the recall and the F1-score by a factor of 1.41 and 1.27, respectively, as well as it achieves a significant reduction of the object categorization entropy (58.8%) when compared to a two-stage video object detection method used as baseline, at the cost of small time overheads (120 ms) and precision loss (0.92).</p

Mathematical Model of Interstitial Fluid Flow and Characterization of the Lacunar Canalicular System in Cortical Bone

While the mechanotransductive capability of skeletal tissue has been acknowledged for decades, the exact mechanisms that enable bone to sense and respond to external stimuli have remained elusive. Numerous theories have evolved to explain this behavior, most notably those involving fluid movement through the tissue’s hierarchical structure. Within mineralized bone, osteocytes reside in micro and nanoporosities, known as lacunae and canaliculi, which house the cell body and their long cellular processes, respectively. Through this lacunar-canalicular system (LCS), osteocytes form an interconnected network, which allow signaling and communication with surrounding osteocytes via gap junctions and secreted factors. It has been theorized that external loading-induced interstitial fluid movement along the cell processes results in shear stresses and/or drag forces that elicit stimulatory responses from osteocytes. While length and mineralized tissue render direct measurements inaccessible, mathematical and computational modeling have been utilized to predict these potential stimulatory mechanisms. However, assumptions regarding the presence of a glycocalyx, which is a pericellular matrix within the interstitial fluid space, are typically made despite the inability to fully characterize its structure. Thus, to investigate the importance of the possible compositions of this glycocalyx, a mathematical model of interstitial fluid flow within a canaliculus was developed, utilizing mixture theory. Resulting sensitivity analyses show that assumptions regarding the glycocalyx greatly influence the profile within the LCS, therefore affecting potential mechanotransductive signals. Additionally, confocal microscopy and a custom, automated reconstruction algorithm, were used to generate three-dimensional renderings of confocal images to further characterize the LCS and improve computational models. Both the mathematical model and reconstruction of the LCS will enhance the development of accurate predictive models and increase understanding of bone’s mechanotransductive abilities

Gaze-Based Human-Robot Interaction by the Brunswick Model

We present a new paradigm for human-robot interaction based on social signal processing, and in particular on the Brunswick model. Originally, the Brunswick model copes with face-to-face dyadic interaction, assuming that the interactants are communicating through a continuous exchange of non verbal social signals, in addition to the spoken messages. Social signals have to be interpreted, thanks to a proper recognition phase that considers visual and audio information. The Brunswick model allows to quantitatively evaluate the quality of the interaction using statistical tools which measure how effective is the recognition phase. In this paper we cast this theory when one of the interactants is a robot; in this case, the recognition phase performed by the robot and the human have to be revised w.r.t. the original model. The model is applied to Berrick, a recent open-source low-cost robotic head platform, where the gazing is the social signal to be considered