Understanding as an interpretation in a shape understanding system

A method of extracting, classifying and modelling non-rigid shapes froman image sequence ispresented. Shapes areapproximated by polygonswherethe number of sides is related to the physical features of a shape class ratherthan any particular shape. A method of ‘seeding’ the polygonal approxima-tionisgivenwhere‘seeds’ areautomaticallyextracted fromasetofdata. Mul-tiple models are built using polygons with different numbers of sides to al-low for feature occlusion. Principal component analysis (PCA) is performedon vector representations of the sides of the polygons which are normalisedby the total perimeter. This removes the need for normalisation of scale andtranslationas required in thePointDistributionModel [16]. A ‘fit score’ met-ricisdefinedwhich givesan indicationof howwellagivenshapefitsamodel. 1 Introduction There has been much work carried out on the extraction and classification of non-rigidshapes from image sequences. In this paper a new method of classifying images automat-ically from an image sequence is proposed. This method is applied in the monitoring ofcattle movement to identifya particularnon-rigidobject in a scene (a cow in ourexample)and gain knowledge about the way in which this object changes with time.Kass et al [11] use an active shape model (snake) in which an energy function is min-imisedtoextractanobjectfromascene. TerzopoulosandSzeliski[15]havemadesomere-finementstothistechniquewiththeinclusionofKalmanfiltertechniques(Kalmansnakes).Blake et al [3] have further refined the technique by adding ‘templates’to this process toincorporate a search for a specific shape. In recent years theadvantage of such model-based approaches has been shown where prior informationabout a class of shapes is usedin extraction and classification. The point distributionmodel (PDM) described by Cooteset al [16] is a useful way of describing a class of non-rigid shapeswhere a shape class isdescribed by a fixed number of points. A model is built from a group of shapes fitted withthisfixed number of ‘reference points’ and principalcomponent analysis (PCA) is used toextract ‘modesof variation’fromthedataset. Members ofashapeclass aredescribed withreference to an eigenspace where the axes are the eigenvectors produced from the princi-pal componentanalysis. A ‘valid’shape isconstructedby takingan area inthiseigenspacecentred around the mean shape (origin) and bounded by maximum deviations along eachprincipalaxes whichare related totheeigenvaluesproduced by PCA and thusthevariancewithinthe data set. PDMs rely on accurate positioningof pointsand can only describe ac-curately shape classes which deform in a linear fashion which can result in invalid shapesbeing included in the model.There havebeen a numberof methodsproposedforpointfittingaroundapre-extractedcontour [1, 2] which involve finding a number of reference points (points of high curva-ture,extreme pointsetc) and addingextrapointsspaced evenlyroundthecontoursuch thatthe contour may be approximated by a spline. Hill et al [8] have proposed a new schemewhereby a contour is approximated by an arbitrary number of straight lines as defined bythe critical point detection algorithm described by Zhu and Chirlian [19]. In the compari-sonoftwoshapestheaveragenumberofpointsrequiredtorepresent theshapes isusedand

The research presented in this paper refers to classification of geometric shapes (cubes, pyramids and cylinders) using multilayer neural network. The input data of the algorithm are the images of shapes placed in different positions and distances from the camera. The classification is based on feature vectors that are obtained using methods of digital image processing. Feature vectors are inputs of neural network. Supervised training of neural network is performed. Reduction algorithm was used in aim of dimension reduction of feature vectors, so the classification results can be displayed graphically. Recognition and classification of geometric shapes may be of interest for realization of many robotic tasks, especially those related to catching of objects with robotic arm or movement of a robot with a set of obstacles.

In this paper we propose a new framework for modeling 2D shapes. A shape is first described by a sequence of local features (e.g., curvature) of the shape boundary. The resulting description is then used to build a profile hidden Markov model (PHMM) representation of the shape. PHMMs are a particular type of hidden Markov models (HMMs) with special states and architecture that can tolerate considerable shape contour perturbations, including rigid and non-rigid deformations, occlusions and missing contour parts. Different from traditional HMM-based shape models, the sparseness of the PHMM structure allows efficient inference and learning algorithms for shape modeling and analysis. The new framework can be applied to a wide range of problems, from shape matching and classification to shape segmentation. Our experimental results show the effectiveness and robustness of this new approach in the three application domains.

When ontology description data by different authors would become widespread in the world, we will be faced with the difficulties of the ontology alignment (OA) problem required for integration and interoperability of ontologies. The OA problem is the problem to find couples of semantically same classes / properties between two ontologies, and includes points of different naming of classes / properties, polysemous naming of classes / properties, different granularity of classes / properties, different hierarchical structures, and so on. We applied our semantic category matching (SCM) tool to the ontology alignment problems. Our approach found pairs of semantically corresponding categories from two different classification hierarchies such as Yahoo directory or library classification as UDC or NDC, based on natural language processing, similarity searching of huge vector spaces, and structural consistency analysis. We tackled problems of the EON2004 Ontology Alignment Contest. For examples, the Contest's random name problems (#201, #202) could not be solved using conventional character string resemblance techniques. However, when we applied SCM to these problems, the results showed that SCM had improved the accuracy as compared to the conventional method (F-measure: 0.021=>0.949, 0.021=>0.580), and exceeded the accuracy average in all problem areas by over 10 % as compared to conventional methods. Our team participated as a competitor in EON OA Contest and could obtain satisfactory results.

Las investigaciones contemporáneas en psicología y neurociencia destacan la importancia de las emociones en la persecución de nuestros objetivos, la calidad de la deliberación y de nuestras relaciones sociales. La filosofía y la ciencia ya no ven a la razón como enfrentada a las emociones; su cooperación logra que nuestra racionalidad práctica se potencie. De ahí el interés en la búsqueda de herramientas que permitan educar nuestras emociones. A partir de investigaciones psicológicas, neurológicas y filosóficas sobre las emociones, este artículo postula, desde la filosofía de la educación, ciertos elementos cruciales para permitir la implementación de una educación emocional, entre ellos la autorreflexión, las narraciones y la metodología educativa de la comunidad de indagación, que incluye los dos elementos anteriores.

Philosophers of mind and epistemologists are increasingly making room in their theories for epistemic emotions (E-emotions) and, drawing on metacognition research in psychology, epistemic – or noetic or metacognitive – feelings (E-feelings). Since philoso- phers have only recently begun to draw on empirical research on E-feelings, in particular, we begin by providing a general characterization of E-feelings (section 1) and reviewing some highlights of relevant research (section 2). We then turn to philosophical work on E-feelings and E-emotions, situating the contributions to the focus section (two articles devoted to E-feelings and two devoted to E-emotions) with respect to both the existing literature and each other (section 3). We conclude by briefly describing some promising avenues for further philosophical research on E-feelings and E-emotions (section 4).

In recent years, a considerable amount of work has been developed concerning a partial differential equations-based approach to image processing. This work has been focussed on the interplay between geometric motion and non-linear evolution equations, and its applicability to problems in image enhancement and denoising, image segmentation, and shape recognition and classification. In this review article, we provide an overview for some of these ideas.

We introduce a novel neural network architecture for encoding and synthesis of 3D shapes, particularly their structures. Our key insight is that 3D shapes are effectively characterized by their hierarchical organization of parts, which reflects fundamental intra-shape relationships such as adjacency and symmetry. We develop a recursive neural net (RvNN) based autoencoder to map a flat, unlabeled, arbitrary part layout to a compact code. The code effectively captures hierarchical structures of man-made 3D objects of varying structural complexities despite being fixed-dimensional: an associated decoder maps a code back to a full hierarchy. The learned bidirectional mapping is further tuned using an adversarial setup to yield a generative model of plausible structures, from which novel structures can be sampled. Finally, our structure synthesis framework is augmented by a second trained module that produces fine-grained part geometry, conditioned on global and local structural context, leading to a full generative pipeline for 3D shapes. We demonstrate that without supervision, our network learns meaningful structural hierarchies adhering to perceptual grouping principles, produces compact codes which enable applications such as shape classification and partial matching, and supports shape synthesis and interpolation with significant variations in topology and geometry.

This article discusses the potential implications of panpsychism for the study and pursuit of spiritual flourishing, with a focus on emerging scientific and philosophical research on psychedelics. Psychedelics research (1) has the means to reliably and safely produce the conditions in which transformative experiences routinely occur, thereby allowing for robust neuroscientific and psychological research, and (2) has attracted a growing body of philosophical and theological work on the metaphysical and epistemological possibilities of such experiences. I begin with a discussion of recent scientific work on psychedelics. I then discuss the epistemic status of psychedelic experiences, where the metaphysics of panpsychism is particularly interesting. I suggest there exists a mutually reinforcing relationship between panpsychism and the metaphysical possibility of a veridical interpretation of psychedelic states, and that this conceptual congruence has important implications for research on spiritual flourishing. This flourishing need not be understood in a theological manner, although it is, I suggest, entirely consistent with at least some naturalistic theological frameworks: the main goal of the article is to map the conceptual terrain in which conversations about spiritual flourishing, psychedelics, and panpsychism might take place.

Heart disease is the leading cause of death in the modern world. Cardiac image processing is now routinely applied for detecting, classification and diagnosis of heart diseases. One of the most common uses of processing methods that are now widely applied are cardiac segmentation [1] and registration methods [2], that are used in order to extract the detailed anatomy and function of the heart. Automatic segmentation plays a central role when inspecting reconstructed 3D cardiac images from CT or MR scanners. An accurate classification of the different cardiac regions is usually the first step of tasks like: cardiac visualization, coronary artery inspection, measurement of the ejection fraction for the left and right ventricles and wall motion analysis. In a clinical context, physicians often mentally integrate image information from different modalities. Automatic registration, based on computer programs, might, however, offer better accuracy and repeatability and save time [3]. Cardiac image registration remains a challenge because of the numerous specific problems mainly related to the different motion sources (patient, respiration, heart) and to the specificity of each imaging modality. Up to now, no general method could automatically register any modality with any other modality so far. This paper presents a survey of shape modeling applications to cardiac image analysis from MRI, CT, echocardiography, PET, and SPECT and aims to introduce new methodologies in this field, classify major contributions in image-based cardiac modeling, provide a tutorial to beginners to initiate their own studies, and introduce the major challenges of registration and segmentation and provide practical examples. Image-driven processing methods, such as thresholding, region-based or edge-based techniques, or else pixel classification [4-6], provide a limited framework for further medical image analysis. They can include geometrical information, as well as high level information, in the so-called shape prior based segmentation framework, or through active shape models and active appearance models. At last, atlas guided segmentation also make use of a set of manually segmented images. By using different analysis software of cardiac images, CAD prototypes can be used in clinical routines in order to provide a computer output as a second reader to assist physicians in the detection of abnormalities, quantification of disease progression and differential diagnosis of lesions [7]. Computerized analysis of cardiac images in combination with artificial intelligence can be used in clinical practice and may contribute to more efficient diagnosis.

ABSTRACTThis study examines whether the semantic meaning of Library of Congress Classification (LCC) class names provides additional insight beyond the system's hierarchical structure for organizing knowledge. Using SBERT, a natural language processing (NLP) model for generating semantic embeddings, we investigate the relationship between the semantic meaning of LCC subclass names and the word usage patterns of the texts assigned to these subclasses. Our results show that although LCC subclass names with similar semantic meanings occasionally correspond to similar word usage patterns, there is no consistent relationship between the two. In contrast, whether two subclasses belong to the same main class reliably predicts the similarity of word usage in the texts assigned to them. While semantic embeddings of LCC subclass names offer intriguing possibilities, our findings indicate that the hierarchical structure of the LCC system remains more robust for knowledge organization.

In this dissertation, the application of smart polymers as actuators was investigated, with focuses on shape memory polymers and twisted-then-coiled artificial muscles. Thermomechanical models have been developed for various polymeric actuators, so as to facilitate interpretation of the underlying mechanisms and to provide guidance for future design. The classical one-way shape memory effect in amorphous shape memory polymers was first reproduced. The amorphous shape memory polymer was treated as a frozen-phase matrix with active-phase inclusions embedded in it. A phase evolution law was proposed from the physics perspective and the Mori-Tanaka approach was used to predict the effective mechanical properties. Then, a phenomenological constitutive model was developed based on the multiple natural configurations framework for the semi-crystalline two-way shape memory effect. The model elucidated how the programming procedure affect the crystallization behavior and eventually determine the two-way shape memory effect via storage of internal stress. Artificial muscles with hierarchical chiral structure that can offer a hundredfold increase in power over natural muscles of equivalent lengths have recently been demonstrated experimentally. To investigate the physical origin behind the remarkable tensile actuation behavior and, therefore, the correlation between the actuation performance and the intrinsic material parameters, a multi-scale modeling framework from macro-scale helical spring structure top-down to the molecular chain interaction has been developed Then, based on the prediction results of the multi-scale model, a new type of hierarchical chiral structured artificial muscle was fabricated using two-way shape memory polymer fiber. The usual improvement in the axial actuation of the twisted-then-coiled muscles were demonstrated both experimentally and theoretically.

Graph Clustering and Model Learning by Data Compression

This article reviews the current and emerging status of qualitative research in psychology. The particular value of diverse philosophical paradigms and varied inquiry approaches to the advancement of psychology generally, and multicultural psychology specifically, is emphasized. Three specific qualitative inquiry approaches anchored in diverse philosophical research paradigms are highlighted: consensual qualitative research, grounded theory, and participatory action research. The article concludes by highlighting important ethical considerations in multicultural qualitative research.

Restorative justice and mutual involvement Christopher Marshalls is convinced that restorative justice has to do with care for relational involvement. That view is intuitively convincing but not particularly clear. Recent psychological and philosophical research has recently devoted much attention to notions such as ‘collective intentionality’ (Searle, Tomasello) and ‘joint commitment’ (Gilbert). This kind of research is situated and discussed in this paper. We indicate how the restoration of mutual involvement can be reinterpreted from the perspective of this research. This exercise shows that caring for relationships should not be interpreted in a sentimental way. In law and in restorative justice, it does not have to be the intention to enter into more personal relationships with each other. Rather, it is the intention to make it possible once again to develop mutual understanding, respect and compassion from our common universal human commitment.