1,721,033 research outputs found
Semi-supervised feature selection for gender classification
No full textWe apply a semi-supervised learning method to perform gender determination. The aim is to select the most discriminating feature components from the eigen-feature representation of faces. By making use of the information provided by both labeled and unlabeled data, we successfully reduce the size of the labeled data set required for gender feature selection, and improve the classification accuracy. Instead of using 2D brightness images, we use 2.5D facial needle-maps which reveal more directly facial shape information. Principal geodesic analysis (PGA), which is a generalization of principal component analysis (PCA) from data residing in a Euclidean space to data residing on a manifold, is used to obtain the eigen-feature representation of the facial needle-maps. In our experiments, we achieve 90.50% classification accuracy when 50% of the data are labeled. This performance demonstrates the effectiveness of this method for gender classification using a small labeled set, and the feasibility of gender classification using the facial shape information
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Geometric interpretations of the relation between the image of the absolute conic and sphere images
No full textA spherical object has been introduced into camera calibration for several years through utilizing the properties of an image conic, which is the projection of the occluding contour of a sphere in the perspective image. However, in literature, only an algebraic interpretation was presented for the relation between the image of the absolute conic and sphere images. In this paper, we propose two geometric interpretations of this relation and two novel camera calibration methods using sphere images are derived from these geometric interpretations.http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000241195700012&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701Computer Science, Artificial IntelligenceEngineering, Electrical & ElectronicSCI(E)EIPubMed28ARTICLE122031-20362
Few Exemplar-Based General Medical Image Segmentation via Domain-Aware Selective Adaptation
Full text linkMedical image segmentation poses challenges due to domain gaps, data modality variations, and dependency on domain knowledge or experts, especially for low- and middle-income countries (LMICs). Whereas for humans, given a few exemplars (with corresponding labels), we are able to segment different medical images even without extensive domain-specific clinical training. In addition, current SAM-based medical segmentation models use fine-grained visual prompts, such as the bounding rectangle generated from manually annotated target segmentation mask, as the bounding box (bbox) prompt during the testing phase. However, in actual clinical scenarios, no such precise prior knowledge is available. Our experimental results also reveal that previous models nearly fail to predict when given coarser bbox prompts. Considering these issues, in this paper, we introduce a domain-aware selective adaptation approach to adapt the general knowledge learned from a large model trained with natural images to the corresponding medical domains/modalities, with access to only a few (e.g. less than 5) exemplars. Our method mitigates the aforementioned limitations, providing an efficient and LMICs-friendly solution. Extensive experimental analysis showcases the effectiveness of our approach, offering potential advancements in healthcare diagnostics and clinical applications in LMICs
Moments of Superellipsoids and their Application to Range Image Registration
Full text linkAbstract— Cartesian moments are frequently used global geometrical features in computer vision for object pose estimation and recognition. In the paper we derive a closed form expression for 3D Cartesian moment of order p+q+r of a superellipsoid in its canonical coordinate system. We also show how 3D Cartesian moment of a globally deformed superellipsoid in general position and orientation can be computed as a linear combination of 3D Cartesian moments of the corresponding non-deformed superellipsoid in canonical coordinate system. Additionally, moments of objects that are compositions of superellipsoids can be computed as simple sums of moments of individual parts. To demonstrate practical application of the derived results we register pairs of range images based on moments of recovered compositions of superellipsoids. We use a standard technique to find centers of gravity and principal axes in pairs of range images while third-order moments are used to resolve the fourway ambiguity. Experimental results show expected improvement of recovered rigid transformation based on moments of recovered superellipsoids as compared to the registration based on moments of raw range image data. Beside object pose estimation the presented results can be directly used for object recognition with moments and/or moment invariants as object features
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