Camera distortion self-calibration using the plumb-line constraint and minimal Hough entropy

In this paper we present a simple and robust method for self-correction of camera distortion using single images of scenes which contain straight lines. Since the most common distortion can be modelled as radial distortion, we illustrate the method using the Harris radial distortion model, but the method is applicable to any distortion model. The method is based on transforming the edgels of the distorted image to a 1-D angular Hough space, and optimizing the distortion correction parameters which minimize the entropy of the corresponding normalized histogram. Properly corrected imagery will have fewer curved lines, and therefore less spread in Hough space. Since the method does not rely on any image structure beyond the existence of edgels sharing some common orientations and does not use edge fitting, it is applicable to a wide variety of image types. For instance, it can be applied equally well to images of texture with weak but dominant orientations, or images with strong vanishing points. Finally, the method is performed on both synthetic and real data revealing that it is particularly robust to noise.Comment: 9 pages, 5 figures Corrected errors in equation 1

South African surgical registrar perceptions of the research project component of training: Hope for the future?

Background. The Health Professions Council of South Africa requires that a research project be submitted and passed before registration as a specialist.Objective. To describe surgical registrars’ perceptions of the compulsory research project.Method. Ethics clearance was received before commencing the study. A questionnaire was developed to collect feedback from surgical registrars throughout South Africa (SA). Completed questionnaires underwent descriptive analysis using MS Excel. Fisher’s exact test and the χ2 test were used to compare perceptions of the research-experienced and research-naive groups.Results. All medical schools in SA were sampled, and 51.5% (124/241) of surgical registrars completed the questionnaire. Challenges facing registrars included insufficient time (109/124), inadequate training in the research process (40/124), inadequate supervision (31/124), inadequate financial resources (25/124) and lack of research continuity (11/124). Of the registrars sampled, 67.7% (84/124) believed research to be a valuable component of training. An overwhelming percentage (93.5%, 116/124) proposed a dedicated research block of time as a potential solution to overcoming the challenges encountered. Further proposals included attending a course in research methodology (79/124), supervision by a faculty member with an MMed or higher postgraduate degree (73/124), and greater research exposure as an undergraduate (56/124). No statistically significant differences were found between the perceptions of the researchexperienced and research-naive groups.Conclusions. Challenges facing surgical registrars in their efforts to complete their research projects were identified and solutions to these problems proposed. It is heartening that respondents have suggested solutions to the problems they encounter, and view research as an important component of their careers

On Performance Discrepancies Across Local Homophily Levels in Graph Neural Networks

Research on GNNs has highlighted a relationship between high homophily (i.e., the tendency for nodes of a similar class to connect) and strong predictive performance in node classification. However, recent research has found the relationship to be more nuanced, demonstrating that even simple GNNs can learn in certain heterophilous settings. To bridge the gap between these findings, we revisit the assumptions made in previous works and identify that datasets are often treated as having a constant homophily level across nodes. To align closer to real-world datasets, we theoretically and empirically study the performance of GNNs when the local homophily level of a node deviates at test-time from the global homophily level of its graph. To aid our theoretical analysis, we introduce a new parameter to the preferential attachment model commonly used in homophily analysis to enable the control of local homophily levels in generated graphs, enabling a systematic empirical study on how local homophily can impact performance. We additionally perform a granular analysis on a number of real-world datasets with varying global homophily levels. Across our theoretical and empirical results, we find that (a)~ GNNs can fail to generalize to test nodes that deviate from the global homophily of a graph, (b)~ high local homophily does not necessarily confer high performance for a node, and (c)~ GNN models designed to handle heterophily are able to perform better across varying heterophily ranges irrespective of the dataset's global homophily. These findings point towards a GNN's over-reliance on the global homophily used for training and motivates the need to design GNNs that can better generalize across large local homophily ranges