An den Herrn Conrad Arnold Schmid /

Signatures: A-C⁸ (C8 v. blank).

Arnold Boecklin; zwei Aufsaetze.

"Zuerst erschienen in der Kunstzeitschrift 'Pan', 1897 III, 2 und 1898 IV, 1."

Jean Baptiste Francois Pompallier, Thomas Arnold and Julian Edmund Tenison Woods and their contribution to the formation and development of Catholic schooling in Australasia

Histories of Catholic education have received little attention by Church historians and are usually written by members of the Catholic clergy, with a strong emphasis placed on the spiritual and building accomplishments of the bishops. This thesis examines the provision of Catholic Education in Australasia, with a focus on the contribution of three men, Jean Baptiste Francois Pompallier, Thomas Arnold and Julian Edmund Tenison Woods. These men received support from the female religious orders in the regions where they worked, frequently with little recognition or praise by Catholic Church authorities. The tenets of their faith gave Pompallier and Woods strength and reinforced their determination to succeed. Arnold, however, possessed a strong desire to change society. All three believed in the desirability of providing Catholic schooling for the poor, with the curriculum facilitating the acquisition of socially desirable values and traits, including obedience, honesty, moral respectability and a strong adherence to Catholic religious values. The beneficiaries included society, future employers, the Church, the children and their parents. With the exception of promoting distinctly Catholic religious values, Roman Catholic schools and National schools in Australasia shared identical objectives. Historians have neglected the contributions of these men.

Random projections on manifolds of symmetric positive definite matrices for image classification

Recent advances suggest that encoding images through Symmetric Positive Definite (SPD) matrices and then interpreting such matrices as points on Riemannian manifolds can lead to increased classification performance. Taking into account manifold geometry is typically done via (1) embedding the manifolds in tangent spaces, or (2) embedding into Reproducing Kernel Hilbert Spaces (RKHS). While embedding into tangent spaces allows the use of existing Euclidean-based learning algorithms, manifold shape is only approximated which can cause loss of discriminatory information. The RKHS approach retains more of the manifold structure, but may require non-trivial effort to kernelise Euclidean-based learning algorithms. In contrast to the above approaches, in this paper we offer a novel solution that allows SPD matrices to be used with unmodified Euclidean-based learning algorithms, with the true manifold shape well-preserved. Specifically, we propose to project SPD matrices using a set of random projection hyperplanes over RKHS into a random projection space, which leads to representing each matrix as a vector of projection coefficients. Experiments on face recognition, person re-identification and texture classification show that the proposed approach outperforms several recent methods, such as Tensor Sparse Coding, Histogram Plus Epitome, Riemannian Locality Preserving Projection and Relational Divergence Classification.

Matching image sets via adaptive multi convex hull

Traditional nearest points methods use all the samples in an image set to construct a single convex or affine hull model for classification. However, strong artificial features and noisy data may be generated from combinations of training samples when significant intra-class variations and/or noise occur in the image set. Existing multi-model approaches extract local models by clustering each image set individually only once, with fixed clusters used for matching with various image sets. This may not be optimal for discrimination, as undesirable environmental conditions (eg. illumination and pose variations) may result in the two closest clusters representing different characteristics of an object (eg. frontal face being compared to non-frontal face). To address the above problem, we propose a novel approach to enhance nearest points based methods by integrating affine/convex hull classification with an adapted multi-model approach. We first extract multiple local convex hulls from a query image set via maximum margin clustering to diminish the artificial variations and constrain the noise in local convex hulls. We then propose adaptive reference clustering (ARC) to constrain the clustering of each gallery image set by forcing the clusters to have resemblance to the clusters in the query image set. By applying ARC, noisy clusters in the query set can be discarded. Experiments on Honda, MoBo and ETH-80 datasets show that the proposed method outperforms single model approaches and other recent techniques, such as Sparse Approximated Nearest Points, Mutual Subspace Method and Manifold Discriminant Analysis.

Automatic classification of human epithelial type 2 cell indirect immunofluorescence images using cell pyramid matching

This paper describes a novel system for automatic classification of images obtained from Anti-Nuclear Antibody (ANA) pathology tests on Human Epithelial type 2 (HEp-2) cells using the Indirect Immunofluorescence (IIF) protocol. The IIF protocol on HEp-2 cells has been the hallmark method to identify the presence of ANAs, due to its high sensitivity and the large range of antigens that can be detected. However, it suffers from numerous shortcomings, such as being subjective as well as time and labour intensive. Computer Aided Diagnostic (CAD) systems have been developed to address these problems, which automatically classify a HEp-2 cell image into one of its known patterns (eg. speckled, homogeneous). Most of the existing CAD systems use handpicked features to represent a HEp-2 cell image, which may only work in limited scenarios. We propose a novel automatic cell image classification method termed Cell Pyramid Matching (CPM), which is comprised of regional histograms of visual words coupled with the Multiple Kernel Learning framework. We present a study of several variations of generating histograms and show the efficacy of the system on two publicly available datasets: the ICPR HEp-2 cell classification contest dataset and the SNPHEp-2 dataset.

Portrait of Yael Arnold Portraits Women

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Portrait of Rolf Arnold Portraits Men

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Sculptures by Ellen Bernkopf nee Catzenstein in the home of her daughter Yael Arnold; Waldstetten.

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Sculpture by Ellen Bernkopf nee Catzenstein in the home of her daughter Yael Arnold; Waldstetten Artworks

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Interior of the Konservenfabrik E. Arnold und Co. (Arnold and Co. canned goods factory); Fuerth, Bavaria Business Establishments; Factories

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Photographic reproduction of charcoal portrait of Arnold Gottlieb Portraits Men

Signed by artist bottom right