Reconstruction over ruins : rebuilding Dresden's Frauenkirche

The debate over the reconstruction of Dresden’s Frauenkirche, the city’s landmark Protestant cathedral destroyed by aerial bombing in 1945, exemplifies the conflicts inherent in the treatment of war-related cultural heritage. Although initially preserved only by virtue of some local citizens’ determination to rebuild the church, in time the Frauenkirche ruins emerged in their own right as an arresting antiwar symbol and one of the foremost sites of war memory and commemoration in the divided Germany. This development created a certain conundrum, for if the church ever were to be rebuilt such a project could only materialise by disturbing the ruins, which supporters claimed were deserving of preservation in their unaltered state. With the advent of reunification, the kind of heritage to be preserved at the site—and the way in which it was to be conserved—came under renewed and reintensified scrutiny and debate. By tracing the shifting dynamics during a half-century of debate over how the Frauenkirche site should be conserved, this chapter examines the impact that struggles over war memory and commemoration can have on cultural heritage. It surveys the arguments for and against rebuilding the Frauenkirche before, during, and after reunification, and considers what aspirations conflicting sides had for expressing personal, national, and international memories of war, loss, and the German national past. Finally, it explores how anastylosis rebuilding principles were used to find a compromise by incorporating, somewhat controversially, parts of the existing ruins into the new church after a local citizens’ initiative successfully appealed for worldwide support to reconstruct the Frauenkirche in the wake of Germany’s reunification.

Generation of 3D sparse feature models using multiple stereo views

Augmented Reality (AR) renders virtual information onto objects in the real world. This new user interface paradigm presents a seamless blend of the virtual and real, where the convergence of the two is difficult to discern. However, errors in the registration of the real and virtual worlds are common and often destroy the AR illusion. To achieve accurate and efficient registration, the pose of real objects must be resolved in a quick and precise manner.

Nonnegative blind source separation by sparse component analysis based on determinant measure

The problem of nonnegative blind source separation (NBSS) is addressed in this paper, where both the sources and the mixing matrix are nonnegative. Because many real-world signals are sparse, we deal with NBSS by sparse component analysis. First, a determinant-based sparseness measure, named D-measure, is introduced to gauge the temporal and spatial sparseness of signals. Based on this measure, a new NBSS model is derived, and an iterative sparseness maximization (ISM) approach is proposed to solve this model. In the ISM approach, the NBSS problem can be cast into row-to-row optimizations with respect to the unmixing matrix, and then the quadratic programming (QP) technique is used to optimize each row. Furthermore, we analyze the source identifiability and the computational complexity of the proposed ISM-QP method. The new method requires relatively weak conditions on the sources and the mixing matrix, has high computational efficiency, and is easy to implement. Simulation results demonstrate the effectiveness of our method.

Non-uniform sparsity in rapid compressive sensing MRI

Magnetic Resonance Imaging (MRI) is one of the prominent medical imaging techniques. This process is time-consuming and can take several minutes to acquire one image. The aim of this research is to reduce the imaging process time of MRI. This issue is addressed by reducing the number of acquired measurements using theory of Compressive Sensing (CS). Compressive Sensing exploits sparsity in MR images. Randomly under sampled k-space generates incoherent noise which can be handled using a nonlinear image reconstruction method. In this paper, a new framework is presented based on the idea to exploit non-uniform nature of sparsity in MR images, where local sparsity constrains were used instead of traditional global constraint, to further reduce the sample set. Experimental results and comparison with CS using global constraint are demonstrated.

Kenzo Tange's forgotten master plan for the reconstruction of Skopje

After the 1963 earthquake, which is said to have destroyed seventy-five per cent of the urban fabric, Skopje, capital city of the Republic of Macedonia (then in Yugoslavia) became a centre of architectural activity. The United Nations held a limited competition for the reconstruction of Skopje, inviting four foreign firms and four Yugoslavian firms to participate. Tange's submission received sixty per cent of the first prize, co-operating with Yugoslav architects to develop the design idea. What can this project tell us about modernism re-inscribed in Japan, and the kinds of internationalism that the United Nations constructed? Japanese Metabolism, of which Tange was a pioneer, heralded Japan as a new centre for innovation in architecture; a new nationalism re-oriented the suffering after Hiroshima and Nagasaki. Tange developed and realised in Skopje the striking planning ideas he began in his Tokyo Bay proposal. This article examines Tange's master plan for Skopje. It argues that his key elements, the City Wall and the City Gate, exemplify Tange's drive for a new vision in the context of destruction, and that these remain definitive elements today even in the context of a messy transition from a communist to a capitalist society.

Sparse subspace representation for spectral document clustering

We present a novel method for document clustering using sparse representation of documents in conjunction with spectral clustering. An ℓ1-norm optimization formulation is posed to learn the sparse representation of each document, allowing us to characterize the affinity between documents by considering the overall information instead of traditional pair wise similarities. This document affinity is encoded through a graph on which spectral clustering is performed. The decomposition into multiple subspaces allows documents to be part of a sub-group that shares a smaller set of similar vocabulary, thus allowing for cleaner clusters. Extensive experimental evaluations on two real-world datasets from Reuters-21578 and 20Newsgroup corpora show that our proposed method consistently outperforms state-of-the-art algorithms. Significantly, the performance improvement over other methods is prominent for this datasets.

Object categorization via sparse representation of local features

Sparse representation has been introduced to address many recognition problems in computer vision. In this paper, we propose a new framework for object categorization based on sparse representation of local features. Unlike most of previous sparse coding based methods in object classification that only use sparse coding to extract high-level features, the proposed method incorporates sparse representation and classification into a unified framework. Therefore, it does not need a further classifier. Experimental results show that the proposed method achieved better or comparable accuracy than the well known bag-of-features representation with various classifiers.

Sparse subspace clustering via group sparse coding

We propose in this paper a novel sparse subspace clustering method that regularizes sparse subspace representation by exploiting the structural sharing between tasks and data points via group sparse coding. We derive simple, provably convergent, and computationally efficient algorithms for solving the proposed group formulations. We demonstrate the advantage of the framework on three challenging benchmark datasets ranging from medical record data to image and text clustering and show that they consistently outperforms rival methods.

Human identification from ECG signals via sparse representation of local segments

This work proposes a novel framework to extract compact and discriminative features from Electrocardiogram (ECG) signals for human identification based on sparse representation of local segments. Specifically, local segments extracted from an ECG signal are projected to a small number of basic elements in a dictionary, which is learned from training data. A final representation is extracted by performing a max pooling procedure over all the sparse coefficient vectors in the ECG signal. Unlike most of existing methods for human identification from ECG signals which require segmentation of individual heartbeats or extraction of fiducial points, the proposed method does not need to segment individual heartbeats or detect any fiducial points. The method achieves an 99.48% accuracy on a 100 subjects dataset constructed from a publicly available database, which demonstrates that both local and global structural information are well captured to characterize the ECG signals.

Learning sparse latent representation and distance metric for image retrieval

The performance of image retrieval depends critically on the semantic representation and the distance function used to estimate the similarity of two images. A good representation should integrate multiple visual and textual (e.g., tag) features and offer a step closer to the true semantics of interest (e.g., concepts). As the distance function operates on the representation, they are interdependent, and thus should be addressed at the same time. We propose a probabilistic solution to learn both the representation from multiple feature types and modalities and the distance metric from data. The learning is regularised so that the learned representation and information-theoretic metric will (i) preserve the regularities of the visual/textual spaces, (ii) enhance structured sparsity, (iii) encourage small intra-concept distances, and (iv) keep inter-concept images separated. We demonstrate the capacity of our method on the NUS-WIDE data. For the well-studied 13 animal subset, our method outperforms state-of-the-art rivals. On the subset of single-concept images, we gain 79:5% improvement over the standard nearest neighbours approach on the MAP score, and 45.7% on the NDCG.