Spatial justice: law and the geography of withdrawal

While spatial justice could be the most radical offspring of law’s recent spatial turn, it remains instead a geographically informed version of social justice. The majority of the existing literature on the subject has made some politically facile assumptions about space, justice and law, thereby subsuming the potentially radical into the banal. In this article, I suggest that the concept of spatial justice is the most promising platform on which to redefine, not only the connection between law and geography, but more importantly, the conceptual foundations of both law and space. More concretely, the article attempts two things: first, a radical understanding of legal spatiality. Space is not just another parameter for law, a background against which law takes place, or a process that the law needs to take into consideration. Space is intertwined with normative production in ways that law often fails to acknowledge, and part of this article is a re-articulation of the connection. Second, to suggest a conception of spatial justice that derives from a spatial law. Such a conception cannot rely on given concepts of distributive or social justice. Instead, the concept of spatial justice put forth here is informed by post-structural, feminist, post-ecological and other radical understandings of emplacement and justice, as well as arguably the most spatial of philosophical discourses, that of Deleuze–Guattari and the prescribed possibilities of space as manifold.

3D Reconstruction of Medical Images from Slices Automatically Landmarked with Growing Neural Models

In this study, we utilise a novel approach to segment out the ventricular system in a series of high resolution T1-weighted MR images. We present a brain ventricles fast reconstruction method. The method is based on the processing of brain sections and establishing a fixed number of landmarks onto those sections to reconstruct the ventricles 3D surface. Automated landmark extraction is accomplished through the use of the self-organising network, the growing neural gas (GNG), which is able to topographically map the low dimensionality of the network to the high dimensionality of the contour manifold without requiring a priori knowledge of the input space structure. Moreover, our GNG landmark method is tolerant to noise and eliminates outliers. Our method accelerates the classical surface reconstruction and filtering processes. The proposed method offers higher accuracy compared to methods with similar efficiency as Voxel Grid.

Image, video and 3D data registration: medical, satellite and video processing applications with quality metrics

Data registration refers to a series of techniques for matching or bringing similar objects or datasets together into alignment. These techniques enjoy widespread use in a diverse variety of applications, such as video coding, tracking, object and face detection and recognition, surveillance and satellite imaging, medical image analysis and structure from motion. Registration methods are as numerous as their manifold uses, from pixel level and block or feature based methods to Fourier domain methods. This book is focused on providing algorithms and image and video techniques for registration and quality performance metrics. The authors provide various assessment metrics for measuring registration quality alongside analyses of registration techniques, introducing and explaining both familiar and state–of–the–art registration methodologies used in a variety of targeted applications.