Consider a finite sequence of linear contractions ${{S}_{j}}(x)\,=\,\rho x\,+{{d}_{j}}$ and probabilities $pj\,>\,0$ with $\Sigma \,{{p}_{j}}\,=\,1$ . We are interested in the self-similar measure $\mu \,=\,\Sigma \,{{p}_{j}}\,\mu \,{}^\circ \,s_{j}^{-1}$ , of finite type. In this paper we study the multi-fractal analysis of such measures, extending the theory to measures arising from non-regular probabilities and whose support is not necessarily an interval.

Under some mild technical assumptions, we prove that there exists a subset of supp $\mu $ of full $\mu $ and Hausdorff measure, called the truly essential class, for which the set of (upper or lower) local dimensions is a closed interval. Within the truly essential class we show that there exists a point with local dimension exactly equal to the dimension of the support. We give an example where the set of local dimensions is a two element set, with all the elements of the truly essential class giving the same local dimension. We give general criteria for these measures to be absolutely continuous with respect to the associated Hausdorff measure of their support, and we show that the dimension of the support can be computed using only information about the essential class.

To conclude, we present a detailed study of three examples. First, we show that the set of local dimensions of the biased Bernoulli convolution with contraction ratio the inverse of a simple Pisot number always admits an isolated point. We give a precise description of the essential class of a generalized Cantor set of finite type, and show that the $k$ -th convolution of the associated Cantor measure has local dimension at $x\,\in \,(0,\,1)$ tending to 1 as $k$ tends to infinity. Lastly, we show that within a maximal loop class that is not truly essential, the set of upper local dimensions need not be an interval. This is in contrast to the case for finite type measures with regular probabilities and full interval support.

This article reviews and reflects on the use of the geographic information system (GIS) as a tool, or geographic information science (GIScience) as a research methodology, and associated techniques of analysis in an empirical study-in-progress on the law and history of early twentieth century British Hong Kong. The article begins by introducing the study and its objectives, as well as the rationale for adopting GIS/GIScience as one of its research methodologies. It then highlights the preliminary findings of the current project and compares them with those of earlier research on the legal history of early twentieth century Beijing using GIS. The article also discusses the difficulties involved in adopting such a digital tool and methodology in historical research. It concludes by reflecting on what GIS can help scholars understand about the social history of law in Hong Kong, beyond what is already known, and how specialists in law, history, and geography can collaborate in a digital law and history project involving the use of GIS. This article also gives an overview of the use of GIS in conducting empirical research in the humanities (including but not limited to history and legal history research) and points to digital sources and web sites useful to researchers who may need tools and data to launch a GIS study in law and history.

In this paper, we study to use nonlocal bounded variation (NLBV) techniques to decompose an image intensity into the illumination and reflectance components. By considering spatial smoothness of the illumination component and nonlocal total variation (NLTV) of the reflectance component in the decomposition framework, an energy functional is constructed. We establish the theoretical results of the space of NLBV functions such as lower semicontinuity, approximation and compactness. These essential properties of NLBV functions are important tools to show the existence of solution of the proposed energy functional. Experimental results on both grey-level and color images are shown to illustrate the usefulness of the nonlocal total variation image decomposition model, and demonstrate the performance of the proposed method is better than the other testing methods.

We present a multi-phase image segmentation method based on the histogram of the Gabor feature space, which consists of a set of Gabor-filter responses with various orientations, scales and frequencies. Our model replaces the error function term in the original fuzzy region competition model with squared 2-Wasserstein distance function, which is a metric to measure the distance of two histograms. The energy functional is minimized by alternative minimization method and the existence of closed-form solutions is guaranteed when the exponent of the fuzzy membership term being 1 or 2. We test our model on both simple synthetic texture images and complex natural images with two or more phases. Experimental results are shown and compared to other recent results.