Local and territorial determinants in the realization of public–private–partnerships: an empirical analysis for Italian provinces

ABSTRACT Relational networks and intangible factors are crucial elements for the competitiveness of a territory. Public–Private–Partnerships (PPPs), in particular, allow for the provision of goods and services that favour the exploitation of complementarities between public and private resources. They aim at promoting an increase in the overall efficiency of investment projects through a complex mechanism that distributes risk and revenues among stakeholders. This paper examines the local and territorial determinants of PPPs through an econometric analysis based upon Italian municipal data, grouped at the provincial level. Using a tobit model, we analyse the relationship between the realization of successful PPP initiatives and different sets of factors, including less analysed local and territorial determinants. We stress the role of the local management of infrastructure assets, the administrative efficiency of local authorities and the diffusion of previous local development initiatives. Local management and territorial context factors explain most of the occurrence of successful PPP initiatives in the pre-crisis period while usual determinants (infrastructure endowment and financial distress) display a weaker effect

Detection of Duplicated Regions in Tampered Digital Images by Bit-Plane Analysis

In this paper we present a new method for searching duplicated areas in a digital image. The goal is to detect if an image has been tampered by a copy-move process. Our method works within a convenient domain. The image to be analyzed is decomposed in its bit-plane representation. Then, for each bitplane, block of bits are encoded with an ASCII code, and a sequence of strings is analyzed rather than the original bit-plane. The sequence is lexicographically sorted and similar groups of bits are extracted as candidate areas, and passed to the following plane to be processed. Output of the last planes indicates if, and where, the image has been altered

Fiscal federalism, regional public investment and spatial interaction processes: the case of Italy

The aim of this paper is to examine interregional interactions in public expenditure (for NUTS I and NUTS II level regions) using a new database on Italian Regional Public Accounts (RPA) over the period 1996-2007. Intergovernmental interactions are particularly important for assessing the impact of the reform towards fiscal federalism which is currently under way in Italy. As pointed out by Salmon (1987,2002), a more decentralized system implies that governments situated at the same level in a multi-level governmental system compete each other as well as with those located along the hierarchy. Competitive behavior is also a key element in many recent models of local government behavior (Brueckner, 1997, 2000) and is now the focus of a growing empirical literature based on strategic interaction in local policy decisions analyzed through the estimation of a reaction function (Millimet, 2002; Revelli, 2003). The paper provides empirical evidence on complementary/competitive relationships in terms of capital public expenditure using the approach originally developed by Dendrinos and Sonis (1988, 1990). This model has been applied to income variables in several papers (Hewings et al. 1996, Magalhaes et al.1999, Dall'erba et al., 2003) but the use of policy variables has not been explored yet. By investigating the occurrence of competitive versus complementary interactions in regional public expenditure, the paper suggests that the definition of a fiscal federalism scheme should take into account adequately both direct and indirect effects

Scale detection via keypoint density maps in regular or near-regular textures

In this paper we propose a new method to detect the global scale of images with regular, near regular, or homogenous textures. We define texture ‘‘scale’’ as the size of the basic elements (texels or textons) that most frequently occur into the image. We study the distribution of the interest points into the image, at different scale, by using our Keypoint Density Maps (KDMs) tool. A ‘‘mode’’ vector is built computing the most frequent values (modes) of the KDMs, at different scales. We observed that the mode vector is quasi linear with the scale. The mode vector is properly subsampled, depending on the scale of observation, and compared with a linear model. Texture scale is estimated as the one which minimizes an error function between the related subsampled vector and the linear model. Results, compared with a state of the art method, are very encouraging

Copy-Move Forgery Detection by Matching Triangles of Keypoints

Copy-move forgery is one of the most common types of tampering for digital images. Detection methods generally use block-matching approaches, which first divide the image into overlapping blocks and then extract and compare features to find similar ones, or point-based approaches, in which relevant keypoints are extracted and matched to each other to find similar areas. In this paper, we present a very novel hybrid approach, which compares triangles rather than blocks, or single points. Interest points are extracted from the image, and objects are modeled as a set of connected triangles built onto these points. Triangles are matched according to their shapes (inner angles), their content (color information), and the local feature vectors extracted onto the vertices of the triangles. Our methods are designed to be robust to geometric transformations. Results are compared with a state-of-the-art block matching method and a point-based method. Furthermore, our data set is available for use by academic researchers

Detecting Multiple Copies in Tampered Images

Copy-move forgeries are parts of the image that are duplicated elsewhere into the same image, often after being modified by geometrical transformations. In this paper we present a method to detect these image alterations, using a SIFT-based approach. First we describe a state of the art SIFT-point matching method, which inspired our algorithm, then we compare it with our SIFT-based approach, which consists of three parts: keypoint clustering, cluster matching, and texture analysis. The goal is to find copies of the same object, i.e. clusters of points, rather than points that match. Cluster matching proves to give better results than single point matching, since it returns a complete and coherent comparison between copied objects. At last, textures of matching areas are analyzed and compared to validate results and to eliminate false positives

Copy-move Forgery Detection via Texture Description

Copy-move forgery is one of the most common type of tampering in digital images. Copy-moves are parts of the image that are copied and pasted onto another part of the same image. Detection methods in general use block-matching methods, which first divide the image into overlapping blocks and then extract features from each block, assuming similar blocks will yield similar features. In this paper we present a block-based approach which exploits texture as feature to be extracted from blocks. Our goal is to study if texture is well suited for the specific application, and to compare performance of several texture descriptors. Tests have been made on both uncompressed and JPEG compressed images

Saliency Based Image Cropping

Image cropping is a technique that is used to select the most relevant areas of an image and discarding the useless parts. Handmade selection, especially in case of large photo collections, is a time consuming task. Automatic image cropping techniques may help users, suggesting to them which part of the image is the most relevant, according to specific criteria. In this paper we suppose that the most visually salient areas of a photo are also the most relevant ones to the users. We compare three different saliency detection methods within an automatic image cropping system, to study the effectiveness of the related saliency maps for this task. We furthermore extended one of the three methods (our previous work), which is based on the extraction of keypoints from the image. Tests have been conducted onto an online available dataset, made of 5000 images which have been manually labeled by 9 users