ART Neural Networks for Remote Sensing Image Analysis

ART and ARTMAP neural networks for adaptive recognition and prediction have been applied to a variety of problems, including automatic mapping from remote sensing satellite measurements, parts design retrieval at the Boeing Company, medical database prediction, and robot vision. This paper features a self-contained introduction to ART and ARTMAP dynamics. An application of these networks to image processing is illustrated by means of a remote sensing example. The basic ART and ARTMAP networks feature winner-take-all (WTA) competitive coding, which groups inputs into discrete recognition categories. WTA coding in these networks enables fast learning, which allows the network to encode important rare cases but which may lead to inefficient category proliferation with noisy training inputs. This problem is partially solved by ART-EMAP, which use WTA coding for learning but distributed category representations for test-set prediction. Recently developed ART models (dART and dARTMAP) retain stable coding, recognition, and prediction, but allow arbitrarily distributed category representation during learning as well as performance

Problems of Soft Budget Constraints in Intergovernmental Relationships: The Case of Italy

Problems of `soft budget` constraints in intergovernmental relationships are currently at the frontier of research in local public economics. This paper reviews the Italian experience in the field, starting from the mid-1970s up to the present period, compares it with that of other countries, and uses it to comment upon the state of the literature. The paper argues that the soft budget constraint problem has been a rampant one in Italian local public finance, generating efficiency losses, lack of political accountability and undermining the soundness of public finances. The paper inquires into the causes and possible solutions to the problem, and in particular describes and comments upon the decentralization process of the 1990s. Finally, the Italian debate on fiscal federalism of the 1990s is also reviewed, arguing that some of the suggestions of this debate may be of interest more generally.

Multimedia documents description by ordered hierarchies: the ToCAIdescription scheme

The authors present the ToCAI (Table of Content Analytical Index) framework, a description scheme (DS) for content description of audio-visual (AV) documents. The idea for such a description scheme comes from the structures used for indexing technical books (table of content and analytical index). This description scheme provides therefore a hierarchical description of the time sequential structure of a multimedia document (ToC), suitable for browsing, together with an “Analytical Index” (AI) of the key items of the document, suitable for retrieval. The AI allows one to represent in a ordered way the items of the AV document which are most relevant from the semantic point of view. The ordering criteria are therefore selected according to the application context. The detailed structure of the DS is presented by means of UML notation and an application example is also shown

Describing multimedia documents in natural and semantic-driven ordered hierarchies

In this work we present the ToCAI (Table of Content-Analytical Index) framework, a description scheme (DS) for content description of audio-visual (AV) documents. The idea for such a description scheme comes out from the structures used for indexing technical books (table of content and analytical index). This description scheme provides therefore a hierarchical description of the time sequential structure of a multimedia document (ToC), suitable for browsing, together with an analytical index (AI) of the key items of the document, suitable for retrieval. The AI allows to represent in an ordered way the items of the AV document which are most relevant from the semantic point of view. The ordering criteria are therefore selected according to the application context. The detailed structure of the DS is presented by means of UML notation as well and an application example is shown

A Computer Vision-Based Quality Assessment Technique for R2R Printed Silver Conductors on Flexible Plastic Substrates

The demand for flexible large-area optoelectronic devices has been growing significantly during recent years. Roll-to-roll (R2R) printing facilitates the cost-efficient industrial production of different optoelectronic devices. Nonetheless, the performance of these devices is highly dependent on the printing quality and number of defects of R2R printed conductors. The image processing technique is an efficient nondestructive testing (NDT) methodology used to detect such defects. In this study, a computer vision-based assessment tool was utilized to visualize R2R printed silver conductors’ defects on flexible plastic substrates. A multistage defect detection technique was proposed to detect and classify both printing-induced defects and imperfections as well as the misalignment of the printed conductors with respect to the reference design. The method proved to be a very reliable approach that can be used independently or in conjunction with electrical testing methods for quality assurance purposes during the production of R2R prints

Segmentation-based lossless compression of burn wound images

Color images may be encoded by using a gray-scale image compression technique on each of the three color planes. Such an approach, however, does not take advantage of the correlation existing between the color planes. In this paper, a new segmentation-based lossless compression method is proposed for color images. The method exploits the correlation existing among the three color planes by treating each pixel as a vector of three components, performing region growing and difference operations using the vectors, and applying a color coordinate transformation. The method performed better than the Joint Photographic Experts Group (JPEG) standard by an average of 3.40 bits/pixel with a database including four natural color images of scenery, four images of burn wounds, and four fractal images, and it outperformed the Joint Bi-Level Image experts Group (JBIG) standard by an average of 3.01 bits/pixel. When applied to a database of 20 burn wound images, the 24 bits/pixel images were efficiently compressed to 4.79 bits/pixel, then requiring 4.16 bits/pixel less than JPEG and 5.41 bits/pixel less than JBIG

A Passenger Flow Risk Forecasting Algorithm for High-Speed Railway Transport Hub Based on Surveillance Sensor Networks

Passenger flow risk forecasting is a vital task for safety management in high-speed railway transport hub. In this paper, we considered the passenger flow risk forecasting problem in high-speed railway transport hub. Based on the surveillance sensor networks, a passenger flow risk forecasting algorithm was developed based on spatial correlation. Computational results showed that the proposed forecasting approach was effective and significant for the high-speed railway transport hub