Catálogo de publicaciones - libros

In this paper, we describe a visual language, based on the so-called , which allows users to visually build complex sentences for querying commonly used search engines. By using this visual language, no parentheses have to be applied, and no precedence rules have to be known. Promising results about the usability of the proposed interface are reported, on the basis on an experimental between-group study, performed on a Yahoo-based prototype of the proposed graphical environment.

Chorems are schematized representations of territories, and so they can represent a good visual summary of spatial databases. Indeed for spatial decision-makers, it is more important to identify and map problems than facts. Until now, chorems were made manually by geographers based on the own knowledge of the territory. So, an international project was launched in order to automatically discover spatial patterns and layout chorems starting from spatial databases. After examining some manually-made chorems some guidelines were identified. Then the architecture of a prototype system is presented based on a canonical database structure, a subsystem for spatial patterns discovery based on spatial data mining, a subsystem for chorem layout, and a specialized language to represent chorems.

This paper introduces a knowledge based approach that can be used for the identification of jetty/bridge locations in aerial imagery. With the proposed method, the semantic network formalism to represent declarative knowledge embodied in a jetty/bridge image and the appropriate procedural knowledge, the control procedure was established. A knowledge based system was then introduced through image analysis and interpretation, aiming at accurately locating the desired objects from primary vague identification. With the advanced image processing techniques proposed here, the complexity of using knowledge based system for image analysis is reduced and the proposed method can effectively locate the compound geospatial objects of jetties and bridges.

In this paper we propose to revisit the well-known autoregressive model (AR) as a texture representation model. We consider the AR model with causal neighborhoods. First, we will define the AR model and discuss briefly the parameters estimation process. Then, we will present the synthesis algorithm and we will show some experimental results. The causal autoregressive model is applied in content-based image retrieval. Benchmarking conducted on the well-known Brodatz database shows interesting results. Both retrieval effectiveness (relevance) and retrieval efficiency are discussed and compared to the well-known multiresolution simultaneous autoregressive model (MRSAR).

In this paper, we present a multiple representations and multiple queries approach to tackle the problem of invariance in the framework of content-based image retrieval (CBIR), especially in the case of texture. This approach, rather than considering invariance at the representation level, considers it at the query level. We use two models to represent texture visual content, namely the autoregressive model and a perceptual model based on a set of perceptual features. The perceptual model is used with two viewpoints: the original images viewpoint and the autocovariance function viewpoint. After a brief presentation and discussion of these multiple representation models / viewpoints, which are not invariant with respect to geometric and photometric transformations, we present the invariant texture retrieval algorithm, which is based on multiple models / viewpoints and multiple queries approach and consists in two levels of results fusion (merging): 1. The first level consists in merging results returned by the different models / viewpoints (representations) for the same query in one results list using a linear results fusion model; 2. The second level consists in merging each fused list of different queries into a unique fused list using a round robin fusion scheme. Experimentations show promising results.