Abstract
Image segmentation plays a major role in a broad range of applications. Evaluating the adequacy of a segmentation algorithm for a given application is a requisite both to allow the appropriate selection of segmentation algorithms as well as to tune their parameters for optimal performance. However, objective segmentation quality evaluation is far from being a solved problem. In this paper, a generic framework for segmentation evaluation is introduced after a brief review of previous work. A metric based on the distance between segmentation partitions is proposed to overcome some of the limitations of existing approaches. Symmetric and asymmetric distance metric alternatives are presented to meet the specificities of a wide class of applications. Experimental results confirm the potential of the proposed measures.

Abstract
In this paper, we recover a graph interpretation of the mutual partition distance, proposed recently by Cardoso and Corte-Real. We deduce some properties of this measure, and establish a correspondence with the partition distance introduced by Almudevar and Field and Gusfield, and independently by Guigues. We also present some different formulations for the computation of the mutual partition distance. Finally, a comparison is made with similar measures.

Abstract
This paper describes an approach based on the shortest path method for the detection and tracking of vibrating lines. The detection and tracking of vibrating structures, such as lines and cables, is of great importance in areas such as civil engineering, but the specificities of these scenarios make it a hard problem to tackle. We propose a two-step approach consisting of line detection and subsequent tracking. The automatic detection of the lines avoids manual initialization - a typical problem of these scenarios - and favors tracking. The additional information provided by the line detection enables the improvement of existing algorithms and extends their application to a larger set of scenarios.

Abstract
Current evaluation methods either rely heavily on reference information manually annotated or, by completely avoiding human input, provide only a rough evaluation of the performance of video object tracking algorithms. The main objective of this paper is to present a novel approach to the problem of evaluating video object tracking algorithms. It is proposed the use different types of reference information and the combination of heterogeneous metrics for the purpose of approximating the ideal error. This will enable a significant decrease of the required reference information, thus bridging the gap between metrics with different requirements concerning this type of data. As a result, evaluation frameworks can aggregate the benefits from individual approaches while overcoming their weaknesses, providing a flexible and powerful tool to assess and characterize the behavior of the tracking algorithms.

Abstract
Generation of side information for multi-view Distributed Video Coding in multi-camera environments (e.g., video surveillance) poses challenges in scenarios with temporary non-overlapping among views and consequently, no resources for generating the side information at some time instants. In this paper we extend our previous work (Ciobanu and Corte-Real, Multimed Tools Appl 48(3):411-436, 2010) (for scenarios with permanent complete-overlapping among views) and propose a solution to this problem by exploiting the past visual data associated with each view, gathered over time as a panoramic image (sprite). The entire collection of temporal data from all the cameras is subsequently used for generating the side information. We tackle several topics related to these scenarios and propose solutions for the encountered issues. Optimization techniques are also discussed, e.g., temporal tags and block alternatives associated with the sprite contents for an improved generation of side information. This paper also presents a post-processing technique for additional refinement of generated side information. Practical results show an overall significant enhancement of side information by over 2 dB.

Abstract
Vector quantization of images raises problems of complexity in codebook search and subjective quality of images. The family of image vector quantieation algorithms proposed in this paper addresses both of those problems. The Fuzzy Classified Vector Quantizer (FCVQ) is based on fuzzy set theory and consists basically in a method of extracting a subcodebook from the original codebook, biased by the features of the block to be coded. The incidence of each feature on the blocks is represented by a fuzzy set that captures its (possibly subjective) nature. Unlike the Classified Vector Quantizer (CVQ), in the FCVQ a specific subcodebook is extracted for each block to be coded, allowing a better adaptation to the block. The CVQ may be regarded as a special case of the FCVQ. In order to explore the possible correlation between blocks, an estimator for the degree of incidence of features on the block to be coded is included. The estimate is based on previously coded blocks and is obtained by maximizing a possibility; a distribution that intends to represent the subjective knowledge on the feature's possibility of occurrence conditioned to the coded blocks is used. Some examples of the application of a FCVQ coder to two test images are presented. A slight improvement on the subjective quality of the coded images is obtained, together with a significant reduction on the codebook search complexity and, when applying the estimator, a reduction of the bit rate.