Abstract
Until the rise of electronic means for direct debit, bank cheques have been used as the best form of payment, balancing security and ease of use. Its acceptance and generalized use are result of international agreements that define rules for filling and using it. The fast processing of payments and transactions through safer electronic methods has created the need to reduce its usage over the last years. But despite this progressive reduction, bank cheques still are and will continue to be used; therefore, there is the need to optimize processing mechanisms. The existing automatic cheque processing systems are proprietary and not adapted to the Portuguese language, which is crucial for the cheque analysis and recognition. A prototype of an automatic system for the recognition of the amount in Portuguese bank cheques has been implemented and is being used as a test platform for improved intelligent character recognition algorithms.

Abstract
The automatic extraction and analysis of visual information is becoming generalised. The first step in this processing chain is usually separating or segmenting the captured visual scene in individual objects. Obtaining a perceptually correct segmentation is however a cumber some task. Moreover, typical applications relying on object segmentation, such as visual surveillance, introduce two additional requirements: (1) it should represent only a small fraction of the total amount of processing time and (2) realtime overall processing. We propose a technique that tackles these problems using a cascade of change detection tests, including noise-induced, illumination variation and structural changes. An objective comparison of common pixelwise modelling methods is first done. A cost-based partition- distance between segmentation masks is introduced and used to evaluate the methods. Both the mixture of Gaussians and the kernel density estimation are used as a base to detect structural changes in the proposed algorithm. Experimental results show that the cascade technique consistently outperforms the base methods, without additional post-processing and without additional processing overheads. © 2007 ACADEMY PUBLISHER.

Abstract
The extraction of relevant objects (foreground) from a background is an important first step in many applications. We propose a technique that tackles this problem using a cascade of change detection tests, including noise-induced, illumination variation and structural changes. An objective comparison of pixel-wise modelling methods is first presented. Given its best relation performance/complexity, the mixture of Gaussians was chosen to be used in the proposed method to detect structural changes. Experimental results show that the cascade technique consistently outperforms the commonly used mixture of Gaussians, without additional post-processing and without the expense of processing overheads. ©2007 IEEE.

Abstract
In this paper we present a complete system for object tracking over multiple uncalibrated cameras with or without overlapping fields of view. We employ an approach based on the bag-of-visterms technique to represent and match tracked objects. The tracks are compared with a global object model based on an ensemble of individual object models. The system can globally recognise objects and minimise common tracking problems such as track drift or split. The output is a timeline representing the objects present in a given multi-camera scene. The methods employed in the system are online and can be optimized to operate in real-time.

Abstract
Object detection and tracking is an essential preliminary task in event analysis systems (e.g. visual surveillance). Typically objects are extracted and tagged, forming representative tracks of their activity. Tagging is Usually performed by probabilistic data association, however, in systems capturing disjoint areas it is often not possible to establish such associations, as data may have been collected at different times OF in different locations. In this case, appearance matching is a valuable aid. We propose using bag-of-visterms, i.e. an histogram of quantized local feature descriptors, to represent and match tracked objects. This method has proven to be effective for object matching and classification in image retrieval applications, where descriptors can be extracted a priori. An important difference in event analysis systems is that relevant information is typically restricted to the foreground. Descriptors can, therefore, be extracted faster, approaching real-time requirements. Also, unlike image retrieval, objects can change over time and therefore their model needs to be updated Continuously. Incremental or adaptive learning is used to tackle this problem. Using independent tracks of 30 different persons, we show that the bag-of-visterms representation effectively discriminates visual object tracks and that it presents high resilience to incorrect object segmentation. Additionally, this methodology allows the construction of scalable object models that can be used to match tracks across independent views.

Abstract
The primary goal of the research on image segmentation is to produce better segmentation algorithms. In spite of almost 50 years of research and development in this Held, the general problem of splitting in image into meaningful regions remains unsolved. New and emerging techniques are constantly being applied with reduced Success. The design of each of these new segmentation algorithms requires spending careful attention judging the effectiveness of the technique. This paper demonstrates how the proposed methodology is well suited to perform a quantitative comparison between image segmentation algorithms using I ground-truth segmentation. It consists of a general framework already partially proposed in the literature, but dispersed over several works. The framework is based on the principle of eliminating the minimum number of elements Such that a specified condition is met. This rule translates directly into a global optimization procedure and the intersection-graph between two partitions emerges as the natural tool to solve it. The objective of this paper is to summarize, aggregate and extend the dispersed work. The principle is clarified, presented striped of unnecessary supports and extended to sequences of images. Our Study shows that the proposed framework for segmentation performance evaluation is simple, general and mathematically sound.