Temporally stable feature clusters for maritime object tracking in visible and thermal imagery

This paper describes a new approach to detect and track maritime objects in real time. The approach particularly addresses the highly dynamic maritime environment, panning cameras, target scale changes, and operates on both visible and thermal imagery. Object detection is based on agglomerative clustering of temporally stable features. Object extents are first determined based on persistence of detected features and their relative separation and motion attributes. An explicit cluster merging and splitting process handles object creation and separation. Stable object clus- ters are tracked frame-to-frame. The effectiveness of the approach is demonstrated on four challenging real-world public datasets.

Manipulation of milk fatty acid composition in lactating cows: opportunities and challenges

Public health policies recommend a population wide decrease in the consumption of saturated fatty acids (SFA) to lower the incidence of cardiovascular and metabolic diseases. In most developed countries, milk and dairy products are the major source of SFA in the human diet. Altering milk fat composition offers the opportunity to lower the consumption of SFA without requiring a change in eating habits. Supplementing the diet of lactating cows with oilseeds, plant oils and marine lipids can be used to replace the SFA in milk fat with monounsaturated fatty acids (MUFA), and to a lesser extent, polyunsaturated fatty acids (PUFA). Due to ruminal metabolism, the decreases in milk SFA are also accompanied by increases in trans fatty acids (TFA), including conjugated isomers. The potential to lower SFA, enrich cis MUFA and PUFA, and alter the abundance and distribution of individual TFA in milk differs according to oil source, form of lipid supplement and degree of oilseed processing, and the influence of other components in the diet. The present review summarises recent evidence on changes in milk fat composition that can be achieved using dietary lipid supplements and highlights the challenges to commercial production of modified milk and dairy products. A meta-analysis on the effects of oilseeds on milk fatty acid composition is also presented.

Topological triangle characterization with application to object detection from images

A novel mathematical framework inspired on Morse Theory for topological triangle characterization in 2D meshes is introduced that is useful for applications involving the creation of mesh models of objects whose geometry is not known a priori. The framework guarantees a precise control of topological changes introduced as a result of triangle insertion/removal operations and enables the definition of intuitive high-level operators for managing the mesh while keeping its topological integrity. An application is described in the implementation of an innovative approach for the detection of 2D objects from images that integrates the topological control enabled by geometric modeling with traditional image processing techniques. (C) 2008 Published by Elsevier B.V.

Chaotic phase synchronization and desynchronization in an oscillator network for object selection

Object selection refers to the mechanism of extracting objects of interest while ignoring other objects and background in a given visual scene. It is a fundamental issue for many computer vision and image analysis techniques and it is still a challenging task to artificial Visual systems. Chaotic phase synchronization takes place in cases involving almost identical dynamical systems and it means that the phase difference between the systems is kept bounded over the time, while their amplitudes remain chaotic and may be uncorrelated. Instead of complete synchronization, phase synchronization is believed to be a mechanism for neural integration in brain. In this paper, an object selection model is proposed. Oscillators in the network representing the salient object in a given scene are phase synchronized, while no phase synchronization occurs for background objects. In this way, the salient object can be extracted. In this model, a shift mechanism is also introduced to change attention from one object to another. Computer simulations show that the model produces some results similar to those observed in natural vision systems.

Cross-situational learning of object-word mapping using Neural Modeling Fields

The issue of how children learn the meaning of words is fundamental to developmental psychology. The recent attempts to develop or evolve efficient communication protocols among interacting robots or Virtual agents have brought that issue to a central place in more applied research fields, such as computational linguistics and neural networks, as well. An attractive approach to learning an object-word mapping is the so-called cross-situational learning. This learning scenario is based on the intuitive notion that a learner can determine the meaning of a word by finding something in common across all observed uses of that word. Here we show how the deterministic Neural Modeling Fields (NMF) categorization mechanism can be used by the learner as an efficient algorithm to infer the correct object-word mapping. To achieve that we first reduce the original on-line learning problem to a batch learning problem where the inputs to the NMF mechanism are all possible object-word associations that Could be inferred from the cross-situational learning scenario. Since many of those associations are incorrect, they are considered as clutter or noise and discarded automatically by a clutter detector model included in our NMF implementation. With these two key ingredients - batch learning and clutter detection - the NMF mechanism was capable to infer perfectly the correct object-word mapping. (C) 2009 Elsevier Ltd. All rights reserved.

Minimizing the object dimensions in circle and sphere packing problems

Given a fixed set of identical or different-sized circular items, the problem we deal with consists on finding the smallest object within which the items can be packed. Circular, triangular, squared, rectangular and also strip objects are considered. Moreover, 2D and 3D problems are treated. Twice-differentiable models for all these problems are presented. A strategy to reduce the complexity of evaluating the models is employed and, as a consequence, instances with a large number of items can be considered. Numerical experiments show the flexibility and reliability of the new unified approach. (C) 2007 Elsevier Ltd. All rights reserved.

Manipulation in Newspaper Articles : A Political Discourse Analysis of Lexical Choice and Manipulation in Japanese Newspaper Crisis Reporting in the case of North Korea

This paper analyzes some forms of linguistic manipulation in Japanese in newspapers when reporting on North Korea and its nuclear tests. The focus lies on lexical ambiguity in headlines and journalist’s voices in the body of the articles, that results in manipulation of the minds of the readers. The study is based on a corpus of nine articles from two of Japan’s largest newspapers Yomiuri Online and Asahi Shimbun Digital. The linguistic phenomenon that contribute to create manipulation are divided into Short Term Memory impact or Long Term Memory impact and examples will be discussed under each of the categories.The main results of the study are that headlines in Japanese newspapers do not make use of an ambiguous, double grounded structure. However, the articles are filled with explicit and implied attitudes as well as attributed material from people of a high social status, which suggests that manipulation of the long term memory is a tool used in Japanese media.