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... is a benchmark in visual object category recognition and detection, providing the vision and machine learning communities with a standard dataset of images and annotation, and standard evaluation procedures. Organised annually from 2005 to present, the challenge and its associated dataset has become accepted as the benchmark for object detection. This paper describes the dataset and evaluation procedure. We review the state-of-the-art in evaluated methods for both classification and detection, analyse whether the methods are statistically different, what they are learning from the images (e.g. the object or its context), and what the methods find easy or confuse. The paper concludes with lessons learnt in the three year history of the challenge, and proposes directions for future improvement and extension.

Most past and present work in machine perception has involved extensive static analysis of passively sampled data. However, it should be axiomatic that perception is not passive, but active. Perceptual activity is exploratory, probing, searching; percepts do not simply fall onto sensors as rain falls onto ground. We do not just see, we look. And in the course,

We introduce a methodology for radiometric reconstruction, the simultaneous recovery of multiple illuminants and surface albedoes from multiple views, assuming that the geometry of the scene and of the cameras is known. We formulate the linear theory of multiple illuminants and show its similarities with the theory of geometric recovery of multiple views. Linear and non-linear implementations are proposed; simulation results are discussed; and, finally, results on real images are presented.

of the resources a host system must provide in order to make use of the algorithm, and an implementation used in the natural language generation component of the IDAS system.

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and orientations. In fact in our application this is the information we wish to determine from the image feature matches. Features are detected in two or more images and characterised using affine texture invariants. The problem of window effects is explicitly addressed by our method - our feature characterisation

this evaluation formula with an appropriate correlation measure and a heuristic search strategy. CFS was evaluated by experiments on artificial and natural datasets. Three machine learning algorithms were used: C4.5 (a decision tree learner), IB1 (an instance based learner), and naive Bayes. Experiments

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In this lecture I want to think about the relationship between the globalization process and that complex human condition we call ‘culture’. But first I need to say very briefly what I understand by globalization. Globalization is a complex process because it involves