Bob Fisher
University of Edinburgh, Informatics, Faculty Member
We propose a system for describing skin lesions images based on a human perception model. Pigmented skinlesions including melanoma and other types of skin cancer as well as non-malignant lesions are used. Works onclassification of skin... more
We propose a system for describing skin lesions images based on a human perception model. Pigmented skinlesions including melanoma and other types of skin cancer as well as non-malignant lesions are used. Works onclassification of skin lesions already exist but they mainly concentrate on melanoma. The novelty of our work isthat our system gives to skin lesion images a semantic label in a manner similar to humans. This work consists of two parts: first we capture they way users perceive each lesion, second we train a machine learning system thatsimulates how people describe images. For the first part, we choose 5 attributes: colour (light to dark), colouruniformity (uniform to non-uniform), symmetry (symmetric to non-symmetric), border (regular to irregular),texture (smooth to rough). Using a web based form we asked people to pick a value of each attribute for eachlesion. In the second part, we extract 93 features from each lesions and we trained a machine learning algorithmusing such features as input and the values of the human attributes as output. Results are quite promising,especially for the colour related attributes, where our system classifies over 80% of the lesions into the samesemantic classes as humans.
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ABSTRACT We address the analysis of fish trajectories in unconstrained underwater videos to help marine biologist to detect new/rare fish behaviours and to detect environmental changes which can be observed from the abnormal behaviour of... more
ABSTRACT We address the analysis of fish trajectories in unconstrained underwater videos to help marine biologist to detect new/rare fish behaviours and to detect environmental changes which can be observed from the abnormal behaviour of fish. The fish trajectories are separated into normal and abnormal classes which indicate the common behaviour of fish and the behaviours that are rare/ unusual respectively. The proposed solution is based on a novel type of hierarchical classifier which builds the tree using clustered and labelled data based on similarity of data while using different feature sets at different levels of hierarchy. The paper presents a new method for fish trajectory analysis which has better performance compared to state-of-the-art techniques while the results are significant considering the challenges of underwater environments, low video quality, erratic movement of fish and highly imbalanced trajectory data that we used. Moreover, the proposed method is also powerful enough to classify highly imbalanced real-world datasets.
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ABSTRACT Long-term monitoring of the underwater environment is still labour intensive work. Using underwater surveillance cameras to monitor this environment has the potential advantage to make the task become less labour intensive. Also,... more
ABSTRACT Long-term monitoring of the underwater environment is still labour intensive work. Using underwater surveillance cameras to monitor this environment has the potential advantage to make the task become less labour intensive. Also, the obtained data can be stored making the research reproducible. In this work, a system to analyse long-term underwater camera footage (more than 3 years of 12 hours a day underwater camera footage from 10 cameras) is described. This system uses video processing software to detect and recognise fish species. This footage is processed on supercomputers, which allow marine biologists to request automatic processing on these videos and afterwards analyse the results using a web-interface that allows them to display counts of fish species in the camera footage.