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Aquatic macroinvertebrate biomonitoring is an efficient way of assessment of slow and subtle anthropogenic changes and their effect on water quality. It is imperative to have reliable identification and counts of the various taxa occurring in samples as these form the basis for the quality indices used to infer the ecological status of the aquatic ecosystem. In this paper, we try to close the gap...
The types and numbers of benthic macroinvertebrates found in a water body reflect water quality. Therefore, macroinvertebrates are routinely monitored as a part of freshwater ecological quality assessment. The collected macroinvertebrate samples are identified by human experts, which is costly and time-consuming. Thus, developing automated identification methods that could partially replace the human...
In this paper, we propose a novel algorithm for Single-hidden Layer Feed forward Neural networks training which is able to exploit information coming from both labeled and unlabeled data for semi-supervised action classification. We extend the Extreme Learning Machine algorithm by incorporating appropriate regularization terms describing geometric properties and discrimination criteria of the training...
While single-view human action recognition has attracted considerable research study in the last three decades, multi-view action recognition is, still, a less exploited field. This paper provides a comprehensive survey of multi-view human action recognition approaches. The approaches are reviewed following an application-based categorization: methods are categorized based on their ability to operate...
Clustering-based Discriminant Analysis (CDA) is a well-known technique for supervised feature extraction and dimensionality reduction. CDA determines an optimal discriminant subspace for linear data projection based on the assumptions of normal subclass distributions and subclass representation by using the mean subclass vector. However, in several cases, there might be other subclass representative...
In this paper, we propose a person identification method exploiting human motion information. A Self Organizing Neural Network is employed in order to determine a topographic map of representative human body poses. Fuzzy Vector Quantization is applied to the human body poses appearing in a video in order to obtain a compact video representation, that will be used for person identification and action...
One of the major issues that activity recognition methods should be able to face is the style variations observed in the execution of activities performed by different humans. In order to address this issue we propose a person-specific activity recognition framework in which human identification proceeds activity recognition. After recognizing the ID of the human depicted in a video stream, a person-specific...
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