1,631 research outputs found
Automated artemia length measurement using U-shaped fully convolutional networks and second-order anisotropic Gaussian kernels
The brine shrimp Artemia, a small crustacean zooplankton organism, is universally used as live prey for larval fish and shrimps in aquaculture. In Artemia studies, it would be highly desired to have access to automated techniques to obtain the length information from Anemia images. However, this problem has so far not been addressed in literature. Moreover, conventional image-based length measurement approaches cannot be readily transferred to measure the Artemia length, due to the distortion of non-rigid bodies, the variation over growth stages and the interference from the antennae and other appendages. To address this problem, we compile a dataset containing 250 images as well as the corresponding label maps of length measuring lines. We propose an automated Anemia length measurement method using U-shaped fully convolutional networks (UNet) and second-order anisotropic Gaussian kernels. For a given Artemia image, the designed UNet model is used to extract a length measuring line structure, and, subsequently, the second-order Gaussian kernels are employed to transform the length measuring line structure into a thin measuring line. For comparison, we also follow conventional fish length measurement approaches and develop a non-learning-based method using mathematical morphology and polynomial curve fitting. We evaluate the proposed method and the competing methods on 100 test images taken from the dataset compiled. Experimental results show that the proposed method can accurately measure the length of Artemia objects in images, obtaining a mean absolute percentage error of 1.16%
Representation Learning: A Review and New Perspectives
The success of machine learning algorithms generally depends on data
representation, and we hypothesize that this is because different
representations can entangle and hide more or less the different explanatory
factors of variation behind the data. Although specific domain knowledge can be
used to help design representations, learning with generic priors can also be
used, and the quest for AI is motivating the design of more powerful
representation-learning algorithms implementing such priors. This paper reviews
recent work in the area of unsupervised feature learning and deep learning,
covering advances in probabilistic models, auto-encoders, manifold learning,
and deep networks. This motivates longer-term unanswered questions about the
appropriate objectives for learning good representations, for computing
representations (i.e., inference), and the geometrical connections between
representation learning, density estimation and manifold learning
Sparse Coral Classification Using Deep Convolutional Neural Networks
Autonomous repair of deep-sea coral reefs is a recent proposed idea to
support the oceans ecosystem in which is vital for commercial fishing, tourism
and other species. This idea can be operated through using many small
autonomous underwater vehicles (AUVs) and swarm intelligence techniques to
locate and replace chunks of coral which have been broken off, thus enabling
re-growth and maintaining the habitat. The aim of this project is developing
machine vision algorithms to enable an underwater robot to locate a coral reef
and a chunk of coral on the seabed and prompt the robot to pick it up. Although
there is no literature on this particular problem, related work on fish
counting may give some insight into the problem. The technical challenges are
principally due to the potential lack of clarity of the water and platform
stabilization as well as spurious artifacts (rocks, fish, and crabs). We
present an efficient sparse classification for coral species using supervised
deep learning method called Convolutional Neural Networks (CNNs). We compute
Weber Local Descriptor (WLD), Phase Congruency (PC), and Zero Component
Analysis (ZCA) Whitening to extract shape and texture feature descriptors,
which are employed to be supplementary channels (feature-based maps) besides
basic spatial color channels (spatial-based maps) of coral input image, we also
experiment state-of-art preprocessing underwater algorithms for image
enhancement and color normalization and color conversion adjustment. Our
proposed coral classification method is developed under MATLAB platform, and
evaluated by two different coral datasets (University of California San Diego's
Moorea Labeled Corals, and Heriot-Watt University's Atlantic Deep Sea).Comment: Thesis Submitted for the Degree of MSc Erasmus Mundus in Vision and
Robotics (VIBOT 2014
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