0000000000253549
AUTHOR
Arne Wiklund
Using Ericsson NorARC's frameworks as test bed for dynamic change of behavior based on CompositeStates
Masteroppgave i informasjons- og kommunikasjonsteknologi 2003 - Høgskolen i Agder, Grimstad Frameworks are a widely used re-use technique in the object-oriented community today. A framework re-uses both code and design and makes it easier to develop new components. Ericsson NorARC (Norwegian Applied Research Center) has developed a set of frameworks; JavaFrame, ActorFrame and ServiceFrame to aid advance service development. JavaFrame is a modeling kit for Java with good support for state machines. ActorFrame is a framework emphasizing the actor and role notion. ServiceFrame emphasize the modeling of service functionality. UML is today’s de facto standard for visualizing, specifying, constru…
Temperate Fish Detection and Classification: a Deep Learning based Approach
A wide range of applications in marine ecology extensively uses underwater cameras. Still, to efficiently process the vast amount of data generated, we need to develop tools that can automatically detect and recognize species captured on film. Classifying fish species from videos and images in natural environments can be challenging because of noise and variation in illumination and the surrounding habitat. In this paper, we propose a two-step deep learning approach for the detection and classification of temperate fishes without pre-filtering. The first step is to detect each single fish in an image, independent of species and sex. For this purpose, we employ the You Only Look Once (YOLO) …
Biometric Fish Classification of Temperate Species Using Convolutional Neural Network with Squeeze-and-Excitation
Our understanding and ability to effectively monitor and manage coastal ecosystems are severely limited by observation methods. Automatic recognition of species in natural environment is a promising tool which would revolutionize video and image analysis for a wide range of applications in marine ecology. However, classifying fish from images captured by underwater cameras is in general very challenging due to noise and illumination variations in water. Previous classification methods in the literature relies on filtering the images to separate the fish from the background or sharpening the images by removing background noise. This pre-filtering process may negatively impact the classificat…
Biometric Fish Classification of Temperate Species Using Convolutional Neural Network with Squeeze-and-Excitation
Our understanding and ability to effectively monitor and manage coastal ecosystems are severely limited by observation methods. Automatic recognition of species in natural environment is a promising tool which would revolutionize video and image analysis for a wide range of applications in marine ecology. However, classifying fish from images captured by underwater cameras is in general very challenging due to noise and illumination variations in water. Previous classification methods in the literature relies on filtering the images to separate the fish from the background or sharpening the images by removing background noise. This pre-filtering process may negatively impact the classificat…