506 research outputs found
A learning approach to swarm-based path detection and tracking
Dissertação para obtenção do Grau de Mestre em
Engenharia Electrotécnica e de ComputadoresThis dissertation presents a set of top-down modulation mechanisms for the modulation of the swarm-based visual saliency computation process proposed by Santana et al. (2010) in context of path detection and tracking. In the original visual saliency computation process, two swarms of agents sensitive to bottom-up conspicuity information interact via pheromone-like signals so as to converge on the most likely location of the path being sought. The behaviours ruling the agents’motion are composed of a set of perception-action rules that embed top-down knowledge about the path’s overall layout. This reduces ambiguity in the face of distractors. However, distractors with a
shape similar to the one of the path being sought can still misguide the system. To mitigate this issue, this dissertation proposes the use of a contrast model to modulate the conspicuity computation and
the use of an appearance model to modulate the pheromone deployment. Given the heterogeneity of the paths, these models are learnt online. Using in a modulation context and not in a direct image processing, the complexity of these models can be reduced without hampering robustness.
The result is a system computationally parsimonious with a work frequency of 20 Hz. Experimental results obtained from a data set encompassing 39 diverse videos show the ability of the proposed model to localise the path in 98.67 % of the 29789 evaluated frames
A Survey on Evolutionary Computation for Computer Vision and Image Analysis: Past, Present, and Future Trends
Computer vision (CV) is a big and important field
in artificial intelligence covering a wide range of applications.
Image analysis is a major task in CV aiming to extract, analyse
and understand the visual content of images. However, imagerelated
tasks are very challenging due to many factors, e.g., high
variations across images, high dimensionality, domain expertise
requirement, and image distortions. Evolutionary computation
(EC) approaches have been widely used for image analysis with
significant achievement. However, there is no comprehensive
survey of existing EC approaches to image analysis. To fill
this gap, this paper provides a comprehensive survey covering
all essential EC approaches to important image analysis tasks
including edge detection, image segmentation, image feature
analysis, image classification, object detection, and others. This
survey aims to provide a better understanding of evolutionary
computer vision (ECV) by discussing the contributions of different
approaches and exploring how and why EC is used for
CV and image analysis. The applications, challenges, issues, and
trends associated to this research field are also discussed and
summarised to provide further guidelines and opportunities for
future research
A Lite Fireworks Algorithm with Fractal Dimension Constraint for Feature Selection
As the use of robotics becomes more widespread, the huge amount of vision
data leads to a dramatic increase in data dimensionality. Although deep
learning methods can effectively process these high-dimensional vision data.
Due to the limitation of computational resources, some special scenarios still
rely on traditional machine learning methods. However, these high-dimensional
visual data lead to great challenges for traditional machine learning methods.
Therefore, we propose a Lite Fireworks Algorithm with Fractal Dimension
constraint for feature selection (LFWA+FD) and use it to solve the feature
selection problem driven by robot vision. The "LFWA+FD" focuses on searching
the ideal feature subset by simplifying the fireworks algorithm and
constraining the dimensionality of selected features by fractal dimensionality,
which in turn reduces the approximate features and reduces the noise in the
original data to improve the accuracy of the model. The comparative
experimental results of two publicly available datasets from UCI show that the
proposed method can effectively select a subset of features useful for model
inference and remove a large amount of noise noise present in the original data
to improve the performance.Comment: International Conference on Pharmaceutical Sciences 202
Optimal Design of Switched Reluctance Motor Using PSO Based FEM-EMC Modeling
This paper aims to optimize the design of a prototype of a 6/4 Switched Reluctance Motor (SRM) using the Particle Swarm Optimization (PSO) algorithm. The geometrical parameters to optimize are the widths of the stator and rotor teeth due to their significant effects on the prototype design and the performances in terms of increased average torque and reduced torque ripple. The studied 3kW SRM is modeled using a numerical-analytical approach based on a coupled Finite Element Method with Equivalent Magnetic Circuit (FEM-EMC). The simulations are performed under MATLAB environment with user-friendly software. The optimal results found are discussed, compared against those obtained by the Genetic Algorithms (GA) and showed a significant improvement in average torque
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