9 research outputs found
ВЫДЕЛЕНИЕ УГЛОВ НА ИЗОБРАЖЕНИЯХ НА ОСНОВЕ ОРИЕНТИРОВАННОГО ГРАДИЕНТА
Рассматриваются алгоритмы автоматического выделения углов на полутоновых и цветныхизображениях, основанные на использовании ориентированного градиента. В отличие от известных алгоритмов они позволяют не только надежно выделить на изображении вершины углов заданной величины, но и оценить ориентацию их сторон
Evolving Reinforcement Learning Algorithms
We propose a method for meta-learning reinforcement learning algorithms by
searching over the space of computational graphs which compute the loss
function for a value-based model-free RL agent to optimize. The learned
algorithms are domain-agnostic and can generalize to new environments not seen
during training. Our method can both learn from scratch and bootstrap off known
existing algorithms, like DQN, enabling interpretable modifications which
improve performance. Learning from scratch on simple classical control and
gridworld tasks, our method rediscovers the temporal-difference (TD) algorithm.
Bootstrapped from DQN, we highlight two learned algorithms which obtain good
generalization performance over other classical control tasks, gridworld type
tasks, and Atari games. The analysis of the learned algorithm behavior shows
resemblance to recently proposed RL algorithms that address overestimation in
value-based methods.Comment: ICLR 2021 Oral. See project website at
https://sites.google.com/view/evolvingr
Learning spatio-temporal representations for action recognition: A genetic programming approach
Extracting discriminative and robust features from video sequences is the first and most critical step in human action recognition. In this paper, instead of using handcrafted features, we automatically learn spatio-temporal motion features for action recognition. This is achieved via an evolutionary method, i.e., genetic programming (GP), which evolves the motion feature descriptor on a population of primitive 3D operators (e.g., 3D-Gabor and wavelet). In this way, the scale and shift invariant features can be effectively extracted from both color and optical flow sequences. We intend to learn data adaptive descriptors for different datasets with multiple layers, which makes fully use of the knowledge to mimic the physical structure of the human visual cortex for action recognition and simultaneously reduce the GP searching space to effectively accelerate the convergence of optimal solutions. In our evolutionary architecture, the average cross-validation classification error, which is calculated by an support-vector-machine classifier on the training set, is adopted as the evaluation criterion for the GP fitness function. After the entire evolution procedure finishes, the best-so-far solution selected by GP is regarded as the (near-)optimal action descriptor obtained. The GP-evolving feature extraction method is evaluated on four popular action datasets, namely KTH, HMDB51, UCF YouTube, and Hollywood2. Experimental results show that our method significantly outperforms other types of features, either hand-designed or machine-learned
Faster and better: a machine learning approach to corner detection
The repeatability and efficiency of a corner detector determines how likely
it is to be useful in a real-world application. The repeatability is importand
because the same scene viewed from different positions should yield features
which correspond to the same real-world 3D locations [Schmid et al 2000]. The
efficiency is important because this determines whether the detector combined
with further processing can operate at frame rate.
Three advances are described in this paper. First, we present a new heuristic
for feature detection, and using machine learning we derive a feature detector
from this which can fully process live PAL video using less than 5% of the
available processing time. By comparison, most other detectors cannot even
operate at frame rate (Harris detector 115%, SIFT 195%). Second, we generalize
the detector, allowing it to be optimized for repeatability, with little loss
of efficiency. Third, we carry out a rigorous comparison of corner detectors
based on the above repeatability criterion applied to 3D scenes. We show that
despite being principally constructed for speed, on these stringent tests, our
heuristic detector significantly outperforms existing feature detectors.
Finally, the comparison demonstrates that using machine learning produces
significant improvements in repeatability, yielding a detector that is both
very fast and very high quality.Comment: 35 pages, 11 figure
Robotic Goal-Based Semi-Autonomous Algorithms Improve Remote Operator Performance
The focus of this research was to determine if reliable goal-based semi-autonomous algorithms are able to improve remote operator performance or not. Two semi-autonomous algorithms were examined: visual servoing and visual dead reckoning. Visual servoing uses computer vision techniques to generate movement commands while using internal properties of the camera combined with sensor data that tell the robot its current position based on its previous position. This research shows that the semi-autonomous algorithms developed increased performance in a measurable way. An analysis of tracking algorithms for visual servoing was conducted and tracking algorithms were enhanced to make them as robust as possible. The developed algorithms were implemented on a currently fielded military robot and a human-in-the-loop experiment was conducted to measure performance
Implementação de um modelo de geometric semantic genetic programming para aplicação naval
Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementRecaí sob a responsabilidade da Marinha Portuguesa a gestão da Zona Económica Exclusiva de Portugal, assegurando a sua segurança da mesma face a atividades criminosas. Para auxiliar a tarefa, é utilizado o sistema Oversee, utilizado para monitorizar a posição de todas as embarcações presentes na área afeta, permitindo a rápida intervenção da Marinha Portuguesa quando e onde necessário. No entanto, o sistema necessita de transmissões periódicas constantes originadas nas embarcações para operar corretamente – casos as transmissões sejam interrompidas, deliberada ou acidentalmente, o sistema deixa de conseguir localizar embarcações, dificultando a intervenção da Marinha.
A fim de colmatar esta falha, é proposto adicionar ao sistema Oversee a capacidade de prever as posições futuras de uma embarcação com base no seu trajeto até à cessação das transmissões. Tendo em conta os grandes volumes de dados gerados pelo sistema (históricos de posições), a área de Inteligência Artificial apresenta uma possível solução para este problema. Atendendo às necessidades de resposta rápida do problema abordado, o algoritmo de Geometric Semantic Genetic Programming baseado em referências de Vanneschi et al. apresenta-se como uma possível solução, tendo já produzido bons resultados em problemas semelhantes.
O presente trabalho de tese pretende integrar o algoritmo de Geometric Semantic Genetic Programming desenvolvido com o sistema Oversee, a fim de lhe conceder capacidades preditivas. Adicionalmente, será realizado um processo de análise de desempenho a fim de determinar qual a ideal parametrização do algoritmo. Pretende-se com esta tese fornecer à Marinha Portuguesa uma ferramenta capaz de auxiliar o controlo da Zona Económica Exclusiva Portuguesa, permitindo a correta intervenção da Marinha em casos onde o atual sistema não conseguiria determinar a correta posição da embarcação em questão