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Among the symptoms of schizophrenia, deficits in the recognition of intention is one of the most studied. However, there is no cognitive model of intention recognition that takes into account both innate and environmental/developmental factors. This work p ...
Object recognition is one of the most important problems in computer vision. However, visual recognition poses many challenges when tried to be reproduced by artificial systems. A main challenge is the problem of variability: objects can appear across huge ...
In recent years time series data has become ubiquitous thanks to affordable sensors and advances in embedded technology. Large amount of time-series data are continuously produced in a wide spectrum of applications, such as sensor networks, medical monitor ...
Pedestrians follow different trajectories to avoid obstacles and accommodate fellow pedestrians. Any autonomous vehicle navigating such a scene should be able to foresee the future positions of pedestrians and accordingly adjust its path to avoid collision ...
Keyword-based image color re-rendering is a convenient way to enhance the color of images. Most methods only focus on the color characteristics of the image while ignoring the semantic meaning of different regions. We propose to incorporate semantic inform ...
The fully connected conditional random field (CRF) with Gaussian pairwise potentials has proven popular and effective for multi-class semantic segmentation. While the energy of a dense CRF can be minimized accurately using a linear programming (LP) relaxat ...
We contribute a learning from demonstration approach for robots to acquire skills from multi-modal high-dimensional data. Both latent representations and associations of different modalities are proposed to be jointly learned through an adapted variational ...
Recent past has seen a lot of developments in the field of image-based dietary assessment. Food image classification and recognition are crucial steps for dietary assessment. In the last couple of years, advancements in the deep learning and convolutional ...
Event-based temporal contrast vision sensors such as the Dynamic Vison Sensor (DVS) have advantages such as high dynamic range, low latency, and low power consumption. Instead of frames, these sensors produce a stream of events that encode discrete amounts ...
Background: Automated segmentation of brain structures is an important task in structural and functional image analysis. We developed a fast and accurate method for the striatum segmentation using deep convolutional neural networks (CNN). New method: T1 ma ...