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In this paper, we propose a new approach for the classification of reaching targets before movement onset, during visually-guided reaching in 3D space. Our approach combines the discriminant power of two-dimensional Electroencephalography (EEG) signals (i.e., EEG images) built from short epochs, with the feature extraction and classification capabilities of deep learning (DL) techniques, such as the...
This work describes a methodology for plant classification based on the analysis of leaf textures by combining a multi-resolution technique, such as the two-dimensional (2D) Discrete Wavelet Transform (2D-DWT), statistical models and Gray-Level Co-occurrence Matrices (GLCM) in which some invariance (e.g. rotation and scale) are achieved. As a second step, an Artificial Neural Network (ANN) model is...
The growth of motion capture systems have contributed to the proliferation of human motion database, mainly because human motion is important in many applications, ranging from games entertainment and films to sports and medicine. However, the captured motions normally attend specific needs. As an effort for adapting and reusing captured human motions in new tasks and environments and improving the...
Plant identification and classification play an important role in ecology, but the manual process is cumbersome even for experimented taxonomists. Technological advances allows the development of strategies to make these tasks easily and faster. In this context, this paper describes a methodology for plant identification and classification based on leaf shapes, that explores the discriminative power...
In this study, we propose to investigate the presence of anticipatory patterns in electroencephalography (EEG) signals while driving a virtual car to determine two specific actions (1) turn left and (2) turn right, a few milliseconds before such actions take place. Our results show the feasibility of using anticipatory brain signals for detecting and classifying game interaction before it happens.
We explore an approach to full-body motion editing with linear motion models, prioritized constraint-based optimization and latent-space interpolation. By exploiting the mathematical connections between linear motion models and prioritized inverse kinematics (PIK), we formulate and solve the motion editing problem as an optimization function whose differential structure is rich enough to efficiently...
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