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Found 14 papers, 0 papers with code
From Image to Stability: Learning Dynamics from Human Pose
We propose and validate two end-to-end deep learning architectures to learn foot pressure distribution maps (dynamics) from 2D or 3D human pose (kinematics).
EscherNet 101
A deep learning model, EscherNet 101, is constructed to categorize images of 2D periodic patterns into their respective 17 wallpaper groups.
Stock Market Prediction via Deep Learning Techniques: A Survey
Existing surveys on stock market prediction often focus on traditional machine learning methods instead of deep learning methods.
Novel 3D Scene Understanding Applications From Recurrence in a Single Image
We demonstrate the utility of recurring pattern discovery from a single image for spatial understanding of a 3D scene in terms of (1) vanishing point detection, (2) hypothesizing 3D translation symmetry and (3) counting the number of RP instances in the image.
Image-based Stability Quantification
Quantitative evaluation of human stability using foot pressure/force measurement hardware and motion capture (mocap) technology is expensive, time consuming, and restricted to the laboratory.
From Kinematics To Dynamics: Estimating Center of Pressure and Base of Support from Video Frames of Human Motion
To gain an understanding of the relation between a given human pose image and the corresponding physical foot pressure of the human subject, we propose and validate two end-to-end deep learning architectures, PressNet and PressNet-Simple, to regress foot pressure heatmaps (dynamics) from 2D human pose (kinematics) derived from a video frame.
Learning Dynamics from Kinematics: Estimating 2D Foot Pressure Maps from Video Frames
In biomechanics, Center of Pressure (CoP) is used in studies of human postural control and gait.
Beyond Planar Symmetry: Modeling human perception of reflection and rotation symmetries in the wild
Humans take advantage of real world symmetries for various tasks, yet capturing their superb symmetry perception mechanism with a computational model remains elusive.
Regularity-Driven Facade Matching Between Aerial and Street Views
We present an approach for detecting and matching building facades between aerial view and street-view images.
Symmetry reCAPTCHA
This is a reaction to the poor performance of symmetry detection algorithms on real-world images, benchmarked since CVPR 2011.
Local Regularity-driven City-scale Facade Detection from Aerial Images
We propose a novel regularity-driven framework for facade detection from aerial images of urban scenes.
GRASP Recurring Patterns from a Single View
We propose a novel unsupervised method for discovering recurring patterns from a single view.
Tracking Sports Players with Context-Conditioned Motion Models
Instead, we introduce a set of Game Context Features extracted from noisy detections to describe the current state of the match, such as how the players are spatially distributed.
Texture replacement in real images
To achieve convincing replacement results we have to detect texture patterns and estimate the lighting map from a given image.
