Automatic Instrument Tracking in Video Based on Optical Flow (PoOL) for Planar Targets with Planar Spatial Structure – We propose a new technique to capture and characterize the behavior of a multi-dimensional robot arm in the hand of a robot pilot. By means of this technique, we show that the arm movements can be observed from camera observations and in a novel way, which is consistent with human-robot interaction. The arm’s movements are observed with the robot’s hand in the robot arm, and thus is a natural representation of human arm behaviors, which can be further visualized by a robot’s hand. We provide a new way to learn the arm movement from camera images (using a non-Gaussian approach), and we further extend this approach to model the relationship between the robot’s hands and arm using the robot’s hand. Using these two inputs, the arm’s motion is recorded as a function of all the robot’s motions, which we then use to classify the arms by using the human’s hands as visualizations. Our results indicate that the robot arm pose accurately and accurately predicts the arm motion according to human hand. We discuss our approach in a new perspective on the arm interaction process.
We develop a new algorithm for the task of detection of human joints in 3D images. The proposed method consists of two stages, detecting human joints in 3D images and comparing their characteristics over all possible combinations. A joint is classified as having three or more attributes: a solidified shape, a structure (dura) and an affine surface. For a complete classification process of joints, we define joints based on the shapes and affine surfaces. We also propose a novel framework for the classification of joints and identify the relevant joints. The proposed method can be viewed as a method for joint labeling and its implementation can be used in different 3D applications.
Learning from non-deterministic examples
Efficient Stochastic Dual Coordinate Ascent
Automatic Instrument Tracking in Video Based on Optical Flow (PoOL) for Planar Targets with Planar Spatial Structure
Neural network classification based on membrane lesion detection and lesion structure selectionWe develop a new algorithm for the task of detection of human joints in 3D images. The proposed method consists of two stages, detecting human joints in 3D images and comparing their characteristics over all possible combinations. A joint is classified as having three or more attributes: a solidified shape, a structure (dura) and an affine surface. For a complete classification process of joints, we define joints based on the shapes and affine surfaces. We also propose a novel framework for the classification of joints and identify the relevant joints. The proposed method can be viewed as a method for joint labeling and its implementation can be used in different 3D applications.