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Found 9 papers, 4 papers with code
Brain tumor multi classification and segmentation in MRI images using deep learning
The segmentation model is based on the U-Net architecture and is trained to accurately segment the tumor from the MRI images.
Brain Tumor classification and Segmentation using Deep Learning
In this paper, we present a machine learning-based system designed to assist healthcare professionals in the classification and diagnosis of brain tumors using MRI images.
Synthesizing Physical Character-Scene Interactions
These scene interactions are learned using an adversarial discriminator that evaluates the realism of a motion within the context of a scene.
Human-Aware Object Placement for Visual Environment Reconstruction
In fact, we demonstrate that these human-scene interactions (HSIs) can be leveraged to improve the 3D reconstruction of a scene from a monocular RGB video.
Stochastic Scene-Aware Motion Prediction
A long-standing goal in computer vision is to capture, model, and realistically synthesize human behavior.
Populating 3D Scenes by Learning Human-Scene Interaction
Second, we show that POSA's learned representation of body-scene interaction supports monocular human pose estimation that is consistent with a 3D scene, improving on the state of the art.
Ranked #4 on
Contact Detection
on BEHAVE
Generating 3D People in Scenes without People
However, this is a challenging task for a computer as solving it requires that (1) the generated human bodies to be semantically plausible within the 3D environment (e. g. people sitting on the sofa or cooking near the stove), and (2) the generated human-scene interaction to be physically feasible such that the human body and scene do not interpenetrate while, at the same time, body-scene contact supports physical interactions.
Resolving 3D Human Pose Ambiguities with 3D Scene Constraints
To motivate this, we show that current 3D human pose estimation methods produce results that are not consistent with the 3D scene.
A Self-Adaptive Network Protection System
In this treatise we aim to build a hybrid network automated (self-adaptive) security threats discovery and prevention system; by using unconventional techniques and methods, including fuzzy logic and biological inspired algorithms under the context of soft computing.
