49 citations · 51 across the 3 of their papers we have counts for
8 papers · 1 filter
On the Minimal Recognizable Image Patch
Mark Fonaryov, Michael Lindenbaum
In contrast to human vision, common recognition algorithms often fail on partially occluded images. We propose characterizing, empirically, the algorithmic limits by finding a mini…
DPDist : Comparing Point Clouds Using Deep Point Cloud Distance
Dahlia Urbach, Yizhak Ben-Shabat, Michael Lindenbaum
We introduce a new deep learning method for point cloud comparison. Our approach, named Deep Point Cloud Distance (DPDist), measures the distance between the points in one cloud an…
Enhancing Generic Segmentation with Learned Region Representations
Or Isaacs, Oran Shayer, Michael Lindenbaum
Current successful approaches for generic (non-semantic) segmentation rely mostly on edge detection and have leveraged the strengths of deep learning mainly by improving the edge d…
Learning Pixel Representations for Generic Segmentation
Oran Shayer, Michael Lindenbaum
Deep learning approaches to generic (non-semantic) segmentation have so far been indirect and relied on edge detection. This is in contrast to semantic segmentation, where DNNs are…
Seeing Things in Random-Dot Videos
Thomas Dagès, Michael Lindenbaum, Alfred M. Bruckstein
Humans possess an intricate and powerful visual system in order to perceive and understand the environing world. Human perception can effortlessly detect and correctly group featur…
Nesti-Net: Normal Estimation for Unstructured 3D Point Clouds using Convolutional Neural Networks
Yizhak Ben-Shabat, Michael Lindenbaum, Anath Fischer
In this paper, we propose a normal estimation method for unstructured 3D point clouds. This method, called Nesti-Net, builds on a new local point cloud representation which consist…