Search Results for author: Michael Werman

Found 26 papers, 6 papers with code

An approach to robust ICP initialization

1 code implementation10 Dec 2022 Alexander Kolpakov, Michael Werman

In this note, we propose an approach to initialize the Iterative Closest Point (ICP) algorithm to match unlabelled point clouds related by rigid transformations.

Robust affine point matching via quadratic assignment on Grassmannians

2 code implementations5 Mar 2023 Alexander Kolpakov, Michael Werman

Robust Affine matching with Grassmannians (RAG) is a new algorithm to perform affine registration of point clouds.

DecisioNet: A Binary-Tree Structured Neural Network

1 code implementation3 Jul 2022 Noam Gottlieb, Michael Werman

DNNs perform well due to their representational learning capabilities, while DTs are computationally efficient as they perform inference along one route (root-to-leaf) that is dependent on the input data.

Image Classification

An Epipolar Line from a Single Pixel

no code implementations28 Mar 2017 Tavi Halperin, Michael Werman

In this paper we propose a new method to compute the epipolar geometry from a video stream, by exploiting the following observation: For a pixel p in Image A, all pixels corresponding to p in Image B are on the same epipolar line.

IQ of Neural Networks

no code implementations29 Sep 2017 Dokhyam Hoshen, Michael Werman

IQ tests are an accepted method for assessing human intelligence.

Camera Calibration from Dynamic Silhouettes Using Motion Barcodes

no code implementations CVPR 2016 Gil Ben-Artzi, Yoni Kasten, Shmuel Peleg, Michael Werman

The use of motion barcodes leads to increased speed, accuracy, and robustness in computing the epipolar geometry.

Camera Calibration

Epipolar Geometry Based On Line Similarity

no code implementations17 Apr 2016 Gil Ben-Artzi, Tavi Halperin, Michael Werman, Shmuel Peleg

This paper proposes a similarity measure between lines that indicates whether two lines are corresponding epipolar lines and enables finding epipolar line correspondences as needed for the computation of epipolar geometry.

Stereo Matching Stereo Matching Hand

Fundamental Matrices from Moving Objects Using Line Motion Barcodes

no code implementations26 Jul 2016 Yoni Kasten, Gil Ben-Artzi, Shmuel Peleg, Michael Werman

Corresponding epipolar lines have similar motion barcodes, and candidate pairs of corresponding epipoar lines are found by the similarity of their motion barcodes.

General Deformations of Point Configurations Viewed By a Pinhole Model Camera

no code implementations29 May 2015 Yirmeyahu Kaminski, Michael Werman

This paper is a theoretical study of the following Non-Rigid Structure from Motion problem.

Event Retrieval Using Motion Barcodes

no code implementations3 Dec 2014 Gil Ben-Artzi, Michael Werman, Shmuel Peleg

We introduce a simple and effective method for retrieval of videos showing a specific event, even when the videos of that event were captured from significantly different viewpoints.

Retrieval

Complex-Valued Hough Transforms for Circles

no code implementations2 Feb 2015 Marcelo Cicconet, Davi Geiger, Michael Werman

This paper advocates the use of complex variables to represent votes in the Hough transform for circle detection.

Quantum Pairwise Symmetry: Applications in 2D Shape Analysis

no code implementations2 Feb 2015 Marcelo Cicconet, Davi Geiger, Michael Werman

A pair of rooted tangents -- defining a quantum triangle -- with an associated quantum wave of spin 1/2 is proposed as the primitive to represent and compute symmetry.

Two view constraints on the epipoles from few correspondences

no code implementations22 Oct 2018 Yoni Kasten, Michael Werman

We show how it can be used to reduce the number of required points for the epipolar geometry when some information about the epipoles is available and demonstrate this with a buddy search app.

Vocal Bursts Valence Prediction

Sketch based Reduced Memory Hough Transform

no code implementations15 Nov 2018 Levi Offen, Michael Werman

This paper proposes using sketch algorithms to represent the votes in Hough transforms.

Image declipping with deep networks

no code implementations15 Nov 2018 Shachar Honig, Michael Werman

We present a deep network to recover pixel values lost to clipping.

Image Declipping

The Generalized Laplacian Distance and Its Applications for Visual Matching

no code implementations CVPR 2013 Elhanan Elboer, Michael Werman, Yacov Hel-Or

The graph Laplacian operator, which originated in spectral graph theory, is commonly used for learning applications such as spectral clustering and embedding.

Clustering Template Matching +1

Clear Skies Ahead: Towards Real-Time Automatic Sky Replacement in Video

no code implementations6 Mar 2019 Tavi Halperin, Harel Cain, Ofir Bibi, Michael Werman

Digital videos such as those captured by a smartphone often exhibit exposure inconsistencies, a poorly exposed sky, or simply suffer from an uninteresting or plain looking sky.

Cameras Viewing Cameras Geometry

no code implementations28 Nov 2019 Danail Brezov, Michael Werman

A basic problem in computer vision is to understand the structure of a real-world scene given several images of it.

Autonomous Vehicles

On Min-Max affine approximants of convex or concave real valued functions from $\mathbb R^k$, Chebyshev equioscillation and graphics

no code implementations5 Dec 2018 Steven B. Damelin, David L. Ragozin, Michael Werman

We study Min-Max affine approximants of a continuous convex or concave function $f:\Delta\subset \mathbb R^k\xrightarrow{} \mathbb R$ where $\Delta$ is a convex compact subset of $\mathbb R^k$.

Using a Supervised Method without supervision for foreground segmentation

no code implementations26 Oct 2020 Levi Kassel, Michael Werman

Neural networks are a powerful framework for foreground segmentation in video acquired by static cameras, segmenting moving objects from the background in a robust way in various challenging scenarios.

Foreground Segmentation

On a realization of motion and similarity group equivalence classes of labeled points in $\mathbb R^k$ with applications to computer vision

no code implementations24 Mar 2021 Steven B. Damelin, David L. Ragozin, Michael Werman

We study a realization of motion and similarity group equivalence classes of $n\geq 1$ labeled points in $\mathbb R^k,\, k\geq 1$ as a metric space with a computable metric.

Fully Convolutional Fractional Scaling

1 code implementation20 Mar 2022 Michael Soloveitchik, Michael Werman

We introduce a fully convolutional fractional scaling component, FCFS.

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