2 code implementations • 17 Jun 2023 • Luca Ferranti, Jani Boutellier
This paper introduces \textsc{FuzzyLogic. jl}, a Julia library to perform fuzzy inference.
1 code implementation • 20 Feb 2023 • Masud Fahim, Ilona Söchting, Luca Ferranti, Juho Kannala, Jani Boutellier
Usually the query images have been acquired with a camera that differs from the imaging hardware used to collect the 3D database; consequently, it is hard to acquire accurate ground truth poses between query images and the 3D database.
1 code implementation • 1 Nov 2022 • Masud An-Nur Islam Fahim, Jani Boutellier
Methods for improving deep neural network training times and model generalizability consist of various data augmentation, regularization, and optimization approaches, which tend to be sensitive to hyperparameter settings and make reproducibility more challenging.
no code implementations • 16 Jun 2022 • Jani Boutellier, Bo Tan, Jari Nurmi
Collaborative inference has received significant research interest in machine learning as a vehicle for distributing computation load, reducing latency, as well as addressing privacy preservation in communications.
no code implementations • 22 Mar 2022 • Abol Basher, Jani Boutellier
Dense point cloud generation from a sparse or incomplete point cloud is a crucial and challenging problem in 3D computer vision and computer graphics.
no code implementations • 26 Mar 2021 • Abol Basher, Muhammad Sarmad, Jani Boutellier
Recently, several works have addressed modeling of 3D shapes using deep neural networks to learn implicit surface representations.
no code implementations • 1 Oct 2020 • Luca Ferranti, Xiaotian Li, Jani Boutellier, Juho Kannala
Camera pose estimation in large-scale environments is still an open question and, despite recent promising results, it may still fail in some situations.
no code implementations • 20 May 2020 • Luca Ferranti, Kalle Åström, Magnus Oskarsson, Jani Boutellier, Juho Kannala
Given a network of receivers and transmitters, the process of determining their positions from measured pseudoranges is known as network self-calibration.
no code implementations • 1 Aug 2018 • Mir Khan, Heikki Huttunen, Jani Boutellier
This representation completely eliminates the need for floating point multiplications and additions and decreases both the computational load and the memory footprint compared to a full-precision network implemented in floating point, making it well-suited for resource-constrained environments.