Results 71 to 80 of about 12,040,618 (293)

Semantic Image Segmentation in Duckietown

Vestnik NSU. Series: Information Technologies, 2021
The article is devoted to evaluation of the applicability of existing semantic segmentation algorithms for the “Duckietown” simulator. The article explores classical semantic segmentation algorithms as well as ones based on neural networks. We also examined machine learning frameworks, taking into account all the limitations of the “Duckietown ...
D. E. Shabalina, K. S. Lanchukovskaya, T. V. Liakh, K. V. Chaika   +3 more
openaire   +2 more sources

Is an Apple an Orange? A Large Language Model Benchmark for Candidate Term Extraction and Subclass Decisions Against Upper Ontologies in Engineering and Materials Science

Advanced Engineering Materials, EarlyView.
Building machine‐readable vocabularies for materials science is slow, expert‐driven work. This study benchmarks 13 large language models on two of its first steps: finding candidate terms in engineering articles and deciding where they belong in a class hierarchy.
Thomas Bjarsch, Maximilian Holland, Klaus Drechsler, Johannes Schilp   +3 more
wiley   +1 more source

SDDS-Net: Space and Depth Encoder-Decoder Convolutional Neural Networks for Real-Time Semantic Segmentation

IEEE Access, 2023
In this paper, we propose novel convolutional encoder-decoder architectures for real-time semantic segmentation based on an image-to-image translation approach via the space-to-depth and depth-to-space modules.
Hatem Ibrahem, Ahmed Salem, Hyun-Soo Kang   +2 more
doaj   +1 more source

Semantically Adaptive Image-to-image Translation for Domain Adaptation of Semantic Segmentation

Proceedings of the British Machine Vision Conference 2020, 2020
Paper will appear on BMVC ...
Luigi Musto, Andrea Zinelli
openaire   +3 more sources

A Lightweight Procedural Layer for Hybrid Experimental–Computational Workflows in Materials Science

Advanced Engineering Materials, EarlyView.
We unveil a prototype hybrid‐workflow framework that fuses automatedcomputation with hands‐on experiments. Built atop pyiron, a lightweight, parameterized layer translates procedure descriptions into executable manual steps, syncing instrument settings, human interventions, and data capture in real‐time today.
Steffen Brinckmann, Sarath Menon, Raphael Röske, Johannes Steinhülb, Michael Selzer, Jan Janssen, Tilmann Hickel, Ruth Schwaiger, Jörg Neugebauer   +8 more
wiley   +1 more source

Distance Regularized Level Set Evolution for Medical Image Segmentation [PDF]

, 2013
Medical image is an important tool because it can be used for surgical planning and simulation, radiotherapy planning, and tracking the progress of disease. To analyze the medical image, it must partitioned into different segment using image segmentation
Pranowo, Pranowo, Rianto , Indra
core   +1 more source

Multi-scale Gaussian representation and outline-learning based cell image segmentation [PDF]

, 2013
BACKGROUND: High-throughput genome-wide screening to study gene-specific functions, e.g. for drug discovery, demands fast automated image analysis methods to assist in unraveling the full potential of such studies.
Rämö, P., Mario Emmenlauer, Ruusuvuori, P., Pekka Ruusuvuori, Dehio, C., Yli-Harja, O., Christoph Dehio, Emmenlauer, M., Olli Yli-Harja, Farhan, M., Muhammad Farhan, Pauli Rämö   +11 more
core   +1 more source

PTRSegNet: A Patch-to-Region Bottom–Up Pyramid Framework for the Semantic Segmentation of Large-Format Remote Sensing Images

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Semantic segmentation is a basic task in the interpretation of remote sensing images. Mainstream deep-learning-based semantic segmentation algorithms typically process images with small sizes.
Shiyan Pang, Yepeng Shi, Hanchun Hu, Lizhi Ye, Jia Chen   +4 more
doaj   +1 more source

Adversarial Examples for Semantic Image Segmentation

CoRR, 2017
ICLR 2017 workshop ...
Volker Fischer 0003, Mummadi Chaithanya Kumar, Jan Hendrik Metzen, Thomas Brox   +3 more
openaire   +3 more sources

Current Status and Challenges in Data Collection for Aerospace Coatings Deposited by Plasma Spraying

Advanced Engineering Materials, EarlyView.
An innovative approach has been integrated into the GRENAT project to optimize plasma spraying and coating performance. Raw materials are accelerated and melted in the plasma generated by torches, creating coatings. Monitoring sensors collect process data which are combined with ex situ characterization data.
Lila Randriamananjara, Vincent Rat, Quentin Dubreuil, Patrice Valorge, Simon Goutier, Alain Denoirjean, Olivier Messe, Céline Ruelle, Jean‐Philippe Poli   +8 more
wiley   +1 more source