Results 21 to 30 of about 5,580,472 (385)
Advances in adversarial attacks and defenses in computer vision: A survey [PDF]
IEEE Access, 2021 Deep Learning is the most widely used tool in the contemporary field of computer vision. Its ability to accurately solve complex problems is employed in vision research to learn deep neural models for a variety of tasks, including security critical ...
Naveed Akhtar +3 more
semanticscholar +1 more source
QUANTITATIVE COMPARISON BETWEEN NEURAL NETWORK- AND SGM-BASED STEREO MATCHING [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2019 Over the last decades, various methods for three-dimensional detection of the environment have been developed and successfully used. This work considers classical stereo methods, which can determine depth information by the means of correspondence ...
A. Frenzel, N. Deckers, R. Reulke
doaj +1 more source
Do Datasets Have Politics? Disciplinary Values in Computer Vision Dataset Development [PDF]
Proc. ACM Hum. Comput. Interact., 2021 Data is a crucial component of machine learning. The field is reliant on data to train, validate, and test models. With increased technical capabilities, machine learning research has boomed in both academic and industry settings, and one major focus has
M. Scheuerman, Emily L. Denton, A. Hanna
semanticscholar +1 more source
ROBUST AERIAL OBJECT TRACKING IN HIGH DYNAMIC FLIGHT MANEUVERS [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2015 Integrating drones into the civil airspace is one of the biggest challenges for civil aviation, responsible authorities and involved com- panies around the world in the upcoming years. For a full integration into non-segregated airspace such a system has
A. Nussberger, H. Grabner, L. van Gool
doaj +1 more source
Sim-to-Real Transfer for Object Detection in Aerial Inspections of Transmission Towers
IEEE Access, 2023 Training deep learning models for object detection usually requires a large amount of data, a condition that is not common for most real-world applications, especially in the context of aerial imagery.
Augusto J. Peterlevitz +15 more
doaj +1 more source
A Review on Machine Learning Styles in Computer Vision—Techniques and Future Directions
IEEE Access, 2022 Computer applications have considerably shifted from single data processing to machine learning in recent years due to the accessibility and availability of massive volumes of data obtained through the internet and various sources.
Supriya V. Mahadevkar +6 more
semanticscholar +1 more source
Deep learning-enabled medical computer vision
npj Digital Medicine, 2021 A decade of unprecedented progress in artificial intelligence (AI) has demonstrated the potential for many fields—including medicine—to benefit from the insights that AI techniques can extract from data.
A. Esteva +9 more
semanticscholar +1 more source
Deep reinforcement learning in computer vision: a comprehensive survey [PDF]
Artificial Intelligence Review, 2021 Deep reinforcement learning augments the reinforcement learning framework and utilizes the powerful representation of deep neural networks. Recent works have demonstrated the remarkable successes of deep reinforcement learning in various domains ...
Ngan T. H. Le +4 more
semanticscholar +1 more source
The Programming Historian, 2022 This is the first of a two-part lesson introducing deep learning based computer vision methods for humanities research. Using a dataset of historical newspaper advertisements and the fastai Python library, the lesson walks through the pipeline of ...
Daniel van Strien +4 more
doaj +1 more source
CNN Variants for Computer Vision: History, Architecture, Application, Challenges and Future Scope
Electronics, 2021 Computer vision is becoming an increasingly trendy word in the area of image processing. With the emergence of computer vision applications, there is a significant demand to recognize objects automatically.
Dulari Bhatt +7 more
semanticscholar +1 more source