نتایج جستجو برای: image understanding
تعداد نتایج: 882303 فیلتر نتایج به سال:
An effective and realtime image understanding algorithm must meet the challenges of ever-growing image databases, the varieties of features to be understood and the limited human resources for developing this algorithm. In this paper, the reasons that mainstream image understanding algorithms could not meet these challenges were presented. One effective and realtime image understanding platform...
Image Understanding (IU) concerns the issues of how to interpret images. Knowledge-based image understanding studies the theory and techniques of computational image understanding, which uses explicit, independent knowledge about the images, such as their context and objects in them, as well as knowledge about the imaging system. Two related disciplines, Artificial Intelligence (AI) and Image P...
Introduction: The understanding of the psychological characteristics of cosmetic surgery applicants can help in various programs to prevent and treat the psychological problems of these patients.The purpose of this paper was to determine the relationship between emotional disorder and repeated negative thoughts with body image concern due to the mediating role of family support in women applyin...
Subsurface rocks and their fluid content along with their architecture affect reflected seismic waves through variations in their travel time, reflection amplitude, and phase within the field of exploration seismology. The combined effects of these factors make subsurface interpretation by using reflection waves very difficult. Therefore, assistance from other subsurface disciplines is needed i...
Due to the differences between the visible and thermal infrared images, the combination of these two types of images leads to better understanding of the characteristics of targets and the environment. Thermal infrared images are really in distinguishing targets from the background based on the radiation differences and land surface temperature (LST) calculation. However, their spatial resolu...
Abstract State-of-the-art deep learning systems (e.g., ImageNet image classification) typically require very large training sets to achieve high accuracies. Therefore, one of the grand challenges is called few-shot where only a few samples are required for good performance. In this survey, we illuminate key paradigms in meta-learning. These meta-learning methods, by simulating tasks which will ...
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