اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدDipole and Quadrupole Moments in Image Processing
211
0
0.0
(
0
)
تأليف
Amelia Sparavigna
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
This paper proposes an algorithm for image processing, obtained by adapting to image maps the definitions of two well-known physical quantities. These quantities are the dipole and quadrupole moments of a charge distribution. We will see how it is possible to define dipole and quadrupole moments for the gray-tone maps and apply them in the development of algorithms for edge detection.
قيم البحث
اقرأ أيضاً
If gamma-ray bursts originate in the Galaxy, at some level there should be a galactic pattern in their distribution on the sky. We test published galactic models by comparing their dipole and quadrupole moments with the moments of the BATSE 3B catalo
g. While many models have moments that are too large, several models are in acceptable or good agreement with the data.
Instead of evaluating the gradient field of the brightness map of an image, we propose the use of dipole vectors. This approach is obtained by adapting to the image gray-tone distribution the definition of the dipole moment of charge distributions. W
e will show how to evaluate the dipoles and obtain a vector field, which can be a good alternative to the gradient field in pattern recognition.
Dipole and higher moments are physical quantities used to describe a charge distribution. In analogy with electromagnetism, it is possible to define the dipole moments for a gray-scale image, according to the single aspect of a gray-tone map. In this
paper we define the color dipole moments for color images. For color maps in fact, we have three aspects, the three primary colors, to consider. Associating three color charges to each pixel, color dipole moments can be easily defined and used for edge detection.
Quadrupole moments of decuplet baryons and the octet-decuplet transition quadrupole moments are calculated using Morpurgos general QCD parameterization method. Certain relations among the decuplet and the octet to decuplet transition quadrupole momen
ts are derived. These can be used to predict the $Delta$ quadrupole moments which are difficult to measure.
As the computing power of modern hardware is increasing strongly, pre-trained deep learning models (e.g., BERT, GPT-3) learned on large-scale datasets have shown their effectiveness over conventional methods. The big progress is mainly contributed to
the representation ability of transformer and its variant architectures. In this paper, we study the low-level computer vision task (e.g., denoising, super-resolution and deraining) and develop a new pre-trained model, namely, image processing transformer (IPT). To maximally excavate the capability of transformer, we present to utilize the well-known ImageNet benchmark for generating a large amount of corrupted image pairs. The IPT model is trained on these images with multi-heads and multi-tails. In addition, the contrastive learning is introduced for well adapting to different image processing tasks. The pre-trained model can therefore efficiently employed on desired task after fine-tuning. With only one pre-trained model, IPT outperforms the current state-of-the-art methods on various low-level benchmarks. Code is available at https://github.com/huawei-noah/Pretrained-IPT and https://gitee.com/mindspore/mindspore/tree/master/model_zoo/research/cv/IPT
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
