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Register a new userColor Dipole Moments for Edge Detection
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Added by
Amelia Sparavigna
Publication date
2009
fields
Informatics Engineering
and research's language is
English
Authors
Amelia Sparavigna
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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.
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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.
In this work, we present a generalized and robust facial manipulation detection method based on color distribution analysis of the vertical region of edge in a manipulated image. Most of the contemporary facial manipulation method involves pixel correction procedures for reducing awkwardness of pixel value differences along the facial boundary in a synthesized image. For this procedure, there are distinctive differences in the facial boundary between face manipulated image and unforged natural image. Also, in the forged image, there should be distinctive and unnatural features in the gap distribution between facial boundary and background edge region because it tends to damage the natural effect of lighting. We design the neural network for detecting face-manipulated image with these distinctive features in facial boundary and background edge. Our extensive experiments show that our method outperforms other existing face manipulation detection methods on detecting synthesized face image in various datasets regardless of whether it has participated in training.
Searches for permanent electric dipole moments of fundamental particles and systems with spin are the experiments most sensitive to new CP violating physics and a top priority of a growing international community. We briefly review the current status of the field emphasizing on the charged leptons and lightest baryons.
With large active volume sizes dark matter direct detection experiments are sensitive to solar neutrino fluxes. Nuclear recoil signals are induced by $^8$B neutrinos, while electron recoils are mainly generated by the pp flux. Measurements of both processes offer an opportunity to test neutrino properties at low thresholds with fairly low backgrounds. In this paper we study the sensitivity of these experiments to neutrino magnetic dipole moments assuming 1, 10 and 40 tonne active volumes (representative of XENON1T, XENONnT and DARWIN), 0.3 keV and 1 keV thresholds. We show that with nuclear recoil measurements alone a 40 tonne detector could be as competitive as Borexino, TEXONO and GEMMA, with sensitivities of order $8.0times 10^{-11},mu_B$ at the $90%$ CL after one year of data taking. Electron recoil measurements will increase sensitivities way below these values allowing to test regions not excluded by astrophysical arguments. Using electron recoil data and depending on performance, the same detector will be able to explore values down to $4.0times 10^{-12}mu_B$ at the $90%$ CL in one year of data taking. By assuming a 200-tonne liquid xenon detector operating during 10 years, we conclude that sensitivities in this type of detectors will be of order $10^{-12},mu_B$. Reducing statistical uncertainties may enable improving sensitivities below these values.
Pursuing more complete and coherent scene understanding towards realistic vision applications drives edge detection from category-agnostic to category-aware semantic level. However, finer delineation of instance-level boundaries still remains unexcavated. In this work, we address a new finer-grained task, termed panoptic edge detection (PED), which aims at predicting semantic-level boundaries for stuff categories and instance-level boundaries for instance categories, in order to provide more comprehensive and unified scene understanding from the perspective of edges.We then propose a versatile framework, Panoptic Edge Network (PEN), which aggregates different tasks of object detection, semantic and instance edge detection into a single holistic network with multiple branches. Based on the same feature representation, the semantic edge branch produces semantic-level boundaries for all categories and the object detection branch generates instance proposals. Conditioned on the prior information from these two branches, the instance edge branch aims at instantiating edge predictions for instance categories. Besides, we also devise a Panoptic Dual F-measure (F2) metric for the new PED task to uniformly measure edge prediction quality for both stuff and instances. By joint end-to-end training, the proposed PEN framework outperforms all competitive baselines on Cityscapes and ADE20K datasets.
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