3D Building Synthesis Based on Images and Affine Invariant Salient Features

2021 ◽  
Author(s):  
Chenxi Li
2003 ◽  
Vol 762 ◽  
Author(s):  
J. David Cohen

AbstractThis paper first briefly reviews a few of the early studies that established some of the salient features of light-induced degradation in a-Si,Ge:H. In particular, I discuss the fact that both Si and Ge metastable dangling bonds are involved. I then review some of the recent studies carried out by members of my laboratory concerning the details of degradation in the low Ge fraction alloys utilizing the modulated photocurrent method to monitor the individual changes in the Si and Ge deep defects. By relating the metastable creation and annealing behavior of these two types of defects, new insights into the fundamental properties of metastable defects have been obtained for amorphous silicon materials in general. I will conclude with a brief discussion of the microscopic mechanisms that may be responsible.


2021 ◽  
Vol 13 (2) ◽  
pp. 274
Author(s):  
Guobiao Yao ◽  
Alper Yilmaz ◽  
Li Zhang ◽  
Fei Meng ◽  
Haibin Ai ◽  
...  

The available stereo matching algorithms produce large number of false positive matches or only produce a few true-positives across oblique stereo images with large baseline. This undesired result happens due to the complex perspective deformation and radiometric distortion across the images. To address this problem, we propose a novel affine invariant feature matching algorithm with subpixel accuracy based on an end-to-end convolutional neural network (CNN). In our method, we adopt and modify a Hessian affine network, which we refer to as IHesAffNet, to obtain affine invariant Hessian regions using deep learning framework. To improve the correlation between corresponding features, we introduce an empirical weighted loss function (EWLF) based on the negative samples using K nearest neighbors, and then generate deep learning-based descriptors with high discrimination that is realized with our multiple hard network structure (MTHardNets). Following this step, the conjugate features are produced by using the Euclidean distance ratio as the matching metric, and the accuracy of matches are optimized through the deep learning transform based least square matching (DLT-LSM). Finally, experiments on Large baseline oblique stereo images acquired by ground close-range and unmanned aerial vehicle (UAV) verify the effectiveness of the proposed approach, and comprehensive comparisons demonstrate that our matching algorithm outperforms the state-of-art methods in terms of accuracy, distribution and correct ratio. The main contributions of this article are: (i) our proposed MTHardNets can generate high quality descriptors; and (ii) the IHesAffNet can produce substantial affine invariant corresponding features with reliable transform parameters.


Author(s):  
Jennifer Duncan

AbstractThe Brascamp–Lieb inequalities are a very general class of classical multilinear inequalities, well-known examples of which being Hölder’s inequality, Young’s convolution inequality, and the Loomis–Whitney inequality. Conventionally, a Brascamp–Lieb inequality is defined as a multilinear Lebesgue bound on the product of the pullbacks of a collection of functions $$f_j\in L^{q_j}(\mathbb {R}^{n_j})$$ f j ∈ L q j ( R n j ) , for $$j=1,\ldots ,m$$ j = 1 , … , m , under some corresponding linear maps $$B_j$$ B j . This regime is now fairly well understood (Bennett et al. in Geom Funct Anal 17(5):1343–1415, 2008), and moving forward there has been interest in nonlinear generalisations, where $$B_j$$ B j is now taken to belong to some suitable class of nonlinear maps. While there has been great recent progress on the question of local nonlinear Brascamp–Lieb inequalities (Bennett et al. in Duke Math J 169(17):3291–3338, 2020), there has been relatively little regarding global results; this paper represents some progress along this line of enquiry. We prove a global nonlinear Brascamp–Lieb inequality for ‘quasialgebraic’ maps, a class that encompasses polynomial and rational maps, as a consequence of the multilinear Kakeya-type inequalities of Zhang and Zorin-Kranich. We incorporate a natural affine-invariant weight that both compensates for local degeneracies and yields a constant with minimal dependence on the underlying maps. We then show that this inequality generalises Young’s convolution inequality on algebraic groups with suboptimal constant.


Genetics ◽  
1999 ◽  
Vol 153 (2) ◽  
pp. 573-583 ◽  
Author(s):  
Henriette M Foss ◽  
Kenneth J Hillers ◽  
Franklin W Stahl

AbstractSalient features of recombination at ARG4 of Saccharomyces provoke a variation of the double-strand-break repair (DSBR) model that has the following features: (1) Holliday junction cutting is biased in favor of strands upon which DNA synthesis occurred during formation of the joint molecule (this bias ensures that cutting both junctions of the joint-molecule intermediate arising during DSBR usually leads to crossing over); (2) cutting only one junction gives noncrossovers; and (3) repair of mismatches that are semirefractory to mismatch repair and/or far from the DSB site is directed primarily by junction resolution. The bias in junction resolution favors restoration of 4:4 segregation when such mismatches and the directing junction are on the same side of the DSB site. Studies at HIS4 confirmed the predicted influence of the bias in junction resolution on the conversion gradient, type of mismatch repair, and frequency of aberrant 5:3 segregation, as well as the predicted relationship between mismatch repair and crossing over.


Author(s):  
Kapil Sharma ◽  
Naresh Surineni ◽  
Sayani Das ◽  
Shivajirao L. Gholap

A concise and divergent chiron approach for the first total synthesis of (+)-Pseudonocardide A, (+)-Pseudonocardide C and an epimer of ent-Pseudonocardide D is reported starting from D-ribose. The salient features...


2020 ◽  
Vol 6 (4) ◽  
pp. 178-179
Author(s):  
Santa Heede ◽  
Stephan Johannes Linke

Heavy eye syndrome is an important type of myopia-induced strabismus. We provide an overview of heavy eye syndrome, from its history to its most salient features. The theory of the orbital and rectus muscle pulley system as it relates to heavy eye syndrome and the prevailing theories on the pathophysiology of heavy eye syndrome in the current literature are discussed. We also highlight the presentation of heavy eye syndrome, its typical features on imaging, and differential diagnosis. Finally, we provide an overview on the management of heavy eye syndrome, including a description of several current surgical techniques.


Author(s):  
Steve Cooke

AbstractAnimal agriculture predominantly involves farming social animals. At the same time, the nature of agriculture requires severely disrupting, eliminating, and controlling the relationships that matter to those animals, resulting in harm and unhappiness for them. These disruptions harm animals, both physically and psychologically. Stressed animals are also bad for farmers because stressed animals are less safe to handle, produce less, get sick more, and produce poorer quality meat. As a result, considerable efforts have gone into developing stress-reduction methods. Many of these attempt to replicate behaviours or physiological responses that develop or constitute bonding between animals. In other words, humans try to mitigate or ameliorate the damage done by preventing and undermining intraspecies relationships. In doing so, the wrong of relational harms is compounded by an instrumentalisation of trust and care. The techniques used are emblematic of the welfarist approach to animal ethics. Using the example of gentle touching in the farming of cows for beef and dairy, the paper highlights two types of wrong. First, a wrong done in the form of relational harms, and second, a wrong done by instrumentalising relationships of care and trust. Relational harms are done to nonhuman animals, whilst instrumentalisation of care and trust indicates an insensitivity to morally salient features of the situation and a potential character flaw in the agents that carry it out.


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