The cellular neural/nonlinear network (CNN) is a powerful tool for image and video signal processing,robotic and biological visions. This paper discusses a general method for designing template of the global connectiv...The cellular neural/nonlinear network (CNN) is a powerful tool for image and video signal processing,robotic and biological visions. This paper discusses a general method for designing template of the global connectivitydetection (GCD) CNN, which provides parameter inequalities for determining parameter intervals for implementing thecorresponding functions. The GCD CNN has stronger ability and faster rate for determining global connectivity in binarypatterns than the GCD CNN proposed by Zarandy. An example for detecting the connectivity in complex patterns isgiven.展开更多
High-resolution U–Pb(ID-TIMS,baddeleyite)ages are presented for mafic dykes from selected swarms in two important Amazonian regions:the Carajás Province in the east,and the Rio Apa block in the southwest–areas
In this study, we present a new andinnovative framework for acquiring high-qualitySVBRDF maps. Our approach addresses the limitations of the current methods and proposes a newsolution. The core of our method is a simp...In this study, we present a new andinnovative framework for acquiring high-qualitySVBRDF maps. Our approach addresses the limitations of the current methods and proposes a newsolution. The core of our method is a simple hardwaresetup consisting of a consumer-level camera, LEDlights, and a carefully designed network that canaccurately obtain the high-quality SVBRDF propertiesof a nearly planar object. By capturing a flexiblenumber of images of an object, our network usesdifferent subnetworks to train different property mapsand employs appropriate loss functions for each ofthem. To further enhance the quality of the maps, weimproved the network structure by adding a novel skipconnection that connects the encoder and decoder withglobal features. Through extensive experimentation usingboth synthetic and real-world materials, our resultsdemonstrate that our method outperforms previousmethods and produces superior results. Furthermore,our proposed setup can also be used to acquire physicallybased rendering maps of special materials.展开更多
文摘The cellular neural/nonlinear network (CNN) is a powerful tool for image and video signal processing,robotic and biological visions. This paper discusses a general method for designing template of the global connectivitydetection (GCD) CNN, which provides parameter inequalities for determining parameter intervals for implementing thecorresponding functions. The GCD CNN has stronger ability and faster rate for determining global connectivity in binarypatterns than the GCD CNN proposed by Zarandy. An example for detecting the connectivity in complex patterns isgiven.
文摘High-resolution U–Pb(ID-TIMS,baddeleyite)ages are presented for mafic dykes from selected swarms in two important Amazonian regions:the Carajás Province in the east,and the Rio Apa block in the southwest–areas
基金supported by the Nature Science Fund of Guangdong Province(No.2021A1515011849)the Key Area Research and Development of Guangdong Province(No.2022A0505050014).
文摘In this study, we present a new andinnovative framework for acquiring high-qualitySVBRDF maps. Our approach addresses the limitations of the current methods and proposes a newsolution. The core of our method is a simple hardwaresetup consisting of a consumer-level camera, LEDlights, and a carefully designed network that canaccurately obtain the high-quality SVBRDF propertiesof a nearly planar object. By capturing a flexiblenumber of images of an object, our network usesdifferent subnetworks to train different property mapsand employs appropriate loss functions for each ofthem. To further enhance the quality of the maps, weimproved the network structure by adding a novel skipconnection that connects the encoder and decoder withglobal features. Through extensive experimentation usingboth synthetic and real-world materials, our resultsdemonstrate that our method outperforms previousmethods and produces superior results. Furthermore,our proposed setup can also be used to acquire physicallybased rendering maps of special materials.
