Image translation plays a significant role in realistic image synthesis,entertainment tasks such as editing and colorization,and security including personal identification.In Edge GAN,the major contribution is attribu...Image translation plays a significant role in realistic image synthesis,entertainment tasks such as editing and colorization,and security including personal identification.In Edge GAN,the major contribution is attribute guided vector that enables high visual quality content generation.This research study proposes automatic face image realism from freehand sketches based on Edge GAN.We propose a density variant image synthesis model,allowing the input sketch to encompass face features with minute details.The density level is projected into non-latent space,having a linear controlled function parameter.This assists the user to appropriately devise the variant densities of facial sketches and image synthesis.Composite data set of Large Scale CelebFaces Attributes(ClebA),Labelled Faces in theWild(LFWH),Chinese University of Hong Kong(CHUK),and self-generated Asian images are used to evaluate the proposed approach.The solution is validated to have the capability for generating realistic face images through quantitative and qualitative results and human evaluation.展开更多
Several recent successes in deep learning(DL),such as state-of-the-art performance on several image classification benchmarks,have been achieved through the improved configuration.Hyperparameters(HPs)tuning is a key f...Several recent successes in deep learning(DL),such as state-of-the-art performance on several image classification benchmarks,have been achieved through the improved configuration.Hyperparameters(HPs)tuning is a key factor affecting the performance of machine learning(ML)algorithms.Various state-of-the-art DL models use different HPs in different ways for classification tasks on different datasets.This manuscript provides a brief overview of learning parameters and configuration techniques to show the benefits of using a large-scale handdrawn sketch dataset for classification problems.We analyzed the impact of different learning parameters and toplayer configurations with batch normalization(BN)and dropouts on the performance of the pre-trained visual geometry group 19(VGG-19).The analyzed learning parameters include different learning rates and momentum values of two different optimizers,such as stochastic gradient descent(SGD)and Adam.Our analysis demonstrates that using the SGD optimizer and learning parameters,such as small learning rates with high values of momentum,along with both BN and dropouts in top layers,has a good impact on the sketch image classification accuracy.展开更多
A novel and fast shape classification and regularization algorithm for on-line sketchy graphics recognition is proposed. We divide the on-line graphics recognition process into four stages: preprocessing,shape classif...A novel and fast shape classification and regularization algorithm for on-line sketchy graphics recognition is proposed. We divide the on-line graphics recognition process into four stages: preprocessing,shape classification,shape fitting,and regularization. Attraction Force Model is employed to progressively combine the vertices on the input sketchy stroke and reduce the total number of vertices before the type of shape can be determined. After that ,the shape is fitted and gradually rectified to a regular one,thus the regularized shape fits the user intended one precisely.Experimental results show that this algorithm can yield good recognition precision(averagely above 90% )and fine regularization effect but with fast speed. Consequently,it is especially suitable to computational critical environment such as PDAs,which solely depends on a pen-based user interface.展开更多
基金The authors received no specific funding for this study.
文摘Image translation plays a significant role in realistic image synthesis,entertainment tasks such as editing and colorization,and security including personal identification.In Edge GAN,the major contribution is attribute guided vector that enables high visual quality content generation.This research study proposes automatic face image realism from freehand sketches based on Edge GAN.We propose a density variant image synthesis model,allowing the input sketch to encompass face features with minute details.The density level is projected into non-latent space,having a linear controlled function parameter.This assists the user to appropriately devise the variant densities of facial sketches and image synthesis.Composite data set of Large Scale CelebFaces Attributes(ClebA),Labelled Faces in theWild(LFWH),Chinese University of Hong Kong(CHUK),and self-generated Asian images are used to evaluate the proposed approach.The solution is validated to have the capability for generating realistic face images through quantitative and qualitative results and human evaluation.
文摘Several recent successes in deep learning(DL),such as state-of-the-art performance on several image classification benchmarks,have been achieved through the improved configuration.Hyperparameters(HPs)tuning is a key factor affecting the performance of machine learning(ML)algorithms.Various state-of-the-art DL models use different HPs in different ways for classification tasks on different datasets.This manuscript provides a brief overview of learning parameters and configuration techniques to show the benefits of using a large-scale handdrawn sketch dataset for classification problems.We analyzed the impact of different learning parameters and toplayer configurations with batch normalization(BN)and dropouts on the performance of the pre-trained visual geometry group 19(VGG-19).The analyzed learning parameters include different learning rates and momentum values of two different optimizers,such as stochastic gradient descent(SGD)and Adam.Our analysis demonstrates that using the SGD optimizer and learning parameters,such as small learning rates with high values of momentum,along with both BN and dropouts in top layers,has a good impact on the sketch image classification accuracy.
文摘A novel and fast shape classification and regularization algorithm for on-line sketchy graphics recognition is proposed. We divide the on-line graphics recognition process into four stages: preprocessing,shape classification,shape fitting,and regularization. Attraction Force Model is employed to progressively combine the vertices on the input sketchy stroke and reduce the total number of vertices before the type of shape can be determined. After that ,the shape is fitted and gradually rectified to a regular one,thus the regularized shape fits the user intended one precisely.Experimental results show that this algorithm can yield good recognition precision(averagely above 90% )and fine regularization effect but with fast speed. Consequently,it is especially suitable to computational critical environment such as PDAs,which solely depends on a pen-based user interface.
