0000000000074503

AUTHOR

Alexander Hepburn

showing 3 related works from this author

PerceptNet: A Human Visual System Inspired Neural Network for Estimating Perceptual Distance

2019

Traditionally, the vision community has devised algorithms to estimate the distance between an original image and images that have been subject to perturbations. Inspiration was usually taken from the human visual perceptual system and how the system processes different perturbations in order to replicate to what extent it determines our ability to judge image quality. While recent works have presented deep neural networks trained to predict human perceptual quality, very few borrow any intuitions from the human visual system. To address this, we present PerceptNet, a convolutional neural network where the architecture has been chosen to reflect the structure and various stages in the human…

FOS: Computer and information sciencesComputer Science - Machine LearningVisual perceptionComputer scienceImage qualitymedia_common.quotation_subjectFeature extractionMachine Learning (stat.ML)02 engineering and technology01 natural sciencesConvolutional neural networkhuman visual systemMachine Learning (cs.LG)010309 opticsStatistics - Machine LearningPerception0103 physical sciences0202 electrical engineering electronic engineering information engineeringFOS: Electrical engineering electronic engineering information engineeringperceptual distancemedia_commonArtificial neural networkbusiness.industryDeep learningImage and Video Processing (eess.IV)Pattern recognitionElectrical Engineering and Systems Science - Image and Video Processingneural networksHuman visual system model020201 artificial intelligence & image processingArtificial intelligencebusiness
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Enforcing Perceptual Consistency on Generative Adversarial Networks by Using the Normalised Laplacian Pyramid Distance

2019

In recent years there has been a growing interest in image generation through deep learning. While an important part of the evaluation of the generated images usually involves visual inspection, the inclusion of human perception as a factor in the training process is often overlooked. In this paper we propose an alternative perceptual regulariser for image-to-image translation using conditional generative adversarial networks (cGANs). To do so automatically (avoiding visual inspection), we use the Normalised Laplacian Pyramid Distance (NLPD) to measure the perceptual similarity between the generated image and the original image. The NLPD is based on the principle of normalising the value of…

FOS: Computer and information sciencesComputer Science - Machine LearningComputer scienceImage qualitymedia_common.quotation_subjectComputer Vision and Pattern Recognition (cs.CV)Computer Science - Computer Vision and Pattern RecognitionComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONMachine Learning (stat.ML)Translation (geometry)Image (mathematics)Machine Learning (cs.LG)Consistency (database systems)Statistics - Machine LearningPerceptionFOS: Electrical engineering electronic engineering information engineeringmedia_commonbusiness.industryDeep learningImage and Video Processing (eess.IV)Contrast (statistics)Pattern recognitionGeneral MedicineImage segmentationElectrical Engineering and Systems Science - Image and Video ProcessingGenerative Adversarial NetworkPerceptionArtificial intelligencebusiness
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Disentangling the Link Between Image Statistics and Human Perception

2023

In the 1950s Horace Barlow and Fred Attneave suggested a connection between sensory systems and how they are adapted to the environment: early vision evolved to maximise the information it conveys about incoming signals. Following Shannon's definition, this information was described using the probability of the images taken from natural scenes. Previously, direct accurate predictions of image probabilities were not possible due to computational limitations. Despite the exploration of this idea being indirect, mainly based on oversimplified models of the image density or on system design methods, these methods had success in reproducing a wide range of physiological and psychophysical phenom…

FOS: Computer and information sciencesComputer Science - Machine LearningComputer Vision and Pattern Recognition (cs.CV)FOS: Biological sciencesQuantitative Biology - Neurons and CognitionComputer Science - Computer Vision and Pattern RecognitionNeurons and Cognition (q-bio.NC)ArticleMachine Learning (cs.LG)
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