A central goal of neuroscience is to understand the representations formed by brain activity
patterns and their connection to behaviour. The classic approach is to investigate how …

Periodic visual stimulation and analysis of the resulting steady-state visual evoked potentials
were first introduced over 80 years ago as a means to study visual sensation and …

This book argues that there is a joint in nature between seeing and thinking, perception, and
cognition. Perception is constitutively iconic, nonconceptual, and nonpropositional, whereas …

Batch Normalization (BN) is a milestone technique in the development of deep learning,
enabling various networks to train. However, normalizing along the batch dimension …

We describe an image compression method, consisting of a nonlinear analysis
transformation, a uniform quantizer, and a nonlinear synthesis transformation. The …

We propose a multi-task end-to-end optimized deep neural network (MEON) for blind image
quality assessment (BIQA). MEON consists of two sub-networks-a distortion identification …

Normalization layers are a staple in state-of-the-art deep neural network architectures. They
are widely believed to stabilize training, enable higher learning rate, accelerate …

Most publicly available image quality databases have been created under highly controlled
conditions by introducing graded simulated distortions onto high-quality photographs …

Despite great efforts over several decades, our best models of primary visual cortex (V1) still
predict spiking activity quite poorly when probed with natural stimuli, highlighting our limited …

We propose a referenceless perceptual fog density prediction model based on natural
scene statistics (NSS) and fog aware statistical features. The proposed model, called Fog …