IEEE transactions on neural networks and learning systems
Jun 1, 2018
In this paper, we propose an efficient visual tracker, which directly captures a bounding box containing the target object in a video by means of sequential actions learned using deep neural networks. The proposed deep neural network to control track...
Primate brains and state-of-the-art convolutional neural networks can recognize many faces, objects and scenes, though how they do so is often mysterious. New research unveils some of the mystery, revealing unexpected complexity in the recognition st...
In this paper, we focused on building a universal haptic texture models library and automatic assignment of haptic texture models to any given surface from the library based on image features. It is shown that a relationship exists between perceived ...
Recently, experimental and theoretical research has focused on the brain's abilities to extract information from a noisy sensory environment and how cross-modal inputs are processed to solve the causal inference problem to provide the best estimate o...
IEEE transactions on neural networks and learning systems
Nov 1, 2017
All complex motion patterns can be decomposed into several elements, including translation, expansion/contraction, and rotational motion. In biological vision systems, scientists have found that specific types of visual neurons have specific preferen...
Our visual environment is full of texture-"stuff" like cloth, bark, or gravel as distinct from "things" like dresses, trees, or paths-and humans are adept at perceiving subtle variations in material properties. To investigate image features important...
What are the roles of central and peripheral vision in human scene recognition? Larson and Loschky (2009) showed that peripheral vision contributes more than central vision in obtaining maximum scene recognition accuracy. However, central vision is m...
Philosophical transactions of the Royal Society of London. Series B, Biological sciences
Jan 5, 2017
Even newborn infants are able to extract structure from a stream of sensory inputs; yet how this is achieved remains largely a mystery. We present a connectionist autoencoder model, TRACX2, that learns to extract sequence structure by gradually const...
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Mar 1, 2016
Visual tracking is challenging due to image variations caused by various factors, such as object deformation, scale change, illumination change, and occlusion. Given the superior tracking performance of human visual system (HVS), an ideal design of b...