Fixations on Low Resolution Images

Fixations on low-resolution images.

When an observer looks at an image, his eyes fixate on a few select points. Fixations from different observers are often consistent-observers tend to look at the same locations. We investigate how image resolution affects fixation locations and consistency across humans through an eye-tracking experiment. We showed 168 natural images and 25 pink noise images at different resolutions to 64 obser...

Emergence of Proto-Object Representations via Fixations in Low-Resolution

One prominent feature of our visual system is that the fovea – the highest-resolution portion of the retina – only occupies two visual degrees, while the remaining portion of the retina (parafovea and periphery) are mainly in low-resolution. Therefore, before we make a saccadic eye movement, the potential fixation target is usually located in parafovea or periphery and is perceived in low-resol...

Face Model Fitting on Low Resolution Images

Active Appearance Models (AAMs) represent the shape and appearance of an object via two low-dimensional subspaces, one for shape and one for appearance. AAMs for facial images are currently receiving considerable attention from the vision community. However, most existing work focuses on fitting AAMs to high-quality facial images. For many applications, effectively fitting an AAM to low-resolut...

Head Pose Estimation on Low Resolution Images

This paper addresses the problem of estimating head pose over a wide range of angles from low-resolution images. Faces are detected using chrominance-based features. Grey-level normalized face imagettes serve as input for linear auto-associative memory. One memory is computed for each pose using a Widrow-Hoff learning rule. Head pose is classified with a winner-takes-all process. We compare res...

Multi Resolution Fuzzy Segmentation of Satellite Images