Eye movements were monitored while participants performed a change detection task with images of natural scenes. An initial and a modified scene image were displayed in alternation, separated by a blank interval (flicker paradigm). In the modified image, a single target object was changed either by deleting that object from the scene or by rotating that object 90 degrees in depth. In Experiment...

Natural scene features stabilize and extend the tracking range of augmented reality (AR) pose-tracking systems. We develop robust computer vision methods to detect and track natural features in video images. Point and region features are automatically and adaptively selected for properties that lead to robust tracking. A multistage tracking algorithm produces accurate motion estimates, and the ...

This paper describes a database collecting natural color images, called the Natural Image Database, and shows how it is used for illuminant estimation problems. First, we present significant features of the database. This database is not only a collection of color images of natural scenes, but also includes scene illuminant spectra and camera calibration data. Because these data sets are availa...

The bag of visual words (BOW) model is an efficient image representation technique for image categorisation and annotation tasks. Building good visual vocabularies, from automatically extracted image feature vectors, produces discriminative visual words which can improve the accuracy of image categorisation tasks. Most approaches that use the BOW model in categorising images ignore useful infor...

The bag of visual words (BOW) model is an efficient image representation technique for image categorisation and annotation tasks. Building good visual vocabularies, from automatically extracted image feature vectors, produces discriminative visual words which can improve the accuracy of image categorisation tasks. Most approaches that use the BOW model in categorising images ignore useful infor...

Providing labeled Arabic text images dataset for scene detection is inherently difficult and costly at the same time. Consequently, only few small datasets are available this task. Previous work has focused on data augmentation technique of datasets; however, generated with these techniques cannot reproduce complexity variability natural images. In paper, we propose a new using Google Street Vi...

Humans can only just recognize images with a geometric resolution of 32× 32 pixels. However, this fact does not mean that it is also possible to assign predefined scene category labels like indoor, outdoor, kitchen or beach to such a tiny image, neither for humans nor for computers. The purpose of our work is to investigate the applicability of common scene categorization methods and the import...

Text recognition in natural scene is a challenging problem due to the many factors affecting text appearance. In this paper, we presents a method that directly transcribes scene text images to text without needing of sophisticated character segmentation. We leverage recent advances of deep neural networks to model the appearance of scene text images with temporal dynamics. Specifically, we inte...