It has previously been suggested that neurons with line and edge selectivities found in primary visual cortex of cats and monkeys form a sparse, distributed representation of natural scenes, and it has been reasoned that such responses should emerge from an unsupervised learning algorithm

We propose a novel approach to learn and recognize natural scene categories. Unlike previous work [9, 17], it does not require experts to annotate the training set. We represent the image of a scene by a collection of local regions, denoted as codewords obtained by unsupervised learning. Each

and achieves high accuracy on a large database of fifteen natural scene categories. The spatial pyramid framework also offers insights into the success of several recently proposed image descriptions, including Torralba’s “gist ” and Lowe’s SIFT descriptors. 1.

The committee, the college and the University of Central Florida are not liable for any use of the materials presented in this study. FAST ALGORITHMS FOR FRAGMENT BASED COMPLETION IN IMAGES OF

We applied independent component analysis (ICA) to hyperspectral images in order to learn an efficient representation of color in natural scenes. In the spectra of single pixels, the algorithm found basis functions that had broadband spectra and basis functions that were similar to natural

. Early stages of visual processing may exploit the characteristic structure of natural visual stimuli. This structure may differ from the intrinsic structure of natural scenes, because sampling of the environment is an active process. For example, humans move their eyes several times a second when

This paper gives an algorithm for detecting and reading text in natural images. The algorithm is intended for use by blind and visually impaired subjects walking through city scenes. We first obtain a dataset of city images taken by blind and normally sighted subjects. From this dataset, we

in positive. We apply the Multiple-Instance learning framework to the problem of learning how to classify natural images. Images are inherently ambiguous since they can represent many different things. A user labels an image as positive if the image somehow contains the concept. Each image is a bag

America A and is made available as an electronic reprint with the permission of OSA. The paper can also be found at the following URL on the OSA website:

Binocular disparity is the input to stereopsis, which is a very strong depth cue in humans. However, the distribution of binocular disparities in natural environments has not been quantitatively measured. In this study, we converted distances from accurate range maps of forest scenes and indoor