Vintage stereoscopes and stereo views are plentiful on eBay, and I confess that my book-fair adventure seems to be blooming into a binge of acquisitiveness. Waiting for me at home, as yet unpacked, is a series of medical stereographs reportedly so grisly that the seller wouldn’t show them on her eBay page. Meanwhile, I’ve scanned my initial collection of stereo cards and posted them on Flickr.
http://www.brutusostling.se/english/main.html
Brutus was appointed to "Nature Photographer of the Nordic countries", 2007-2008.
http://www.cambridgeincolour.com/tutorials/diffraction-photography.htm
Diffraction is an optical effect which can limit the total resolution of your photography-- no matter how many megapixels your camera may have. Ordinarily light travels in straight lines through uniform air, however it begins to disperse or "diffract" when squeezed through a small hole (such as your camera's aperture). This effect is normally negligible, but increases for very small apertures. Since photographers pursuing better sharpness use smaller apertures to achieve a greater depth of field, at some aperture the softening effects of diffraction offset any gain in sharpness due to better depth of field. When this occurs your camera optics are said to have become diffraction limited. Knowing this limit can help you to avoid any subsequent softening, and the unnecessarily long exposure time or high ISO speed required for such a small aperture.
http://www.silverlight.co.uk/tutorials/toc.html
http://www.imaging-resource.com/ARTS/MONCAL/CALIBRATE.HTM
http://www.northlight-images.co.uk/article_pages/cameras/1ds3_af_micoadjustment.html
http://grail.cs.washington.edu/projects/videoenhancement/videoEnhancement.htm
We present a framework for automatically enhancing videos of a static scene using a few photographs of the same scene. For example, our system can transfer photographic qualities such as high resolution, high dynamic range and better lighting from the photographs to the video. Additionally, the user can quickly modify the video by editing only a few still images of the scene. Finally, our system allows a user to remove unwanted objects and camera shake from the video. These capabilities are enabled by two technical contributions presented in this paper. First, we make several improvements to a state-of-the-art multiview stereo algorithm in order to compute view-dependent depths using video, photographs, and structure-from-motion data. Second, we present a novel image-based rendering algorithm that can re-render the input video using the appearance of the photographs while preserving certain temporal dynamics such as specularities and dynamic scene lighting.
http://www.cs.tau.ac.il/~tommer/beautification2008/
When human raters are presented with a collection of shapes and asked to rank them according to their aesthetic appeal, the results often indicate that there is a statistical consensus among the raters. Yet it might be difficult to define a succinct set of rules that capture the aesthetic preferences of the raters. In this work, we explore a data-driven approach to aesthetic enhancement of such shapes. Specifically, we focus on the challenging problem of enhancing the aesthetic appeal (or the attractiveness) of human faces in frontal photographs (portraits), while maintaining close similarity with the original.
http://www1.cs.columbia.edu/CAVE/projects/face_replace/
Advances in digital photography have made it possible to capture large collections of high-resolution images and share them on the internet. While the size and availability of these collections is leading to many exciting new applications, it is also creating new problems. One of the most important of these problems is privacy. Online systems such as Google Street View allow users to interactively navigate through panoramic images of public places created using thousands of photographs. We believe that an attractive solution to the privacy problem is to remove the identities of people in photographs by automatically replacing their faces with ones from a collection of stock images. Automatic face replacement has other compelling applications as well. For example, people commonly have large personal collections of photos on their computers. These collections often contain many photos of the same person(s) taken with different expressions, and under various poses and lighting conditions. One can use such collections to create novel images by replacing faces in one image with more appealing faces of the same person from other images. For group shots, the burst mode available in most cameras can be used to take several images at a time. With an automatic face replacement approach, one could create a single composite image with, for example, everyone smiling and with both eyes open.