Accidental Pinhole and Pinspeck Cameras

We identify and study two types of “accidental” images that can be formed in scenes. The first is an accidental pinhole camera image. The second class of accidental images are “inverse” pinhole camera images, formed by subtracting an image with a small occluder present from a reference image without the occluder. Both types of accidental cameras happen in a variety of different situations. For example, an indoor scene illuminated by natural light, a street with a person walking under the shadow of a building, etc. The images produced by accidental cameras are often mistaken for shadows or interreflections. However, accidental images can reveal information about the scene outside the image, the lighting conditions, or the aperture by which light enters the scene.

[1]  Abelardo Morell.  A Camera in a Room , 1995 .

[2]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Shree K. Nayar,et al.  A Theory of Single-Viewpoint Catadioptric Image Formation , 1999, International Journal of Computer Vision.

[4]  Frédo Durand,et al.  Image and depth from a conventional camera with a coded aperture , 2007, SIGGRAPH 2007.

[5]  Rob Fergus,et al.  Blind deconvolution using a normalized sparsity measure , 2011, CVPR 2011.

[6]  Shree K. Nayar,et al.  Corneal Imaging System: Environment from Eyes , 2006, International Journal of Computer Vision.

[7]  E. Land,et al.  Lightness and retinex theory. , 1971, Journal of the Optical Society of America.

[8]  John E. Tyler,et al.  The nature of light and colour in the open air , 1954 .

[9]  Edward H. Adelson,et al.  Recovering intrinsic images from a single image , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Ramesh Raskar,et al.  Fast separation of direct and global components of a scene using high frequency illumination , 2006, ACM Trans. Graph..

[11]  Edward H. Adelson,et al.  Ground truth dataset and baseline evaluations for intrinsic image algorithms , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[12]  William T. Freeman,et al.  Diffuse reflectance imaging with astronomical applications , 2011, 2011 International Conference on Computer Vision.

[13]  Antonio Torralba,et al.  Accidental pinhole and pinspeck cameras: Revealing the scene outside the picture , 2012, CVPR.

[14]  H. Barrow,et al.  RECOVERING INTRINSIC SCENE CHARACTERISTICS FROM IMAGES , 1978 .

[15]  Yair Weiss,et al.  Deriving intrinsic images from image sequences , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[16]  Andrew T. Young,et al.  Television photometry: The Mariner 9 experience , 1974 .

[17]  Antonio Torralba,et al.  Building a database of 3D scenes from user annotations , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[18]  Adam L. Cohen.  Anti-pinhole Imaging , 1982 .

[19]  Shree K. Nayar,et al.  Lensless Imaging with a Controllable Aperture , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[20]  Frédo Durand,et al.  Image and depth from a conventional camera with a coded aperture , 2007, ACM Trans. Graph..

[21]  Jitendra Malik,et al.  Shape, albedo, and illumination from a single image of an unknown object , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[22]  Derek Nowrouzezahrai,et al.  Learning hatching for pen-and-ink illustration of surfaces , 2012, TOGS.

[23]  James F. O'Brien,et al.  Exposing photo manipulation with inconsistent reflections , 2012, TOGS.

[24]  Edward H. Adelson,et al.  Single Lens Stereo with a Plenoptic Camera , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[25]  Richard Szeliski,et al.  Automatic Estimation and Removal of Noise from a Single Image , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[26]  Ian D. Reid,et al.  Single View Metrology , 2000, International Journal of Computer Vision.