Welcome to the Field Lab. This laboratory investigates questions in vision and psychology with a combination of psychophysical and computational approaches. Below are some highlights from our published research.

List of links on google scholar

Recent Papers

  • Vilankar KP, Golden JR, Chandler DM, Field DJ (2014) Local edge statistics provide information regarding occlusion and nonocclusion edges in natural scenes. Journal of vision 14.9 [link]
  • Alam MM, Vilankar KP, Field DJ, Chandler DM (2014) Local masking in natural images: A database and analysis. Journal of vision 14.8 [link]
  • Chandler DM, Field DJ (2012) Method for estimating the relative contribution of phase and power spectra to the total information in natural-scene patches. Journal of the Optical Society of America A [PDF] [link]
  • Graham DJ, Friedenberg JD, Rockmore DN, Field DJ (2010) Mapping the similarity space of paintings: Image statistics and visual perception. Visual Cognition 18(4) [link]
  • Albert MV, Schnabel A, Field DJ (2008) Innate Visual Learning through Spontaneous Activity Patterns. PLoS Computational Biology 4(8) [PDF] [link]
  • Graham, DJ and Field, DJ. (2008) Global nonlinear compression of natural luminances in painted art. Proceedings of SPIE Conference on Computer Image Analysis in the Study of Art. San Jose, CA 28 January 2008. [PDF]
  • Graham DJ and Field DJ. (2008). " Variations in Intensity Statistics for Representational and Abstract Art, and for Art from the Eastern and Western Hemispheres" Perception in press. [PDF]
  • Kingdom FAA, Field DJ, and Olmos A. (2007). "Does spatial invariance result from insensitivity to change?" Journal of Vision 7(14):11, 1-13, http://journalofvision.org/7/14/11/, doi:10.1167/7.14.11. [PDF]
  • Graham DJ, and Field DJ. (2007). "Statistical regularities of art images and natural scenes: Spectra, sparseness and nonlinearities. Spatial Vision 21, 149-164. [PDF]
  • Chandler DM, and Field DJ. (2007). "Estimates of the Information Content and Dimensionality of Natural Scenes From Proximity Distributions." Journal of the Optical Society of America A, Vol. 24, Issue 4, pp. 922-941. [LINK] [PDF]

Efficiency in visual systems

Sparse coding

Sparse components

The image to the right demonstrates the results of the sparse coding network with natural scenes described by Olshausen and Field (1996). Matlab software to run this algorithm is available from Bruno Olshausen's website. Such sparse components show a number of similarities to receptive fields of V1 simple cells. Several theoretical, computational, and experimental studies suggest that neurons encode sensory information using a small number of active neurons at any given point in time. This strategy, referred to as 'sparse coding', could possibly confer several advantages. First, it allows for increased storage capacity in associative memories; second, it makes the structure in natural signals explicit; third, it represents complex data in a way that is easier to read out at subsequent levels of processing; an d fourth, it saves energy. Recent physiological recordings from sensory neurons have indicated that sparse coding could be a ubiquitous strategy employed in several different modalities across different organisms.

For recent reviews, see:

Related publications include:

Other publications related to theories of efficiency in visual systems:

Modeling the self-similar structure in natural scenes

Fractal self-similar image

Natural scenes are approximately scale invariant with regards to both their power spectra and their phase spectra. Principally because of the phase spectra, wavelet-like transforms are capable of producing a sparse, informative representation of these images. Self-similar codes like the wavelet are effective for many natural phenomena because such phenomena show similar structures to those found in natural scenes. The processing of spatial patterns by the mammalian visual system shows a number of similarities to the 'wavelet transforms' which have attracted considerable interest outside of the study of sensory systems.

Contour integration by the human visual system

Above are examples of stimuli used in Field, Hayes, and Hess 1993. Move your mouse pointer over the images to see the path of elements that has been embedded in an array of randomly oriented elements (non-IE browsers only).

These stimuli were used in experiments to determine the rules that govern the perception of continuity. Using a forced-choice procedure, observers were found to be capable of identifying the path within a field of randomly-oriented elements eve n when the spacing between the elements was considerably larger than the size of any of the individual elements.

When the elements were oriented at angles up to +-60 deg relative to one another, the path was reliably identified. Alignment of the elements along the path was found to play a large role in the ability to detect the path. Small variations in the alignment or aligning the elements orthogonally significantly reduced the observer's ability to detect the presence of a path.

Related publications include:

Art and Natural Scenes

Related publications include:

Blur and Myopia

Related publications include:


Related publications include:


Related publications include:


Related publications include:

Top Cited

The following publications appear on Yury Petrov's list of Top 100 Vision Papers

  • (#31) Olshausen BA, and Field DJ. (1996). "Emergence of Simple-Cell Receptive Field Properties by Learning a Sparse Code for Natural Images." Nature, 381: 607-609. [PDF]
  • (#39) Field DJ. (1987). "Relations Between the Statistics of Natural Images and the Response Profiles of Cortical Cells". Journal of the Optical Society of America A 4 2379-2394. [PDF]
  • (#66) Field DJ. (1994). "What is the Goal of Sensory Coding?" Neural Computation Vol 6: 559-601. [PDF]
  • (#72) Field DJ, Hayes A, and Hess R. (1993). "Contour Integration by the Human Visual System: Evidence for a Local 'Association Field'". Vision Research 33-2, 173-193. [PDF]
  • (#81) Olshausen BA, and Field DJ. (1997). "Sparse Coding with an Overcomplete Basis Set: A Strategy Employed by V1?" Vision Research, 37: 3311-3325. [PDF]