92 Spatial structure and the perceived motion of ob jects of different colors BART FARELL Institute for Sensory Research, Syracuse University, Syracuse, NY 13244-5290, USA The nature of color's contribution to the perception of motion is not completely clear: are motion signals from chromatic and luminance stimuli processed independently or not? Plaids containing 3 component gratings permit study of interactions between ID luminance and chromatic stimuli with identical velocity vectors. These plaids show that whether processing of luminance and chromatic components is independent or interactive depends entirely on spatial variables. Observers see a coherent multicolored plaid only when luminance components have a higher spatial frequency than chromatic components and components of each type have a common orientation. Otherwise luminance and chromatic components do not cohere. The role of luminance edges and relative phase was investigated by combining a chromatic sinusoid and a luminance plaid composed of square waves minus the fundamental. At 0° relative phase, the 3 components are seen to cohere, as found before. Remarkably, a 90° shift in relative phase made the chromatic content of the pattern invisible, leaving behind a thoroughly achromatic plaid. The dependence of perceived motion on spatial parameters suggests that luminance-chromatic interactions are an attempt by the visual system to exploit the expected correlation between luminance and chromatic spatial distributions across objects, while also heavily weighting the contribution of moving luminance edges. Texture processing and image segmentation in man and machines: a unified theory TERRY CAELLI Department of Computer Science, The University of Melbourne, Parkville. Victoria, Australia 3052 Over the past thirty years of investigating the processes that underlie human and machine texture processing and image segmentation a number of computational processes have been shown to be sufficient to attain region identification which confonns to what humans perceive. We attempt to identify, and enumerate the characteristics of these processes and to show how they are common to early processing in both human and machine vision. Finally, we contrast texture or region classification with segmentation problems and argue that the latter is much more fundamental to early visual processing - involving, for stable solutions, more constraints than previously thought in both literatures. Put simply, the main processes involved in texture classification or region classification or segmentation are: ( 1 ) an adaptive form of decomposition - usually expressed either in the form of local auto-correlators, adaptive filter (feature) profiles or the differential weighting of fixed filters; (2) a measure of filter output to 'evidence' feature presence - in particular, invariant region and boundary predicates; (3) a form of constrained clustering with at least one constraint due to spatial contiguity. Evidence for these processes will be given from a variety of biological and machine vision investigations.
Spatial Vision (continued as Seeing & Perceiving from 2010) – Brill
Published: Jan 1, 1993
It’s your single place to instantly
discover and read the research
that matters to you.
Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.
All for just $49/month
Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly
Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.
Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.
Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.
All the latest content is available, no embargo periods.
“Hi guys, I cannot tell you how much I love this resource. Incredible. I really believe you've hit the nail on the head with this site in regards to solving the research-purchase issue.”Daniel C.
“Whoa! It’s like Spotify but for academic articles.”@Phil_Robichaud
“I must say, @deepdyve is a fabulous solution to the independent researcher's problem of #access to #information.”@deepthiw
“My last article couldn't be possible without the platform @deepdyve that makes journal papers cheaper.”@JoseServera