
Submitted by Assigned_Reviewer_1
Q1: Comments to author(s). First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. (For detailed reviewing guidelines, see http://nips.cc/PaperInformation/ReviewerInstructions)
The paper describes a novel way to parameterize stimuli for generating spacetime texture fields modeled after realworld deformations. The intent is to provide a more rigid framework by which scientists may psychophysically test motion perception. The model allows one to generate causal timestructured "Motion Clouds" in realtime. Motion Clouds are textured stimuli, which could be gaborlike or described by other functions. The model includes independent variables to describe motion, position, and warping. Warping describes texture zooming and rotations. They further suggest parameter distributions and stimulus properties that match well with existing psychophysical data. They use their model to generate stimuli for a speed discrimination psychophysical task and demonstrate their ability to assess how the stimulus spatial frequency affects speed discrimination. I interpret their contribution as a GPU ready method for generating realtime stimuli that is parameterized in a meaningful way for studying texturemotion related psychophysical phenomena.
A further advantage of this formalism is that it allows for statements of the model within a Bayesian framework. This sets the stage for psychophysical experiments. The main result of these (although the dataset is small) is that spatial frequency has a positive effect on perceived speed: stimuli of lower frequency with respect to the reference are perceived as going slower. The Bayesian model was able to explain these systematic biases for spatial frequency as shifts in priors on speed during the speed judgements. Overall I find the data part of this paper to be less satisfying because there is insufficient data for detailed analysis within the Bayesian frame work and the authors are unable to interpret some of their results.
Other comments:
The paper has some formatting problems (figure Legends not sufficiently different from text)
that make it difficult to read.
Note: The discussion section is cut off in the second sentence and doesn't continue. It seems as though they lost some text somewhere...
Q2: Please summarize your review in 12 sentences
An interesting method for generating motion stimuli for psychophysical experiments.
Submitted by Assigned_Reviewer_2
Q1: Comments to author(s). First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. (For detailed reviewing guidelines, see http://nips.cc/PaperInformation/ReviewerInstructions)
The paper notes (line 269) that stationary dynamic Gaussian textures
can be produced using existing methods such as Fourier filtering of noise,
and says that the proposed method has the advantage of being able to produce stimuli in realtime.
However why is this necessary? Most psychological stimuli
are prepared in advance, one can imagine ways of easily handing any need
for onthefly generation.
As well, Gaussian textures are a very limited class of images.
Classic work by Julesz in the 1960s70s explored the relation between texture discriminability and secondorder statistics. The results seemed to indicate simply that the idea of nthorder statistics does not correspond in any simple way to human perception. But as well, Julesz was using the full secondorder distribution, which provides much more modeling power than
is available with the Gaussian restriction used here.
Line 94, why is [3] nonparametric? Line 270, missing reference
Q2: Please summarize your review in 12 sentences
This looks like careful and sophisticated work, however I am not able to understand the motivations and relevance for NIPS. It may be better suited for a forum on psychological methods.
Submitted by Assigned_Reviewer_3
Q1: Comments to author(s). First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. (For detailed reviewing guidelines, see http://nips.cc/PaperInformation/ReviewerInstructions)
Weak review.
Discussion section on page 7 appears to be cutoff midsentence. A good discussion section would be helpful for clarifying the importance and contributions of this work.
The impact of this work would be increased by providing the source code for the stimuli generation for use by other psychophysics researchers (as in the original motion cloud work).
p 6 line 308. "assess" is misspelled. p 8 line 421. "dynamic" is misspelled. p 8 line 428. "though" should be "through"
Q2: Please summarize your review in 12 sentences
Paper presents a generative model of dynamic textures, shows that this model can be formulated as a stochastic PDE, and uses it in a psychophysics task to show textures with higher spatial frequencies are perceived to have higher speeds. The model and psychophysics result are important, however, the paper is difficult to follow in places and would benefit from careful editing.
Submitted by Assigned_Reviewer_4
Q1: Comments to author(s). First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. (For detailed reviewing guidelines, see http://nips.cc/PaperInformation/ReviewerInstructions)
This paper
provides a generative model for motion estimation based on small perturbations of observer's position by rotations, scaling and translation. Axiomatic definition of motion cloud stimuli from Ref. 8 was provide together with fast synthesis of naturalistic textures that can efficiently probe motion perception. The psychophysical results on judging the relative speed of moving
dynamical textures were inconclusive with the authors concluding that larger datasets are needed.
The paper appear to be hastily written with substantial number of typos (e.g. lines 101, 108, 270, 264, 366). In some cases the sentences were incomprehesible (e.g. lines 366370).
Q2: Please summarize your review in 12 sentences
This paper considers the generation of dynamic motion cloud stimuli. These stimuli were proposed before but here are derived in a more rigorous manner. Psychophysical results were not conclusive.
Submitted by Assigned_Reviewer_5
Q1: Comments to author(s). First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. (For detailed reviewing guidelines, see http://nips.cc/PaperInformation/ReviewerInstructions)
[Summary] The paper presents a a generative model for motion perception, which can be fitted into a Bayesian inference framework. This is derived from an axiomatic and biologicallydriven model, and then is shown to be a generalization of the wellknown luminance conservation equation.
[Originality and Significance] The contributions of this paper need to be confirmed by other reviewers with expertise.
[Clarity] The paper is mostly well written. But I have some difficulty in understanding some details, since I do not have a solid background in this area.
[Questions & comments] I did not notice major defect in the paper to my knowledge. But it would be more interesting if there could be any discussion of relationship between the proposed model and the dynamic textures by [3] (e.g, how the definition and/or properties are equivalent/relevant). This will provide a broader perspective to understand the framework.
@L94, it is said that "the most prominent method is the nonparametric Gaussian autoregressive (AR) framework of [3]", the model presented in [3], however, is a parametric one (actually, a linear dynamic system). Is there any typo or misinterpretation here?
[Mesc.] Reference at L270 is not properly referred.
Q2: Please summarize your review in 12 sentences
The paper presents a a biologicallyinspired generative model for motion perception, which can be fitted into a Bayesian inference framework. I cannot confidently evaluate the significance here though I do not find any major defects.
Q1:Author
rebuttal: Please respond to any concerns raised in the reviews. There are
no constraints on how you want to argue your case, except for the fact
that your text should be limited to a maximum of 5000 characters. Note
however, that reviewers and area chairs are busy and may not read long
vague rebuttals. It is in your own interest to be concise and to the
point.
We are glad that all reviewers appreciated the
significant technical contributions of our manuscript. There is a
consensus in the range of comments that the manuscript should more clearly
communicate its main contribution on the synthesis of textures optimized
for psychophysics and based on a rigorous mathematical model of natural
image transformations. In this response, we tackle those key points
seeking to clearly demonstrate why we believe our manuscript makes an
important contribution to NIPS. In addition, we will correct all errors
and omissions pointed out in the original submission, some of which we
acknowledge hindered the comprehension of the manuscript.
#Reviewer
1 Q: However I am not able to understand the motivations and relevance
for NIPS. A: We strongly believe that this contribution is directly
relevant to NIPS. Indeed, this work is at the interface between
mathematical modeling and psychophysics. NIPS has been a great avenue for
previous similar work which includes several important works of Weiss and
Simoncelli, two authors whose research groups have made seminal
contributions to Bayesian modelling of perception.
Q: However why
is this necessary? Most psychological stimuli are prepared in advance, one
can imagine ways of easily handing any need for onthefly
generation. A: For experimental purposes, the number of generated video
frames can be enormous. This framework is used in practice in different
labs for psychophysics & neurophysiology and we found it to be
essential for the acquisition of a substantial number of conditions and
trials. In particular, there is a genuine difficulty when very large
dynamic stimulus presentations are used in high resolution or over longer
durations (stimulation for several minutes). We have faced these
limitations in the past and so have many colleagues. The code, which will
be openly available, will make the greatest contribution in these
contexts
Q: As well, Gaussian textures are a very limited class of
images. [...] The results seemed to indicate simply that the idea of
nthorder statistics does not correspond in any simple way to human
perception. A: We agree that Gaussian textures are the simplest
statistical models, and can only account for a limited range of perceptual
phenomena. From a computer graphics perspective, these models have been
shown to be surprisingly effective at capturing microscale details, and
have recently been very popular at SIGGRAPH ("Gabor noise"). Moreover,
these stimuli are designed to be used in perceptual experiments. Gaussian
textures are "worst case scenario" textures, as they are composed of the
densest mixture of random textons. Using sparser texture most often makes
the job "easier" for the neural system; hence their usefulness to assess
its efficiency e.g. for speed discrimination. Finally, related to the
point above, our contribution is a first step toward more generic models,
e.g. nonlinear sPDEs that include higher order correlations. We will add
a short discussion about these issues in the conclusion, which was indeed
lacking.
Q: Line 94, why is [3] nonparametric? A: This is a
typo, which will be corrected.
#Reviewer 2
Q: The paper
appears to be hastily written with substantial number of typos [...] A:
There were indeed some errors in the previous submission. We will do a
full proof checking for the final version.
#Reviewer 3
Q:
[...] any discussion of relationship between the proposed model and the
dynamic textures by [3] A: This is indeed a good suggestion. [3] makes
use of an AR1 dynamical system, so it can only capture 1st order sPDE,
while we make use of 2nd order PDE's. We will add this remark to the final
version of the paper. We found this difference to be absolutely crucial
for visual experiments to capture the correct correlation in time. A
detailed discussion about AR textures can be found in
[17].
#Reviewer 4
Q: The impact of this work would be
increased by providing the source code for the stimuli generation for use
by other psychophysics researchers. A: We strongly agree. We will
opensource the code and direct readers to the online github repository
within the revised text.
#Reviewer 6
Q: I find the data part
of this paper to be less satisfying because there is insufficient
data... A: The psychophysics part was meant as a proof of concept, to
bridge the gap between propositions of the generative model and Bayesian
modelling of speed perception. While more data would indeed extend the
possible interpretations, we have importantly tested the formalised
relationship between likelihoods and the motion cloud parameterization
with our dataset by asking a novel question about frequency related
adjustments to perceived speed.
#Reviewers 4 & 6
Q:
Discussion section on page 7 appears to be cutoff midsentence. A: In
fact, a poor typesetting resulted in a part of the discussion to appear
after Figure 3's caption. We apologise for the
inconvenience. 
