Analyzing Intrinsic Motion Textures

Created from Naturalistic Video Captures

Angus Graeme Forbes

, Christopher Jette

and Andrew Predoehl

School of Information: Science, Technology, and Arts, University of Arizona, Tucson, U.S.A.

Department of Computer Science, University of Arizona, Tucson, U.S.A.

Keywords:

Intrinsic Motion Textures, Psychophysics, Perception, Metrics for Motion, Dynamic Textures.

Abstract:

This paper presents an initial exploration of the plausibility of incorporating subtle motions as a useful modal-

ity for encoding (or augmenting the encoding of) data for information visualization tasks. Psychophysics

research indicates that the human visual system is highly responsive to identifying and differentiating even the

subtlest motions intrinsic to an object. We examine aspects of this intrinsic motion, whereby an object stays in

one place while a texture applied to that object changes in subtle but perceptible ways. We hypothesize that the

use of subtle intrinsic motions (as opposed to more obvious extrinsic motion) will avoid the clutter and visual

fatigue that often discourages visualization designers from incorporating motion. Using transformed video

captures of naturalistic motions gathered from the world, we conduct a preliminary user study that attempts

ascertains the minimum amount of motion that is easily perceptible to a viewer. We introduce metrics which

allow us to categorize these motions in terms of ﬂicker (local amplitude and frequency), ﬂutter (global ampli-

tude and frequency), and average maximum contrast between a pixel and its immediate neighbors. Using these

metrics (and a few others), we identify plausible ranges of motion that might be appropriate for visualization

tasks, either on their own or in conjunction with other modalities (such as color or shape), without increasing

visual fatigue. Based on an analysis of these initial preliminary results, we propose that the use of what we

term “intrinsic motion textures” may be a promising modality appropriate for a range of visualization tasks.

1 INTRODUCTION

Rustling leaves, ﬂickering ﬂames, sunlight sparkling

on water – every day we are continually confronted

with naturalistic motion as we navigate the world.

Information visualization, as a ﬁeld, examines how

meaning is effectively conveyed through visually-

encoded data. However, dynamic visualizations that

encode data using motion have not been as widely ex-

plored, as motion is generally considered to be too

distracting a modality for representing information

effectively. The human visual system is exception-

ally adept at identifying differences in texture (Em-

rith et al., 2010), and observing both the movement

of objects or the movement within an object (Chalupa

et al., 2004). While far from being fully understood,

a growing body of research indicates the presence of

multiple neurological mechanisms for processing ex-

trinsic motion (the movement of an object) and intrin-

sic motion (local movement within a single object)

(Lu and Sperling, 2001; Nishida et al., 1997). Extrin-

sic and intrinsic motion is also termed “ﬁrst-order”

or “second-order,” respectively: ﬁrst-order motion re-

ferring to the perception of a change in luminance

across the visual ﬁeld; second-order motion referring

instead to the perception of changes in texture or con-

trast. While both types of motion can indicate a global

movement of a single entity, in this paper we examine

the use of second-order motion to indicate motions

within a stationary object. We introduce a method for

transforming real-world motion into abstract motion

textures with subtle motions, which we are calling in-

trinsic motion textures. These transformed video cap-

tures of real-world naturalistic motion allow us to ex-

periment with non-distracting motion without includ-

ing their representational aspects. Through the trans-

formation of, say, the movement of water in a stream,

we are able to capture the motion without directly re-

minding users that they are looking at water. We also

introduce an easy-to-calculate set of metrics to char-

acterize these intrinsic motions. While the vast range

of possible motions makes it rather daunting to at-

tempt to encompass all types of movement via a sin-

gle set of metrics, ours capture the main features of

107

Graeme Forbes A., Jette C. and Predoehl A..

Analyzing Intrinsic Motion Textures Created from Naturalistic Video Captures.

DOI: 10.5220/0004660401070113

In Proceedings of the 5th International Conference on Information Visualization Theory and Applications (IVAPP-2014), pages 107-113

ISBN: 978-989-758-005-5

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

Figure 1: Stages of the video capture of naturalistic motion being transformed into an intrinsic motion texture. Here we show

a single frame (A) as it is (B) cropped, (C) desaturated, (D) contrast-compressed with a low pixel range, and (E) pixelated. A

further step uses temporal smoothing to mitigate extra ﬂickering introduced from the pixelation.

intrinsic motion. We conducted a user study asking

participants to evaluate a set of intrinsic motion tex-

tures. We were primarily interested in gathering in-

sight into the following question – What is the least

amount of motion needed in order to easily identify

differences between highly similar motions? We dis-

covered that even intrinsic motion textures with a low

contrast range are easily distinguishable, as long as

certain amounts of ﬂicker, ﬂutter, and average maxi-

mum contrast (deﬁned below) are present.

In the inﬂuential text, “Semiology of Graphics,”

Jacques Bertin introduces a typology of retinal vari-

ables applicable to the communication of informa-

tion. Although explicitly discussing static print me-

dia, Bertin hints at the ability of texture, unlike other

retinal variables, to produce a vibratory effect that is

the “collusion” of physiological and psychological ef-

fects. Although he does not explore this issue of vi-

bratory effects further, he encourages designers “to

make the most of this variation, to obtain the res-

onance without provoking an uncomfortable sensa-

tion, to ﬂirt with ambiguity without succumbing to

it.” Bertin speaks of texture in terms of the vary-

ing thicknesses of lines, points, or shapes to indicate

an overview similarity or differentiation between dif-

ferent qualities, or an ordered-ness within the same

quality. However, he does not believe that the use of

texture is reﬁned enough to allow viewers to perceive

proportionality and therefore is not effective at allow-

ing users to perform quantitative tasks using texture

alone (Bertin, 2010).

Nonetheless, investigations into the use of static

textures in visualization contexts have found that they

can be effective for representing quantitative data in

certain tasks. A seminal paper (Ware and Knight,

1995) uses Gabor patches to parametrically investi-

gate aspects of texture that may be useful in informa-

tion display, such as orientation, size, and contrast.

Applied research introduced in (Kujala and Lukka,

2003) explores the use of a “parameter hierarchy” in

the procedural creation of perceptually distinct tex-

tures for the effective display of information. More

recently, (Byelas and Telea, 2009) uses overlapping

textures to indicate multivariate data in software dia-

grams, explicitly attempting to indicate an increased

number of variables within the diagram. (House et al.,

2006) describes a technique for the perceptual opti-

mization of complex visualizations involving layered

textures. And (Interrante and Kim, 2001) and (Kim

et al., 2004) explore the efﬁcacy of using various ori-

entations and types of textures to facilitate shape per-

ception. These examples are mostly explicitly con-

cerned with the potential expanded range of variables

that can be expressed, while at the same time aware

that the cost of this expanded range might be percep-

tual fatigue or cognitive overload, or worse, an in-

ability to clearly distinguish differences. (Interrante,

2000) examines the use of overlapping naturalistic

textures to indicate multivariate data while mitigating

against the “extraneous stress” that might occur with

synthesized textures. In addition to being a potentially

effective representation of a wide range of quantita-

tive information, the use of certain aspects of natu-

ralistic textures, such as variability, might be used to

indicate extra information, such as uncertainty. Inter-

estingly, recent perceptual experiments, such as (Em-

rith et al., 2010), conﬁrm that humans perceive even

minute alterations in texture, but note that it is in fact

somewhat easier to discern differences between syn-

thetic textures than natural textures.

Motion is often used to signal transitions between

views and contexts, to signal interruptions, and to in-

dicate temporal aspects of data. However, it is less

frequently used as an encoding mechanism for quanti-

tative or qualitative information. (Forbes et al., 2010)

presents a data visualization framework than enables

animation to be mapped to dynamic streams of data,

and (Bostock et al., 2011) describes a framework

that includes “transition” operators for animating data

points. The use of motion in visualization elicits con-

cern about visual clutter and perceptual fatigue, even

while potentially providing an expanded toolset for

representing information. At its most extreme, the in-

judicious use of motion in information might cause

IVAPP2014-InternationalConferenceonInformationVisualizationTheoryandApplications

108

signiﬁcant visual stress (Ware, 2004). One group

of security researchers (Conti et al., 2005) even de-

scribes the potential for malicious hackers to takeover

an information visualization system and alter its vi-

sual output to induce epileptic seizures.

Results from (Bartram and Ware, 2002) show that

small, brief, and graphically simple extrinsic motions

are perceptually efﬁcient ways to distinguish objects

in a crowded display. In particular, they note that a

synchronization of elements is required in order for

them to be effectively recognized as similar. That is,

the timing of the motion is as important as the mo-

tion itself. Research into extrinsic motion cues, or

“moticons” (Bartram et al., 2003), ﬁnds that motion

coding is independent from color and shape coding

and that more subtle motions are less distracting to

users yet easily perceived. A series of experiments

that analyzed extrinsic aspects of motion– velocity,

direction, and on-off blinking– ﬁnds that these prop-

erties are all effective at encoding multiple data values

in a prototype astrophysics simulation, provided they

meet certain basic thresholds of perceptibility (Huber

and Healey, 2005). A technique termed “motion high-

lighting” explores the potential applicability of mo-

tion to node-link diagrams (Ware and Bobrow, 2004).

Results of motion highlighting experiments indicate

that the translating or scaling of node is more useful

for supporting rapid interactive queries on node-link

diagrams than static highlighting methods.

Dynamic textures are sequences of images that

exhibit some form of temporal coherence, or more

speciﬁcally, they are individual images that are “re-

alizations of the output of a dynamical system driven

by an independent and identically distributed pro-

cess” (Doretto et al., 2003). Dynamic textures have

been effectively used in scientiﬁc visualizations and

have been more extensively investigated in computer

graphics and computer vision research. For instance,

(Van Wijk, 2002) uses an iterative series of texture

distortions to represent ﬂuid ﬂows, and (Forbes and

Odai, 2012; Forbes et al., 2013) applies this tech-

nique to creative media arts projects. Work by (Lum

et al., 2003) explores adding moving particles to a

surface texture of a static object in which the parti-

cles are placed along the principal curvature direction

to better indicate the object’s shape and spatial rela-

tionships. Within a more general computer graphics

context, dynamic textures are used in a variety of ap-

plications. For instance, dynamic textures have been

used as a computationally efﬁcient way to add realism

to a scene. (Chuang et al., 2005) presents an interac-

tive system that procedurally generates dynamic tex-

tures from selected components of a single image that

can then be added to a scene. Similarly, a variety of

techniques have been introduced to automatically cre-

ate “temporal textures” from a single image in order

to mimic natural phenomenon such as clouds, water,

and ﬁre (Lai and Wu, 2007; Ruiters et al., 2010; Ok-

abe et al., 2011). In addition to having the potential

to be used as an effective modality for representing

quantitative information, recent research has explored

the use of dynamic textures as a medium for provid-

ing semantically contextualized information. (Lock-

yer et al., 2011) explores the “expressive scope” of

ambient motion textures for “emphasis and more sub-

tle ambient visualization.” In particular, this research

focused on the effective communication of particu-

lar emotions through the use of intrinsic motion cues

within a dynamic texture.

For the most part, research on motion in informa-

tion visualization is concerned with extrinsic motion,

or at least does not differentiate between extrinsic and

intrinsic motion. For instance, (Ware and Bobrow,

2004), also cited above, discusses a motion highlight-

ing technique whereby the animation of a station-

ary link generate a “crawling” motion. Although it

is not presented speciﬁcally as a dynamic texture, it

is clear that this “crawling” motion is of a different

nature than the translation patterns used to highlight

nodes. A recent evaluation found that animated rep-

resentations were more effective than almost all static

representations of link representations (Holten et al.,

2011). It seems reasonable that other visualization

systems could utilize a conﬂation of textures and mo-

tion; rather than attempting to procedurally generate

dynamic textures, we could gather them directly. This

would have the immediate advantage that they were,

at least in some degree, inherently non-distracting for

the simple reason that they occur continually in the

real-world. An earlier (unpublished) study by one of

the authors found that the use of moving sinusoidal

gratings introduced visual fatigue precisely because

of the qualities that made it unrealistic: its ﬁxed ro-

tation, its predictable frequency and amplitude, and

its repetitive sequence of pixel values. instead, Dy-

namic textures using ﬂuctuating, intrinsic, real-world

motion are cohesive without being repetitive; differ-

entiable without being distracting.

In order to expedite the creation of intrinsic mo-

tion textures in order to analyze their potential effec-

tiveness in visualization systems, we gathered real-

world video of natural phenomena containing intrin-

sic motion: ﬁre, water, clouds, etc. Although we be-

lieve that these textures have a representational com-

ponent that might be useful in some visualization cir-

cumstances, for this study we isolated the phenomena

using these steps: (A) record the natural phenomenon;

(B) crop the resulting video; (C) desaturate the video;

AnalyzingIntrinsicMotionTexturesCreatedfromNaturalisticVideoCaptures

109

(D) constrain the pixel range of the video; (E) pixelate

the video; (F) apply temporal smoothing to the video.

The video is thus transformed into a low-contrast, de-

saturated, pixelated, and mostly unrecognizable ver-

sion of itself that nonetheless retains important qual-

ities of the original natural motion. Figure 1 shows

a frame from a naturalistic video as it is processed

through this pipeline. These intrinsic motion textures

can then be deﬁned metrically, in terms of particular

features, and included in studies where we can asso-

ciate these features with empirical observations, such

as discernibility and differentiability.

2 METRIC DEFINITIONS

Much research has been done to develop ways to ef-

fectively and efﬁciently characterize motion. A com-

monly used method, optical ﬂow, assumes that there

is a unique velocity vector at each pixel. Other meth-

ods relax that assumption. For instance, (Langer and

Mann, 2003) introduces “optical snow,” which is able

to characterize motions that have prevalent disconti-

nuities between frames. The intrinsic motions that

we have gathered likewise include large amounts of

ﬂickering that are not captured with optical ﬂow type

analyses. Since we are, for now, interested primar-

ily in the single task of determining the ease of dis-

crimination betweens motions, we constructed a way

to create simpler metrics that deﬁne a video via a set

of eight features that sufﬁciently characterize intrinsic

motions. They are grouped into the following cate-

gories: ﬂicker, ﬂutter, and variability.

We now introduce notation that deﬁnes our met-

rics precisely. Let I

q,t

denote the integer intensity

value of a pixel in the video, at spatial position q

and at time t. Assuming the video pixels lie in a

rectangular grid of width W , height H, and that the

video has duration of T discrete video frames, q ∈

Q = {1, .. .,W } × {1, .. ., H} and t ∈ {1,...,T }. The

ﬁrst characteristic of interest we deﬁne is the contrast

range K of a video:

Deﬁnition 1 (Contrast range).

K = max

q∈Q

1≤t≤T



q,t



− min

q∈Q

1≤t≤T



q,t



In the present study, instead of using K as a video

feature for comparison, we partitioned our test videos

into collections with of similar contrast range, be-

cause two videos with widely differing contrasts are

very obviously distinct. Speciﬁcally, we gathered

videos with contrast ranges of 20 or less, 21 to 40,

41 to 60, and 61 to 80, into groups called G

, G

, and G

, respectively.

The variability metrics reﬂect the spatial variation

in a single video frame. Let Q

⊂ Q be the set of

pixel positions in the interior of the grid. Each pixel

position q ∈ Q

therefore has eight spatially adjacent

neighbors, the set of which we denote N(q). The

roughness R is deﬁned as the average intensity dif-

ference of the highest-contrast neighbors:

Deﬁnition 2 (Roughness).

R =

W H

∑

q∈Q

∑

t=1

max

s∈N(q)

q,t

− I

s,t

Edginess E

is the average number of large-

contrast juxtapositions, per pixel, per frame. The con-

trast is regarded as large if the intensity difference is

at least θ:

Deﬁnition 3 (Edginess).

W H

∑

q∈Q

∑

t=1

{τ : θ≤max

s∈N(q)

q,τ

−I

s,τ

(t).

In the current study, we set the threshold value θ equal

to one-eighth the maximum contrast range of the rele-

vant collection (G

, G

, or G

). For example,

when comparing two videos from group G

, we used

the E

40/8

= E

edginess metric.

Our ﬂicker metrics depend on the local maxima

and minima (peaks and valleys) of pixel intensity in

the time domain. To specify them formally, we intro-

duce deﬁnitions of peak and valley as follows.

Deﬁnition 4 (Peak intensity value). I

q,t

is a peak in-

tensity value of width j, provided

• j > 0,

• 1 < t ≤ T − j,

• I

q,t−1

< I

q,t

= I

q,t+1

= ·· · = I

q,t+ j−1

, and

• I

q,t+ j−1

> I

q,t+ j

Though cumbersome, this deﬁnition is consistent

with an intuitive notion of local maximum. We also

deﬁne valley intensity value analogously for local

minima. Let n

denote the number of peaks of width

1 or more at position q. Let p

1,q

, p

2,q

,. .. , p

be the

peak intensity values at position q, such that p

i,q

is the

intensity value of the ith peak, in chronological order.

Similarly, let v

1,q

2,q

,. .. ,v

denote the valley in-

tensity values (assuming they are equally numerous

as the peaks). Now we can precisely state our met-

rics for ﬂicker. First, the average number of peaks per

pixel, per frame, is called local frequency, F

Deﬁnition 5 (Local frequency).

W H

∑

q∈Q

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110

We deﬁne the average peak-to-valley difference,

per pixel, as local amplitude, A

Deﬁnition 6 (Local amplitude).

W H

∑

q∈Q

∑

i=1

i,q

− v

i,q

if n

> 0, otherwise A

= 0.

We deﬁne local choppiness, C

L,θ

, as the average

number of large intensity jumps per pixel, per frame.

A jump at position q is large if equals or exceeds

threshold value θ.

Deﬁnition 7 (Local choppiness).

L,θ

W H

∑

q∈Q

∑

t=2

{τ : |I

q,τ

−I

q,τ−1

|≥θ}

(t)

Similar to our use of Def. 3, in our study we set

the threshold value here to one-eighth of the contrast

range of whichever videos we are comparing. So

when comparing videos from group G

, we use lo-

cal choppiness metric C

L,5

Our ﬂutter metrics are similar to the ﬂicker met-

rics, but they depend on the average intensity of the

entire video frame at a given moment. Let J

denote

the average pixel intensity of the frame at time t, i.e.,

W H

∑

q∈Q

q,t

. This sequence also has peaks and

valleys, which we assume are m in number. At the

risk of confusion, we will denote its peak values and

valley values (regardless of width) as p

, p

,. .. , p

and v

,. .. ,v

respectively, in chronological order.

(Note that these values have only one subscript.) The

average number of these peaks, per frame, is called

global frequency, F

Deﬁnition 8 (Global frequency).

= m/T.

The average of these peak-to-valley differences is

the global amplitude, A

Deﬁnition 9 (Global amplitude).

∑

i=1

− v

if m > 0, otherwise A

= 0.

We deﬁne global choppiness C

G,θ

as the average

number of large increases in average intensity, with

threshold θ:

Deﬁnition 10 (Global choppiness).

G,θ

∑

t=2

{τ : |J

−J

τ−1

|≥θ}

(t).

Again, in our study we set the this metric’s threshold

just as those of Defs. 3 and 7. So, when comparing

videos from group G

, we compute metric C

G,5

These metrics are easy to calculate and capture

both the global and the local, pixel level aspects of

the videos. They may not however capture larger-

scale movement within the motion textures. However,

since our express aim is to use motion textures that do

not contain large-scale motion, we believe that these

metrics are appropriate as a ﬁrst attempt to character-

ize intrinsic motions.

3 USER STUDY

The main goal of our preliminary user study was

to determine the minimum amount of movement re-

quired in order for a participant to quickly differen-

tiate between similar motions. Since motion can be

highly distracting and since humans are exception-

ally good at noticing differences in motion, by ﬁnding

lower bounds on various parameters that make motion

distinguishable we can identify the minimum values

of easily-discernible features. Future work will use a

more rigorously deﬁned empirical study using tech-

niques to measure just-noticeable difference, as well

as explore user response to visualization tasks incor-

porating motion textures. For this preliminary study

we wanted to obtain an initial sense of what attributes

were most easily noticeable at low-contrast ranges,

and which of these attributes were thought to be the

least distracting.

To ﬁnd this minimum feature set, we created a

study that presented the participant with a pair of

videos. The user was then asked to indicate whether

he or she agreed or disagreed with a series of state-

ments about the videos. We gathered 32 unique

videos of naturalistic motion and processed them as

described in section 3.1. We created 4 “bins” and

made versions of each of these videos with different

levels of contrast. Bin 1 contained videos with a con-

trast range of +/- 10; Bin 2, +/- 20; Bin 3, +/- 30; Bin

4, +/- 40. For Bin 1, it was very difﬁcult to tell most of

the videos apart, especially when looking at a single

(unmoving) frame from the video. In other words, the

contrast was so low that without movement it would

be almost impossible to tell them apart. For Bin 2,

it seemed that about half of the time it was easy to

tell the videos apart and the other half of the time it

was difﬁcult. For Bin 3, it became much easier to tell

any of the videos apart from any of the others. And

ﬁnally, for Bin 4 it was easy to tell all of the videos

apart. However, we thought that if the movements be-

came more chaotic (higher absolute ﬂicker amplitude

AnalyzingIntrinsicMotionTexturesCreatedfromNaturalisticVideoCaptures

111

and frequency) then in those cases the videos in Bin

3 and Bin 4 would be hard to tell apart. We did not

test any of the videos against a video with a different

range of pixel values as it is easy to discern the dif-

ferences in videos when one had a higher maximum

and lower minimum pixel value than the other. Each

of the videos was analyzed with custom software that

output the features described by our metrics system.

We further calculated the absolute difference between

the feature vectors of each video.

We included a series of four Likert items per test

designed to elicit the participant’s opinion about the

discernibility of ﬂicker and frequency. We ran vari-

ous “batches” of our test over the course of a week

and a half on Amazon Mechanical Turk. We received

a total of 144 completed studies. For most of these

batches, we randomly chose one of the 4 bins for each

test. The majority of our “workers,” 107, indicated

that they were from India; 24 were from the United

States; the rest came from the United Kingdom, Mex-

ico, Sri Lanka, Canada, Pakistan, and Nigeria. 2 par-

ticipants chose “Other” as their nationality. There

were an equal number of male and female participants

(72 each). The minimum and maximum age was 19

and 63, respectively, with a median age of 31. Fol-

lowing the suggestions in (Heer and Bostock, 2010),

which describes some of the advantages and disad-

vantages of conducting studies via Mechanical Turk,

we made a substantial effort to encourage reliable par-

ticipation and mitigate inaccurate or random answers,

ultimately obtaining 476 samples from the 144 partic-

ipants.

All features, except for the frequency of the ﬂutter

(the global frequency of a direction change in aver-

age pixel value for a frame) were positively correlated

with easy differentiability. We built a statistical model

to characterize the relationship between the video mo-

tion metrics and the participants’ responses. We fo-

cused on contrast ranges 20 and 40 and modeled the

data as a two-category classiﬁcation problem: in each

video comparison, the videos are either difﬁcult to

distinguish (category C

) or not (category C

), gen-

erated by the user’s Likert responses. For our binary

classiﬁer, any value greater than 2 was given a placed

in category C

, and any value less than or equal to 2

was placed in category C

. Ideally, the model would

effectively predict the category for a video compar-

ison, based only on our video features. We modeled

these two categories by assuming a multivariate Gaus-

sian distribution of the feature vectors (which are the

absolute differences of each of the eight metrics for

the pair of videos being compared). In other words,

we computed the maximum-likelihood mean vector

C,r

and covariance matrix Σ

C,r

of all feature vec-

tors for category C ∈ {C

}, and contrast range

r ∈ {20, 40}. For the purpose of classiﬁcation, we also

make use of the empirical frequency of C

and C

classes, denoted p(C

) and p(C

). Given a new data

vector x for contrast range r, we would classify it in

category C

provided it satisﬁes p(C

|x) > p(C

|x),

where by Bayes’ theorem, for C ∈ {C

p(C|x) =



x; µ

C,r

,Σ

C,r



p(C)

∑

B∈{C

}



x; µ

B,r

,Σ

B,r



p(B)

In the above, N (x; µ,Σ) denotes the probability den-

sity function for the multivariate Gaussian with mean

µ and covariance Σ. One advantage of a Gaussian

characterization is that we can easily marginalize any

subset of features. Thus we can see the average in-

teraction between any two features and can list the

thresholds for the classiﬁer with all other features

marginalized (Table 1). In particular, even small dif-

ferences in ﬂickering (especially in the frequency and

choppiness) at the individual pixel level were the main

predictors of whether or not a video pair was likely to

be easily differentiable.

Table 1: Threshold values between features.

l chop 0.0011901

l amp 0.12419

l freq 0.00081854

g chop 0.048523

g amp 0.037790

g freq 0.0056176

rough 0.57185

edge 0.0033416

4 CONCLUSIONS

This paper presents an initial foray into exploring

the potential usefulness of intrinsic motion textures.

We provide a method for generating cohesive, non-

repetitive, intrinsic motions textures from real-world

video captures; a method for characterizing the fea-

tures of intrinsic motions; a preliminary user study

that indicates minimal differences necessary for dif-

ferentiation between motions; and an analysis of this

study that identiﬁes thresholds on these features. This

initial exploration of motion textures created from

video captures of naturalistic movement seems to in-

dicate that this may be a promising area for future in-

vestigations. Future work will involve the design and

analysis of more rigorous empirical studies to deter-

mine the validity of our claims regarding the notice-

ability and distraction of these types of textures.

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