25 colour recognition grid conected a robot dog

8/3/2019 25 Colour Recognition Grid Conected a Robot Dog

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Velagapudi ramkrishna Siddhartha engineering collegeDEPARTMENT OF COMPUTER SCIENCE

Vijayawada

A paper on

Presented by

Ch.Phani Krishna P.Veerendra

III Year CSE III Year CSE

[email protected] [email protected]
mailto:[email protected]:[email protected]:[email protected]:[email protected]


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1.Abstract:Multimedia data is rapidly gaining

importance along with recent developments

such as the increasing deployment ofsurveillance cameras in public locations. AIBO

is a commercially available quadruped robot

dog equipped with a color CMOS camera. its

main aim to detect a mark on the floor using his

camera and tracking the people.. In a few years

time, analyzing the content of multimedia data

will be a problem of phenomenal proportions, as

digital video may produce data at rates beyond

100 Mb/s, and multimedia archives steadily run

into Petabytes of storage space. Consequently,

for urgent problems in multimedia content

analysis, Grid computing is rapidly becoming

indispensable. This demonstration shows the

viability of wide-area Grid systems in adhering

to the heavy demands of a real-time task in

multimedia content analysis. Specifically, we

show the application of a Sony Aibo robot dog,

capable of recognizing objects from a set of

learned objects, while connected to a large-scale

Grid system comprising of cluster computer

located in Europe, the United States, andAustralia. As such, we demonstrate the effective

integration of state-of the- art results from two

largely distinct research fields: Multimedia

Content Analysis and Grid computing. This

paper briefly introduces the Parallel- Horus

architecture, describes services-based approach

to wide-area multimedia computing.

2.Introduction:In order to create a social robot, able

to interact with people in a natural way, one ofthe things it should be able to do is to follow a

person around an area. This master's research is

aimed at enabling a Sony AIBO robot dog to

watch people closely and follow them around

the house. This is done using the visual data

provided by the robot's nose-mounted camera.

By analyzing the data with respect to colour

distributions and salient features.

This is the time in which information

be it scientific, industrial, or otherwise is

generally composed of multimedia items, i.e. acombination of pictorial, linguistic, and auditory

data.Due to the increasing storage and

connectivity of multimedia data, automatic

multimedia content analysis is becoming an

ever more important area of research.Fundamental research questions in this area

include:

1. Can we automatically find (sub-)genres

in images from a statistical evaluation

of large image sets?

2. Can we automatically learn to find

objects in images and video streams

from partially annotated image sets?

Scientifically, these questions deal

with the philosophical foundations of cognition

and giving names to things. From a societalperspective solutions are urgently needed, given

the rapidly increasing volume of multimedia

data.

Figure(a): Robotic Dog

The existing user transparent

programming model (i.e. Parallel-Horus)is

matched with an execution model based on

Wide-Area Multimedia Services, i.e. highperformance multimedia functionality that can

be invoked from sequential applications running

on a desktop machine. Evaluation of approach

by applying it to a state-of-the-art Visual

recognition task can be done. Specifically,

application of a Sony Aibo robot dog, capable

of recognizing objects from a set of 1,000

objects, while connected to a large-scale Grid

system comprising of cluster systems in Europe

and Australia.

3. Mark Detection:

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Two solutions are possible to detect the

mark in the picture:

1. Color recognition: By knowing in

advance the color of the mark, position

in the picture can be computed by using

a virtual mask moving on all pixels: Themean color inside the mask is calculated

to find the mark position in the picture.

Figure(b): robotic dog recognizing the ball

2. Shape recognition: Image processing

algorithms allows finding circles or lines

in the picture. In the image processing

course of Mr. Jourlin, circle recognition

needed first different filters applied onthe picture to detect the edges. After

extracting edges, circles can be detected

by using shape parameters and lines are

detected by using Hough transform.

For the 4 following reasons, the color recogni-

-tion method is adopted:

1. The camera perspective distortion

modify the shape of the mark and could

disturb the shape recognition.

2. The shape recognition algorithms need

more computational power due to theimage filtering and edge extraction.

3. The floor's color will be very different

from the mark color, the color

recognition technique can then be

efficient.

4. Color recognition algorithms are easier

to implement than shape recognition.

The existing user transparent

programming model (i.e. Parallel-Horus)is

matched with an execution model based on

Wide-Area Multimedia Services, i.e. high

performance multimedia functionality that can

be invoked from sequential applications running

on a desktop machine.

4. Parrallel-Horus:

Parallel-Horus is a cluster programmingframework that allows programmers to

implement parallel multimedia applications as

fully sequential programs. The Parallel-Horus

framework consists of commonly used

multimedia data types and associated

operations, implemented in C++ and MPI. The

librarys API is made identical to that of an

existing sequential library: Horus.

4.1. Unary pixel operation:

Operations in which a unary functionis applied to each pixel in the image. The

functions sole argument is the pixel value

itself.

E.g.: negation, absolute value, square root

4.2. Binary pixel operation:

Operations in which a binary function

is applied to each pixel in the image. The

functions first argument is the pixel value

itself.The second argument is either a single value or

a pixel value obtained from image itself.

E.g.: addition, multiplication, threshold

4.3. Global reduction:

Operations in which all pixels in the

image are combined to obtain a single result

value.

E.g.: sum ,product, maximum

4.4. Neighborhood operation: Operations in which several pixel

values in the neighborhood of each pixel in the

image are combined.

E.g.:percentile, median

4.5. Generalized convolution:

Special case of neighborhood

operation.The combination of pixels in the

neighborhood of each pixel is expressed in

terms of two binary operations.

E.g.: convolution, guass, dilation

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4.6. Geometric Transformations:

Operations in which the images

domain is transformed.

E.g.: rotation, scaling, reflection

Current developments include patterns foroperations on large datasets, as well as patterns

on increasingly important data structures, such

as feature vectors obtained from earlier

calculations on image and video data.

For reasons of efficiency, all Parallel-

Horus operations are capable of adapting to the

performance characteristics of a parallel

machine at hand, i.e. by being flexible in the

partitioning of data structures.

5. Services-Based Multimedia GridComputing:

From the users perspective, Grid

computing is still far from being more than just

an academic concept. Essentially, this is because

Grids do not yet have the full basic functionality

needed for extensive use. Consequently, as long

as programming and usage is hard, most

researchers in multimedia computing will not

regard Grids as a viable alternative to more

traditional computer systems.6. Performance Evaluation:In this section the assessment of the

architecture is given for the effectiveness in

providing significant performance gains. At the

end , the wide-area execution of a state-of-the-

art vision task is described. Specifically, the

application of a Sony Aibo robot dog, capable

of recognizing objects is presented, while

connected to a large-scale Grid system

comprising of clusters in Europe and Australia.

6.1 Object Recognition by a Sony Aibo Robot

Dog:

The example application demonstrates

object recognition performed by a Sony Aibo

robot dog (see Figure (c)). Irrespective of the

application of a robot, the general problem of

object recognition is to determine which, if any,

of a given repository of objects appears in an

image or video stream. It is a computationally

demanding problem that involves a non-trivial

tradeoff between specificity of recognition (e.g.,discriminating between different faces) and

invariance (e.g., to different lighting

conditions). Due to the rapid increase in the size

of multimedia repositories consisting of

known objects, state-of-the-art sequential

computers no longer can live up to the

computational demands, making high performance distributed computing

indispensable.

Fig. (c). Object recognition by our robot dog:

(1) an object is shown to the dogs camera;

(2) video frames are processed on a per-cluster basis;

(3) given the resulting feature vectors describing the

scene, a database of known objects is searched;

(4) in case of recognition, the dog reacts accordingly

6.2. Local Histogram Invariants.

In the robot application, localhistograms of invariant features for each aspectof an object are explained. The color invariant

features are highly invariant to illumination

color, shadow effects, and shading of the object.

The features are derived from a kernel based

histogram of feature responses. By exploiting

natural image statistics, these histograms are

modeled by parameterized density functions.

The parameters act as a new class of

photometric and geometric invariants, yielding a

very condensed representation of local imagecontent.

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figure (d):illumination direction variationIn the learning phase of this system, a

single condensed representation of each

observed object is stored in a database.

Subsequently, object recognition is achieved by

matching local histograms extracted from the

video stream generated by the camera in the

dogs nose against the learned database.

In this approach, first transform each

pixels RGB value to an opponent color

representation,

The rationale behind this is that the

RGB sensitivity curves of the camera are

transformed to Gaussian basis functions , being

the Gaussian and its first and second order

derivative. Hence, the transformed values

represent an opponent color system, measuring

intensity, yellow versus blue, and red versus

green Spatial scale is incorporated by

convolving the opponent color images with a

Gaussian filter. Photometric invariance is nowobtained by considering two non-linear

transformations. The first color invariant W

isolates intensity variation from chromatic

variation, i.e. edges due to shading, cast shadow,

and albedo changes of the object surface. The

second invariant feature C measures all

chromatic variation in the image, disregarding

intensity variation, i.e. all variation where the

color of the pixels change. These invariants

measure point-properties of the scene, and are

referred to as point-based invariants.

channel histograms of the invariant gradients

{Ww, Cw, Cw}, and the edge detectors {Wx,

Wy, Cx, Cy, Cx, Cy}, separately.

6.3. Histogram Parameterization:

From natural image statistics research, itis known that histograms of derivative filters

can be well modeled by simple distributions.

from the previous it is shown that histograms of

Gaussian derivative filters in a large collection

of images follow a Weibull type distribution.

Furthermore, the gradient magnitude for

invariants W and C given above follow a

Weibull distribution,

(1)

where r represents the response for one of the

invariants {Ww, Cw, Cw}.

The local histogram of invariants of

derivative filters can be well modelled by an

integrated Weibull type distribution ,

.(2)

In this case, r represents the response for one of

the invariants {Wx, Wy,Cx,Cy, Cx, Cy}.

Furthermore, represents the complete Gamma

function,

(3)

In our implementation we convert the

histogram density of all local invarianthistograms to Weibull form by first centering

the data at the mode of the distribution. Then,

multiplying each bin frequency p(ri) by its

absolute response value |ri|, and normalizing the

distribution. These transformations allow the

estimation of the Weibull density parameters

and , indicating the (local) edge contrast, and

the (local) roughness or textureness,

respectively .

6.4. Color-Based Object Recognition:

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In the robot dog system a simple

algorithm is applied for object recognition and

localization, based on the described invariant

features.

In the first learning phase of theexperiment, an object is characterized by

learning the invariant Weibull parameters at

fixed locations in the image, representing a sort

of fixed retina of receptive fields positioned at

a hexagonal grid, 2 apart, on three rings from

the center of the image. Hence, a total of

1+6+12+18 = 37 histograms are constructed.

For each histogram, the invariant Weibull

parameters are estimated. In the learning phase

dog is presented with a set of 1,000 objects

under a single visual setting. For each ofthese objects, the learned set of Weibull

parameters is stored in a database.

+ =

Figure (f) : learning phase

In the second recognition phase, the

learning step is validated by showing the sameobjects again, under many different

appearances, with varying lighting direction,

lighting color, and viewing position, using the

same retinal structure. In this manner, the robot

dog has learned each of the 1,000 objects from

only one example, while being capable of

recognizing more than 300 of these under

a diversity of imaging conditions that may occur

in everyday life. Interestingly, this recognition

rate is higher than the recognition rate of around

200 objects reported for a real dog.The important thing is the fact that

the algorithm runs at around 1 frame per 4

seconds. Moreover, voting is done by the results

obtained from 8 consecutive frames. As a result,

object recognition takes approximately 30

seconds, which is not even close to real-time

performance. This problem is overcome

by applying high-performance distributed

computing at a very large scale.

7. Benefits:

There are many benefits in using the colorbased object recognition in robot dogs,

1.Robotic pets can replace the real ones.

2.Robotic pets can provide services and

health benefits for senior citizens.

figure(g): Robot dog in household environment

3.These robotic dogs are flexible to move

and can bring the objects according to the user

requirements.

9. Conclusion: The results obtained by matching the

Parallel-Horus programming model with an

execution model based on so-called Wide-Area

Multimedia Services are described above

Considering the above features, it can be

decieded that system have succeeded in

designing and building a framework. These

have served well in showing the potential of

applying Grid resources in state-of-the-artmultimedia processing in a coordinated manner.

However, there is still room for improvement, in

functionality, reliability, fault tolerance, and

performance. With such improvements,

Parallel-Horus will continue to have an

immediate and stimulating effect on the study of

the many computationally demanding problems

in multimedia content analysis. The robot dog

application is merely one of these.

10. References:1. G. Alonso, F. Casati, H. Kuno, and V.

Machiraju. Web Services -

Concepts,Architectures and

Applications. Springer-Verlag, 2004.

2. H.E. Bal et al. The Distributed ASCI

Supercomputer Project. Operating

SystemsReview, 34(4):7696, 2000.

3. D. Comaniciu, V. Ramesh, and P. Meer.

Kernel-based Object Tracking.

IEEETransactions on Pattern Analysis

and Machine Intelligence, 25(4):564577, 2003.

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4. D. Crookes, P.J. Morrow, and P.J.

McParland. IAL: A Parallel Image

ProcessingProgramming Language. IEE

Proceedings, Part I, 137(3):176182,

June 1990.

5. J.M. Geusebroek et al. Color Invariance.IEEE Transactions on Pattern Analysis

and Machine Intelligence, 23(12):1338

1350, 2001.

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25 colour recognition grid conected a robot dog

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