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INTERACTIVE NATURAL IMAGE SEGMENTATION

VIA SPLINE REGRESSION

Abstract

We propose an interactive algorithm for segmentation of natural images in this project.

The task is formulated as a problem of spline regression, in which the spline is derived in

Sobolev space and has a form of a combination of linear and Greens functions. Besides its

nonlinear representation capabilit , one advantage of this spline in usage is that, once it has been

constructed, no parameters need to be tuned to data. We define this spline on the user specified

foreground and background pi!els, and solve its parameters "the combination coefficients offunctions# from a group of linear e$uations. To speed up spline construction, %&means clustering

algorithm is emplo ed to cluster the user specified pi!els. B taking the cluster centers as

representatives, this spline can be easil constructed. The foreground object is finall cut out

from its background via spline interpolation. The computational comple!it of the proposed

algorithm is linear in the number of the pi!els to be segmented. The proposed algorithm is

implemented and tested using '(T)(B simulation

BLOCK DIAGRAM


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EXISTING SYSTEM

*olor +mage Segmentation (dvances (nd -rospects +mage segmentation is ver

essential and critical to image processing and pattern recognition. This surve provides asummar of color image segmentation techni$ues available now.

DISADVANTAGES

Basicall , color segmentation approaches are based on monochrome segmentation

approaches operating in different color spaces.

PROPOSED SYSTEM

We proposed a novel algorithm for natural image segmentation. We formulated the task

as a problem of spline regression. The spline is a combination of linear and Greens functions,

with adaptabilit to diverse natural images. We also anal /ed the connections of our spline

regression algorithm to other algorithms, including those developed in inductive learning setting,

transductive learning setting, regulari/ation on graph and general functional spaces. *omparative

e!periments illustrate the validit of our method.

ADVANTAGES

(dvantage of this spline in usage is that, once it has been constructed, no parameters

need to be tuned to data.


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To speed up spline construction, %&means clustering algorithm is emplo ed to cluster the

user specified pi!els. B taking the cluster centers as representatives, this spline can be

easil constructed.

DOMAIN: DIGITAL IMAGE PROCESSING

0igital +mage processing is the use of computer algorithms to perform image processing

on 0igital +mages. (s a subfield of digital signal processing, digital image processing has man

advantages over analog image processing1 it allows a much wider range of algorithms to be

applied to the input data, and can avoid problems such as the build&up of noise and signal

distortion during processing.

SOFTWARE REQUIREMENT

'(T)(B 2.3 and above

'(T)(B is a high&performance language for technical computing. +t integrates

computation, visuali/ation, and programming in an eas &to&use environment where problems and

solutions are e!pressed in familiar mathematical notation. T pical uses include

'ath and computation

(lgorithm development

'odeling, simulation, and protot ping

0ata anal sis, e!ploration, and visuali/ation

Scientific and engineering graphics

(pplication development, including Graphical 4ser +nterface building

'(T)(B is an interactive s stem whose basic data element is an arra that does not re$uire

dimensioning. This allows ou to solve man technical computing problems, especiall those

with matri! and vector formulations, in a fraction of the time it would take to write a program in

a scalar non&interactive language such as * or 567T7(8.


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INTRODUCTION:

9!tracting the foreground objects in natural images is one of the most fundamental tasks

in image processing and understanding. Generall , this task can be formulated as a problem of

image segmentation. The efforts in segmentation have surged in recent decades, with the

development of numerous approaches and proposals for real world applications. +n spite of man

thoughtful attempts, it is still ver difficult to develop a general framework which can ield

satisfactor segmentations for diverse natural images. The difficulties lie in the comple!it of

perceiving and modeling the numerous visual patterns in natural images and the intrinsic

ambiguit of grouping them to be the needed objects.

To reduce the comple!it and intrinsic ambiguit , one method is to design interactive

frameworks, which can allow the user to specif the foreground and background according to

her:his own understanding about the image. +n such a work setting, the user can also act as a

judge to accept or refuse the current segmentation results, or add more strokes to obtain better

segmentation. +n view of image perception, the user specified strokes give us the visual hints to

model and group the visual patterns. With such supervised information, man e!isting algorithms

developed in machine learning can be emplo ed to formulate the task of image segmentation.

The goal of interactive image segmentation is to cut out a foreground object from its

background with modest user interaction . There are two main methods. 9dge based and region based. 9dge&based methods need the user to label the points near the object boundar . 7ecentl ,

researches mainl focus on region&based methods. +n this method, the interaction st le is largel

improved. The user can label the regions of foreground object and its background b simpl

dragging the mouse. The main advantage is that it does not re$uire the user to stare at and stroke

along the object boundar . With the help of statistical inference or machine learning algorithms,

the developed region&based interaction frameworks have achieved great success.

SCOPE OF T E PRO!ECT:


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To develop a novel algorithm for natural image segmentation. The task is formulated as a

problem of spline regression. The line is a combination of linear and Greens functions, with an

adaptabilit to diverse natural images. (nd to anal /e the connections of our spline regression

algorithm to other algorithms, including those developed in inductive learning setting,

transductive learning setting, regulari/ation on graph and general functional spaces.

MODULE SEPERATION:

'604)9 ; -roblem formulation and Spline 7egrssion

'604)9 < 5eature =ectors of -i!els

'604)9 > *lustering the 4ser Specified -i!els

'604)9 ? Spatial 8eighbourhood (ssignment

'604)9 @ -ost processing steps

'604)9 ; Spline 7egrssion A 5eature =ectors of -i!els

'604)9 < *lustering the 4ser Specified -i!els A Spatial 8eighbourhood (ssignment

'604)9 > 8atural +mage Segmentation

MODULE DESCRIPTION:

MODULE ":

+t is a task of data classification. 'an e!isting classification methods, such as )00 %&

8 as classifier , semi&supervised classification , graph cut , random walks can be applied to this

task. Cere spline regression is followed. 5irst a spline function is reconstructed. This task can be

considered in a general regulari/ation framework containing data fitting and function

smoothness.

Cere, each pi!el is described as a @&0 feature vector, i.e , Xi =[R G B X Y D in which "7 G

B# is the normali/ed color for each pi!el and "!, # is the spatial coordinate normali/ed with

image width and height. The reason that we consider the spatial coordinates is that the

discrimination between pi!els with similar colors can be enhanced, especiall when the

foreground object and its background contain e!actl identical colors.


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MODULE #:

Generall , there are thousands of pi!els which ma be scribbled b the user. (ccordingl ,

linear e$uations are to be solved with a large number of unknown parameters. Since the

coefficient matri! is a dense matri!, the computation comple!it of solving the linear e$uations

have to be solved here. Cowever, in most cases, the foreground object and its background onl

consists of a few number of different colors. Thus the user specified foreground and background

pi!els can be clustered and emplo the cluster centers as their representatives.

+n our spline regression framework, once the spline is constructed, it can be used to map

the unlabeled pi!els one b one. Thus, the spatial relationship between pi!els on the image grid

will be simpl ignored. To utili/e the spatial structure of the image, we assign the regressed value

of each pi!el to its neighbors .

MODULE $:

+n this section, three post processing methods will be introduced for user to obtain better

segmentation. The are connectivit anal sis, edge fairing, and segmenting with more strokes.

5inall the Segmented output is given for the input natural images.

ALGORIT M USED:Spline 7egression

FUTURE EN ANCEMENT:

Some e!isting incorrect small blobs can be corrected or deleted via connectivit anal sis

and area statistics.

ADVANTAGES:(dvantage of this spline in usage is that, once it has been constructed, no parameters

need to be tuned to data.

+t has an adaptabilit to diverse natural images.

9ach part of this segmentation achieves the highest accurac .


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APPLICATIONS:

7emote sensing

'edical image processing

'orphological image processing.

LITERATURE SURVEY:

E;D 6bject Based +mage 9diting William (. Barrett barrettFcs.b u.edu(lan S. *hene chene aFcs.b u.edu

0epartment of *omputer Science, Brigham oung 4niversit

This paper introduces 6bject Based +mage 9diting "6B+9# for real&time animation and

manipulation of static digital photographs. +ndividual image objects are selected, scaled,

stretched, bent, warped or even deleted "with automatic hole filling # Hat the object, rather than

the pixel level & using simple gesture motions with a mouse. 6B+9 gives the user direct, local

control over object shape, si/e, and placement while dramaticall reducing the time re$uired to

perform image editing tasks. 6bject selection is performed b manuall collecting "subobject#

regions detected b a watershed algorithm. 6bjects are tessellated into a triangular mesh,

allowing shape modification to be performed in real time using 6penG)s te!ture mapping

hardware. Through the use of anchor points , the user is able to interactivel perform editing

operations on a whole object, or just part"s# of an object & including moving, scaling, rotating,

stretching, bending, and deleting. Indirect manipulation of object shape is also provided through

the use of sliders and Be/ier curves. Coles created b movement are filled in real&time based on

surrounding te!ture. When objects stretch or scale, we provide a method for preserving texture

granularity or scale. +t present a texture brush , which allows the user to IpaintJ te!ture into

different parts of an image, using e!isting image te!ture"s#. 6B+9 allows the user to performinteractive, high& level editing of image objects in a few seconds to a few tens of seconds.

%#& I't(ract)*( I+a,( S(,+('tat)-' .s)', a' a/a0t)*(GMMRF +-/(1

(. Blake, *. 7other, '. Brown, -. -ere/, and -. Torr
mailto:[email protected]:[email protected]:[email protected]


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'icrosoft 7esearch *ambridge 4%,2 KK Thomson (venue, *ambridge *B> 35B, 4%.

http ::www.research.microsoft.com:vision:cambridge

The problem of interactive foreground:background segmentation in still images is of

great practical importance in image editing. The state of the art in interactive segmentation is

probabl represented b the graph cut algorithm of Bo kov and Koll "+**=


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image segmentation. We also demonstrate an interesting Gestalt e!ample. ( fast implementation

of segmentation method is possible via a new ma!&flow algorithm.

REFERNCES:

E;D W. (. Barrett and (. S. *hene , I6bject&based image editing,J in roc! "IGGR# $ , San

(ntonio, TM,

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