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CHAPTER 1INTRODUCTION

1.1 INTRODUCTION TO OUR PROJECTImages are considered as one of the most important medium of conveying information. Understanding images and extracting the information from them such that the information can be used for other tasks in an important aspect of machine learning.One of the first steps in direction of understanding images is to segment them and find out different objects in them. Thus image segmentation plays a vital role towards conveying information that is represented by an image also assists in understanding the image

1.2 AIM OF OUR PROJECTOur main aim of the project is to distinguish the objects in the image from the background through their boundaries.

1.3 TOOLS REQUIRED/USED MATLAB R2012a programming language can be used for implementation of the proposed project importantly with image processing tool box.1.4 ORGANIZATION OF REPORT

The project is organized as follows. Chapter 2 gives the concept of image segmentation and various approaches. Chapter 3 reviews the general active contour model designed for automatic image segmentation. Chapter 4 presents lattice Boltzmann method which incorporates the user input and the initial segmentation giving result to an active contour model Chapter 5 shows the experimental results to demonstrate the effectiveness of our proposed method. Finally, Chapter 6 concludes this project.

CHAPTER 2IMAGE SEGMENTATION

2. IMAGE SEGMENTATIONImage Segmentation is a process of dividing the given image in to regions homogeneous with respect to certain features, and which hopefully correspond to real objects in the actual scene. Segmentation plays a vital role to exact information from an image to create homogeneous regions by classifying pixels in to groups thus forming regions of similarity. The homogeneous regions formed as a result of segmentation indwell pixels having similarity in which regions according to particular selection criteria e.g. intensity, color etc.There are two main approaches in image segmentation: edge- and region- based. Edge based segmentation partitions an image based on discontinuities among sub-regions, while region-based segmentation does the same function based on the uniformity of a desired property within a sub-region. In this chapter, we briefly discuss existing image segmentation technologies as background.2.1 Edge-based segmentation: Edge-based segmentation looks for discontinuities in the intensity of an image. It is more likely edge detection or boundary detection rather than the literal meaning of image segmentation. An edge can be defined as the boundary between two regions with relatively distinct properties. The assumption of edge-based segmentation is that every sub-region in an image is sufficiently uniform so that the transition between two sub-regions can be determined on the basis of discontinuities alone. When this assumption is not valid, region-based segmentation, discussed in the next section, usually provides more reasonable segmentation results.Basically, the idea underlying most edge-detection techniques is the computation of a local derivative operator. The gradient vector of an image I(x, y), given by

It is obtained by the partial derivatives and at every pixel location. The local derivative operation can be done by convolving an image with kernels shown in fig 2.1

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Figure 2.1: Examples of gradient kernels along: (a) vertical direction, (b) horizontal directionThe magnitude of the first derivate

determines the presence of edges in an image The Laplacian of an image function I(x, y) is the sum of the second-order derivatives, defined as

The general use of the Laplacian is in finding the location of edges using its zero-crossings. A critical disadvantage of the gradient operation is that the derivative enhances noise. As a second-order derivative, the Laplacian is even more sensitive to noise. An alternative is convolving an image with the Laplacian of a Gaussian (LoG) function given by

where a two-dimensional Gaussian function with the standard deviation is defined as

The LoG function produces smooth edges as the Gaussian filtering provides smoothing effect.Sobel operation is performed by convolving an image with kernels shown in figure 2.2. Sobel operators have the advantage of providing both a derivative and smoothing effect gradient kernels shown in figure 2.1 because the derivative enhances noise.

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(a) (b)Figure 2.2: Sobel operators along: (a) vertical direction, (b) horizontal direction

Edge detection by gradient operations generally work well only in the images with sharp intensity transitions and relatively low noise. Due to its sensitivity to noise, some smoothing operation is generally required as preprocessing, and the smoothing effect consequently blurs the edge information. However, the computational cost is relatively lower than other segmentation methods because the computation can be done by a local filtering operation, i.e. convolution of an image with a kernel. Edge-based active contour models use the magnitude of gradient to determine the position of edges.

2.2 Region-based segmentation: Region-based segmentation looks for uniformity within a sub-region, based on a desired property, e.g. intensity, color, and texture. Region growing is a technique that merges pixels or small sub-regions into a larger sub-region. The simplest implementation of this approach is pixel aggregation, which starts with a set of seed points and grows regions from these seeds by appending neighboring pixels if they satisfy the given criteria. Figure 2.3 shows a simple example of pixel aggregation.248

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Figure 2.3: Pixel aggregation: (a) original image with seeds underlined; (b) segmentation result with =4Segmentation starts with two initial seeds, and then the regions grow if they satisfy a criterion such as

Despite the simple nature of the algorithm, there are fundamental problems in region growing: the selection of initial seeds and suitable properties to grow the regions. Selecting initial seeds can be often based on the nature of applications or images. For example, the ROI is generally brighter than the background in IR images. In this case, choosing bright pixels as initial seeds would be a proper choice. Additional criteria that utilize properties to grow the regions lead region growing into more sophisticated methods, e.g. region competition. Region competition merges adjacent sub-regions under criteria involving the uniformity of regions or sharpness of boundaries. Strong criteria tend to produce over-segmented results, while weak criteria tend to produce poor segmentation results by over-merging the sub-regions with blurry boundaries. An alternative of region growing is split-and-merge, which partitions an image initially into a set of arbitrary, disjointed sub-regions, and then merges and/or split the sub-regions in an attempt to satisfy the segmentation criteria.

2.3 Histogram based methods: Histogram-based methods are very efficient when compared to other image segmentation methods because they typically require only one pass through thepixels. In this technique, a histogram is computed from all of the pixels in the image, and the peaks and valleys in the histogram are used to locate theclustersin the image.Colororintensitycan be used as the measure.A refinement of this technique is torecursivelyapply the histogram-seeking method to clusters in the image in order to divide them into smaller clusters. This is repeated with smaller and smaller clusters until no more clusters are formed. One disadvantage of the histogram-seeking method is that it may be difficult to identify significant peaks and valleys in the image. In this technique of image classification distance metric and integrated region matching are familiar.Histogram-based approaches can also be quickly adapted to occur over multiple frames, while maintaining their single pass efficiency. The histogram can be done in multiple fashions when multiple frames are considered. The same approach that is taken with one frame can be applied to multiple, and after the results are merged, peaks and valleys that were previously difficult to identify are more likely to be distinguishable. The histogram can also be applied on a per pixel basis where the information result are used to determine the most frequent color for the pixel location. This approach segments based on active objects and a static environment, resulting in a different type of segmentation useful inVideo tracking.

CHAPTER 3ACTIVE CONTOUR MODEL

3. ACTIVE CONTOUR MODEL

Active contours are connectivity-preserving relaxation methods, applicable to the image segmentation problems. Active contours have been used for image segmentation and boundary tracking since the first introduction of snakes by Kass. The basic idea is to start with initial boundary shapes represented in a form of closed curves, i.e. contours, and iteratively modify them by applying shrink/expansion operations according to the constraints of the image. Those shrink/expansion operations, called contour evolution, are performed by the minimization of an energy function like traditional region-based segmentation methods or by the simulation of a geometric partial differential equation (PDE). An advantage of active contours as image segmentation methods is that they partition an image into sub-regions with continuous boundaries, while the edge detectors based on threshold or local filtering, e.g. Canny or Sobel operator, often result in discontinuous boundaries. The use of level set theory has provided more flexibility and convenience in the implementation of active contours. Depending on the implementation scheme, active contours can use various properties used for other segmentation methods such as edges, statistics, and texture.

3.1 Active ContoursThe method of active contours has become quite popular for a range of applications, mainly image segmentation and motion tracking, through the last decade. This methodology is based upon the use of deformable contours which match to various object shapes and motions. This section provides a theoretical setting of active contours and an indication of existing active contour methods. There are two main approaches in active contours based on the mathematic implementation: snakes and level sets. Snakes explicitly shift predefined snake points based on an energy minimization method, while level set approaches move contours completely as a particular level of a function. As image segmentation methods, there are two kinds of active contour models according to the force evolving the contours: edge- and region-based. Edge- based active contours apply an edge detector, typically based on the image gradient, to locate the boundaries of sub-regions and to draw the contours to the detected boundaries. Edge-based approaches are closely connected to the edge-based segmentation. Region based active contours apply the statistical information of image intensity inside each subset instead of searching geometrical boundaries. Region-based approaches are also closely connected to the region-based segmentation.

3.2 Level Set Method

Level set theory, a formulation to apply active contours, was proposed by Osher and Sethian. They represented a contour implicitly via a two dimensional Lipschitz - continuous function defined on the image plane. The function is called level set function, and a particular level, generally the zero level, of is defined as the contour. The level set technique is a general framework for tracking dynamic interfaces and shapes. It was first developed as a way to model fluid boundaries, such as a flame front. In computer vision and pattern recognition the level set method (LSM) had been widely used for image segmentation. The attractive advantage of the LSM is its ability to extract complex contours and to automatically handle topological changes, such as splitting and merging. The LSM belongs to the active contours models (ACMs) which is based on the Eulerian framework, i.e., the geometric representation of the active contour instead of the parametric representation which is based on the Lagrangian framework. The basic idea of the LSM is to evolve the zero-level of the level set function (LSF) in the image domain until it reaches the boundaries of the regions of interest. The active curve evolution is governed by the level set equation (LSE). It is a partial differential equation expressed as

where is the level set function, V is the speed function which drives and attracts the active contour towards the region boundaries. The second term of the right hand represents the curvature and is used to smooth the contour. It is non-linear and computational expansive. and are user-controlling parameters. Two approaches are usually used to stop the evolving curve on the boundaries of the desired objects. The first one uses an edge indicator depending on the gradient of the image like in classical snakes and active contour models, and the methods belonging to this class are effective when detecting objects boundaries defined by edges, i.e., high gradient but are more sensitive to noise and ineffective when the object of interest is without edges. The second approach uses some regional attributes to stop the evolving curve, and the methods belonging to this class are robust to noise, effective when detecting objects without edges but have some difficulties when the boundaries between the object and the background are only defined by high gradient.

3.2.1 Two phase level set method

In our project, we propose a two phase level set method which can allows the realization of a binary segmentation, and can be easily expanded to the multi-phase case. The method uses both edge and region information in order to effectively detect both objects defined by edges and without edges. The sensitivity to noise introduced by local information, i.e., edge information, is handled by the 2D gray-scale histogram based constraint. The lattice Boltzmann method(LBM) is used as an alternative approach to solve the LSE. It can better handle the problem of time consuming because the non-linear term that is curvature term in LSE. LBM is discussed in the next chapter in detail.

Contour Initialization For any active contour method, the contour needs to be initialized before the contour evolution process.

CHAPTER 4LATTICE BOLTZMANN METHOD

4.1 LATTICE BOLZMANN METHOD Instead of using some computational expensive classical numerical methods based on finite difference, finite element or finite volume approximations, the lattice bolzmann method is as an alternative approach to solve the LSE. The LBM is of recent use in image segmentation, but is highly promising because of its simplicity, explicit and highly parallelizable nature. It is second order accuracy both in time and space and can better handle the problem of time consuming because the non-linear term in the LSE, i.e., the curvature term, is implicitly computed. The LBM is firstly designed to simulate NavierStokes equations for an incompressible fluid. The evolution equation of LBM is

where fi is the particle distribution function and is, in this paper, the Bhatnager-Gross-Krook (BGK) collision model with body force .

where represents the relaxation time and the local equilibrium particle distribution which can be expressed as follows when modeling typical diffusion phenomenon, with where is the macroscopic fluid density. By performing the Chapman-Enskog expansion the following diffusion equation can be recovered from LBM . Substituting by the signed distance function in above Equation,the LSE can be recovered. In our model we use the D2Q5 LBM lattice structure to solve the proposed new LSE. The body force F acts as the link with image statistics for the LBM solver and is defined from the proposed LSE.

A statistical description of a system can be explained by distribution function where is the number of molecules at time t positioned between r and r+dr which have velocities between c and c+dc, as mentioned in the previous chapter. An external force F acting on a gas molecule of unit mass will change the velocity of the molecule from c to c+Fdt and its position from r to r+cdt.The number of molecules, f(r,c,t). before applying the external force is equal to the number of molecules after the disturbance, f(r+cdt,c+Fdt,t+dt), if no collisions take place between the molecules. Hence, (1)However, if collisions take place between the molecules there will be a net difference between the numbers of molecules in the interval drdc: The rate of change between final and initial status of the distribution function is called collision operator, . Hence, the equation for evolution of the number of the molecules can be written as,

Fig:Position and velocity vector for a particle after and before applying a force, F

(2)Dividing the above equation by dtdrdc and as the limit dt0, yields (3)The above equation states that the total rate of change of the distribution function is equal to the rate of the collision. Since f is a function of r, c and t, then the total rate of change can be expanded as, (4)Dividing by dt, yields (5)The vector r can be expressed in 3-D Cartesian coordinate system as r =xi + yj + zk, where i, j, and k are unit vectors along x, y, and z-direction, respectively.Above equation can be written as, (6)

where a is equal to dc/dt, the acceleration and can be related to force F by Newtons second law, a=F/m: Therefore, the Boltzmann transport equation (3) can be written as, (7)The is a function of and need to be determined to solve the Boltzmann equation. For system without an external force, the Boltzmann equation can be written as (8)Note that c and f are vectors.Equation(8) is an advection equation with a source term (), or advection with a reaction term, which can be solved exactly along the characteristic lines that is tangent to the vector c, if is explicitly known. The problem is that is a function of f and Eq(8) is an integro-differential equation, which is difficult to solve. The relation between the above equation and macroscopic quantities such as fluid density, ; fluid velocity vector , and internal energy e, is as follows (9) (10) (11)where m is the molecular mass and the particle velocity relative to the fluid velocity, the peculiar velocity, =c _ u. Equations(9),(10) and (11) are conservation of mass, momentum, and energy, respectively. From the kinetic theory, as discussed before, the internal energy can be expressed as,

The BGKW Approximation

It is difficult to solve Boltzmann equation because the collision term is very complicated. The outcome of two body collisions is not likely to influence significantly, the values of many measured quantities (Cercignani, 1990). Hence, it is possible to approximate the collision operator with simple operator without introducing significant error to the outcome of the solution. Bhatnagar, Gross and Krook (BGK) in 1954 introduced a simplified model for collision operator. At the same time Welander (1954), independently, introduced similar operator. The collision operator is replaced as, (13) where =1/ The coefficient is called the collision frequency and is called relaxation factor. The local equilibrium distribution function is denoted by , which is MaxwellBoltzmann distribution function. After introducing BGKW approximation, the Boltzmann equation (Eq(8) without external forces) can be approximated as, (14)In Lattice Boltzmann method, the above equation is discretized and assumed it is valid along specific directions, linkages. Hence, the discrete Boltzmann equation can be written along a specified direction as, (15)The above equation is the working horse of the lattice Boltzmann method and replaces NavierStokes equation in CFD simulations. It is possible to derive NavierStokes equation from Boltzmann equation. We can make comments as the followings about Eq.(15)1. The equation is a linear partial differential equation.2. The equation looks like an advection equation with a source term.3. The right-hand side of the equation represents the advection (streaming).4. The left-hand side term represents the collision process, source term.Equation(15) can be discretized as (16)The local equilibrium distribution function with a relaxation time determine the type of problem needed to be solved. The beauty of this equation lies in its simplicity and can be applied for many physics by simply specifying a different equilibrium distribution function and source term (external force). Adding a source term (force term) to the above equation is straightforward. However, there are a few concerns, which will be discussed in the following chapters. Also, the details of implementing the above equation for different problems, such as momentum, heat and mass diffusion, advectiondiffusion without and with external forces, will be presented in the following chapters. It is possible to use finite difference or finite volume to solve partial differential equation(15). Some authors used this approach to solve fluid dynamic problems on non-uniform grids. The main focus of the book is to solve Eq(15) in two steps, collision and streaming.In LBM, the solution domain need to be divided into lattices. At each lattice node, the factitious particles (distribution function) reside. Some of these particles streams (move) along specified directions to the neighboring nodes. The number of direction, linkage, depends on the lattice arrangement. Different lattice arrangements will be discussed in the following section.

Lattice Arrangements

The common terminology used in LBM is to refer to the dimension of the problem and the number of speed is using DnQm, where n represent the dimension of the problem (1 for 1-D, 2 for 2-D and 3 for 3-D) and m refers to the speed model, number of linkages.

One-Dimensional

In general, two models can be used for lattice arrangements, called D1Q3Q and D1Q5Q. D1Q3 is the most popular one. The black nodes are the central node, while the gray nodes are neighboring nodes. The factitious particles stream from the central node to neighboring nodes through linkages with a specified speed, called lattice speed.

D1Q3 and D1Q2

For D1Q3, there are three velocity vectors (c0; c1 and c2) for f0; f1and f2; which equal to 0, 1, and -1, respectively. Note that we assumed that dx =dt; otherwise, and , where and are the linkage length and time step, respectively. For this arrangement, the total number of factitious particles at any instant of time cannot exceed three particles. One stagnant particle (zero velocity) resides on the central site. The other two particles move either to the left or to the right node in the streaming process. The weighting factors, ; has values of 4/6, 1/6 and 1/6 for f0; f1and f2; respectively. The speed of sound, ; in lattice units for D1Q3 is . It is also possible to use other arrangement called D1Q2. The weighting factors, has values of 1/2 and 1/2 for f1and f2; respectively. The speed of sound for this arrangement is 1/ . These schemes, D1Q2 and D1Q3, are mostly used. However, it is possible to involve more sites and use higher order schemes, such as, D1Q5.

D1Q5

For this arrangement, the total number of factitious particles at any instant of time cannot exceeded five particles. The weighting factors, are 6/12, 2/12, 2/12,1/12, and 1/12 for f0; f1; f2; f3; and f4; respectively. The speed of the sound in lattice units is 1/.

Two-Dimensional

4.2 D2Q5 and D2Q4 Lattice Structure

D2Q5 model has four velocity vectors issued from the central nodes as shown in above figure. One of the particle resides at the central node, hence its speed is zero, noted as c(0,0). The distribution function f1and f2 move with c(1,0) and c(-1,0) (to the east and west), respectively, while f3and f4 move with speed c(0,1) and c(0,-1) (to the north and south), respectively. Note that it is assumed that . The weighting factors for f0; f1; f2; f3; and f4 are 2/6, 1/6, 1/6, 1/6, and 1/6, respectively. It is worthy to mention that this arrangement cannot be used to simulate fluid flows. This issue will be discussed latter on. D2Q4 has four velocity vectors and there is no particle residing on the centre node. The weighting factor for each direction is 1/4. We will discuss implementations and applications of each scheme in the following chapters. The numbering of lattice links is arbitrary.However, for computer programming algorithm, proper numbering may reduce simplifying the computer code.

4.3 GPUThe GPU has been developed for general-purpose computing. Since increasing the clock speeds drive transistors to thermal limits, the multi-core technique became the evident solution to increase the processing speed of computing hardware. The GPU has, therefore, been recognized as one of the most promising techniques to accelerate scientific computations. The GPU architecture favors dense data and local computations because the communications between microprocessor is time consuming. Since the majority of LBM is local, it is thus suitable for GPU-based computation. In this paper, the proposed algorithm is accelerated using an NVIDIA graphics processing units. The proposed method is efficient and effective when detecting object with well-defined edges, with weak edges and without edges. Furthermore the method is robust against noise, although it uses edge information, and fast to enable real-time image segmentation. Comparing to some mean-shift and graph-based segmentation method, the advantages of the proposed method rely on the intrinsic nature of the level set method which allows the straightforward passage to higher dimensions, for example from 2D to 3D. In addition, contrary to graph-cut method, sub-pixel accuracy can be reached.

CHAPTER 5EXPERIMENTAL RESULTS

5. EXPERIMENTAL RESULTS5.1 Parameter Setting

Our proposed method has a few parameters, including {lambda, gamma, tau, mu, a}.The parameter allows the user to control and adjust the impact of F on the active contour motion. We empirically set to be 550 in our experiments.The parameter a is set to 0.5, although the results are quite robust to different values of a.The parameters gamma and tau are relaxation coefficients. tau is made equal to (9*gamma+2)/4; where gamma is set to 0.5.The parameter is set to 2 empirically. Note that all the parameters are set in the same way for all the experiments.Number of iterations=2

5.2 Implementation

Some real images are read in to matlab and are initially converted in to gray scale images.Later a default initial contour is given according to the programming in matlab which sets level set function .Then body force of the images are developed depending on the image.Then Lattice Boltzmann method is used to evolve the contour to align along the boundaries of the objects in the image using above equations with the help of the parameters.

The following figures show the implementation of our method on different images in the following figures 5.1, 5.2, 5.3, 5.4,6.1,6.2,6.3,6.4,6.5(a),(b)

Fig 5.1 An example image of bird for simulation

Fig 5.2 Initial contour given on the gray scale image

Fig 5.3 Updated contour along the boundaries of the bird

Fig5.4 Final binary image distinguishing the bird from the background through the boundary.

Fig 6.1 Another example of image for simulation

Fig 6.2 gray scale image for the above image with contour.

Fig 6.3 Resulting contour for above image

Fig 6.4 final binary image.

Fig 6.5 (a) (b)

Two figures illustrating the difference between our proposed method (a)and chan vese method(b).

5.3 Limitations

Nevertheless, the algorithm has the limitation of the lattice Boltzmann method which results in the non-negligible memory complexity when storing the distribution functions. Future work will be to handle this problem in order to use the method for GPU cluster accelerated real-time volume images segmentation.

CHAPTER 6CONCLUSION

6. CONCLUSION6.1 Conclusion

In this paper, we have proposed an edge, region and 2D histogram information based level set model. The use of the lattice Boltzmann method to solve the level set equation enables the algorithm to be highly parallelizable and fast when implemented using an NVIDIA graphics processing units. The method is effective when segmenting objects with and without edges, robust against noise and quietly independent to the position of the initial contour. Experimental results on a variety kind of images have demonstrated subjectively and objectively the effectiveness of the proposed model.

6.2 Applications:

a) Medical imaging:Medical imagingis the technique and process used to createimagesof thehuman body(or parts and function thereof) for clinical purposes (medical proceduresseeking to reveal,diagnose, or examinedisease) or medical science (including the study of normalanatomyandphysiology). Although imaging of removedorgansandtissuescan be performed for medical reasons, such procedures are not usually referred to as medical imaging, but rather are a part ofpathology.

b) Object detection:Object detectionis a computer technology related tocomputer visionandimage processingthat deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos. Well-researched domains of object detection includeface detectionandpedestrian detection. Object detection has applications in many areas of computer vision, includingimage retrievalandvideo surveillance.

c) Face detection:Face detectionis a computer technology that determines the locations and sizes of human faces in arbitrary (digital) images. It detects facial features and ignores anything else, such as buildings, trees and bodies.Face detection can be regarded as a specific case ofobject-class detection. In object-class detection, the task is to find the locations and sizes of all objects in an image that belong to a given class. Examples include upper torsos, pedestrians, and cars.Face detection can be regarded as a more general case offace localization. In face localization, the task is to find the locations and sizes of a known number of faces (usually one). In face detection, one does not have this additional information.Early face-detection algorithms focused on the detection of frontal human faces, whereas newer algorithms attempt to solve the more general and difficult problem of multi-view face detection. That is, the detection of faces that are either rotated along the axis from the face to the observer (in-plane rotation), or rotated along the vertical or left-right axis (out-of-plane rotation), or both. The newer algorithms take into account variations in the image or video by factors such as face appearance, lighting, and pose.Face detection is used inbiometrics, often as a part of (or together with) afacial recognition system. It is also used in video surveillance, human computer interface and image database management. Some recent digital cameras use face detection for autofocus.Face detection is also useful for selecting regions of interest in photo slideshows that use a pan-and-scaleKen Burns effect.Face detection is gaining the interest of marketers. A webcam can be integrated into a television and detect any face that walks by. The system then calculates the race, gender, and age range of the face. Once the information is collected, a series of advertisements can be played that is specific toward the detected race/gender/age.

d) Facial recognition system:A facial recognition system is a computer application for the automatically identifying orverifyingapersonfrom adigital imageor avideo framefrom a videosource. One of the ways to do this is by comparing selectedfacial featuresfrom the image and a facialdatabase.It is typically used insecurity systemsand can be compared to other biometricssuch asfingerprintor eyeirisrecognition systems.

e) Iris recognition:Iris recognitionis an automated method ofbiometricidentification that uses mathematical pattern-recognition techniques on video images of the iridesof an individual'seyes, whose complex random patterns are unique and can be seen from some distance.Not to be confused with another, less prevalent, ocular-based technology,retina scanning, iris recognition uses camera technology with subtle infraredillumination to acquire images of the detail-rich, intricate structures of the iris. Digital templates encoded from these patterns by mathematical and statistical algorithms allow the identification of an individual or someone pretending to be that individual.Databases of enrolled templates are searched by matcher engines at speeds measured in the millions of templates per second per (single-core) CPU, and with infinitesimally small False Match rates.Many millions of persons in several countries around the world have been enrolled in iris recognition systems, for convenience purposes such as passport-free automated border-crossings, and some national ID systems based on this technology are being deployed. A key advantage of iris recognition, besides its speed of matching and its extreme resistance to False Matches is the stability of the iris as an internal, protected, yet externally visible organ of the eye.

f) Machine vision:Machine vision(MV) is the technology and methods used to provide imaging-based automatic inspection and analysis for such applications as automatic inspection, process control, and robot guidance in industryMachine vision methods are defined as both the process of defining and creating a MV solution,and as the technical process that occurs during the operation of the solution. Here the latter is addressed. As of 2006, there was little standardization in theinterfacingand configurations used in MV. This includes user interfaces, interfaces for the integration of multi-component systems and automated data interchange.Nonetheless, the first step in the MV sequence of operation isacquisition of an image, typically using cameras, lenses, and lighting that has been designed to provide the differentiation required by subsequent processing.MVsoftwarepackages then employ variousdigital image processingtechniques to extract the required information, and often make decisions (such as pass/fail) based on the extracted information.Though the vast majority of machine vision applications are still solved using 2 dimensional imaging, machine vision applications utilizing 3D imaging are growing niche within the industry.

g) Content based Image retrieval:Content-based image retrieval(CBIR), also known asquery by image content(QBIC) andcontent-based visual information retrieval(CBVIR) is the application ofcomputer visiontechniques to theimage retrievalproblem, that is, the problem of searching fordigital imagesin largedatabases(see this surveyfor a recent scientific overview of the CBIR field). Content based image retrieval is opposed toconcept based approaches(see concept based image indexing)."Content-based" means that the search will analyze the actual contents of the image rather than themetadatasuch as keywords, tags, and/or descriptions associated with the image. The term 'content' in this context might refer to colors, shapes, textures, or any other information that can be derived from the image itself. CBIR is desirable because most web based image search engines rely purely onmetadataand this produces a lot of garbage in the results.Also having humans manually enter keywords for images in a large database can be inefficient, expensive and may not capture every keyword that describes the image. Thus a system that can filter images based on their content would provide better indexing and return more accurate results.

6.3 Future ScopeThis paper can be extended in a few ways. For example, it might be beneficial to apply the lattice Boltzmann method for other segmentation problems, such as image matting or video segmentation. Also, it was interesting to adopt some advanced evaluation method, such as the user simulation-based approach proposed in to fully evaluate the performance of different interactive image segmentation methods

BIBLIOGRAPHY

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APPENDIX

SOURCE CODE

UI

START SEGMENTATIONLOAD IMAGEOPPRE-PROCESSINGUIUser Interference OPOut put Image

Code Algorithm

CODE

%%%%%%%%%%%%%%%%%% START %%%%%%%%%%%%%%%%%%%clcclear allclose all% INITIAL DISTANCE FIELDI= imread ('bird.jpg');I=im2double(I);I=rgb2gray(I);lx = size(I,1);ly = size(I,2);Imean = zeros(lx,ly);for x = 2:lx-1 for y = 2:ly-1 Imean(x,y) = (I(x-1,y-1)+I(x-1,y)+I(x-1,y+1)+I(x,y-1)+I(x,y)+I(x,y+1)+I(x+1,y-1)+I(x+1,y)+I(x+1,y+1))/9; endend figure(1);imshow(I);hold on;[m n]=size(I);phi=ones(m,n);phi(50:m-50,50:n-50)=-1;contour(phi, [0 0], 'r')hold off; % D2Q5 LATTICE CONSTANTSex = [0, 1, 0, -1, 0];ey = [0, 0, 1, 0, -1];A = [1/3, 1/6, 1/6, 1/6, 1/6];% INITIALIZATION OF THE LBM DISTRIBUTION FUNCTIONFeq = zeros(5,lx,ly);D = zeros(5,lx,ly);Fout = zeros(5,lx,ly);phi2 = phi;for x = 1:lx for y = 1:ly for li = 1:5 Feq(li,x,y) = phi2(x,y).* A(li); end endend Fint = Feq; % SETTING OF THE RELAXATION COEFFICIENTgamma = 0.5;tau = (9*gamma+2)/4; % EDGE INFORMATIONn = 2;[Dx, Dy] = gradient(I);G = 1./(sqrt(Dx.^2 + Dy.^2) + 1).^n; % PERFORMING STREAMING-COLLISIONSlambda = 550;mu = 2;a = 0.5;F = zeros(lx,ly);tic; for iterations = 1:2 % COMPUTATION OF THE EXTERNAL FORCE %calculation of interior and exterior meansImg=I;u=phi;c1 = sum(sum(Img.*(u0)));%exterior mean for x = 1:lx for y = 1:ly F(x,y)= lambda*(I(x,y)-(0.95*c1+0.05*c2))*(G(x,y)+exp(mu*(abs(I(x,y)-Imean(x,y))-a))); end end for x = 1:lx for y = 1:ly for li = 1:5 Feq(li,x,y) = phi2(x,y).* A(li); end end end for x = 2:lx-1 for y = 2:ly-1 for li = 1:5 Fout(li,x,y)= Fint(li,x,y)-1/tau .* (Fint(li,x,y)-Feq(li,x,y))+(2*tau-1)/(2*tau) .* F(x,y); end end end for x = 2:lx-1 for y = 2:ly-1 for li = 1:5 Fint(li,x+ex(li),y+ey(li)) = Fout(li,x,y); end end end % UPDATING OF THE DISTANCE VALUEphi3= zeros(lx,ly); for x = 1:lx for y = 1:ly for li = 1:5 phi3(x,y)=phi3(x,y) + Fint(li,x,y); end end end figure;imshow(I); hold on; contour(phi3,[0 0], 'r'); endCPU_time = tocphi4=zeros(lx,ly);for x = 1:lx for y = 1:ly if phi3(x,y)