数字图像处理论文中英文对照资料外文翻译文献

中英文对照资料外文翻译文献

原文

To image edge examination algorithm research

Abstract ：Digital image processing took a relative quite young discipline, is following the computer technology rapid development, day by day obtains the widespread application.The edge took the image one kind of basic characteristic, in the pattern recognition, the image division, the image intensification as well as the image compression and so on in the domain has a more widespread application.Image edge detection method many and varied, in which based on brightness algorithm, is studies the time to be most long, the theory develops the maturest method, it mainly is through some difference operator, calculates its gradient based on image brightness the change, thus examines the edge, mainly has Robert, Laplacian, Sobel, Canny, operators and so on LOG。

First as a whole introduced digital image processing and the edge detection survey, has enumerated several kind of at present commonly used edge detection technology and the algorithm, and selects two kinds to use Visual the C language programming realization, through withdraws the image result to two algorithms the comparison, the research discusses their good and bad points.

Foreword：In image processing, as a basic characteristic, the edge of theimage, which is widely used in the recognition, segmentation,intensification and compress of the image, is often applied tohigh-level domain.There are many kinds of ways to detect the edge. Anyway, there aretwo main techniques: one is classic method based on the gray grade ofevery pixel; the other one is based on wavelet and its multi-scalecharacteristic. The first method, which is got the longest research,get the edge according to the variety of the pixel gray.

The maintechniques are Robert, Laplace, Sobel, Canny and LOG algorithm. The second method, which is based on wavelet transform, utilizes theLipschitz exponent characterization of the noise and singular signaland then achieve the goal of removing noise and distilling the realedge lines. In recent years, a new kind of detection method, which basedon the phase information of the pixel, is developed. We need hypothesizenothing about images in advance. The edge is easy to find in frequencydomain. It’s a reliable method.

In chapter one, we give an overview of the image edge. And inchapter two, some classic detection algorithms are introduced. Thecause of positional error is analyzed, and then discussed a moreprecision method in edge orientation. In chapter three, wavelet theoryis introduced. The detection methods based on sampling wavelettransform, which can extract maim edge of the image effectively, andnon-sampling wavelet transform, which can remain the optimum spatialinformation, are recommended respectively. In the last chapter of thisthesis, the algorithm based on phase information is introduced. Usingthe log Gabor wavelet, two-dimension filter is constructed, many kindsof edges are detected, including Mach Band, which indicates it is aoutstanding and bio-simulation method。May all the work in this paper is of some value to research and applications of image edge detection.

First chapter introduction

§1.1 image edge examination introduction

The image edge is one of image most basic characteristics, often is carrying image majority of informations。But the edge exists in the image irregular structure and in not the steady phenomenon, also namely exists in the signal point of discontinuity place,

these spots have given the image

outline position, these outlines

are frequently we when the

imagery processing needs the

extremely important some

representative condition, this

needs us to examine and to withdraw its edge to an image。But the edge examination algorithm is in the imagery processing question one of classical technical difficult problems, its solution carries on the high level regarding us the characteristic description, the recognition and the understanding and so on has the significant influence; Also because the edge examination all has in many aspects the extremely important use value, therefore how the people are devoting continuously in study and solve the structure to leave have the good nature and the good effect edge examination operator question。In the usual situation, we may the signal in singular point and the point of discontinuity thought is in the image peripheral point, its nearby gradation change situation may reflect from its neighboring picture element gradation distribution gradient。

According to this characteristic, we proposed many kinds of edge examination operator: If Robert operator, Sobel operator, Prewitt operator, Laplace operator and so on.These methods many are wait for the processing picture element to carry on the gradation analysis for the central neighborhood achievement the foundation, realized and has already obtained the good processing effect to the image edge extraction.。But this kind of method simultaneously also exists has the edge picture element width, the noise jamming is serious and so on the shortcomings, even if uses some auxiliary methods to perform the denoising, also corresponding can bring the flaw which the edge fuzzy and so on overcomes with difficulty。Along with the wavelet analysis appearance， its good time frequency partial characteristic by the widespread application in the imagery processing and in the pattern recognition domain, becomes in the signal processing the commonly used method and the powerful tool。Through the wavelet analysis, may interweave decomposes in the same place each kind of composite signal the different frequency the block signal， but carries on the edge examination through the wavelet transformation, may use its multi-criteria and the multi-resolution nature fully， real effective expresses the image the edge characteristic。When the wavelet transformation criterion reduces, is more sensitive to the image detail; But when the criterion increases, the image detail is filtered out, the examination edge will be only the thick outline.This

characteristic is extremely useful in the pattern recognition, we may be called

this thick outline the image the main edge.If will be able an image main edge

clear integrity extraction, this to the goal division, the recognition and so

on following processing to bring the enormous convenience.Generally speaking,

the above method all is the work which does based on the image luminance information。

In the multitudinous scientific research worker under, has obtained the very

good effect diligently.But, because the image edge receives physical condition

and so on the illumination influences quite to be big above, often enables many

to have a common

shortcoming based on

brightness edge detection

method, that is the edge is

not continual, does not seal

up.Considered the phase

information in the image

importance as well as its

stable characteristic, causes using the phase information to carry on the imagery processing into new research topic。

In this paper soon introduces one kind based on the phase image characteristic examination method - - phase uniform method.It is not uses the

image the luminance information, but is its phase characteristic, namely supposition image Fourier component phase most consistent spot achievement characteristic point.Not only it can examine brightness characteristics and so

on step characteristic, line characteristic, moreover can examine Mach belt phenomenon which produces as a result of the human vision sensation characteristic.Because the phase uniformity does not need to carry on any supposition to the image characteristic type, therefore it has the very strong versatility。

§1.2 image edge definition

The image majority main information all exists in the image edge, the main

performance for the image partial characteristic discontinuity, is in the image the gradation change quite fierce place, also is the signal which we usually said has the strange change place。The strange signal the gradation change which moves towards along the edge is fierce, usually we divide the edge for the step shape and the roof shape two kind of types (as shown in Figure 1-1).In the step edge two side grey levels have the obvious change; But the roof shape edge is located the gradation increase and the reduced intersection point.May portray the peripheral point in mathematics using the gradation derivative the change, to the step edge, the roof shape edge asks its step, the second time derivative separately。

To an edge, has the possibility simultaneously to have the step and the line edge characteristic. For example on a surface, changes from a plane to the normal direction different another plane can produce the step edge; If this surface has the edges and corners which the regular reflection characteristic also two planes form quite to be smooth, then works as when edges and corners smooth surface normal after mirror surface reflection angle, as a result of the regular reflection component, can produce the bright light strip on the edges and corners smooth surface, such edge looked like has likely superimposed a line edge in the step edge. Because edge possible and in scene object important characteristic correspondence, therefore it is the very important image characteristic。For instance, an object outline usually produces the step edge, because the object image intensity is different with the background image intensity。

§1.3 paper selected topic theory significance

The paper selected topic originates in holds the important status and the function practical application topic in the image project.The so-called image project discipline is refers foundation discipline and so on mathematics, optics principles, the discipline which in the image application unifies which accumulates the technical background develops.The image project content is extremely rich, and so on divides into three levels differently according to the abstract degree and the research technique: Imagery processing, image analysis and image understanding。As shown in Figure 1-2, in the chart, the image

division is in between the image analysis and the imagery processing, its meaning is, the image division is from the imagery processing to the image analysis essential step, also is further understands the image the foundation。

The image division has the important influence to the characteristic.The image division and based on the division goal expression, the characteristic extraction and the parameter survey and so on transforms the primitive image as a more abstract more compact form, causes the high-level image analysis and possibly understands into.But the edge examination is the image division core content, therefore the edge examination holds the important status and the function in the image project.Therefore the edge examination research always is in the image engineering research the hot spot and the focal point, moreover the people enhance unceasingly to its attention and the investment。

§1.4 this article prime tasks

§1.4.1 Algorithm content

Introduced and has analyzed the classics image edge examination algorithm, summarized each algorithm good and bad points, has given the image edge examination result, and emphatically take the LOG algorithm as the example, embarked from the noise and the edge shape viewpoint has analyzed the reason which the edge position error produced; Introduced in one kind of LOG algorithm the quite precise definite edge method。

§1.4.2 Wavelet theory

Has studied the wavelet elementary theory, summarized the signal as well as the noise Lip index nature, and in based on in the non-sampling wavelet transformation image characteristic extraction algorithm foundation, unifies the auto-adapted denoising method, has made certain improvement to this method, obtained the quite satisfactory effect, denoising ability had the quite big enhancement; Then introduced one kind based on the sampling wavelet examination image main edge method。

§1.4.3 Novel algorithm

The system has studied one kind quite novel based on the phase image

characteristic extraction algorithm - - phase uniform algorithm, and has given its simple algorithm.Has given in the unidimensional situation algorithm simulation step, analyzed expanded to the two-dimensional method, and explained by the edge examination result the phase uniform algorithm conformed to the human vision characteristic。

§1.5 this article content arrangement

In the first chapter, the author has given an outline explanation to the image edge examination, and explained carries on the image edge examination the vital significance.In second chapter, the system introduced the quite classical image edge examination operator and the concrete realization principle, have analyzed each algorithm existence insufficiency by the edge examination result.Finally, from the noise influence and edge shape obtaining, take the LOG algorithm as the example, has analyzed the reason which the false edge as well as edge shifting produces.Finally introduced in one kind of LOG algorithm the quite precise definite edge method.

In third chapter, the author system introduced the present quite popular wavelet theory, and introduced emphatically the multi-criterion concept and the signal Lip index, and by the noise and the signal Lip index characteristic, carries on the extraction in the non-sampling wavelet transformation foundation to the image edge.In order to strengthen the edge image anti-chirp ability, but also the algorithm has made certain improvement regarding this, the auto-adapted denoising method will use in the edge detection, has obtained the satisfying effect.Finally also introduces one kind based on the sampling wavelet examination image main edge method.In the this article fourth chapter, introduced one kind quite novel based on the phase image characteristic extraction algorithm - - phase uniform algorithm.From unidimensional algorithm introduction obtaining, has given under the unidimensional signal simulation result, and expands gradually two-dimensionally.Explained through the simulation result this algorithm robustness quite is strong, moreover conforms to humanity's visual system performance.

Second chapter classical image edge examination algorithm

This chapter first simply introduced a classics step edge examination essential method in 2.1.Then 2.2 and 2.3 distinctions elaborated specifically the classical derivative operator and the linear filtering operator realization method, and has given each algorithm result comparison in 2.4.In 2.5, compared with the concrete analysis noise and the edge shape the reason which produced to the edge pointing accuracy influence as well as the false edge, and has given in the unidimensional situation simulation result, has drawn the conclusion.In 2.6, the image positive and negative edge which picks out using the LOG algorithm, compared with the precise localization image real edge, the final output was two value single picture element image.

§2.1 classical edge examination essential method

We knew that, the edge examination essence is uses some algorithm to withdraw in the image the object and the background junction demarcation line.We define the edge for the image in the gradation occur the rapid change region boundary.The image gradation change situation may use the image gradation distribution the gradient to reflect, therefore we may use the partial image differential technology to obtain the edge examination operator.

The edge examination algorithm has the following four steps ( its process as shown in Figure2-1):

Filter: The edge examination algorithm mainly is based on an image intensity step and the second time derivative, but the derivative computation is very sensitive to the noise, therefore must use the filter to improve and the noise related edge detector performance.Needs to point out that, the majority filter have also caused the edge intensity loss while noise reduction, therefore, strengthens the edge and between the noise reduction needs compromised.

Enhancement: Strengthens the edge the foundation is determines the image each neighborhood intensity the change value.The enhancement algorithm may (or partial) the intensity value has the neighborhood the remarkable change spot to reveal suddenly.The edge strengthens is generally completes through the computation gradient peak-to-peak value.

Examination: Has many point gradient peak-to-peak value in the image quite

to be big, but these in the specific application domain not all is the edge, therefore should use some method to determine which select is the peripheral points.The simple edge examination criterion is the gradient peak-to-peak value threshold value criterion.

Localization: If some application situation request definite edge position, then the edge position may come up the estimate in the sub-picture element resolution, the edge position also may estimate.In the edge examination algorithm, the first three steps use extremely universally.This is because under the majority situations, needs the edge detector to point out merely the edge appears in image some picture element neighbor, but is not unnecessary to point out the edge the exact location or the direction.

The edge examines the error usually is refers to the edge to classify the error by mistake, namely distinguished the vacation edge the edge retains, but distinguished the real edge the vacation edge removes.The edge error of estimation is describes the edge position and the lateral error with the probability statistical model.We examine the edge the error and the edge error of estimation differentiate, is because their computational method is completely different, its error model completely is also different.

The edge examination is examines the image partial remarkable change the most fundamental operation. In the unidimensional situation, the step edge concerns with the image first derivative partial peak value. The gradient is the function change one kind of measure, but an image may regard as is the image intensity continuous function sampling point array. Therefore, is similar with the unidimensional situation, the image grey level remarkable change available gradient discrete approximation function examines. The gradient is first derivative two-dimensional equivalent -like, defines for the vector as a result of each kind of reason, the image always receives the stochastic noise the disturbance, may say the noise is ubiquitous.Because the classical edge examination method has introduced each form differentiate, thus causes inevitably to the noise extremely sensitive, carries out the edge examination result is frequently examines the noise regard peripheral point, but but the

genuine edge also as a result of receives the noise jamming not to examine.Thus regarding has the noise image, one good edge examination method should have the good noise abatement ability, simultaneously also has the complete edge maintenance characteristic。

2-11 looks by Figure place, does not have in the noise situation in the image, the Prewitt operator, the Robert operator, the Sobel operator as well as the differential gradient operator, all can the quite accurate examination edge.But, after joins the white gaussian noise, the Robert operator receives the influence is smallest, next is the Prewitt operator, receives affects in a big way is the Sobel operator, but regarding the differential gradient operator, then is the image overall contrast gradient has obvious depression.2-17 may see by Figure, in does not have in the noise situation, the Canny operator, the LOG operator and the Laplace operator all may obtain the quite good examination effect, but, the LOG operator always can produce the false edge, this and its zero crossing examination method concerns.After adds on the noise, traditional examination operator (Laplace operator) the examination quality dropped obviously, but the LOG operator has produced more false edges under the noise condition.But the Canny operator examination result is continuously extremely satisfying. About the noise to the edge algorithm influence, we in the next section, take the LOG algorithm as the example, makes the quite exhaustive analysis and the elaboration.

This chapter first from the tradition based on image gradation first derivative edge obtaining, introduced the classics edge examination operator as well as afterwards developed the linear filtering edge examination method, compared with they respective characteristic, as well as in each kind of noise situation examination ability, and has given the simulation result, finally discovered, the Canny operator and withdrew the edge aspect in the noise elimination to have the quite good effect.In this chapter final, but also take the LOG operator as the example, has analyzed the image noise and the edge shape to its examination result analysis, and has given the simulation result, explained under the traditional fixed criterion examination method, the image

edge symmetry as well as the criterion choice to examines the result to have the very tremendous influence.Finally, we according to Amlan the Kundu recommendation 20 kind of template shapes, examines in the LOG algorithm in the edge image, withdraws the positive and negative boundary the central position, takes our final two value single picture element boundary output.

Third chapter based on wavelet thought image edge examination

Although the edge detection had the differential gradient operator, the Laplace operator, the Sobel operator, the LOG operator as well as the Canny operator and so on many methods, but these algorithms do not have the automatic focal variation thought.But in fact, as a result of reasons and so on physics and illumination, in each image edge usually produces in the different criterion scope, forms the different type the edge (for example edges and so on step, roof), these informations are unknown.Moreover, in the image always has the noise, therefore, according to the image characteristic, can auto-adapted examine the image the edge is correctly extremely difficult. Easy to imagine, is not impossible to examine all edges with the sole criterion edge examination operator, simultaneously, for avoids affecting the edge examination during filtration noise the accuracy, examines the edge with the multi-criterion method more and more to bring to people's attention.Because the wavelet transformation has the good time frequency localization characteristic as well as multi-criterion analysis ability [10,11,12], has “the focal variation” in the different criterion the function, suits in the examination sudden change signal, is examines this kind of signal the powerful tool, therefore obtained the widespread application.This chapter on take the wavelet transformation as the foundation, after the use wavelet filter the image characteristic, in sampling and in the non-sampling foundation, the examination image edge, and gave some improvement comment.Based on the non-sampling wavelet transformation edge detection, the spatial information which the maintenance optimizes, has obtained the quite good effect, but, when we merely are interested to the image approximate outline time, the sampling wavelet transform pair we are perhaps more practical. The sampling wavelet transformation is the Mallat tower system algorithm which the face

explains things in front of this chapter mentioned, it can obtain the primitive image in different criterion detail.When the decomposition criterion increases, not only has filtered the noise, but also withdrew the image approximate outline.We may use under the great criterion the edge image to instruct the small criterion lower limb the extraction.Thus, has removed the small detail which the very sensitive majority of noises and we is not cared about very much under the small criterion, is only the image main outline which retains us to care about.Its flow chart as shown in Figure 3-7.Must pay attention in here, the transformation criterion do not have too to be big, otherwise made the instruction the edge too to be short, easy to create the edge information to lose too many, the outline was stiff, was not gentle. And after the decomposition criterion excessively are many, a shifting can cause slightly in the great criterion in the small criterion the very big mistake.Among them, this algorithm difficulty provides the instruction in the great criterion edge for the small criterion the part.Because the great criterion edge will be sampling later the obtained data, therefore above it in a point correspondence and small criterion four spots.If in small criterion four spot intensity enough big, we on determine it as the boundary point.This algorithm simulation result as shown in Figure 3-8.In here, our have not used the wavelet inverse transformation which in the usual method uses, but, has still obtained the quite good result.

§3.5 this chapter section

This chapter in after introduction wavelet edge examination principle, application, introduced uses in scoring the signal irregularity Lip index, and explained under certain condition, also may estimate function f (t) with the wavelet transformation in a t0 Lip index.Thus, related the wavelet transformation and the signal Lip index.When carries on the edge examination using the wavelet, applies non-sampling the wavelet transformation, and has made some improvements to the traditional method, unifies the auto-adapted filter method with the signal irregularity examination, in the noise quite big situation, has obtained the quite satisfying denoising effect.In this chapter final, but also discussed in the sampling wavelet transformation foundation, carries on

the image the main edge extraction method.This method is takes with the great criterion under edge image instructs under the small criterion the edge image to carry on the choices, the simulation result indicated this method is effective.

Fourth chapter based on phase information image edge examination algorithm We knew that, the image characteristic examination is imagery processing, pattern recognition, based on content domain and so on image retrieval key technologies.How examines as well as the effective description image characteristic, since long ago receives the multitudinous disciplines continuously the attention.The overwhelming majority image characteristic examination algorithm all is carries on based on the image brightness gradient.Notes the phase information the importance and the stability, causes to carry on the imagery processing into new research topic using the phase information.This chapter, we will launch the discussion on this quite novel topic, will infer its rationale, and explained by its simulation result based on the phase characteristic extraction algorithm superiority.

§4.1 signal phase information

We knew that, classical, the simple edge examination method is inspects in its some neighborhood to primitive image each picture element the gradation change, uses nearby the edge a step or two step directional derivative change rule examines the edge, for example the differential gradient operator, the Sobel operator, the Laplace operator, the Canny operator and so on, their common characteristic is: To the primitive image according to the picture element some neighborhood structure edge, examines the image partial characteristic discontinuity.In the two-dimensional picture edge examination, the Laplace operator is simple and the effective method. Because the image receives the stochastic noise frequently the disturbance, when carries on the edge examination with these operators, although has the computation load small merit, but by the differentiate inherent characteristic, they is extremely sensitive to the noise jamming, like this they when carry on the edge examination can regard as frequently the noise the edge to examine, but but the genuine edge as a result

of has also not been examined the noise jamming.Therefore, obtains the edge often has flaws and so on gap, burr.

Considered the phase information the importance and the stability, cause using the phase information to carry on the imagery processing into new research topic, here proposed the phase uniformity, is precisely based on this realization.Phase uniformity (PC:Phase Congruency) the basic concept is the image Fourier component phase most consistent spot achievement characteristic point.When for example, the square-wave launches for the Fourier's series, all Fourier component all is a sine wave, as shown in Figure 4-1.In a step synchronism, the phase is 90o or 270o, all is changing in the square-wave other single phase value, causes the phase coincidence degree to reduce.Similar, the phase coincidence degree is biggest in the triangular wave apex, as shown in Figure 4-2, namely 0o or 180o are biggest.The use phase uniform important characteristic is does not need to the profile to carry on any supposition, only is simply phase coincidence seeks the characteristic point in the Fourier transformation territory according to. Each kind of characteristic types and so on step, line, roof all may cause the phase uniformity high spot appearance, thus obtains the examination result, even can examine the Mach belt phenomenon which produces as a result of the human vision sensation characteristic.

§4.2 signal phase characteristic

The phase uniformity is the performance extremely outstanding image characteristic examination operator, has the very strong versatility, also conforms to the human vision sensation characteristic.Oppenheim and Lim[22] have given the phase information important classical demonstration.They carry on separately two charts the Fourier transformation, takes chart the phase information, again takes other chart the scope information, combines a new image, and makes the inverse Fourier transform.We may discover, on new synoptic map, although the image stripe information quite is disorderly, but, may clear seeing provide the phase information the image outline.This demonstration result as shown in Figure 4-3.Since the image phase information had been proven it has the so vital role in the entire image, the nature we can to give the quite key

attention based on the image phase information image characteristic extraction algorithm.

In Morrone and Owens[23] conducts after the research to the Mach belt phenomenon, confirmed the phase information in image characteristic examination many superiority, and causes this method more widespread application in the machine vision this chapter first two image Fourier transformation, exchanges the two separately the scope and the phase information, has obtained two synoptic maps.Explained the phase information by this experiment in the image importance.Then the introduction phase uniform definition, its approximate expression - partial energy as well as based on the wavelet phase uniformity computational method, has given the computation step simulation result.Meets down, after has compared the Gabor wavelet, explained log the Gabor wavelet merit, and take log the Gabor wavelet as a foundation, has designed the 2D filter, has analyzed its performance, and through has proven our analysis to each kind of image examination result.Finally has given the phase uniform algorithm to the Mach belt phenomenon processing result, explained this algorithm conforms to humanity's visual system performance.

Full text summary

Without a doubt, in the information highly developed modern society, seeps along with the information technology to the production and life each domain.Pattern recognition technology application is more widespread.But the image edge detection is precisely the pattern recognition, application and so on machine vision foundations and the premise.This paper has carried on the review on the present quite mature classics edge examination algorithm, and applied many to at present has made the introduction based on the wavelet characteristic examination algorithm, finally, but also introduced recently developed by the image phase information extraction edge technology.Generally speaking, this article very clear divides into three parts, between them both relative independence, and layer upon layer progressives.

The first part has given a total explanation to the present image edge examination algorithm, elaborated the paper selected topic theory