CN104008555B - A kind of curve detection method cumulative based on backtracking - Google Patents

Abstract

The invention discloses a kind of curve detection method cumulative based on backtracking, by analyzing the characteristic measure of the gray feature curve construction of curve；Structure energy function makes the energy minimum of curve point；Any chosen in curve scans for obtaining characteristic image as starting point, use shortest path first；Each cumulative obtaining of backtracking that click on searched is recalled cumulative figure；Selected threshold in the cumulative figure of backtracking, extracts and obtains final curve.The present invention can the curve extracted in image rapidly and efficiently in the case of having only to use a starting point.

Description

Curve detection method based on backtracking accumulation

Technical Field

The invention relates to the technical field of information science, in particular to a curve detection method based on backtracking accumulation.

Background

At present, the shortest path technology has a good application in detecting simple open curves in images. The shortest path technique can be used to search a path and minimize the total energy of the search, and can be solved by the following mathematical problem:

$U_{p_s}\left(p\right)=\underset{A(p_s,p)}{min}\left\{E\left(l\right)\right\}=\underset{A(p_s,p)}{min}\left\{{\∫}_{\mathrm{\Ω}}\left(P\right(l\left(s\right))+\mathrm{\ω})ds\right\}---\left(1\right)$

wherein,is shown as starting point p_sMinimum energy to an arbitrary point p, A (p)_sP) denotes the starting point p_sSet of paths to an arbitrary point p in the image, E (l) energy of curve l, l(s) ∈ RⁿIs a parameterized form at arc length s, P (l(s)) Representing potential energy, and omega is a correction term.

Short path techniques were first used for active edge models, extracting curves by minimizing internal and external energies, due to the use of energy minimization criteria, but active edge models are sensitive to the location of the initial active edge. Later, other scholars obtained curves by solving Eikonal's equation to minimize the total energy, setting the start and end points of the curve, but were more susceptible in the continuous region due to the correlation of this method with the active edge. Recently, some scholars have proposed methods using less a priori information, such as a shortest path method called keypoint detection, which only needs to provide a starting point to search forward the whole curve, but still needs to know the length of the curve to terminate the whole search process.

It is easy to find that these methods require a lot of a priori knowledge, such as the start and end points of a given curve, and the length of the curve as a priori condition if only one start point is given. When the curve has many branches, one end point is required for each branch to obtain a good detection result.

Therefore, the main research in the aspect of curve detection at present is to obtain a good curve extraction effect on the premise of only needing little prior knowledge.

Disclosure of Invention

The invention aims to solve the technical problem of overcoming the dependence of the existing curve detection method on prior knowledge, and provides a curve detection method based on backtracking accumulation, which can efficiently extract a curve in an image on the premise of only acquiring one starting point. The backtracking accumulation means that: after the shortest path search, after each point is traced back to the starting point or a certain number of points in the reverse direction, the curve point is visited with high frequency, but the non-curve point is visited less, so that the curve point is successfully separated from the non-curve point, and finally the curve contour is drawn.

In order to achieve the purpose, the invention adopts the technical scheme that:

the invention relates to a curve detection method based on backtracking accumulation, which comprises the following steps:

step 1: analyzing the gray scale feature of any point p in the image I to be processed, constructing a feature metric value M (p) of the curve feature of the description point p, and constructing a curve point p_cCharacteristic metric value of (M) (p)_c) Greater than the non-curve point p_non-cCharacteristic metric value of (M) (p)_non-c) I.e. M (p)_c)＞M(p_non-c)；

Step 2: constructing an energy function P (p) by means of the constructed characteristic metric values M (p), the constructed energy function corresponding to the curve point p_cEnergy P (P) of_c) Less than the non-curve point p_non-cEnergy P (P) of_non-c) I.e. P (P)_c)＜P(p_non-c)；

And step 3: selecting any point p on the curve_sAs starting point, p_s∈p_cSearching the image I to be processed by using the shortest path algorithm, and searching the passing point p_psdCharacteristic metric value of (M) (p)_psd) Form a characteristic image I_f；

And 4, step 4: in the obtained characteristic image I_fRecord the point p passed by the search_psdFor each point p through which the search passes_psdBacktracking and accumulating to obtain a backtracking and accumulating graph

And 5: for backtracking accumulation chartSelecting a threshold lambda_fSaid threshold value λ_fIn the range of [10^-3，10³]So thatIs greater than threshold lambda_fIs a curve point p_cAll curve points p_cForm the finally obtained graph I_c。

Further, the constructed energy function p (p) is the inverse of the characteristic metric m (p).

Further, the shortest path algorithm searches the image to be processed I without specifying a search end point.

Further, for the backtracking accumulation graphAt any point p in_bkFrom the characteristic image I_fPoint p searched in_psdBacktracking to the starting point by p_bk，p_bkAccumulating graphs in backtrackingValue of (A)For all backtracking passes p_bkP of (a)_psdCharacteristic metric value of (M) (p)_psd) And (4) summing.

Compared with the prior art, the invention has the beneficial effects that:

the method comprises the steps of firstly constructing curve feature expression according to the gray level distribution features of the curve, further representing potential energy of each point in an image, then searching under the condition of giving a starting point by using a shortest path algorithm, backtracking each point according to a feature graph obtained by searching the shortest path, accumulating the feature value of each point backtracked to the starting point, and finally giving a proper threshold value to filter out the points with smaller accumulated values, thereby obtaining the final curve. The invention can quickly and effectively detect and extract the curve in the image on the premise of only needing one starting point and not needing any other prior information.

Drawings

FIG. 1 is a diagram of a simulation of an image to be processed according to the present invention;

FIG. 2 is a feature image of a simulated graph obtained using a shortest path algorithm in accordance with the present invention;

FIG. 3 is a graph of curve I extracted from a simulation graph based on a backtracking accumulation method according to the present invention_c；

FIG. 4 is a diagram of an actual crack of an image to be processed according to the present invention;

FIG. 5 is a feature image of an actual crack map obtained using the shortest path algorithm of the present invention;

FIG. 6 is a graph of an actual fracture map extracted by the backtracking accumulation-based method of the present invention.

Detailed Description

The present invention is further illustrated by the following examples, which are intended to be purely exemplary and are not intended to limit the scope of the invention, as various equivalent modifications of the invention will occur to those skilled in the art upon reading the present disclosure and fall within the scope of the appended claims.

As shown in fig. 1, a graph of a simulation of an image to be processed is used to illustrate a specific trace-back accumulation-based curve detection method, which includes the following steps:

step 1: analyzing the difference of the gray scale characteristics of the curve and the background in the analog image to be processed in fig. 1, and defining a curve characteristic metric m (p) for describing the curve characteristics of any point p in fig. 1; the feature metric M (p) is constructed using the difference of the curve gray feature and the background gray feature such that the curve point p_cCurve characteristic metric value of M (p)_c) Greater than the non-curve point p_non-cThe curve characteristic measure of, i.e. M (p)_c)＞M(p_non-c). The gray level of the curve point is lower than the average gray level of the background;the curve is a thin line, the proportion of curve points in a certain window is small, and the background area does not have the characteristic, so that the curve points and the background area can be separated.

Further, the window width is set to w for a known arbitrary point p_i,jWith p_i,jCentered, within a window of w × w, p is calculated_i,jInverse ratio (p) of the sum of the gray scale differences with other points in the window_i,j) I.e. byFor curve point p_cWith ratio (p)_c)＞ratio(p_non-c) (ii) a Wherein m, n are two variables, I (p)_i,j) Represents p_i,jOf the pixel, correspondingly, I (p)_i+m,j+n) Representing other points p within the window_i+m,j+nThe gray value of (a); the curve feature metric value is then defined as m (p) i (p) ratio (p).

Step 2: constructing an energy function P (p) by the curve characteristic metric value M (p) to make a curve point p_cEnergy of (4) is minimal;

from M (p)_c)＞M(p_non-c) In order to make the curve point p_cThe potential energy P (p) of any point p in the constructed image is the reciprocal of the characteristic measure M (p), namely the potential energy P (p) is easier to search

$P\left(p\right)=\frac{1}{M\left(p\right)}---\left(2\right)$

And step 3: selecting any point p in the curve which is known_sAs starting point, p_s∈p_cUsing the shortest pathThe image I to be processed is searched by the path algorithm, and the passing point p is searched_psdCurve characteristic metric value of M (p)_psd) Form a characteristic image I_f；

Specifically, as shown in FIG. 1, a, b, and c in FIG. 1 are all points on the curve, i.e., a ∈ p_c,b∈p_c,c∈p_cTaking a point a as a starting point, performing shortest path search by using Dijkstra algorithm in the shortest path algorithm, performing the search in a neighborhood taking the point a as a center, recording the accumulated cost from the point to the starting point when a certain point is searched, and selecting a point with the minimum accumulated cost for continuous calculation each time; searching through points p_psdCurve characteristic metric value of M (p)_psd) The feature image is formed, as shown in fig. 2, the point with the gray value greater than 0 in fig. 2 is the searched point p_psdE.g. points b, d, e; and the point with the gray value of 0 is the point p which is not searched_non-psdE.g., point f in the figure.

And 4, step 4: in the obtained characteristic image I_fFrom each search through a point p_psdStarting, backtracking and accumulating to the starting point to obtain a backtracking and accumulating graph

Specifically, for the characteristic image I obtained in step 3_f(FIG. 2), since we describe the curve characteristics, the curve points b and c in FIG. 1 have larger characteristic metric M (p)_c) Value and smaller potential energy P (P)_c) (ii) a For p accessed from step 3_psdPoints, such as points b, d, e in fig. 2, are traced back with their characteristic metric value M (p)_psd) Is accumulated to the point p passed in the backtracking process_bkGet the backtracking accumulation chartNamely, it is

Wherein, the backtracking passes through the point p_bkBelonging to an arbitrary point p searched for_psd. E.g., point d, backward search back to the starting point a₁、d₂、d₃When the point b is reached, the point a is returned, and similarly, the point e passes through the point e when backtracking to the starting point a₁、e₂、e₃、e₄When the point b is reached, the point a is returned, so that the curve points a and b are more frequently visited than the non-curve points d and e during the backtracking, and therefore

And 5: accumulating graphs in backtrackingMiddle selection threshold lambda_fSaid threshold value λ_fIn the range of [10^-3，10³]，Is greater than threshold lambda_fIs a curve point p_cAll curve points p_cForm the finally obtained graph I_cAs shown in fig. 3.

In particular, becauseI.e. the curve point p_cOfThe value will be larger, setting the threshold lambda_fSo thatThus at a given threshold λ_fObtaining a final curve point set graph I_cAs indicated by the black curve in FIG. 3, wherein

Evaluation of effects

Firstly, a simulation graph with a curve is used, as shown in fig. 1, to extract the curve, wherein the average gray value of the curve in the simulation graph is lower than the background value, and the background is generated randomly, and the curve graph I is obtained by the method of the invention_cAs shown in fig. 3, the detected curve is marked with a black line in fig. 3. In order to verify the application effect of the invention in practical situations, a real crack diagram is used, for example, an experiment is carried out in fig. 4, and besides the properties of the simulated diagram, the crack diagram has the influence of multiple textures, shadows, illumination and the like, so that the crack diagram is more challenging. With a starting point p as shown in fig. 4_sUsing the constructed energy function P (p) as a starting point, and searching by using a shortest path algorithm to obtain a characteristic image I_fAs shown in fig. 5; next, for the characteristic image I shown in FIG. 5_fEach point in the graph is backtracked and accumulated to obtain a backtracking accumulation graphSetting a certain threshold lambda_fSo thatTo obtain the final graph I_cAs shown in fig. 6, fig. 6 indicates the detected curve using a black line. Experimental results show that the method disclosed by the invention has the extraction efficiency and quality in both the synthetic diagram and the crack diagram.

Visual evaluation: by observing the extraction results of fig. 3 and 6, we can see that the curve in the graph can be extracted quickly and effectively by using the method of the present invention under the condition of only providing one starting point.

Claims (4)

1. A curve detection method based on backtracking accumulation is characterized in that the backtracking accumulation refers to the following steps: after the shortest path is searched, after each point is reversely traced to a starting point or a certain point number, the curve point can be accessed at high frequency, and the non-curve point is accessed less, so that the curve point and the non-curve point are successfully separated, and the curve outline is finally drawn; the method comprises the following steps:

step 1: analyzing the gray scale feature of any point p in the image I to be processed, constructing a curve feature metric value M (p) for describing the curve feature of the point p,and construct a curve point p_cCurve characteristic metric value of M (p)_c) Greater than the non-curve point p_non-cCurve characteristic metric value of M (p)_non-c) I.e. M (p)_c)＞M(p_non-c)；

Step 2: constructing an energy function P (p) by means of the constructed curve characteristic metric values M (p), the constructed energy function corresponding to the curve points p_cEnergy P (P) of_c) Less than the non-curve point p_non-cEnergy P (P) of_non-c) I.e. P (P)_c)＜P(p_non-c)；

And step 3: selecting any point p on the curve_sAs starting point, p_s∈p_cSearching the image I to be processed by using the shortest path algorithm, and searching the passing point p_psdCurve characteristic metric value of M (p)_psd) Form a characteristic image I_f；

And 4, step 4: in the obtained characteristic image I_fRecord the point p passed by the search_psdFor each point p through which the search passes_psdBacktracking and accumulating to obtain a backtracking and accumulating graph

And 5: for backtracking accumulation chartSelecting a threshold lambda_fSaid threshold value λ_fIn the range of [10^-3，10³]So thatIs greater than threshold lambda_fIs a curve point p_cAll curve points p_cForm the finally obtained graph I_c。

2. The method of claim 1, wherein the constructed energy function p (p) is the inverse of the curve feature metric value m (p).

3. The curve detection method according to claim 1, wherein the shortest path algorithm searches the image to be processed I without specifying a search end point.

4. The curve detection method according to claim 1, wherein the backtracking cumulative graph is obtained by performing a back-tracking cumulative graphAt any point p in_bkFrom the characteristic image I_fPoint p searched in_psdBacktracking to the starting point by p_bk，p_bkAccumulating graphs in backtrackingValue of (A)For all backtracking passes p_bkP of (a)_psdCurve characteristic metric value of M (p)_psd) And (4) summing.