A Quadric Image Segmentation for the Feature Extraction of Tire Surface Wear

Chao Zhang and Yin-Hang Cheng School of Electronics and Information Engineering, Beijing Jiao Tong University

SY1003, Beijing , China 100044 zhangchao@zzu.edu.cn

Abstract

As an advanced analysis tool image processing has been made a wide use in solving complex image information, such as CT, metallic erosion detect, vehicle engineering etc. However, adopting singular image process is often constrained in its limited scope of application, and even fails in dealing. Thus we proposed quadric image segmentation based on the machine vision for extracting wear feature in this paper. The method consists of thresholding and region labeling, of which the optimization iterating of the given threshold and seed filling are used to divide the image into the multi blocks. After that the local characters could be refined with respective and independent outlines. And the corresponding cause of the formation could be interpreted judging by wear mechanism as well. Finally we compare this method with Ostu, watershed and active contour method by an instance. It shows that the method is quite a tolerant in uncertain positions, clearly segmented, quick, and fit for wear analysis.

1. Introduction

In recent years, with the sustaining improvement on traffic environment and vehicle itself, the driving situation has been lifted in a high level. It is however followed by negatives of quickening tire wears and faults, which will induce the frequent traffic accidents directly or indirectly. All these things constitute a big

threat to human life whether driving or taking. It is therefore very important and meaningful to make an early forecast for the tire’s working state, warning what had happened, informing what should do in daily operation or an emergency.

When speaking of the tire monitoring technology, it is concentrated on the research and development of the devices, e.g. SAW (surface acoustic wave) sensor, TPMS (tire pressure monitor system), for measuring pressure, temperature, and frictional coefficient. And it is simplex, large volume, and also absent of interfaces to the future intelligent vehicle [1~4], especially the graphic information about tread texture is often put aside. As for the tire study, it ranges in a large span, but it focus on road disturbance [5], friction effect test of tire rubber by rolling machine [6]. Unfortunately the tire’s most important surface image information is often neglected which has recorded large amounts of features from environment and vehicle, and should have been the best proof for the tire analysis. While optimization of the tread pattern design [7], and the structure parameters optimizing [8] has made use of the image process for their analysis and design, they are often depreciated by their singular image processing. Just the saying goes that where the object passes, there its trail must be left. Image processing has a promising way to monitor and analyze the static or dynamic behaviors of tires. So we proposed quadric image segmentation for extracting feature of tire wear surface in this paper.

As we know, tire is the only touching media between the vehicle and the surface. Under the

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working condition, resultant forces put the tires attach to the road, and contact is thereby formed, which could reflect the features on acceleration, deceleration, and turn of the vehicle, especially chassis and its relevant parts [7]. Moreover, during the course of the driving, the environment also leaves some marks on it, which is often neglected by drivers. Even if being aware of it, some times they will care less about how serious the wear is, never thinking why. In this way much important information is lost. From this point, the method here is meaningful.

The tread surface includes large amounts of texture information, such as pattern, block, pitch, slot, and also friction mark. The tire has a regular pattern (curve and block), with the abnormal friction. It means the friction mark could be distinguished from numerous pattern collections. There existed much noise from the patterns and friction areas (illegible details) in the dealt image. So we propose quadric segmentation - thresholding as the initial segmentation to sketch the outline out, then region labeling as further segmentation to pick out details of it, which is in turn to make out the wear cause in terms of the judging rules. In general, we take several steps to perform quadric segmentation for feature extraction of the tire: firstly we create an Access database to store snapshots of the tire; then the images are customized as the requirement, mainly in terms of size, direction, and type; next the proper image is chosen, loaded and tested by the proposed method; lastly by the above we give the analysis result and its corresponding interpretation.

The rest of this paper is organized as follows: in section 2 we make a detail account of the algorithm, in section 3 an example is taken to test and compare the quality of this method, also with the result analysis, and in section 4 we draw a conclusion and future task.

2. Quadric image segmentation

To get an effective feature extraction of tread texture, the algorithm here is motivated by the tire’s special

pattern. In the tread surface its pattern is a determinate permutation and combination of pitches and slots. Among them, lines and squares are main filling styles. In this way seed filling is introduced to signify the critical parameters, such as size, proportion, head, and position. By means of those the irregular and regular blocks will be transformed into rectangle (tire pattern is often in square style like a knife) to fill, by which the pixels with their own borders would be statistically silhouetted, labeled and accumulated. Well, that is the identification’s essence. Through the preliminary thresholding segmentation we eliminate the noises in part, and the more clear structure of the tread is thus far mapped out. After that the area, the circumference, the centroid, such critical factors could be evaluated by further segment-region labeling, in turn for giving the cause of the formation by rules.

2.1. Thresholding segmentation

As for analyzing and identifying the object, thresholding segments the peculiar and interested regions out. Compared with the following region labeling, it spent less time to prune the outline out from the original coarse image. It plays an important role from image procession to analysis. It is the basis of the object interpretation, and influences the character detecting. By object expressing, feature extracting and parameter measuring the original image is converted into a more abstract and compact form, which could make it possible to analyze and explain image in a high level. The principle of thresholding is how to choose the threshold (1) of the ROI (region of interest), which is a gray eigen value in almost uniform gray region. When there is a comparatively conspicuous contrast between the background and the segmented regions, it will get a good work, and vice versa. Yet the accuracy of segmentation varies with the threshold choosing. In common it is often practiced more and more to obtain a rational one [9]. The procedure is as follow:

Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications (ISDA'06) 0-7695-2528-8/06 $20.00 © 2006

( ) ( )( )⎩

⎨⎧

<≥

=tyxftyxf

yxg,,0,,1

, (1)

f(x,y) is gray value of the image, t-threshold, and a binary image’s gray value g(x,y) of segmentation is realized by t which is calculated by (2).

( ) ( ) ( ) ( ) ( )( )22121

2 tututwtwt −××=σ (2)

w1(t), w2(t) is the amount of pixel in c0 and c1 respectively, by which the pixel of the image could be dived into two parts, c0 and c1. c0 consists of the pixel in [0~t], while c1 corresponds to [t+1~L], with the gray level L. σ(t)2 is the variance between c0 and c1. While t changes from 0 to L-1, the optimist thresh T will be gained at the time of getting maximum value of σ(t)2 displayed as (3).

[ ]

( )tArgLt

2

1,1,0T σ

−∈=

K

(3)

Because the variance is a kind of evaluation over the gray uniform, the more the variance is, the difference between the two parts of the image will be larger. It means the probability of wrong segmentation will be less when the object and background is mistaken for each other, but in contrary condition it runs worse. Although it is not optimist in most situations, it is the most stable. So we adopt that as first segmentation.

After thresholding segmented, the units of the image could rise out of their neighbour and get ready for the next region labeling.

2.2. Region labeling

Considered the inevitable fault, some times fatal for thresholding in the less contrast between object and background, and also the improving qualification, we make use of the region labeling to make a more delicate segmentation, namely quadric segmentation.

Supposing pixels are arranged in grids, so that means either 4 or 8 ways connecting the centered point in direction (left up, right up, left bottom, right bottom, or adding up, bottom, left and right of center).

Following that the pixel unit will be marked with its own group labels specified as rule (4).

))(max,1(max 21 ii TkSkK(i) −= (4)

After defining the connections of the corresponding direction, the dimension of group is defined as Si and Ti for each i, which makes up of the connected region’s pixel sum in cluster. k1, k2 is the respective weight. The corresponding operator K(i) could be calculated after. In the same way, i is ergotic through the pixel grid to trace down the linear offset between i and all around it. Thereby the region candidate will be identified out. The pseudo code of it is as follows:

‘ Thresholding Read image data and get original pixel group Initialize the threshold, array pointer and size of the

image For row sweeping For column sweeping Judge the class by thresholding Next column Next row ‘update the pixel group For class accumulating varying in [0~255] Sum up various classes’ moment Next class For calculating the maximum threshold in [0~ 255] Sort the maximum out of all classes Next ‘get the optimist threshold and corresponding

dealt pixel group ‘ region labeling Segment the image according to the optimist Define the shape style Execute morphological operator ‘refine the image Use seed filling to outline the boundary unit For all units’ calculating parameters (area, perimeter,

centroid etc Calculating respective parameters

Next ‘ the required feature information for analysis ‘Feature extraction For all the parameters’ current value Classify the data by wear’s position, proportion

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Next Return the final wear feature and its cause ‘end Now we have necessary data for feature extraction

of wear, then make a comprehensive evaluation for them by the rule proposed, the wear feature is thereby decided. The general principle is that different wear type has different feature distribution in image (detailed in 2.3). The whole process is shown by Fig. 1.

Figure1. Flow chart of the algorithm

What needs to be noticed is the “disturb reduction” will properly be added into the process according to whether the primary segmentation has excessive blurs. So the successive manipulation will go well.

2.3. Tire texture data

In this paper, the structure of pattern block, outline, density and tendency of pitches and slots are much emphasized rather than the tire’s tribology mechanism. From reflecting features, the tire texture data mainly includes line, area, heading, and other characters. As far as the tire wear type is concerned, it generally includes uneven wear, camber wear, local wear,

feathered wear, diagonal wear, and wheel and toe wear, shown as Fig.2. But no matter what type the wear is, normal or abnormal, it has its own special distribution and shape. For example, camber wear is characterized that one side wears more serious than another in shoulder by the cause of driving under worse arc condition; uneven wear in shoulder caused by low pressure, in tread by high pressure; featured wear in front tire rather than other tires by improper running; local wear in local area by accidental brake; diagonal wear in diagonal direction by emergency buffer or stop; wheel and toe wear by long time no displacing tires. All in word some wear is curved in sidewall, some in tread, some in crown, and each has peculiar wear characters. The tire is a pneumatic object, which destines itself not to have a uniform force. That denotes the difference of the various wears in heading and position, and in turn to make it possible to become the cause of the judging rule of the wear type. That’s the original intention of this paper and also the essence of feature extraction.

Figure 2. Common wear feature instant of tire

Through thresholding and region labeling, the image is transformed into a binary image with conspicuous region border and relevant structure data. Namely the normal and abnormal region will be reflected. Then based on the judging criteria of the texture mentioned above, the reason for wear is obtained.

3. Example

In order to test the performance of quadric image segmentation proposed in this paper, we take the tire image snapped from a digital camera as an example (Fig.3 left). This binary image is 253×165, and made

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a pretreatment by the edge operator- erosion and dilation at first, so a comparatively clear circumference of the units could be obtained before the later algorithm processing (Fig.3 middle and right).

Figure 3. Process of image pretreatment

We define a relation-match where the correlation function could be translated into a local peak. For this example, the correlation performed well given in half side style (Fig.4 left). There are only little incorrect matches background caused, which could be easily screened as the peak value in imparity part. Hence the method could have a good pixel distribution spectrum handled by the correlation function (Fig. 4 right).

Figure 4. Relation and value of correlation

distribution After the beforehand pretreatment, we begin to

perform further segment- region labeling, during the course of which the pixel density and position relevant to structure could be displayed as Fig. 5. The Left represents the binary segmentation effect, middle as the region classification, and right as the final result. It is obvious that the wear region is accurately labeled from other regions, also with its centroid (circle point) and close area belonging to.

Figure 5. Image of quadric segmentation and

its corresponding colorful surf Table I gives us a detail diagnosis results, that is, the

amount of the abnormal region, the centroid position, the area, the dimension and also the wear type.

Table 1. Test results of the instance

blocksCentroid

（x/ y）cm

Area

cm*cm

dimension

（L/W）

cm

Wear

type

1 （46. 9919,

89.4212） 2348 165*253

Cambe

r wear

From table 1, we could get that only one abnormal region is found in the left shoulder, while others are basically normal. It hints us the tire is at an improper camber situation, which leads one shoulder worn more seriously than another.

Additionally, we make a comparison with Ostu method, watershed method, and active contour method to illustrate its qualification (Fig.6).

Figure 6. Results in different method(Ostu,

watershed, active contour) From Fig.6, we could get the following information: 1) The first is too sensitive to the noise, and couldn’t

get a balance between local borders, even difficult to form close outline in the discontinuous border.

2) The first’s situation occurs to the second, but comparatively better, however still depreciated in the long discontinuous.

3) Active contour has a better segmentation than the other two, yet it is inferior to quadric segmentation. It is motivated by internal of energy field, and the border gradually comes into being by area overspreading which spends much time in iterating.

And the real investigation is as follows: 1) As for the tire style, it belongs to block pattern,

Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications (ISDA'06) 0-7695-2528-8/06 $20.00 © 2006

arrayed independently. Tread pattern is arranged by sipe, zigzag pitch, small size block. The edge effect of patch and sipe could lift the cohesion ability in the ice and snow surface, and also the performance in brake, driving and control. It is commonly used in winter.

2) The abnormal tire wear region is only found in the left side. The reason is that its camber angle is larger. That attributes to driving in the improper arc and the tire needs to be realigned.

From above it indicates that the algorithm’s analysis is in accord with the investigation, and meeting the expectation: outline is fined in single pixel width; continuous in the closed border; precisely pitched in respective position. Compared with other methods, it could get a clear outline, short calculating time, and abundant information.

4. Conclusion

Tire is probably the most complex component of a vehicle, and tire’s performance, especially at high speed, is critical for the vehicle safety. It is common that deformation, temperature rise and eventual failure will occur to each tire. So it is meaningful that if the current state of the tire could be analyzed as early as possible. Using the proposed algorithm the different effects on tire could be visualized, and analyzed in a more efficient way. Although the image here is offline snapped, the data process could be independent of it. In the examination of other wear images, we also get an agreeable expectation, and it shows that away from the parameter survey, compared with other device measuring (like TPMS), other image process methods, the method here has its superiority and adapts to tyre monitoring. And it will be helpful and meaningful for guiding traffic safety in future.

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