Adaptive Image Segmentation based on Fast Thresholding and Image Merging

Ye Zhang1,2

, Hongsong Qu1,2

,Yanjie Wang1

1

Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences 2Graduate School of Chinese Academy of Sciences

E-mail: zhang.leaflet@gmail.com

Abstract

Image segmentation is the first essential and

important step of low level vision. This paper proposes a novel algorithm for adaptive image segmentation, based on thresholding technique and segments merging according to their characteristics combine with spatial position. Our earlier work of getting the entire information of the histogram could help choose the multiple thresholds. However, not all the peaks of the histogram correspond to obvious structural unit in the image. Spatial information must be involved. This paper also suggests subjoining segments matching for video image tracking. They will give great help to image segmentation. The proposed algorithm can meet the real-time requirement and lead to higher segmentation accuracy, some types of texture can also be segmented well; it can be applied in many conditions, including complex target segmented. We describe the algorithm in detail and perform simulation experiments. The computation based on pixels can fully parallel processing to save time. 1. Introduction

Image segmentation is an important part in object recognition as well as in high level image interpretation and understanding. It is a process of segmenting an image to non-overlapping regions whose interior pixels’ characteristics are similar but different to their adjacent region. The already proposed segmentation algorithm can be classified into three major categories: boundary based segmentation, region based segmentation and thresholding based segmentation.

1) Boundary based segmentation

Boundaries represent sharp changes in image intensity. Boundary detected operator can be simple and efficient like Sobel. But far more time-consuming

3) Thresholding based segmentation Thresholding is an old, simple and popular

technique for image segmentation. Nikhil R. Pal and Sankar K. Pal have done a well review work on image thresholding segmentation technique. Refer to the global and local information, thresholding based segmentation can be classified into global thresholding and local thresholding. The former is a statistical method of the entire image, regardless the spatial information. The latter depends on the sub-region classification. Refer to the number of the thresholds; thresholding can be classified into bi-level thresholding and multi-thresholding. Since the image is always composed of several objects with different surface characteristics, most of the recent works on thresholding have been focused on multi-thresholding.

Proceedings of the 16th International Conference onArtificial Reality and Telexistence--Workshops (ICAT'06)0-7695-2754-X/06 $20.00 © 2006

In our earlier work, a real-time multi-thresholding algorithm is proposed [6].

In this paper, we propose a novel algorithm for image segmentation and segments merging. With the thresholds based segmentation algorithm, we can segment the image into different regions with homogeneity gray level. In the post-procedure, segment merging is contained. The algorithm can simultaneously segment multiple objects in various conditions.

This paper is organized as follows: our earlier work of thresholding segmentation is described in section II; the proposed algorithm for segments merging is described in section III in detail; in section IV, a method about terminal criterion is contained. In section V, we present simulated results of real scene image segmentation. Section VI is devoted to the conclusions. 2. Thresholding segmentation

The simplest approach of segmentation is basing solely on pixel values. Our main idea of the technique is to utilize the thresholding based segmentation, which could help to simple the region growing steps. First of all, we suppose most meaningful structure in an image will help to compose a peak in the histogram, but the inverse can be incertitude. In our earlier work, a deep analysis of the histogram is proposed. The rules of the peak selection is utilizing the climbing or dropping trend to extract the local maximum. In other words, the peaks must be the local maximum, but the local maximum may not be the expected peaks, for it may be in a climbing or dropping trend procession. Then we record their positions in the histogram, and set the thresholds between each pair of neighboring peaks. Figure 1 shows an example of getting local maximum and thresholds selection. The processing time of this thresholding method is less than 1ms.

After the thresholds based segmentation, the image can be segmented into several parts with different gray level ranges. In region based segmentation, the segmented images can be separated into two parts: agglomerate regions and scattered regions, as Fig. 2 shows. And the following sequence described in section III helps to attach the scattered points to more reasonable agglomerate region. Since not all the peaks correspond to meaningful structures, the segments merging technique based on merging weight must be involved.

Fig. 1. The picture named ‘camera man’ and its histogram

analysis.

(a) (b) (c)

Fig. 2. (a) shows one of the segmented image; (b) extract (a)’s agglomerate region; (c) extract (a)’s scattered region.

3. Segments merging

The segmentation based on multi-threshold usually results in a large number of regions. However, it always looks similar in some neighboring region either in the pixel domain or the feature domain, also for the scattered points, which can be attached to the agglomerate region. Segments merging can be decided by examination of the distance between the groups. For gray-scale images, the distance is the sum of the spatial position | |i jd d− and the luminance

differences| |i JI I− . In the simulation experiments we could confirm that

besides the Eulidean distance ED , the simpler

Manhattan distance MD is already sufficient for object tracking purposes, These two distances between vectors ( 1x ,…, nx ) and ( 1y ,…, ny ) are defined as

2 21 1( ) ...( )E n nD x y x y= − + − (1)

And 1 1| | ... | |M n nD x y x y= − + + − (2)

When n=1, ED = MD . In order to treat all object features with equal criterions, it’s necessary to normalize the feature. One possible way is timing them by the computation weights. The computation weight

1M and 2M can be adjusted according to varies

images, in most condition, 1M can be decided by the

size of the picture, and 2M can be decided by the dynamic range of the histogram.

, 1 2*(| | 1) *(| | 1)i J i j i JD M d d M I I= − + + − + (3)

Proceedings of the 16th International Conference onArtificial Reality and Telexistence--Workshops (ICAT'06)0-7695-2754-X/06 $20.00 © 2006

Fig. 3. Detailed description of the used image segmentation algorithm based on fast thresholding and region merging.

The algorithm is given in Fig.3 and proceeds as follows. First, according to pairs of thresholds, the gray level based segmented image is got. Then we divide each image into agglomerate region and scattered region. We labeled the agglomerate regions and record their mean gray level, and then calculate the scattered parts’ distance to its neighboring agglomerate region. If

1, ,i J i othersD D< and 1,i J thresholdD D< , the

scattered point i is supposed to be a part of agglomerate region J1. The above operation is repeated as long as scattered points exist. Finally, if no scattered points left, the process finishes and every agglomerate region is considered to be a segment. In the post operation, two neighboring segments with similar gray levels can be merged.

Fig 4 shows an example, after the thresholding technique, according to the gray level and the distance, the picture can be grouped into four parts, including three agglomerate regions and one scattered region. After the weight computation, the scattered region can be attached to group 1, and group 2 and 3 can be merged.

Fig. 4. Explanation of the proposed image segmentation

and segments merging algorithm

4. Terminal criterion

The method introduced above can be simple when combined with termination criterion. Take image “camera man” for an example, the image can be segmented into more than 50 segments after the image merging process. However, some segments cannot be merged according to the distance-based criterion; most of them are not the interesting part or meaning less to be separated. In order to solve this problem, we involved termination criterion (T-C). The criterion can be set above all the procedures.

For the simplest condition, in the thresholding procedures, we select one threshold enough if the T-C is 2, and segment the image into two parts. The rule for selecting thresholds is: ensure most segments have more pixels and more distinct gray level.

Terminal criterion helps to bring forward segmented ceasing time when more segmented work is not necessary. It prevents over segmenting and saves time. The T-C selection can be varied in different condition and for different purposes. The proceeds of using T-C is shown in Fig. 5:

Fig. 5. the description of the T-C utilization

5. Experiments results

This section presents simulated results of the image segmentation algorithm on grey image “camera man”. Without termination criterion, the image can be segmented into six gray ranges according to the fast thresholding segmentation method as figure 6 shows.

Proceedings of the 16th International Conference onArtificial Reality and Telexistence--Workshops (ICAT'06)0-7695-2754-X/06 $20.00 © 2006

This segmentation may contain some scattered part and some improper regions which should be merged. If we include terminal criterion, the experimental results can be showed in figure 6.

(a) T-C=2 (b) T-C=4

(c) T-C=30 and segments merging result

(d) another image (e) T-C=10

(f) T-C=40 and segments merging result

Fig. 6. The segmented result with different T-C, if we choose large T-C, more details will be extracted, but the image will also be over segmented

6. Conclusion

We propose an approach for the automatic image segmentation technique using thresholding and segments merging. The technique of thresholding is simple and powerful; it is shown to be highly efficient for the multi-class data classification problem. It just like our first impression, we get the glancing structure form of the picture from this procedure. Compared with region growing method, the preceding thresholding procedure helps to determine the growing borderlines beforehand, and the growing steps can be reduced. The terminal criterion also helps to determine

the segmentation precision. It helps us not be involved in the insignificance segmentation and prevent over segmenting. Our experiments indicated that the proposed algorithm could adaptively offer the image structuring without human interaction and the priori knowledge. Since the procedure of thresholding and connection region judgment mainly based on pixels, the application of the algorithm can be paralleled to meet the real-time requirement. 7. References [1] Jianping Fan, David. K. Y. Yau, Ahmed. K. Elmagarmid and Walid G. Aref, Automatic Image Segmentation by Intergrating Color-Edge Extraction and Seeded Region Growing, IEEE Transactions on Image Processing, Vol. 10, October 2001. [2] Wee Kheng Leow and Seet Chong Lua, An Improved Neural Network for Segmenting Objects’ Boundaries in Real Images, National University of Singapore, IEEE,1997 [3] T. Morimoto, Y. Harada, T.Koide and H. J. Mattausch, Pixel-parallel digital CMOS Implementation of Image Segmentation by Region Growing, IEEE Proc-Circuits Devices Syst., Vol. 152, No. 6, December 2005. [4] T. Morimoto, O. Kiriyama, Y. Harada, H. Adachi, T. Koide and H. J. Mattausch, Object Tracking in Video Pictures based on Image Segmentation and Pattern Matching. [5] Nikhil R. Pal and Sankar K. Pal, a Review on Image Segmentation Techniques, Indian Statistical Institute, Pattern Recognition, Vol. 26, No. 9, pp. 1277, 1294, 1993. [6] Zhang Ye and Wang Yanjie, High accuracy real-time automatic thresholding for centroid tracker, ICO20 (the 20th international conference of optics), Proc. Of SPIE Vol. 6027P, 2006 [7] P. K. Sahoo, S. Soltani and A. K. C. Wong, a Survey of Thresholding Techniques, Computer vision, Graphics and Image Processing 41, 223-260, 1988. [8] K. S. Thyagarajan, Helge Bohlmann, Image coding based on segmentation using region growing. CH2396-0/0000-0752, 1987, IEEE.

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