IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 19, NO. 12, DECEMBER /10/4

Introduction Connected Components Labeling Data Structure Algorithm Experimental Result And Comparison Conclusion

Connected components in a binary image is one of the fundamental image analysis algorithms. This paper present a more efficient approach to the problem of finding the connected components in binary images. The first advantage is the reduced execution time compared to existing techniques. This is achieved by making a single pass regular access algorithm that only requires access to the three most recently processed image lines at any given time.

One knows for a certain image which pixels are foreground pixels or object pixels and one needs to list each separate object within this image.

Region descriptors data unit : pta a pointer to the first line segments of the region ( pta ) ptd a pointer to the last line segments of the region ( ptd ) ptb ptc Two add pointers that are used internally by our connected components algorithm ( ptb & ptc ) Line segment data unit : start column end column row of the line segment a pointer to another line segments

Label B is reserved for the background pixels

S S is the processing row of the image. ptbrow< S – 1 ptb points to the last line segment of part row < S – 1 ptc row = S - 1 ptc points to the last line segment row = S - 1 Processed Rows S S - 1 < S - 1 pta ptb ptc ptd Processing Row

The algorithm repeatedly performs the following operations until the last row of the input image is processed: 1)delineate a new line segment in the current row; 2)group the freshly created line segment and every connected line segment of the previous row into region descriptors; 3)if the last line segment of the current row is processed, proceed to the next row.

Depending upon the number of connected line segments, three situations can occur: 1)The current line segment is not connected with any line segment. 2)The current line segment is connected with exactly one line segment. 3)The current line segment is connected with two or more line segments.

1)The current line segment is not connected with any line segment. pta, ptc ptd ptb NULL If the current line segment is not connected with any line segment, it means the current line segment is the first part of a new region. Consequently, we create a new region descriptor and add the current line segment to this region descriptor then let pta, ptc and ptd point to the new line segment, ptb point to NULL. S S - 1 < S - 1

2)The current line segment is connected with exactly one line segment. If the current line segment is connected with exactly one line segment, it means the current line segment is a continuation of the region to which the connected line segment belongs. We, thus, lookup the corresponding region descriptor and append the current line segment to the end of the linked list contained in this region descriptor. How to find the right region descriptor for a new line segment ?

3)The current line segment is connected with two or more line segments. If the current line segment is connected with two or more line segments, all these line segments must be in the same region. However, at this point the line segments may still belong to different region descriptors which must be merged. How to merge two region descriptors ?

Searching the right region descriptor for a new line segment: S S - 1 < S - 1 pta ptb ptc ptd S S - 1 < S - 1 pta ptb ptc ptd pta ptc ptd S S - 1 ptb = NULL

Merging two region descriptors : A is the final merged region descriptor. 1)B.ptc - > next = A.ptb - > next 2)A.ptb - > next = B.ptb - > next 3)B.ptb - > next = A.pta 4)A.pta = B.pta pta ptb ptc ptd ptb pta ptc ptbptaptcptdptc ptd pta ptb S S - 1 < S - 1 AB

When current row is processed, update region descriptors before process the next row. 1)ptb = ptc 2)ptc = ptdpta ptb ptc ptd S S - 1 < S - 1

He et al.’s technique Author compare his connected components algorithm with, to there knowledge, the fastest conventional connected component labeling algorithm, namely He et al.’s technique. Image size 8192 by 8192 filled whih Bernoulli noise

Resize 300 images to size 8192 by 8192 and threshold the resized image to create two binary images. One with the light pixels as foreground pixels and one with the dark pixels as foreground pixels. Thus, in total we have 600 binary images in our test dataset. Our algorithm calculates the connected components of the natural images on average 7.4 times faster than He’s technique. The final output of the connected components of the natural images uses on average times less memory with our data structure compared to the region label image.

The disadvantage of using our region coding data structure is that it does not provide a direct way to locate the region of a certain pixel. The following operations need to be done to find the region of pixel ( r, c ): 1)find the line segment within row which contains pixel ; 2)follow the linked list until the last line segment is reached; 3)read out the region descriptor via the pointer of the last line segment.

With extensive experimental analysis we have shown our algorithm to be faster than the fastest conventional algorithm. Additionally, our novel region coding data structure uses less memory in typical cases than the region label image. We also proved that our algorithm is linear with the number of pixels for any possible image.