Yuyan Chao

A Run-Based Two-Scan Labeling Algorithm

We present an efficient run-based two-scan algorithm for labeling connected components in a binary image. Unlike conventional label-equivalence-based algorithms, which resolve label equivalences between provisional labels, our algorithm resolves label equivalences between provisional label sets. At any time, all provisional labels that are assigned to a connected component are combined in a set, and the smallest label is used as the representative label. The corresponding relation of a provisional label and its representative label is recorded in a table. Whenever different connected components are found to be connected, all provisional label sets concerned with these connected components are merged together, and the smallest provisional label is taken as the representative label. When the first scan is finished, all provisional labels that were assigned to each connected component in the given image will have a unique representative label. During the second scan, we need only to replace each provisional label by its representative label. Experimental results on various types of images demonstrate that our algorithm outperforms all conventional labeling algorithms.

A Linear-Time Two-Scan Labeling Algorithm

This paper presents a fast linear-time two-scan algorithm for labeling connected components in binary images. In the first scan, provisional labels are assigned to object pixels in the same way as do most conventional labeling algorithms. To improve efficiency, we use corresponding equivalent label sets and a representative label table for resolving label equivalences. When the first scan is finished, all provisional labels belonging to each connected component in a given image are combined in the corresponding equivalent label set, and they are assigned a unique representative label with the representative label table. During the second scan, by use of the completed representative label table, all provisional labels belonging to each connected component are replaced by their representative label. Our algorithm is very simple in principle, and is easy to implement. Experimental results demonstrated that the efficiency of our algorithm is superior to that of other labeling algorithms.

An Algorithm for Connected-Component Labeling, Hole Labeling and Euler Number Computing

Labeling connected components and holes and computing the Euler number in a binary image are necessary for image analysis, pattern recognition, and computer (robot) vision, and are usually made independently of each other in conventional methods. This paper proposes a two-scan algorithm for labeling connected components and holes simultaneously in a binary image by use of the same data structure. With our algorithm, besides labeling, we can also easily calculate the number and the area of connected components and holes, as well as the Euler number. Our method is very simple in principle, and experimental results demonstrate that our method is much more efficient than conventional methods for various kinds of images in cases where both labeling and Euler number computing are necessary.

A RUN-BASED ONE-AND-A-HALF-SCAN CONNECTED-COMPONENT LABELING ALGORITHM

This paper presents a run- and label-equivalence-based one-and-a-half-scan algorithm for labeling connected components in a binary image. Major differences between our algorithm and conventional label-equivalence-based algorithms are: (1) all conventional label-equivalence-based algorithms scan all pixels in the given image at least twice, whereas our algorithm scans background pixels once and object pixels twice; (2) all conventional label-equivalence-based algorithms assign a provisional label to each object pixel in the first scan and relabel the pixel in the later scan(s), whereas our algorithm assigns a provisional label to each run in the first scan, and after resolving label equivalences between runs, by using the recorded run data, it assigns each object pixel a final label directly. That is, in our algorithm, relabeling of object pixels is not necessary any more. Experimental results demonstrated that our algorithm is highly efficient on images with many long runs and/or a small number of object pixels. Moreover, our algorithm is directly applicable to run-length-encoded images, and we can obtain contours of connected components efficiently.

Two Efficient Label-Equivalence-Based Connected-Component Labeling Algorithms for 3-D Binary Images

Whenever one wants to distinguish, recognize, and/or measure objects (connected components) in binary images, labeling is required. This paper presents two efficient label-equivalence-based connected-component labeling algorithms for 3-D binary images. One is voxel based and the other is run based. For the voxel-based one, we present an efficient method of deciding the order for checking voxels in the mask. For the run-based one, instead of assigning each foreground voxel, we assign each run a provisional label. Moreover, we use run data to label foreground voxels without scanning any background voxel in the second scan. Experimental results have demonstrated that our voxel-based algorithm is efficient for 3-D binary images with complicated connected components, that our run-based one is efficient for those with simple connected components, and that both are much more efficient than conventional 3-D labeling algorithms.

Configuration-Transition-Based Connected-Component Labeling

This paper proposes a new approach to label-equivalence-based two-scan connected-component labeling. We use two strategies to reduce repeated checking-pixel work for labeling. The first is that instead of scanning image lines one by one and processing pixels one by one as in most conventional two-scan labeling algorithms, we scan image lines alternate lines, and process pixels two by two. The second is that by considering the transition of the configuration of pixels in the mask, we utilize the information detected in processing the last two pixels as much as possible for processing the current two pixels. With our method, any pixel checked in the mask when processing the current two pixels will not be checked again when the next two pixels are processed; thus, the efficiency of labeling can be improved. Experimental results demonstrated that our method was more efficient than all conventional labeling algorithms.

A Very Fast Algorithm for Simultaneously Performing Connected-Component Labeling and Euler Number Computing

Labeling connected components and calculating the Euler number in a binary image are two fundamental processes for computer vision and pattern recognition. This paper presents an ingenious method for identifying a hole in a binary image in the first scan of connected-component labeling. Our algorithm can perform connected component labeling and Euler number computing simultaneously, and it can also calculate the connected component (object) number and the hole number efficiently. The additional cost for calculating the hole number is only O(H) , where H is the hole number in the image. Our algorithm can be implemented almost in the same way as a conventional equivalent-label-set-based connected-component labeling algorithm. We prove the correctness of our algorithm and use experimental results for various kinds of images to demonstrate the power of our algorithm.

The connected-component labeling problem: A review of state-of-the-art algorithms

Connected-component labeling (CCL) is indispensable for pattern recognition.Many connected-component labeling algorithms have been proposed.The state-of-the-art CCL algorithms presented in the last decade are reviewed. This article addresses the connected-component labeling problem which consists in assigning a unique label to all pixels of each connected component (i.e., each object) in a binary image. Connected-component labeling is indispensable for distinguishing different objects in a binary image, and prerequisite for image analysis and object recognition in the image. Therefore, connected-component labeling is one of the most important processes for image analysis, image understanding, pattern recognition, and computer vision. In this article, we review state-of-the-art connected-component labeling algorithms presented in the last decade, explain the main strategies and algorithms, present their pseudo codes, and give experimental results in order to bring order of the algorithms. Moreover, we will also discuss parallel implementation and hardware implementation of connected-component labeling algorithms, extension for n-D images, and try to indicate future work on the connected component labeling problem.

A novel bit-quad-based Euler number computing algorithm

AbstractThe Euler number of a binary image is an important topological property in computer vision and pattern recognition. This paper proposes a novel bit-quad-based Euler number computing algorithm. Based on graph theory and analysis on bit-quad patterns, our algorithm only needs to count two bit-quad patterns. Moreover, by use of the information obtained during processing the previous bit-quad, the average number of pixels to be checked for processing a bit-quad is only 1.75. Experimental results demonstrated that our method outperforms significantly conventional Euler number computing algorithms.

A new run-based algorithm for Euler number computing

The Euler number of a binary image is an important topological feature for many image processing, image analysis, pattern recognition, and computer vision applications. This paper proposes a new run-based Euler number computation algorithm. The conventional run-based algorithm processes rows of the given image one-by-one from top to bottom in a single phase. For each row, it finds the runs in the row and records the start and end locations of each run to compute neighbor runs. In contrast, our algorithm calculates the Euler number of an image in two phases. In the first phase, we process odd rows alternately to find runs and only record its end location. In the second phase, we process each of the remaining even rows to find runs and calculate neighboring runs between the current row and the rows immediately above and below using the recorded run data. Using this method, the number of accesses required to compute the Euler number decreases in almost all cases. Analysis of the time complexity and experimental results demonstrate that our algorithm outperforms conventional Euler number computation algorithms.