This paper presents a new connected component labeling algorithm. The proposed algorithm scans image lines every three lines and processes pixels three by three. When processing the current three pixels, we also utilize the information obtained before to reduce the repeated work for checking pixels in the mask. Experimental results demonstrated that our method is more efficient than the fastest conventional labeling algorithm.

This paper proposes an efficient two-scan labeling algorithm for binary hexagonal images. Unlike conventional labeling algorithms, which process pixels one by one in the first scan, our algorithm processes pixels two by two. We show that using our algorithm, we can check a smaller number of pixels. Experimental results demonstrated that our method is more efficient than the algorithm extended straightly from the corresponding labeling algorithm for rectangle binary images.

Given a binary image I and a threshold t, the size-thresholded binary image I(t) defined by I and t is the binary image after removing all connected components consisting of at most t pixels. This paper presents space-efficient algorithms for computing a size-thresholded binary image for a binary image of n pixels, assuming that the image is stored in a read-only array with random-access. With regard to the problem, there are two cases depending on how large the threshold t is, namely, Relatively large threshold where t = Ω(), and Relatively small threshold where t = O(). In this paper, a new algorithmic framework for the problem is presented. From an algorithmic point of view, the problem can be solved in O() time and O() work space. We propose new algorithms for both the above cases which compute the size-threshold binary image for any binary image of n pixels in O(nlog n) time using only O() work space.

This paper proposes a new first-scan method for two-scan labeling algorithms. In the first scan, our proposed method first scans every fourth image line, and processes the scan line and its two neighbor lines. Then, it processes the remaining lines from top to bottom one by one. Our method decreases the average number of times that must be checked to process a foreground pixel will; thus, the efficiency of labeling can be improved.

We describe a cell-based connected component labeling algorithm to calculate the 0th and 1st moment features as the attributes for labeled regions. These can be used to indicate their sizes and positions for multi-object extraction. Based on the additivity in moment features, the cell-based labeling algorithm can label divided cells of a certain size in an image by scanning the image only once to obtain the moment features of the labeled regions with remarkably reduced computational complexity and memory consumption for labeling. Our algorithm is a simple-one-time-scan cell-based labeling algorithm, which is suitable for hardware and parallel implementation. We also compared it with conventional labeling algorithms. The experimental results showed that our algorithm is faster than conventional raster-scan labeling algorithms.

In this paper, an adaptive block-based text line extraction algorithm is proposed. Three global and two local parameters are defined to adapt the method to various handwritings in different languages. A document image is segmented into several overlapping blocks. The skew of each block is estimated. Text block is de-skewed by using the estimated skew angle. Text regions are detected in the de-skewed text block. A number of data points are extracted from the detected text regions in each block. These data points are used to estimate the paths of text lines. By thinning the background of the image including text line paths, text line boundaries or separators are estimated. Furthermore, an algorithm is proposed to assign to the extracted text lines the connected components which have intersections with the estimated separators. Extensive experiments on different standard datasets in various languages demonstrate that the proposed algorithm outperforms previous methods.

Text detection in color images has become an active research area in the past few decades. In this paper, we present a novel approach to accurately detect text in color images possibly with a complex background. The proposed algorithm is based on the combination of connected component and texture feature analysis of unknown text region contours. First, we utilize an elaborate color image edge detection algorithm to extract all possible text edge pixels. Connected component analysis is performed on these edge pixels to detect the external contour and possible internal contours of potential text regions. The gradient and geometrical characteristics of each region contour are carefully examined to construct candidate text regions and classify part non-text regions. Then each candidate text region is verified with texture features derived from wavelet domain. Finally, the Expectation maximization algorithm is introduced to binarize each text region to prepare data for recognition. In contrast to previous approach, our algorithm combines both the efficiency of connected component based method and robustness of texture based analysis. Experimental results show that our proposed algorithm is robust in text detection with respect to different character size, orientation, color and language and can provide reliable text binarization result.

We propose a novel method for Web page grouping based only on hyperlink information. Because of the explosive growth of the World Wide Web, page grouping is expected to provide a general grasp of the Web for effective information search and netsurfing. The Web can be regarded as a gigantic digraph where pages are vertices and links are arcs. Our grouping method is a generalization of decomposition into strongly connected components, in which each group is constructed as a subset of a strongly connected component. Moreover, group sizes can be controlled by adjusting a parameter, called the threshold parameter. We call the resulting groups parameterized connected components (PCCs). The algorithm is simple and admits parallelization. Notably, we apply Dijkstra's shortest path algorithm in our grouping method. This paper also includes experimental results for 15 million Web pages, which show the contribution of our method to efficient Web surfer navigation.

In this paper, we present simulation algorithms among enhanced mesh models. The enhanced mesh models here include reconfigurable mesh and mesh with multiple broadcasting. A reconfigurable mesh (RM) is a processor array that consists of processors arranged to a 2-dimensional grid with a reconfigurable bus system. The bus system can be used to dynamically obtain various interconnection patterns among the processors during the execution of programs. A horizontal-vertical RM (HV-RM) is obtained from the general RM model, by restricting the network topology it can take to the ones in which each bus segment must be along row or column. A mesh with multiple broadcasting (MWMB) is an enhanced mesh, which has additional broadcasting buses endowed to every row and column. We present two algorithms:1) an algorithm that simulates a HV-RM of size nn time-optimally in θ(n) time on a MWMB of size nn, and 2) an algorithm that simulates a RM of size nn in θ(log2 n) time on a HV-RM of size nn. Both algorithms use a constant number of storage in each processor. Furthermore, we show that a RM of size nn can be simulated in θ((n/m)2 log n log m) time on a HV-RM of size mm, in θ ((n/m)2 m log n log m) time on a MWMB of size mm (m < n). These simulations use θ((n/m)2) storage in each processor, which is optimal.

A practical structural representation of a segmented image is presented. The practicalness is defined according to whether or not the representation can be directly generated from its corresponding segmented image. Two structural representations have been proposed in the literature. They are hierarchical structure and relational graph. Because they are defined totally on the basis of human perception, neither of the representations can be directly generated from the corresponding segmented image. The structural representation described in this paper, however, is based on the relations among pattern primitives and generated by applying some human-oriented constraints.

Circuit simulators are used to verify circuit functionality and to obtain detailed timing information before the expensive fabrication process takes place. They have become an essential CAD tool in an era of sub-micron technology. We have developed a new event-driven MOS circuit simulator to replace a direct method circuit simulator. In our simulator, partitioned subcircuits are analyzed by a direct method matrix solver, and these are controlled by an event-driven scheme to maintain accuracy. The key of this approach is how to manage events for circuit simulation. We introduced two types of events: self-control events for a subcircuit and prediction correcting events between subcircuits. They control simulation accuracy, and bring simulation efficiency through multi-rate behavior of a large scale circuit. The event-driven scheme also brings some useful functions which are not available from a direct method circuit simulator, such as a selected block simulation function and a batch simulation function for load variation. We simulated logic modules (buffer, adder, and counter) with about 1000 MOSFETs with our event-driven MOS circuit simulator. Our simulator was 5-7 times faster than a SPICE-like circuit simulator, while maintaining the less than 1% error accuracy. The selected block simulation function enables to shorten simulation time without losing any accuracy by selecting valid blocks in a circuit to simulate specified node waveforms. Using this function, the logic modules were simulated 13-28 times faster than the SPICE-like circuit simulator while maintaining the same accuracy.

The subject of the paper is to propose a simple O(|V|+|E|) algorithm for finding all 3-edge-components of a given undirected multigraph G=(V, E). An 3-edge-connected component of G is defined as a maximal set of vertices such that G has at least three edge-disjoint paths between every pair of vertices in the set. The algorithm is based on the depth-first search (DFS) technique. For any fixed DFS-tree T of G, cutpairs of G are partitioned into two types: a type 1 pair consists of an edge of T and a back edge; a type 2 pair consists of two edges of T. All type 1 pairs can easily be determined in O(|V|+|E|) time. The point is that an edge set KE(T) in which any type 2 pair is included can be found in O(|V|+|E|) time. All 3-edge-components of G appear as connected components if we delete from G all edges contained in type 1 pairs or in the edge set KE(T).