Ling-Hwei Chen

A fast motion estimation algorithm based on the block sum pyramid

In this correspondence, a fast approach to motion estimation is presented. The algorithm uses the block sum pyramid to eliminate unnecessary search positions. It first constructs the sum pyramid structure of a block. Successive elimination is then performed hierarchically from the top level to the bottom level of the pyramid. Many search positions can be skipped from being considered as the best motion vector and, thus, the search complexity can be reduced. The algorithm can achieve the same estimation accuracy as the full search block matching algorithm with much less computation time.

A fast search algorithm for vector quantization using mean pyramids of codewords

One of the most serious problems for vector quantization, especially for high dimensional vectors, is the high computational complexity of searching for the closest codeword in the codebook design and encoding phases. Although quantizing high dimensional vectors rather than low dimensional vectors results in better performance, the computation time needed for vector quantization grows exponentially with the vector dimension. This makes high dimensional vectors unsuitable for vector quantization. To overcome this problem, a fast search algorithm, under the assumption that the distortion is measured by the squared Euclidean distance, is proposed. Using the mean pyramids of codewords, the algorithm ran reject many codewords that are impossible matches and hence save a great deal of computation time. The algorithm is efficient for high dimensional codeword searches. Experimental results confirm the effectiveness of the proposed method. >

A fast iterative scheme for multilevel thresholding methods

Abstract The previously published optimal thresholding techniques based on some objective functions are very efficient in the bi-level thresholding case, but they are impractical when extended to multilevel thresholding. The reason for this is their computational complexity which grows exponentially with the number of thresholds. In this paper, an iterative scheme is proposed to render these optimal thresholding techniques more practical. The proposed algorithm starts with a bi-level thresholding, then uses the initial results to obtain higher-order thresholds. This algorithm is iterative and the convergence is proved. We also introduce some useful programming techniques to make the computation more efficient. The proposed algorithm can therefore determine the number of thresholds automatically as well as save a significant amount of computing time.

High-speed closest codeword search algorithms for vector quantization

Abstract One of the most serious problems for vector quantization is the high computational complexity involved in searching for the closest codeword through a codebook in both codebook design and encoding phases. In this paper, based on the assumption that the distortion is measured by the squared Euclidean distance, two high-speed search methods will be proposed to speed up the search process. The first one uses the difference between the mean values of two vectors to reduce the search space. The second is to find the Karhunen-Loeve transform (KLT) for the distribution of the set of training vectors and then applies the partial distortion elimination method to the transformed vectors. Experimental results show that the proposed methods can reduce lots of mathematical operations.

A new non-iterative approach for clustering

Abstract In this paper, a new non-iterative clustering method is proposed. It consists of two passes. In the first pass, the mean distance from one object to its nearest neighbor is estimated. Based on this distance, those noises far away from objects are extracted and removed. In the second pass, the mean distance from the remaining objects to their nearest neighbors is computed. Based on the distance, all the intrinsic clusters are then found. The proposed method is non-iterative and can automatically determine the number of clusters. Experimental results also show that the partition generated by the proposed method is more reasonable than that of the well-known c -means algorithm in many complicated object distributions.

New method for multilevel thresholding using the symmetry and duality of the histogram

A new approach for multilevel thresholding based on two interesting properties of the histogram is proposed. First, a peak-finding algorithm is presented based on the symmetry of a histogram, for which the hillsides of each hill are symmetrical about the central curve. Next, the duality that the peaks and valleys are opposite is presented in order to identify the valleys of the histogram. Based on this property, we propose a valley-finding algorithm to construct the hierarchical order of the various valleys. The proposed method is computationaily faster than traditional ones such as the variance-based and entropy-based methods. Compared with recent work proposed by Lim, the proposed method has some advantages in constructing the hierarchical order of various valleys.

An interactive flower image recognition system

In this paper, we present an interactive system for recognizing flower images taken by digital cameras. The proposed system provides an interactive interface allowing each user to draw an appropriate bounding window that contains the interested flower region. Then, a flower boundary tracing method is developed to extract the flower region as accurately as possible. In addition to the color and shape features of the whole flower region, the color and shape features of the pistil/stamen area will also be used to represent the flower characteristics more precisely. Experiments conducted on two distinct databases consisting of 24 species and 102 species have shown that our proposed system outperforms other approaches in terms of the recognition rate.

Image contrast enhancement using classified virtual exposure image fusion

In our daily life, digital cameras and smart phones have been widely used to take pictures. However, digital cameras and smart phones have a limited dynamic range, which is much lower than that human eyes can perceive. Thus, the photographs taken in high dynamic range scenes often exhibit under-exposure or over-exposure artifacts in shadow or highlight regions. In this study, an image fusion based approach, called classified virtual exposure image fusion (CVEIF), is proposed for image enhancement. First, a function imitating the F-stop concept in photography is designed to generate several virtual images having different intensity. Then, a classified image fusion method, which blends pixels in distinct luminance classes using different fusion functions, is proposed to produce a fused image in which every image region is well exposed. Experimental results on four different kinds of generic images, including a normal image, a low-contrast images, a backlight image, and a dark scene image, have shown that the proposed CVEIF approach produced more pleasingly enhanced images than other methods.

A high-speed algorithm for line detection

A high-speed method for line detection is proposed in this paper. By shifting the black points in a black/white image I, a parallel line for each straight line on I will be generated. Through the use of the geometric property on a pair of parallel lines, the parameter sets of those lines possibly on I can be obtained immediately. Since the transform from image space to parameter space is one to few points, the proposed method can significantly reduce the number of transforms for evaluating possible parameter sets. Experimental results are also given to show the correctness and effectiveness of the proposed method.

A novel approach for semantic event extraction from sports webcast text

Semantic event extraction is helpful for video annotation and retrieval. For sports video, most previous works detect events by video content itself. Some useful external knowledge has been researched recently. In this paper, we proposed an unsupervised approach to extract semantic events from sports webcast text. First, unrelated words in the descriptions of webcast text are filtered out, and then the filtered descriptions are clustered into significant event categories. Finally, the keywords for each event category are extracted. According to our experimental results, the proposed approach actually extracts significant text events, which can be used for further video indexing and summarization. Furthermore, we also provide a hierarchical searching scheme for text event retrieval.