Rae-Hong Park

Performance study of several global thresholding techniques for segmentation

Abstract A comparative performance study of five global thresholding algorithms for image segmentation was investigated. An image database with a wide variety of histogram distribution was constructed. The histogram distribution was changed by varying the object size and the mean difference between object and background. The performance of five algorithms was evaluated using the criterion functions such as the probability of error, shape, and uniformity measures Attempts also have been made to evaluate the performance of each algorithm on the noisy image. Computer simulation results reveal that most algorithms perform consistently well on images with a bimodal histogram. However, all algorithms break down for a certain ratio of population of object and background pixels in an image, which in practice may arise quite frequently. Also, our experiments show that the performances of the thresholding algorithms discussed in this paper are data-dependent. Some analysis is presented for each of the five algorithms based on the performance measures.

Object matching algorithms using robust Hausdorff distance measures

A Hausdorff distance (HD) is one of commonly used measures for object matching. This work analyzes the conventional HD measures and proposes two robust HD measures based on m-estimation and least trimmed square (LTS) which are more efficient than the conventional HD measures. By computer simulation, the matching performance of the conventional and proposed HD measures is compared with synthetic and real images.

Integrated position estimation using aerial image sequences

Presents an integrated system for navigation parameter estimation using sequential aerial images, where the navigation parameters represent the positional and velocity information of an aircraft for autonomous navigation. The proposed integrated system is composed of two parts: relative position estimation and absolute position estimation. Relative position estimation recursively computes the current position of an aircraft by accumulating relative displacement estimates extracted from two successive aerial images. Simple accumulation of parameter values reduces the reliability of the extracted parameter estimates as an aircraft goes on navigating, resulting in a large positional error. Therefore, absolute position estimation is required to compensate for the positional error generated by the relative position estimation. Absolute position estimation algorithms using image matching and digital elevation model (DEM) matching are presented. In the image matching, a robust-oriented Hausdorff measure (ROHM) is employed, whereas in the DEM matching, an algorithm using multiple image pairs is used. Experiments with four real aerial image sequences show the effectiveness of the proposed integrated position estimation algorithm.

A fast hierarchical motion vector estimation algorithm using mean pyramid

In transmitting moving pictures, interframe coding is shown to be effective for compressing video data. A hierarchical motion vector estimation algorithm using mean pyramid is proposed. Using the same measurement window at each level of a pyramid, the proposed algorithm, based on the tree pruning, reduces the computational complexity greatly with its performance comparable to that of the full search (FS). By varying the number of candidate motion vectors which are to be used as the initial search points for motion vector estimation at the next level, the mean squared error of the proposed algorithm varies, ranging between those of the FS and three step search (TSS) methods. Also, depending on the number of candidate motion vectors, the computational complexity of the proposed hierarchical algorithm ranges from 1/8-1/2 of that of the FS. The computer simulation results of the proposed technique compared with the conventional methods are given for various test sequences. >

Robust adaptive segmentation of range images

We propose a novel image segmentation technique using the robust, adaptive least kth order squares (ALKS) estimator which minimizes the kth order statistics of the squares of residuals. The optimal value of k is determined from the data, and the procedure detects the homogeneous surface patch representing the relative majority of the pixels. The ALKS shows a better tolerance to structured outliers than other recently proposed similar techniques. The performance of the new, fully autonomous, range image segmentation algorithm is compared to several other methods.

Block-based noise estimation using adaptive Gaussian filtering

We propose a fast noise estimation algorithm using a Gaussian pre-filter. The coefficients of the Gaussian filter are selected according to the standard deviation of the Gaussian noise estimated from an input noisy image. The standard deviation is estimated from the difference image between the noisy input image and its filtered image. This algorithm can be applied to noise reduction in commercial image- or video-based applications such as digital cameras and digital television (DTV) for its performance and simplicity.

A fast feature-based block matching algorithm using integral projections

Block-by-block motion compensation algorithms are studied for video-conference/video-telephone television signals. A fast feature-based block matching algorithm using integral projections for the motion vector estimation is proposed. The proposed algorithm reduces the motion estimation computations by a factor of two by calculating the one-dimensional cost functions rather than the two-dimensional ones. Also, the low sensitivity of the proposed algorithm to the presence of additive noise is shown experimentally. Simulation results based on the original and noisy image sequences are presented. >

Weighted-adaptive motion-compensated frame rate up-conversion

In this paper, we propose a frame rate upconversion algorithm using the weighted-adaptive motion-compensated interpolation (WAMCI) that reduces the block artifacts due to the failure of motion estimation and block-based processing. The proposed method is based on the interpolation scheme by weighted sum of multiple motion-compensated interpolation (MCI) images. Also, in the proposed method, the block artifacts on the block boundaries are reduced by applying a technique similar to the overlapped block motion compensation (OBMC). To reduce the blurring of overlapping processing, the proposed method uses the motion analysis to determine the type of motion and applies the OBMC adaptively. Experimental results indicate good performance of the proposed scheme with significantly reduced block artifacts.

Document image binarization based on topographic analysis using a water flow model

Abstract This paper proposes a local adaptive thresholding method based on a water flow model, in which an image surface is considered as a three-dimensional (3-D) terrain. To extract characters from backgrounds, we pour water onto the terrain surface. Water flows down to the lower regions of the terrain and fills valleys. Then, the thresholding process is applied to the amount of filled water for character extraction, in which the proposed thresholding method is applied to gray level document images consisting of characters and backgrounds. The proposed method based on a water flow model shows the property of locally adaptive thresholding. Computer simulation with synthetic and real document images shows that the proposed method yields effective adaptive thresholding results for binarization of document images.

An efficient algorithm for video sequence matching using the modified Hausdorff distance and the directed divergence

To manipulate a large video database, effective video indexing and retrieval are required. A large number of video retrieval algorithms have been presented for framewise user query or video content query, whereas few video-sequence matching algorithms have been investigated. In this paper, we propose an efficient algorithm for video sequence matching using the modified Hausdorff distance and the directed divergence of histograms between successive frames. To effectively match the video sequences with a low computational load, we use the key frames extracted by the cumulative directed divergence and compare the set of key frames using the modified Hausdorff distance. Experimental results with color video sequences show that the proposed algorithms for video sequence matching yield better performance than conventional algorithms such as histogram difference, histogram intersection, and chi-square test methods.