Dong-Gyu Sim

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.

Robust Hausdorff distance matching algorithms using pyramidal structures

Abstract This paper proposes two Hausdorff distance (HD) matching algorithms, in which robust HD measures are implemented in pyramidal structures. By computer simulations, the matching performance of the conventional HD measures and the proposed robust HD matching algorithms using pyramidal structures is compared, with real images which are degraded by noise and occlusions.

Invariant texture retrieval using modified Zernike moments

Abstract This paper presents an effective texture descriptor invariant to translation, scaling, and rotation for texture-based image retrieval applications. In order to find the minimal matching distance between two descriptors, existing frequency-layout descriptors require a lot of distance calculations with every possible combination of scaling and rotation values because they are not invariant to geometrical transformation. To cope with this problem, a new compact descriptor is proposed that is theoretically invariant to such transformations. The proposed descriptor is obtained by first calculating the power spectrum of an original texture image for translation invariance and then the power spectrum image is normalized for scale invariance. Finally, modified Zernike moments are calculated for rotation invariance. The proposed algorithm is simpler and lower than conventional algorithms in terms of the computational complexity. The effectiveness of the proposed descriptor for invariant texture retrieval is shown with various texture datasets by comparing the retrieval accuracy, the descriptor size, and the matching complexity of the proposed descriptor with those of conventional descriptors.

A modified Zernike moment shape descriptor invariant to translation, rotation and scale for similarity-based image retrieval

Zernike moments are used as a shape descriptor for complex shapes such as trademarks that are difficult to be defined with a single contour for similarity-based image retrieval applications. Zernike moments of a given shape are calculated as correlation values of the shape with Zernike basis functions in that all the pixels of the shape regardless of their positions contribute with the same weight to the Zernike moments. The proposed modified Zernike moment descriptor for a shape is obtained taking account of the importance of the outer form of the shape to human perception. The modified Zernike moment descriptor is obtained by first dividing the original shape into two parts of inner and outer regions with a predetermined radius and then calculating the Zernike moment of the outer part and the inner part of the shape. The proposed descriptor consists of Zernike moments of outer and inner parts. Euclidean distance is used for computing the distance measure between two shapes. For perceptual similarity-based retrieval, the Zernike moments of the outer part are used and for exact-matching retrieval, both of the outer and inner Zernike moments are used. Experimentation under various test conditions shows the effectiveness of the proposed modified Zernike moment descriptor.

Pixel-Wise Unified Rate-Quantization Model for Multi-Level Rate Control

In this paper, we present a pixel-wise unified rate quantization (R-Q) model for a low-complexity rate control on configurable coding units of high efficiency video coding (HEVC). In the case of HEVC, which employs hierarchical coding block structure, multiple R-Q models can be employed for the various block sizes. However, we found that the ratios of distortions over bits for all the blocks are a nearly constant because of employment of the rate distortion optimization technique. Hence, one relationship model between rate and quantization can be derived from the characteristic of similar ratios of distortions over bits regardless of block sizes. Thus, we propose the pixel-wise unified R-Q model for HEVC rate control working on the multi-level for all block sizes. We employ a simple leaky bucket model for bit control. The rate control based on the proposed pixel-wise unified R-Q model is implemented on HEVC test model 6.1 (HM6.1). According to the evaluation for the proposed rate control, the average matching percentage to target bitrates is 99.47% and the average PSNR degradation is 0.76 dB. Based on the comparative study, we found that the proposed rate control shows low bit fluctuation and good RD performance, compared to R-lambda rate control for long sequences.

Implementation of fast HEVC encoder based on SIMD and data-level parallelism

This paper presents several optimization algorithms for a High Efficiency Video Coding (HEVC) encoder based on single instruction multiple data (SIMD) operations and data-level parallelism. Based on the analysis of the computational complexity of HEVC encoder, we found that interpolation filter, cost function, and transform take around 68% of the total computation, on average. In this paper, several software optimization techniques, including frame-level interpolation filter and SIMD implementation for those computationally intensive parts, are presented for a fast HEVC encoder. In addition, we propose a slice-level parallelization and its load-balancing algorithm on multi-core platforms from the estimated computational load of each slice during the encoding process. The encoding speed of the proposed parallelized HEVC encoder is accelerated by approximately ten times compared to the HEVC reference model (HM) software, with minimal loss of coding efficiency.

Translation, scale, and rotation invariant texture descriptor for texture-based image retrieval

A texture descriptor invariant to translation, rotation and scale changes is presented in this paper. A power spectral image is obtained by using the DFT to extract the proposed descriptor for the translation invariance of a given texture. The power spectral image is scale-normalized by a cut-off frequency on the power spectral image that is calculated as the total energy inside a circle with its radius of the cut-off frequency amounts to a predetermined value. Finally, the rotation invariant Zernike moments are calculated on the translation and scale normalized image for a rotation, scale and translation invariant descriptor. The extraction is simple and fast, using fast known DFT and Zernike transformation. The matching is also simple and fast using the Euclidean distance measure between a query and test textures. The effectiveness of the proposed algorithm is shown with various texture databases.

Two-dimensional object alignment based on the robust oriented Hausdorff similarity measure

This paper proposes an oriented Hausdorff similarity (OHS) measure for robust object alignment. The OHS measure is introduced by replacing the distance concept of conventional Hausdoff distance (HD) algorithms by the similarity concept of the Hough transform (HT). The proposed algorithm can be considered as the modified directed HT using the distance transform (DT). The orientation information at each pixel is also used to remove incorrect correspondences.

Real-time objective quality assessment based on coding parameters extracted from H.264/AVC bitstream

In this paper, we propose a new method for objective video quality assessment from coding parameters of an H.264/AVC bitstream as a hybrid/bitstream category. Conventional image and video quality assessment methods evaluate image or video quality using a degraded video or image after fully decoding it. On the other hand, the proposed method assesses video quality not with reconstructed videos but using coding parameters extracted from a bitstream. The parameters can be easily extracted while decoding the H.264/AVC bitstream. To assess video quality, the proposed algorithm is based on statistics of boundary strengths (BS), quantization parameters (QP), and average bitrates. Accuracy of the proposed method is 9.7% higher than those of the conventional algorithms in terms of Pearson correlation. Furthermore, the proposed method has significantly lower computational complexity than the conventional methods.