Transform Coding of Arbitrarily Shaped Regions in Ultrasonic Data

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DOI: https://doi.org/10.1109/76.350781

[2] ISO/IEC JTC1/SC29/WG11 Ad hoc group on MPEG-4 video VM editing, MPEG-4 Video Verification Model Version 5. 0, Nov. (1996).

DOI: https://doi.org/10.1109/cmpcon.1991.128829

[3] J. G. Apostolopoulos and J. S. Lim, Coding of Arbitrarily-Shaped Regions, SPIE Vol. 2501, (1995).

[4] T. Sikora, S. Bauer, and B. Makai, Efficiency of Shape-Adaptive 2-D Transforms for Coding of Arbitrarily Shaped mage Segments, IEEE Trans. on Circuits and Syst. For Video Tech., Vol. 5, No. 3, June (1995).

DOI: https://doi.org/10.1109/76.401104

[5] D. Bimbo, Visual Information Retrieval, Morgan Kaufmann, (2001).

[26] [28] [30] [32] [34] 360. 2 0. 4 0. 6 0. 8 1 bpp PSNR [dB].

[20] [22] [24] [26] [28] 0. 2 0. 4 0. 6 0. 8 1 bpp PSNR [dB]  : The proposed method - - - - - : The LPE padded DCT.

Paper TitlePages

Visual Feature Extraction under Wavelet Domain for Image Retrieval

Authors: Won Bae Park, Eun Ju Ryu, Young Jun Song, Jae Hyeong Ahn

Abstract: In this paper, we propose a new visual feature extraction method for content-based image retrieval (CBIR) based on wavelet transform which has both spatial-frequency and multi-resolution characteristics. We extract visual features for each frequency band in wavelet transformation and use them for CBIR. The lowest frequency band involves utilizing the spatial information of an original image. We extract 64 feature vectors using fuzzy homogeneity in the wavelet domain, which considers both the wavelet coefficients and the spatial information of each coefficient. In addition, we extract 3 feature vectors using the energy values of high frequency bands, and store those to the image database. As a query, we retrieve the most similar image from the image database according to the 10 largest homograms (normalized fuzzy homogeneity vectors) and 3 energy values. Simulation results show that the proposed method has good accuracy in image retrieval using 90 texture images.

206

Estimation of Physical Parameters Using Learning Algorithm in Precision Positioning Control

Authors: Seiji Hashimoto, Hiroki Watanabe, Kenji Nakajima

Abstract: In this paper, a high precision control and a state estimation methods based on the learning algorithm are proposed. By means of the feedback error-learning method, at first, the variation of physical parameter is identified. Then, online estimation can be performed comparing the learned weighting coefficients in the neural network. The proposed method is applied to a precision control system, and is verified through the experiments.

469

A New Digital Watermarking Algorithm Based on DWT-DCT-RLC

Authors: Wen Juan He, Jing Liu, Yuan Yi Hu, Jing Yi Wang

Abstract: The paper presents an imperceptible and robust digital watermarking algorithm using a combination of the DWT-DCT , which improves the robustness via using new embedding strategy. Apply 4-level DWT to the host image. Then, DCT transform of each directional middle frequencies DWT subband is computed; A binary watermarked logo is coded by Run-Length Code(RLC);The watermark bits are embedded in the coefficients of the DCT block based on the energy of each corresponding to coefficient in the highest level selected using the value of RLC. Performance evaluation show that the proposed scheme is invisible and robust against common image manipulations like JPEG compression, noise adding, sharpening, filtering.

188

An Improved Method for CT Image Coding Using PSNR Prediction Model Based on ELM

Authors: Han Yang Zhu, Xin Yu Jin, Jian Feng Shen

Abstract: In telemedicine, medical images are always considered very important telemedicine diagnostic evidences. High transmission delay in a bandwidth limited network becomes an intractable problem because of its large size. It’s important to achieve a quality balance between Region of Interest (ROI) and Background Region (BR) when ROI-based image encoding is being used. In this paper, a research made on balancing method of LS-SVM based ROI/BR PSNR prediction model to optimize the ROI encoding shows it’s much better than conventional methods but with very high computational complexity. We propose a new method using extreme learning machine (ELM) with lower computational complexity to improve encoding efficiency compared to LS-SVM based model. Besides, it also achieves the same effect of balancing ROI and BR.

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