Jing-Ming Guo

License Plate Localization and Character Segmentation With Feedback Self-Learning and Hybrid Binarization Techniques

License Plate Localization (LPL) and Character Segmentation (CS) play key roles in License Plate Recognition System (LPRS). In this study, we dedicate ourselves in these two issues. In LPL, the histogram equalization is employed to solve the low contrast and dynamic range problem; the texture properties, e.g., aspect ratio, and color similarity are used to locate the License Plate (LP). In CS, the hybrid- binarization technique is proposed to effectively segment the characters in the dirt LP. The feedback self-learning procedure is also employed to adjust the parameters in the system. As documented in the experiments, good localization and segmentation results are achieved with the proposed algorithms.

Hybrid pixel-based data hiding and block-based watermarking for error-diffused halftone images

A low computational complexity noise-balanced error diffusion (NBEDF) technique is proposed for embedding watermarks into error-diffused images. The visual decoding pattern can be perceived when two or more similar NBEDF images are overlaid, even in a high activity region. Also, with the modified improved version of NBEDF, two halftone images can be made from two totally different gray-tone images, and still provide a clear and sharp visual decoding pattern. With self-decoding techniques, we can also decode the pattern with only one NBEDF image. However, the NBEDF method is not so robust to damage due to printing or other distortions. Thus, a kernels-alternated error diffusion (KAEDF) technique is proposed. By using them alternately in the halftone process, we find that two well-known kernels (Jarvis, J.F. et al., 1976; Stucki, P., 1981) are compatible. In the decoder, because the spectral distributions of Jarvis and Stucki kernels are different in the 2D fast Fourier transform domain, we use the cumulative squared Euclidean distance criterion to determine each cell in a watermarked halftone image belonging to either Jarvis or Stucki, and then decode the watermark. Furthermore, because the detailed textures of Jarvis and Stucki patterns are somewhat different in the spatial domain, the lookup table (LUT) technique is also used for fast decoding. From simulation results, the correct decoding rates for both techniques are high and extremely robust, even after printing and scanning processes. Finally, we extend the hybrid NBEDF and KAEDF algorithms to two color EDF halftone images, where 8 independent KAEDF watermarks and 16 NBEDF watermarks can be inserted and still achieve a high-quality result.

Hierarchical Method for Foreground Detection Using Codebook Model

This paper presents a hierarchical scheme with block-based and pixel-based codebooks for foreground detection. The codebook is mainly used to compress information to achieve a high efficient processing speed. In the block-based stage, 12 intensity values are employed to represent a block. The algorithm extends the concept of the block truncation coding, and thus it can further improve the processing efficiency by enjoying its low complexity advantage. In detail, the block-based stage can remove most of the backgrounds without reducing the true positive rate, yet it has low precision. To overcome this problem, the pixel-based stage is adopted to enhance the precision, which also can reduce the false positive rate. Moreover, the short-term information is employed to improve background updating for adaptive environments. As documented in the experimental results, the proposed algorithm can provide superior performance to that of the former related approaches.

Fast Background Subtraction Based on a Multilayer Codebook Model for Moving Object Detection

Moving object detection is an important and fundamental step for intelligent video surveillance systems because it provides a focus of attention for post-processing. A multilayer codebook-based background subtraction (MCBS) model is proposed for video sequences to detect moving objects. Combining the multilayer block-based strategy and the adaptive feature extraction from blocks of various sizes, the proposed method can remove most of the nonstationary (dynamic) background and significantly increase the processing efficiency. Moreover, the pixel-based classification is adopted for refining the results from the block-based background subtraction, which can further classify pixels as foreground, shadows, and highlights. As a result, the proposed scheme can provide a high precision and efficient processing speed to meet the requirements of real-time moving object detection.

Content-Based Image Retrieval Using Features Extracted From Halftoning-Based Block Truncation Coding

This paper presents a technique for content-based image retrieval (CBIR) by exploiting the advantage of low-complexity ordered-dither block truncation coding (ODBTC) for the generation of image content descriptor. In the encoding step, ODBTC compresses an image block into corresponding quantizers and bitmap image. Two image features are proposed to index an image, namely, color co-occurrence feature (CCF) and bit pattern features (BPF), which are generated directly from the ODBTC encoded data streams without performing the decoding process. The CCF and BPF of an image are simply derived from the two ODBTC quantizers and bitmap, respectively, by involving the visual codebook. Experimental results show that the proposed method is superior to the block truncation coding image retrieval systems and the other earlier methods, and thus prove that the ODBTC scheme is not only suited for image compression, because of its simplicity, but also offers a simple and effective descriptor to index images in CBIR system.

Improved Block Truncation Coding Based on the Void-and-Cluster Dithering Approach

Block truncation coding (BTC) is an efficient technology for image compression. An improved BTC algorithm, namely ordered dither block truncation coding (ODBTC), is presented in this study. In order to provide better image quality, the void-and-cluster halftoning is combined with the BTC. The ODBTC results show that the image quality is improved when it is operated in high coding gain applications. Another feature of the ODBTC is the dither array look up table (LUT), which significantly reduces the complexity compared to the BTC.

False-positive-free SVD-based image watermarking

Abstract The need of copyright protection and rightful ownership become very urgent in the fast growing Internet environment. The watermarking offers a convenient way to hide specific information via an imaging system for the consumer electronic devices such as digital camera, scanner, and printer. Numerous efforts have been devoted in the Singular Value Decomposition (SVD)-based image watermarking schemes which embed the visual watermark image into the host image before publishing for public usage. However, the main drawback of the SVD-based image watermarking is its false positive problem of which an attacker can easily claim and obtain the correct watermark from an unauthorized image. In this paper, we proposed a new SVD-based image watermarking by embedding the principal component of a watermark into the host image of block based manner using spread spectrum concept. The experimental results demonstrate that the proposed method overcomes the false positive problem, achieves a high payload, and outperforms the former reliable SVD-based watermarking.

Contact-free hand geometry-based identification system

This paper presents an approach for personal identification using hand geometrical features, in which the infrared illumination device is employed to improve the usability of this hand recognition system. In the proposed system, prospective users can place their hand freely in front of the camera without any pegs or templates. Moreover, the proposed system can be widely used under dark environment and complex background scenarios. To achieve better detection accuracy, in total 13 important points are detected from a palm image, and 34 features calculated from these points are used to further recognition. Experimental results demonstrate that the averaged Correct Identification Rate (CIR) is 96.23% and averaged False Accept Rate (FAR) is 1.85%. These results prove that the proposed contact-free system can be considered as an effective identity verification system for practical applications.

Data hiding in halftone images with noise-balanced error diffusion

In this letter, we propose a low-complexity algorithm for embedding watermarks into two or more error-diffused images. The first one is only a regular error-diffused image, and the others are achieved by applying the proposed noise-balanced error diffusion technique (NBEDF) to the original gray-level image. The visual decoding pattern can be perceived when these two or more similar error-diffused images are overlaid each other. Furthermore, with the proposed modified version of NBEDF, the two halftone images can be made from two totally different gray-tone images and still provide a clear and sharp visual decoding pattern.

Reversible Data Hiding Based on Histogram Modification of SMVQ Indices

This work presents a novel reversible data-hiding scheme that embeds secret data into a transformed image and achieves lossless reconstruction of vector quantization (VQ) indices. The VQ compressed image is modified by the side-matched VQ scheme to yield a transformed image. Distribution of the transformed image is employed to achieve high embedding capacity and a low bit rate. Moreover, three configurations, under-hiding, normal-hiding, and over-hiding schemes, are utilized to improve the proposed scheme further for various applications. Experimental results demonstrate that the proposed scheme significantly enhances the compression ratio and embedding capacity. Experimental results also show that the proposed scheme achieves the best performance among approaches in literature in terms of the compression ratio and embedding capacity.