Regularity scalable image coding based on wavelet singularity detection

In this paper, we propose an adaptive algorithm for scalable wavelet image coding, which is based on the general feature, the regularity, of images. In pattern recognition or computer vision, regularity of images is estimated from the oriented wavelet coefficients and quantified by the Lipschitz exponents. To estimate the Lipschitz exponents, evaluating the interscale evolution of the wavelet transform modulus sum (WTMS) over the directional cone of influence was proven to be a better approach than tracing the wavelet transform modulus maxima (WTMM). This is because the irregular sampling nature of the WTMM complicates the reconstruction process. Moreover, examples were found to show that the WTMM representation cannot uniquely characterize a signal. It implies that the reconstruction of signal from its WTMM may not be consistently stable. Furthermore, the WTMM approach requires much more computational effort. Therefore, we use the WTMS approach to estimate the regularity of images from the separable wavelet transformed coefficients. Since we do not concern about the localization issue, we allow the decimation to occur when we evaluate the interscale evolution. After the regularity is estimated, this information is utilized in our proposed adaptive regularity scalable wavelet image coding algorithm. This algorithm can be simply embedded into any wavelet image coders, so it is compatible with the existing scalable coding techniques, such as the resolution scalable and signal-to-noise ratio (SNR) scalable coding techniques, without changing the bitstream format, but provides more scalable levels with higher peak signal-to-noise ratios (PSNRs) and lower bit rates. In comparison to the other feature-based wavelet scalable coding algorithms, the proposed algorithm outperforms them in terms of visual perception, computational complexity and coding efficienc

On the Correlation Property of Multiscaling Coefficients for Signal Denoising

The discrete multiwavelet transform (DMWT) enables a signal to be analyzed in a multiresolution and multidimensional way. While the generated multiwavelet coefficients are vectors in nature, it has been generally understood that correlation exists between the vector elements. This feature has been adopted particularly in image coding applications to allow efficient design of VQ codebook. For a multiresolution analysis, the multiwavelet coefficients are generated from the multiscaling coefficients of the upper level. In this paper, we show that many multiwavelet systems cannot give correlated multiscaling vector elements, as different from the multiwavelet vector elements. But for those that can give correlated multiscaling vector elements, they can provide much information to assist in identifying the "blank" regions in a noisy signal. A new denoising algorithm is then proposed based on this feature and is particularly useful for sparse source signals.APSIPA ASC 2009: Asia-Pacific Signal and Information Processing Association, 2009 Annual Summit and Conference. 4-7 October 2009. Sapporo, Japan. Poster session: Signal Processing Theory and Methods I (6 October 2009)

On the Correlation Property of Multiscaling Coefficients for Signal Denoising

Abstract-The discrete multiwavelet transform (DMWT) enables a signal to be analyzed in a multiresolution and multidimensional way. While the generated multiwavelet coefficients are vectors in nature, it has been generally understood that correlation exists between the vector elements. This feature has been adopted particularly in image coding applications to allow efficient design of VQ codebook. For a multiresolution analysis, the multiwavelet coefficients are generated from the multiscaling coefficients of the upper level. In this paper, we show that many multiwavelet systems cannot give correlated multiscaling vector elements, as different from the multiwavelet vector elements. But for those that can give correlated multiscaling vector elements, they can provide much information to assist in identifying the "blank" regions in a noisy signal. A new denoising algorithm is then proposed based on this feature and is particularly useful for sparse source signals

La escolarización del colectivo gitano en Lleida: de la exclusión a la inclusión

Este artículo pretende analizar la situación actual de la escolarización de la población escolar gitana en Cataluña, así como de las actuaciones que se han realizado en la última décda para su integración en las instituciones escolares de Lleida. Además, propone una reflexión sobre la reubicación de la problemática avenida Tarradellas y la distribución de niños; los programas y servicios que la Administración educativa ofrece para atender a las minorias étnicas; la contratación de un mediador intercultural, y el control del absentismo. Y por último, se centra en los programas europeos y los intentos de conversión de centros-gueto en comunidades de aprendizaje como apuestas de futuro en una sociedad que, al vivir en continuo proceso de transformación, exige la responsabilidad activa de los actores y agentes sociales para dar respuesta a los interrogantes sociales y educativos que se plantean.This article pretends to make a current analysis of the gypsy school population in Catalonia, and the performances that have been carried out in the last decade for their integration in the school institutions of Lleida. It also suggest a reflection about the relocation of the problematic Tarradellas Avenue and the distribution of children, the programs and services of the educational Adininistration for the attention of the ethnic minorities, the recruiting of a mediator intercultural and the control of the absenteeism. Finally, it emphasises in the paper of the European programs and the intents of centre-ghetto conversion in learning corninunities. In a society, in continuous transformation process, it demands the active responsibility of the actors and social agents to give answers to the social and educational you ask that formulate

Teeth Reconstruction Using Artificial Intelligence: Trends, Perspectives, and Prospects

ABSTRACTBackground Artificial intelligence (AI) applications in dental restorative procedures have significantly developed over recent years. However, there is a lack of documentation regarding the types of AI used in tooth reconstruction.Types of Studies Reviewed Studies using AI in tooth reconstruction were electronically searched over three databases PubMed, Cochrane library, and Google Scholar. The relevancy of theme tooth reconstruction was prioritized in searching. Only original research was included without the restriction of publication time or any filters.Results A total of 18 studies were included in this review: 5 reported programmable AI systems based on mathematical models such as statistical estimation, 9 studies used biogeneric tooth libraries, and 4 presented the deep learning models.Practical Implications AI has gained significant progress over the past two decades as a powerful tool for automated tooth reconstruction. However, further studies are required to compare different forms of AIs and to assess their clinical performance in the reconstruction of occlusal surfaces