A Novel DIBR 3D Image Hashing Scheme Based on Pixel Grouping and NMF

Most of the traditional 2D image hashing schemes do not take into account the change of viewpoint when constructing the final hash vector. This result in the classification accuracy rate is unsatisfactory when applied for depth-image-based rendering (DBIR) 3D image identification. In this work, pixel grouping based on histogram shape and nonnegative matrix factorization (NMF) are applied to design DIBR 3D image hashing with better robustness resisting to geometric distortions and higher classification accuracy rate for virtual image identification. Experiments show that the proposed hashing is robust against common signal and geometric distortion attacks, such as additive noise, blurring, JPEG compression, scaling, and rotation. Compared with the state-of-art schemes of traditional 2D image hashing, the proposed hashing achieves better performances under above attacks, especially for virtual image identification.

Pixel Grouping of Digital Images for Reversible Data Hiding

Pixel Grouping (PG) of digital images has been a key consideration in recent development of the Reversible Data Hiding (RDH) schemes. While a PG kernel with neighborhood pixels helps compute image groups for better embedding rate-distortion performance, only horizontal neighborhood pixel group of size 1×3 has so far been considered. In this paper, we formulate PG kernels of sizes 3×1, 2×3 and 3×2 and investigate their effect on the rate-distortion performance of a prominent PG-based RDH scheme. Specially, a kernel of size 3×2 (or 2×3) that creates a pair of pixel-trios having triangular shape and offers a greater possible correlation among the pixels. This kernel thus can be better utilized for improving a PG-based RDH scheme. Considering this, we develop and present an improved PG-based RDH scheme and the computational models of its key processes. Experimental results demonstrated that our proposed RDH scheme offers reasonably better embedding rate-distortion performance than the original scheme.

Uso del procesamiento digital de imágenes para la extracción de datos de medidas experimentales publicados en formato gráfico

In this article, we propose a method as an alternative to obtain experimental measurement data, in the absence of laboratory equipment to perform tests, in a suitable format to perform mathematical operations in order to use them as information to validate: hypotheses, models constitutive and / or research theories focused on technological development. The proposed method uses as a main tool the image segmentation technique by region growth by pixel grouping and the normalization of the coordinates of the positions of the pixels extracted to the axis scale in the corresponding figure. The segmentation of the image separates the coordinates of the pixels that form the axes and the curves, the coordinates of the pixels of the curves are normalized to the scale of the axes. The method is tested with images of the result of experimental tests of stress-strain behavior recovered from [1]. The results of the data extraction are plotted and the averages of each curve extracted as well as the standard deviation are obtained. It is verified that the data obtained can be used to corroborate or support hypotheses in a wide range of investigations.

Uso del procesamiento digital de imágenes para la extracción de datos de medidas experimentales publicados en formato gráfico

In this article, we propose a method as an alternative to obtain experimental measurement data, in the absence of laboratory equipment to perform tests, in a suitable format to perform mathematical operations in order to use them as information to validate: hypotheses, models constitutive and / or research theories focused on technological development. The proposed method uses as a main tool the image segmentation technique by region growth by pixel grouping and the normalization of the coordinates of the positions of the pixels extracted to the axis scale in the corresponding figure. The segmentation of the image separates the coordinates of the pixels that form the axes and the curves, the coordinates of the pixels of the curves are normalized to the scale of the axes. The method is tested with images of the result of experimental tests of stress-strain behavior recovered from [1]. The results of the data extraction are plotted and the averages of each curve extracted as well as the standard deviation are obtained. It is verified that the data obtained can be used to corroborate or support hypotheses in a wide range of investigations.

A comparison between PCA and some enhancement filters for denoising astronomical images

This paper includes a comparison between denoising techniques by using statistical approach, principal component analysis with local pixel grouping (PCA-LPG), this procedure is iterated second time to further improve the denoising performance, and other enhancement filters were used. Like adaptive Wiener low pass-filter to a grayscale image that has been degraded by constant power additive noise, based on statistics estimated from a local neighborhood of each pixel. Performs Median filter of the input noisy image, each output pixel contains the Median value in the M-by-N neighborhood around the corresponding pixel in the input image, Gaussian low pass-filter and Order-statistic filter also be used. Experimental results shows LPG-PCA method gives better performance, especially in image fine structure preservation, compared with other general denoising algorithms.