In this paper we shall propose and examine an VLSI architecture for
the integer-to-integer wavelet transform which is used by JPEG2000 standard for lossless compression. In order to achieve a fully utilization of hardware resources independently of the bit-depth of the input data, on-line arithmetic (digit-serial computation) is proposed to carry out this architecture. Besides, a high throughput is achieved thanks to the high degree of parallelism that on-line arithmetic allows. The design has been simulated and implemented using Xilinx FPGA device, and its main results are provided.
Early detection of tissue changes in a disease process is of utmost interest and a challenge for non-invasive imaging techniques. Texture is an important property of image regions and many texture descriptors have been proposed in the literature. In this paper we introduce a new approach related to texture descriptors and texture grouping. There exist some applications, e.g. shape from texture, that require a more dense sampling as provided by the pseudo-Wigner distribution. Therefore, the first step to the problem is to use a modular pattern detection in textured images based on the use of a Pseudo-Wigner Distribution (PWD) followed by a PCA stage. The second scheme is to consider a direct local frequency analysis by splitting the PWD spectra following a "cortex-like" structure. As an alternative technique, the use of a Gabor multiresolution approach was considered. Gabor functions constitute a family of band-pass filters that gather the most salient properties of spatial frequency and orientation selectivity. This paper presents a comparison of time-frequency methods, based on the use of the PWD, with sparse filtering approaches using a Gabor-based multiresolution representation. Performance the current methods is evaluated for the segmentation for synthetic texture mosaics and for osteoporosis images.
We present an effective method for texture segmentation and analysis using a local spectral method. The method combines the advantages of a high spectral resolution of a joint representation given by the Pseudo-Wigner distribution with an effective adaptive principal component analysis. Performance of the method is evaluated using fabric samples with defects, medical images, and crack detection in metallic surfaces. The examples demonstrate the discrimination power of the present method for detecting even very subtle changes in the homogeneity of textures.