Search results for "ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION"
showing 10 items of 982 documents
An Enhanced Detector of Blurred and Noisy Edges
2007
Detecting edges in digital images is a tricky operation in image processing since images may contain areas with different degrees of noise, blurring and sharpness. Such operation represents an important step of the whole process of similarity shape analysis and retrieval.
Data Compression Using Wavelet and Local Cosine Transforms
2015
The chapter describes an algorithm that compresses two-dimensional data arrays, which are piece-wise smooth in one direction and have oscillating events in the other direction. Seismic, hyper-spectral and fingerprints data, for example, have such a mixed structure. The transform part of the compression process is an algorithm that combines wavelet and local cosine transform (LCT). The quantization and the entropy coding parts of the compression are taken from the SPIHT codec. To efficiently apply the SPIHT codec to a mixed coefficients array, reordering of the LCT coefficients takes place. On the data arrays, which have the mixed structure, this algorithm outperforms other algorithms that a…
NIR and Visible Image Fusion for Improving Face Recognition at Long Distance
2014
Face recognition performance achieves high accuracy in close proximity. However, great challenges still exist in recognizing human face at long distance. In fact, the rapidly increasing need for long range surveillance requires a passage from close-up distances to long distances which affects strongly the human face image quality and causes degradation in recognition accuracy. To address this problem, we propose in this paper, a multispectral pixel level fusion approach to improve the performance of automatic face recognition at long distance. The main objective of the proposed approach is to formulate a method to enhance the face image quality as well as the face recognition rate. First, v…
Fractional wavelet transform
1997
The wavelet transform, which has had a growing importance in signal and image processing, has been generalized by association with both the wavelet transform and the fractional Fourier transform. Possible implementations of the new transformation are in image compression, image transmission, transient signal processing, etc. Computer simulations demonstrate the abilities of the novel transform. Optical implementation of this transform is briefly discussed.
Pattern recognition using sequential matched filtering of wavelet coefficients
1997
Abstract A bank of wavelets is used for pattern recognition by means of sequential filtering. Each element of the bank is matched to a different wavelet coefficient of the target. A sequential process leads to a set of correlation outputs. Post-processing by means of a fast blending method provides the final output correlation. Both computer simulations and optical experiments are presented, showing the discrimination capability for this implementation.
Multiscale Edges Detection by Wavelet Transform for Model of Face Recognition
1996
Publisher Summary The linear auto-associator is a particular case of the linear-associator. The goal of this network is to associate a set of stimuli to itself, which could be used to store and retrieve face images and it also could be applied as a pre-processing device to simulate some psychological tasks—such as categorizing face according to their gender. A technique of learning based on the wavelet transform can improve recognition capability when the pattern images are with a great noise. One of the ways to store and recall face images uses the linear auto-associative memory. This connectionist model is in conjunction with a pixel-based coding of the faces. The image processing using t…
Regularization of optical flow with M-band wavelet transform
2003
The optical flow is an important tool for problems arising in the analysis of image sequences. Flow fields generated by various existing solving techniques are often noisy and partially incorrect, especially near occlusions or motion boundaries. Therefore, the additional information on the scene gained from a sequence of images is usually worse. In this paper, discrete wavelet transform has been adopted in order to enhance the reliability of optical flow estimation. A generalization of the well-known dyadic orthonormal wavelets to the case of the dilation scale factor M > 2 with N vanishing moments has been used, and it has proved to be a useful regularizing tool. The advantages in the comp…
Periodic Spline Wavelets and Wavelet Packets
2014
This chapter presents wavelets and wavelet packets in the spaces of periodic splines of arbitrary order, which, in essence, are the multiple generators for these spaces. The SHA technique provides explicit representation of the wavelets and wavelet packets and fast implementation of the transforms in one and several dimensions.
Improved color interpolation using discrete wavelet transform
2005
New approaches to Color Interpolation based on Discrete Wavelet Transform are described. The Bayer data are split into the three colour components; for each component the Wavelet Coefficient Interpolation (WCI) algorithm is applied and results are combined to obtain the final colour interpolated image. A further anti-aliasing algorithm can be applied in order to reduce false colours. A first approach consists of interpolating wavelet coefficients starting from a spatial analysis of the input image. It was considered an interpolation step based on threshold levels associated to the spatial correlation of the input image pixel. A second approach consists of interpolating wavelet coefficients …
A General Frame-by-Frame Wavelet Transform Algorithm for a Three-Dimensional Analysis with Reduced Memory Usage
2007
The 3D-DWT is a mathematical tool of increasing importance. However, the huge memory requirement of the algorithms that compute it is one of the main drawbacks in practical implementations. In this paper, we introduce a frame-by-frame algorithm to calculate the 3D-DWT with low memory usage. This algorithm is general, in the sense that it can be employed with any wavelet transform and, contrary to other proposals, it gets the same results as the regular wavelet transform. In addition, there is no need to divide the input video sequence into group of frames, and it can be applied in a continuous manner, so that coding efficiency is increased and no blocking artifacts appear.