Complex wavelet structural similarity (CW-SSIM) index has been recognized as a novel image similarity measure of broad potential applications due to its robustness to small geometric distortions such as translation, s...
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Complex wavelet structural similarity (CW-SSIM) index has been recognized as a novel image similarity measure of broad potential applications due to its robustness to small geometric distortions such as translation, scaling and rotation of images. Nevertheless, how to make the best use of it in image classification problems has not been deeply investigated. In this paper, we introduce a series of novel image classification algorithms based on CW-SSIM and use handwritten digit recognition, and face recognition as examples for demonstration. Among the proposed approaches, the best compromise between accuracy and complexity is obtained by the CW-SSIM support vector machine based algorithms, which combines an unsupervised clustering method to divide the training images into clusters with representative images and a supervised learning method based on support vector machines to maximize the classification accuracy. Our experiments show that such a conceptually simple image classification method, which does not involve any registration, intensity normalization or sophisticated feature extraction processes, and does not rely on any modeling of the image patterns or distortion processes, achieves competitive performance with reduced computational cost. (C) 2012 Elsevier B.v. All rights reserved.
This paper introduces a single-image super-resolution approach which is based on sparse representation over dictionaries learned in the wavelet domain. The diagonal detail subband learning and reconstruction is improv...
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ISBN:
(纸本)9781467355636;9781467355629
This paper introduces a single-image super-resolution approach which is based on sparse representation over dictionaries learned in the wavelet domain. The diagonal detail subband learning and reconstruction is improved by designing two diagonal dictionaries;one for the diagonal and another for the anti-diagonal orientations. Four pairs (low resolution and high resolution) of subband dictionaries are designed. The sparse representation coefficients for the respective low and high resolution images are assumed to be the same. The proposed algorithm is compared with the leading super-resolution techniques and is shown to excel both visually and quantitatively, with an average PSNR raise of 0.82 dB over the Kodak set. Moreover, this algorithm is shown to significantly reduce the dictionary learning computational complexity by designing compactly sized structural dictionaries.
imageprocessing has gained an increased usage and impact in modern pavement networks automatic distress severity classification (DSC). DSC defines priorities and maintenance resources optimum allocation in order to a...
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ISBN:
(纸本)9781728133775
imageprocessing has gained an increased usage and impact in modern pavement networks automatic distress severity classification (DSC). DSC defines priorities and maintenance resources optimum allocation in order to achieve a cost-effective rehabilitation process. This paper presents a novel computer vision algorithm having the ability to process, isolate and evaluate the distress severity level of a pavement. A pavement color image is converted to grayscale and then processed for image denoising of the granularity and complex texture that represent and artifact in cracks edge detection. The processing is achieved by a 2D dual-tree double density wavelet transform filter banks that significantly reduces the granularity noise while preserving the pavement cracks for edge detection. The 2D wavelet FIR filters perform analysis, soft thresholding then a synthesis of the image. The second step is then an edge detection process followed by morphological filtering and labeled components size-histogram filter to isolate false edges as residuals of denoising. A final step is performed by two Savitzky-Golay filters for the detection of longitudinal and transverse alligator cracks projections. A weighted score function with multiple parameters is used for DSC.
We introduce an isotropic measure of local contrast for natural images that is based on analytic filters and present the design of directional wavelet frames suitable for its computation. We show how this contrast mea...
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ISBN:
(纸本)0819437646
We introduce an isotropic measure of local contrast for natural images that is based on analytic filters and present the design of directional wavelet frames suitable for its computation. We show how this contrast measure can be used within a masking model to facilitate the insertion of a watermark in an image while minimizing visual distortion.
14-bit or 16-bit pixel depth high dynamic-range images are acquired from visible band cameras and from infrared imaging devices which are more widely used nowadays. Usually, linear mapping is used to display these ima...
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ISBN:
(纸本)9781467355636;9781467355629
14-bit or 16-bit pixel depth high dynamic-range images are acquired from visible band cameras and from infrared imaging devices which are more widely used nowadays. Usually, linear mapping is used to display these images to operators. However, results of the researches done to map images into 0-255 range in recent years show that different techniques result in major differences at image perception and detail visibility. Successful compression of image dynamic range increases the operator awareness for surveillance systems and ensure more effective display of scene details to user. Besides, dynamic-range compression techniques effect the enhancement of the success rate of image target detection and tracking techniques. In this work, scene components are analyzed using wavelet coefficients and intensity distribution of scene components are extracted. Extracted intensity distribution is used to display scene components effectively.
We derive two expressions for roundoff error variance, one for a rounded off random value with a zero mean and a given variance under uniform distribution and another for such a value under a normal. Also, an expressi...
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We derive two expressions for roundoff error variance, one for a rounded off random value with a zero mean and a given variance under uniform distribution and another for such a value under a normal. Also, an expression for truncation error variance for values under uniform distribution is obtained. An application of the expressions to analysis of processing essentially quantized data by a nonrecursive smoothing filter is shown. Also their applications to quantization error (quantization noise) analysis of general linear processing of quantized signals under uniform and normal distributions and to quantization error analysis of essentially quantized discrete transforms like DFT (discrete Fourier transform), DCT (discrete cosine transform), DWT (discrete Walsh transforms), wavelet transforms, and so on, to image, sound (audio), video and to general signalprocessing in many cases can be considered as useful. The effect of accuracy of using these expressions is the more, the more used quantization level and the less maximal signal amplitudes. (C) 2014 Elsevier B.v. All rights reserved.
Most digital signalprocessing methods have an underlying assumption of regularly-spaced data samples. However, many real-world data collection techniques generate data sets which are not sampled at evenly-spaced inte...
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ISBN:
(纸本)0819422134
Most digital signalprocessing methods have an underlying assumption of regularly-spaced data samples. However, many real-world data collection techniques generate data sets which are not sampled at evenly-spaced intervals, or which may have significant data dropout problems. Therefore, a method of interpolation is needed to model the signal on an even grid of arbitrary granularity. We propose the interpolation of nonuniformly sampled fields using a least- square fit of the data to a wavelet basis in a multiresolution setting.
The objective measurement of blockiness plays an important role in many applications, such as the quality assessment of an image, and the design of image and video coding system. However, most of the existing no-refer...
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ISBN:
(纸本)9781479903566
The objective measurement of blockiness plays an important role in many applications, such as the quality assessment of an image, and the design of image and video coding system. However, most of the existing no-reference blockiness metrics do not consider important influences of grid distortion of an image on the performance of the metric. In this paper, we propose a new blockiness metric, which is robust to grid distortion, based on the marginal distribution of local wavelet coefficients and saliency information. Experiments for several public image databases showed that the proposed metric provides consistent correlations with subjective blockiness scores and outperforms other existing no-reference blockiness metrics.
With recent advancement in precision farming, the need for variable rate technology has become apparent. variable rate technology can improve the efficiency of farm operations and lessen farm's environmental impac...
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With recent advancement in precision farming, the need for variable rate technology has become apparent. variable rate technology can improve the efficiency of farm operations and lessen farm's environmental impact. To implement effective variable rate applications, it is essential to gather and process information on crop nitrogen level reliably. This research is intended to develop an image-processing method to assess crop nitrogen level based on multi-spectral images of maize plants. This method first removed unnecessary information from the image and then converted the image into a one-dimensional (1D) signal representing the reflectance of the maize plant across leaves. The obtained data was further processed using the wavelet packet transform to find specific patterns that correspond to crop nitrogen stress. To implement wavelet analysis, the 1D signal was deconstructed into packets of narrow frequency bands to find the lowest level approximations at different levels. The maximum wavelet coefficients were identified for interested signal bands and then compared to SPAD meter readings, which were used as the ground-truth corn nitrogen level. Analysis results indicated that the db4 wavelet at a level 8 deconstruction had the highest linear regression coefficient (R-2 = 0.78) with a high correlation coefficient (r = 0.88) for corn nitrogen levels. (c) 2007 Elsevier B.v. All rights reserved.
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