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This paper presents a novel blur tolerant decor relation scheme for local phase quantization (LPQ) texture descriptor. As opposed to previous methods, the introduced model can be applied with virtually any kind of blur regardless of the point spread function. The new technique takes also into account the changes in the image characteristics originating from the blur itself. The implementation does...
Image quality is often degraded by blur caused by, for example, misfocused optics or camera motion. Blurring may also deteriorate the performance of computer vision algorithms if the image features computed are sensitive to these degradations. In this paper, we present an image descriptor based on local phase quantization that is robust to centrally symmetric blur. The descriptor referred to as local...
This paper introduces a rotation invariant extension to the blur insensitive local phase quantization texture descriptor. The new method consists of two stages, the first of which estimates the local characteristic orientation, and the second one extracts a binary descriptor vector. Both steps of the algorithm apply the phase of the locally computed Fourier transform coefficients, which can be shown...
In this paper, recognition of blurred faces using the recently introduced Local Phase Quantization (LPQ) operator is proposed. LPQ is based on quantizing the Fourier transform phase in local neighborhoods. The phase can be shown to be a blur invariant property under certain commonly fulfilled conditions. In face image analysis, histograms of LPQ labels computed within local regions are used as a face...
In this paper, we propose novel blur and similarity transform (i.e. rotation, scaling and translation) invariant features for the recognition of objects in images. The features are based on blur invariant forms of the log-polar sampled phase-only bispectrum and are invariant to centrally symmetric blur, including linear motion and out of focus blur. An additional advantage of using the phase-only...
We present a novel search algorithm which is suitable for optimizing functions with a high-dimensional discrete-valued parameter vector. The algorithm is designed to find a function local optimum with the minimal number of evaluated points without requiring function derivatives. The algorithm is applied to frame-level rate-distortion (R-D) optimization using Lagrangian relaxation to the rate constraints...
In this paper, we propose an image registration method, which is invariant to centrally symmetric blur. The method utilizes the phase of the images and has its roots on phase correlation (PC) registration. We show how the even powers of the normalized Fourier transform of an image are invariant to centrally symmetric blur, such as motion or out-of-focus blur. We then use these results to propose blur-invariant...
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