Dimensional mismatch between a low-resolution (LR) surveillance face image and its high-resolution (HR) template makes recognition very difficult. A novel method called coupled cross-regression (CCR) is proposed to deal with this problem. Instead of processing in the original observing space directly, CCR projects LR and HR face images into a unified low-embedding feature space. Spectral regression is employed to improve generalization performance and reduce computational complexity. Meanwhile, cross-regression is developed to utilize HR embedding to increase the information of the LR space, thus improving the recognition performance. Experiments on the FERET and CMU PIE face database show that CCR outperforms existing structure-based methods in terms of recognition rate as well as computational complexity.
Color harmonization is an artistic technique to adjust a set of colors in order to enhance their visual harmony so that they are aesthetically pleasing in terms of human visual perception. We present a new color harmonization method that treats the harmonization as a function optimization. For a given image, we derive a cost function based on the observation that pixels in a small window that have similar unharmonic hues should be harmonized with similar harmonic hues. By minimizing the cost function, we get a harmonized image in which the spatial coherence is preserved. A new matching function is proposed to select the best matching harmonic schemes, and a new component-based preharmonization strategy is proposed to preserve the hue distribution of the harmonized images. Our approach overcomes several shortcomings of the existing color harmonization methods. We test our algorithm with a variety of images to demonstrate the effectiveness of our approach.
Two perspective images of a single scene taken by uncalibrated cameras are related by a fundamental matrix, which is the key to solving many computer vision problems. We present a new robust and accurate algorithm to estimate the fundamental matrix, which is suitable for wide baseline stereo pairs. The estimation algorithm includes two stages. The first stage is that more affine invariant matching points are determined by a propagation process. In this stage, an affine iterative optimization model is used to accurately detect matching points at the subpixel level. And a new matching cost function is proposed that is more robust to outliers and more effective for images with single texture and repetitive texture. In the second stage, a resampling model based on the posterior probability is presented in order to optimize the fundamental matrix with more accurate matching points. The experiment results show that our algorithm is very robust to outliers, and the fundamental matrix with high accuracy can be estimated.
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