Current Issue : October - December Volume : 2016 Issue Number : 4 Articles : 5 Articles
This paper presents a video summarization method that is specifically for the static summary of consumer videos.\nConsidering that the consumer videos usually have unclear shot boundaries and many low-quality or meaningless\nframes, we propose a two-step approach where the first step skims a video and the second step performs\ncontent-aware clustering with keyframe selection. Specifically, the first step removes most of redundant frames that\ncontain only little new information by employing the spectral clustering method with color histogram features. As a\nresult, we obtain a condensed video that is shorter and has clearer temporal boundaries than the original. In the\nsecond step, we perform rough temporal segmentation and then apply refined clustering for each of the temporal\nsegments, where each frame is represented by the sparse coding of SIFT features. The keyframe selection from each\ncluster is based on the measure of representativeness and visual quality of frames, where the representativeness is\ndefined from the sparse coding and the visual quality is the combination of contrast, blur, and image skew measures.\nThe problem of keyframe selection is to find the frames that have both representativeness and high quality, which is\nformulated as an optimization problem. Experiments on videos with various lengths show that the resulting\nsummaries closely follow the important contents of videos....
This paper proposes a representative pixel (RP) extraction algorithm and chrominance image recovery algorithm for\nthe colorization-based digital image coding. The colorization-based coding methods reduce the color information of\nan image and achieve higher compression ratio than JPEG coding; however, they take much more computing time. In\norder to achieve low computational cost, this paper proposes the algorithm using the set of multiple-resolution\nimages obtained by colorization error minimizing method. This algorithm extracts RPs from each resolution image\nand colorizes each resolution image utilizing a lower resolution color image, which leads to the reduction of the\nnumber of RPs and computing time. Numerical examples show that the proposed algorithm extracts the RPs and\nrecovers the color image fast and effectively....
Besides a high distinctiveness, robustness (or invariance) to image degradations is very desirable for texture feature\nextraction methods in real-world applications. In this paper, focus is on making arbitrary texture descriptors invariant\nto blur which is often prevalent in real image data. From previous work, we know that most state-of-the-art texture\nfeature extraction methods are unable to cope even with minor blur degradations if the classifier�s training stage is\nbased on idealistic data. However, if the training set suffers similarly from the degradations, the obtained accuracies\nare significantly higher. Exploiting that knowledge, in this approach the level of blur of each image is increased to a\ncertain threshold, based on the estimation of a blur measure. Experiments with synthetically degraded data show that\nthe method is able to generate a high degree of blur invariance without loosing too much distinctiveness. Finally, we\nshow that our method is not limited to ideal Gaussian blur....
In this paper, rotation invariance and the influence of rotation interpolation methods on texture recognition using\nseveral local binary patterns (LBP) variants are investigated.\nWe show that the choice of interpolation method when rotating textures greatly influences the recognition capability.\nLanczos 3 and B-spline interpolation are comparable to rotating the textures prior to image acquisition, whereas the\nrecognition capability is significantly and increasingly lower for the frequently used third order cubic, linear and nearest\nneighbour interpolation. We also show that including generated rotations of the texture samples in the training data\nimproves the classification accuracies. For many of the descriptors, this strategy compensates for the shortcomings of\nthe poorer interpolation methods to such a degree that the choice of interpolation method only has a minor impact.\nTo enable an appropriate and fair comparison, a new texture dataset is introduced which contains hardware and\ninterpolated rotations of 25 texture classes. Two new LBP variants are also presented, combining the advantages of\nlocal ternary patterns and Fourier features for rotation invariance....
Computational stereo is in the fields of computer vision and photogrammetry. In the computational stereo and\nsurface reconstruction paradigms, it is very important to achieve appropriate epipolar constraints during the\ncamera-modeling step of the stereo image processing. It has been shown that the epipolar geometry of linear\npushbroom imagery has a hyperbola-like shape because of the non-coplanarity of the line of sight vectors. Several\nstudies have been conducted to generate resampled epipolar image pairs from linear pushbroom satellites images;\nhowever, the currently prevailing methods are limited by their pixel scales, skewed axis angles, or disproportionality\nbetween x-parallax disparities and height. In this paper, a practical and unified piecewise epipolar resampling\nmethod is proposed to generate stereo image pairs with zero y-parallax, a square pixel scale, and proportionality\nbetween x-parallax disparity and height. Furthermore, four criteria are suggested for performance evaluations of the\nprevailing methods, and experimental results of the method are presented based on the suggested criteria. The\nproposed method is shown to be equal to or an improvement upon the prevailing methods....
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