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We propose a framework to generate customized summarizations of visual data collections, such as collections of images, materials, 3D shapes, and 3D scenes. We assume that the elements in the visual data collections can be mapped to a set of vectors in a feature space, in which a fitness score for each element can be defined, and we pose the problem of customized summarizations as selecting a subset of these elements. We first describe the design choices a user should be able to specify for modeling customized summarizations and propose a corresponding user interface. We then formulate the problem as a constrained optimization problem with binary variables and propose a practical and fast algorithm based on the alternating direction method of multipliers (ADMM). Our results show that our problem formulation enables a wide variety of customized summarizations, and that our solver is both significantly faster than state‐of‐the‐art commercial integer programming solvers and produces better solutions than fast relaxation‐based solvers. [ABSTRACT FROM AUTHOR]

Rational design of high efficient and low cost oxygen evolution reaction (OER) electrodes is critical for electrochemical water splitting. Herein, we report Reevesite Ni−Fe layered double hydroxide (NiFe‐(CO32−)‐LDH) nanosheet arrays as novel OER catalysts in alkaline electrolyte. These self‐supported nanosheet arrays with a composition of Ni6Fe2(CO3)(OH)16 ⋅ 4H2O on Ni foam (NF) were synthesized by a one‐pot hydrothermal method, in which NF served as both the conductive substrate and Ni source. The morphology and OER activity can be tuned by iron concentration. In 1 M KOH, the optimized NiFe‐(CO32−)‐LDH catalyst only required overpotentials of 228 and 264 mV to deliver current densities of 10 and 100 mA cm−2, respectively, with a small Tafel slope of 37 mV dec−1. Remarkably, this catalyst also showed outstanding durability by long‐term operation for 20 h with 96 % retention. Our work provides attractive cost‐effective 3D catalysts for efficient and robust water oxidation at alkaline media. [ABSTRACT FROM AUTHOR]

The bilateral filter (BF) has showed great effectiveness for a variety of problems. However, its brute‐force implementation is time consuming. One way of accelerating a BF is to approximate the nonlinear range kernel of the BF by a set of linear time shiftable kernels. To achieve this goal, only finite values of the kernel of the BF have been used to perform smoothing due to the quantisation of digital images. Thus, the filtering results are not changed by substituting the range kernel with the function having the same values at finite discrete points. The Lagrange interpolation polynomial can exactly pass through predefined points and therefore can be employed to replace original kernels for accurate by accelerating the BF. To speed up the BF at the cost of small approximation error, two approximation methods are proposed to obtain the optimal fitting polynomial. The performance of the proposed method is validated by extensive experiments. [ABSTRACT FROM AUTHOR]

The bilateral filter (BF) as an edge‐preserving lowpass filter is a valuable tool in various image processing tasks, including noise reduction and dynamic range compression. However, its computational cost is too high to apply in the real‐time processing tasks as the range kernel, which acts on the pixel intensities, making the averaging process nonlinear and computationally intensive, particularly when the spatial filter is large. Using the well‐known Hermite polynomials, a BF accelerating method is proposed, which reduces the computational complexity from O(r2n) to O(n), where r denotes the filter size of a BF and n is the total number of pixels in an image. [ABSTRACT FROM AUTHOR]

Due to the heavy computation cost, the bilateral filter cannot be applied to real‐time applications. To deal with this problem, many range kernel approximation based acceleration schemes have been proposed. However, all these methods use predefined basis function and assign the same penalisation for all range values to fulfil range kernel approximation. These constraints block from further improving the filtering accuracy and speed. Employing weighted variable projection, a new acceleration method which achieves state‐of‐the‐art performance is proposed. This is done by: (i) unleashing the constraint of using fixed basis function; (ii) exploiting the colour distribution information of the input image to perform a weighted approximation of the range kernel. Experiments demonstrate the superiority of the proposed method in gaining more accurate filtering results efficiently. [ABSTRACT FROM AUTHOR]