Existing feature descriptor-based methods on retinal image registration are mainly based on scale-invariant feature transform (SIFT) or partial intensity invariant feature descriptor (PIIFD). While these descriptors are often being exploited, they do not work very well upon unhealthy multimodal images with severe diseases. Additionally, the descriptors demand high dimensionality to adequately represent the features of interest. The higher the dimensionality, the greater the consumption of resources (e.g. memory space). To this end, this paper introduces a novel registration algorithm coined low-dimensional step pattern analysis (LoSPA), tailored to achieve low dimensionality while providing sufficient distinctiveness to effectively align unhealthy multimodal image pairs. The algorithm locates hypotheses of robust corner features based on connecting edges from the edge maps, mainly formed by vascular junctions. This method is insensitive to intensity changes, and produces uniformly distributed features and high repeatability across the image domain. The algorithm continues with describing the corner features in a rotation invariant manner using step patterns. These customized step patterns are robust to non-linear intensity changes, which are wellsuited for multimodal retinal image registration. Apart from its low dimensionality, the LoSPA algorithm achieves about twofold higher success rate in multimodal registration on the dataset of severe retinal diseases when compared to the top score among state-of-the-art algorithms.

This work reported a well-fabricated PtSn/SiO 2 catalyst prepared by a modified two-step sol-gel (MTSG) method. The adoption of strong electrostatic adsorption (SEA) and sol-gel (SG) method could give both a significantly high Pt dispersion and a large amount of Lewis acid sites. The homogenous distribution of Sn species could not only provide more opportunity for the Pt precursor to disperse on the support by forming Pt-(O-Sn^) y 2Ày analogous species, but also enhance the synergy between Pt and Sn species.Consequently, an excellent activity was achieved in the hydrogenation of acetic acid (AcOH) with a conversion of 100% and ethanol (EtOH) selectivity of 93%. Investigations on the effect of Sn/Pt molar ratio showed that the dispersion of Pt decreases obviously with the increasing Sn/Pt ratio due to the geometric or electronic effects caused by SnO x species. A balancing effect between Pt active sites and Lewis acid sites was found to be responsible for the superior catalytic performance in AcOH hydrogenation. Moreover, a parallel reaction path model was proposed for the hydrogenation of AcOH over PtSn/SiO 2 catalyst, in which ethanol and ethyl acetate (AcOEt) are formed competitively through the adsorbed ethoxy intermediate. † Electronic supplementary information (ESI) available: Fig. S1 shows the N 2 adsorption-desorption isotherms and pore size distribution of reduced catalysts. Fig. S2 displays the HAADF-STEM and EDX elemental-mapping images of the reduced PtSn1.6/SiO 2 -MTSG catalyst. Fig. S3 shows the TEM images of reduced PtSnx/SiO 2 -MTSG catalysts. Fig. S4 represents the deconvolution of the t about FTIR spectra of chemisorbed pyridine on reduced catalysts. Fig. S5 displays the hydrogenation performance of AcOH over PtSn1.6/SiO 2 -MTSG catalyst as a function of WHSV (AcOH) . Table S1 shows the tted results of H 2 -TPR experiments of catalysts. Table S2 displays the integral quantity of Lewis acid sites of reduced catalysts. Table S3 illustrates the hydrogenation performance of AcOH over PtSn1.6/SiO 2 -MTSG catalysts. See

Leaf image identification is a significant and challenging research work. Here, a unified multi‐scale method is proposed to capture leaf geometric information for plant leaf classification and image retrieval. For each point on the leaf contour, the unified multi‐scale method utilises a simple yet effective three‐step strategy to locate corresponding neighbour points. The descriptor extracted using these neighbour points can provide a coarse‐to‐fine description of leaf contours and is of multi‐scale characteristic intrinsically. More importantly, there is no scale parameter to be adjusted in the method, and hence no optimisation procedure is required. The proposed method is applied to three well‐known contour features to capture geometric information of leaves, including angle, arch‐height, and triangle‐area representation. FFT is applied on the features in unified multi‐scale method for convenient and fast leaf matching. Leaf classification and image retrieval experiments are conducted on four challenging leaf datasets to test the proposed method and evaluated using three standard performance evaluation metrics. The experimental results and comparisons with the state‐of‐art methods indicate that the unified multi‐scale method has remarkable performance.

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