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Image segmentation using a region-based active contour model could present difficulties when its noise distribution is unknown. To overcome this problem, this paper proposes a novel region-based model for the segmentation of objects or structures in images by introducing a local similarity factor, which relies on the local spatial distance within a local window and local intensity difference to improve the segmentation results. By using this local similarity factor, the proposed method can accurately extract the object boundary while guaranteeing certain noise robustness. Furthermore, the proposed algorithm completely avoids the pre-processing steps typical of region-based contour model segmentation, resulting in a higher preservation of image details. Experiments performed on synthetic images and real word images demonstrate that the proposed algorithm, as compared with the state-of-art algorithms, is more efficient and robust to higher noise level manifestations in the images.

The development of novel electrochemical energy storage devices is a grand challenge. Here, an aqueous ammonium‐ion hybrid supercapacitor (A‐HSC), consisting of a layered δ‐MnO2 based cathode, an activated carbon cloth anode, and an aqueous (NH4)2SO4 electrolyte is developed. The aqueous A‐HSC demonstrates an ultrahigh areal capacitance of 1550 mF cm−2 with a wide voltage window of 2.0 V. An amenable peak areal energy density (861.2 μWh cm−2) and a decent capacitance retention (72.2% after 5000 cycles) are also achieved, surpassing traditional metal‐ion hybrid supercapacitors. Ex situ characterizations reveal that NH4+ intercalation/deintercalation in the layered δ‐MnO2 is accompanied by hydrogen bond formation/breaking. This work proposes a new paradigm for electrochemical energy storage.

a b s t r a c tLarge scale 3D shape retrieval has become an important research direction in content based 3D shape retrieval. To promote this research area, two Shape Retrieval Contest (SHREC) tracks on large scale com prehensive and sketch based 3D model retrieval have been organized by us in 2014. Both tracks were based on a unified large scale benchmark that supports multimodal queries (3D models and sketches). This benchmark contains 13680 sketches and 8987 3D models, divided into 171 distinct classes. It was compiled to be a superset of existing benchmarks and presents a new challenge to retrieval methods as it comprises generic models as well as domain specific model types. Twelve and six distinct 3D shape retrieval methods have competed with each other in these two contests, respectively. To measure and compare the performance of the participating and other promising Query by Model or Query by Sketch 3D shape retrieval methods and to solicit state of the art approaches, we perform a more comprehensive comparison of twenty six (eighteen originally participating algorithms and eight additional state of the art or new) retrieval methods by evaluating them on the common benchmark. The benchmark, results, and evaluation tools are publicly available at our websites