Stereo matching algorithms usually consist of four steps, including matching cost calculation, matching cost aggregation, disparity calculation, and disparity refinement. Existing CNN-based methods only adopt CNN to solve parts of the four steps, or use different networks to deal with different steps, making them difficult to obtain the overall optimal solution. In this paper, we propose a network architecture to incorporate all steps of stereo matching. The network consists of three parts. The first part calculates the multi-scale shared features. The second part performs matching cost calculation, matching cost aggregation and disparity calculation to estimate the initial disparity using shared features. The initial disparity and the shared features are used to calculate the feature constancy that measures correctness of the correspondence between two input images. The initial disparity and the feature constancy are then fed to a subnetwork to refine the initial disparity. The proposed method has been evaluated on the Scene Flow and KITTI datasets. It achieves the state-of-the-art performance on the KITTI 2012 and KITTI 2015 benchmarks while maintaining a very fast running time.

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Salient region detection is a challenging problem and an important topic in computer vision. It has a wide range of applications, such as object recognition and segmentation. Many approaches have been proposed to detect salient regions using different visual cues, such as compactness, uniqueness, and objectness. However, each visual cue-based method has its own limitations. After analyzing the advantages and limitations of different visual cues, we found that compactness and local contrast are complementary to each other. In addition, local contrast can very effectively recover incorrectly suppressed salient regions using compactness cues. Motivated by this, we propose a bottom-up salient region detection method that integrates compactness and local contrast cues. Furthermore, to produce a pixel-accurate saliency map that more uniformly covers the salient objects, we propagate the saliency information using a diffusion process. Our experimental results on four benchmark data sets demonstrate the effectiveness of the proposed method. Our method produces more accurate saliency maps with better precision-recall curve and higher F-Measure than other 19 state-of-the-arts approaches on ASD, CSSD, and ECSSD data sets.

• A triboelectric nanogenerator (TENG) and a glucose fuel cell (GFC) were separately designed to harvest biomechanical energy from body motion and biochemical energy from glucose molecules. • A hybrid energy-harvesting system (HEHS) which consisted of TENG and GFC was developed successfully, and it can simultaneously harvest biomechanical energy and biochemical energy. ABSTRACT Various types of energy exist everywhere around us, and these energies can be harvested from multiple sources to power micro-/nanoelectronic system and even personal electronic products. In this work, we proposed a hybrid energy-harvesting system (HEHS) for potential in vivo applications. The HEHS consisted of a triboelectric nanogenerator and a glucose fuel cell for simultaneously harvesting biomechanical energy and biochemical energy in simulated body fluid. These two energy-harvesting units can work individually as a single power source or work simultaneously as an integrated system. This design strengthened the flexibility of harvesting multiple energies and enhanced corresponding electric output. Compared with any individual device, the integrated HEHS outputs a superimposed current and has a faster charging rate. Using the harvested energy, HEHS can power a calculator or a green light-emitting diode pattern. Considering the widely existed biomechanical energy and glucose molecules in the body, the developed HEHS can be a promising candidate for building in vivo self-powered healthcare monitoring system.

Diffusion-based salient region detection has recently received intense research attention. In this paper, we present some effective improvements concerning two important aspects of diffusion-based methods: the construction of the diffusion matrix and the seed vector. First, we construct a two-layer sparse graph, which is generated by connecting each node to its neighboring nodes and the most similar node that shares common boundaries with its neighboring nodes. Compared with the most frequently used two-layer neighborhood graph, our graph not only effectively uses local spatial relationships, but also removes dissimilar redundant nodes. Second, we use the spatial variance of superpixel clusters to obtain the seed vector and, compared with the previously most-used boundary prior, our approach can better distinguish saliency seeds from the background seeds, especially when salient objects appear near the image boundaries. Finally, we calculate two preliminary saliency maps using the saliency and background seed vectors, and more accurate results are obtained using the manifold ranking diffusion method. Integrating these two diffusion-based saliency maps, we obtain the final saliency map. Extensive experiments in which we compare our method with 20 existing state-of-the-art methods on five benchmark data sets: ASD, DUT-OMRON, ECSSD, MSRA5K, and MSRA10K, show that the proposed method performs better in terms of various evaluation metrics.

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