Abstract: Disclosed are a capacitance-to-digital conversion circuit, a capacitance-to-digital conversion method and an electronic chip. The capacitance-to-digital conversion circuit includes a first module, a comparator and an adaptive range-shift module; the first module includes a successive approximation unit, a first adder, a first digital-to-analog converter, a second adder, a third adder and an integrating unit. The first module further includes a second digital-to-analog converter connected to the third adder. The comparator, the adaptive range-shift module and the first adder are connected in series and the comparator is connected to the second digital-to-analog converter. By the present application, the adverse influence caused by the parasitic and interference is well avoided, the capacitance-to-digital conversion circuit may work in a harsh environment, the robustness of the circuit is significantly improved and the application range of the circuit is expanded.

Abstract: A multimodal remote sensing image matching method and system integrate different local feature descriptors for automatic matching of multimodal remote sensing images. First, a local feature descriptor, such as the Histogram of Oriented Gradient (HOG), the local self-similarity (LSS), or the Speeded-Up Robust Feature (SURF), is extracted for each pixel of an image to form a pixel-wise feature representation map. Then, the three-dimensional Fourier transform (namely 3D FFT) is used to establish a fast matching similarity metric in a frequency domain based on the feature representation map, followed by a template matching scheme to achieve control points (CP) between images. In addition, the novel pixel-wise feature representation technique named channel features of orientated gradients (CFOG), which outperforms the pixel-wise feature representation methods based on the traditional local descriptors (e.g., HOG, LSS and SURF) in both matching performance and computational efficiency.

Abstract: A multimodal remote sensing image matching method and system integrate different local feature descriptors for automatic matching of multimodal remote sensing images. First, a local feature descriptor, such as the Histogram of Oriented Gradient (HOG), the local self-similarity (LSS), or the Speeded-Up Robust Feature (SURF), is extracted for each pixel of an image to form a pixel-wise feature representation map. Then, the three-dimensional Fourier transform (namely 3D FFT) is used to establish a fast matching similarity metric in a frequency domain based on the feature representation map, followed by a template matching scheme to achieve control points (CP) between images. In addition, the novel pixel-wise feature representation technique named channel features of orientated gradients (CFOG), which outperforms the pixel-wise feature representation methods based on the traditional local descriptors (e.g., HOG, LSS and SURF) in both matching performance and computational efficiency.