Diagnosing aerosols in extrasolar giant planets with cross-correlation function of water bands

Transmission spectroscopy with ground-based, high-resolution instruments provides key insight into the composition of exoplanetary atmospheres. Molecules such as water and carbon monoxide have been unambiguously identified in hot gas giants through cross-correlation techniques. A maximum in the cross-correlation function (CCF) is found when the molecular absorption lines in a binary mask or model template match those contained in the planet. Here, we demonstrate how the CCF method can be used to diagnose broadband spectroscopic features such as scattering by aerosols in high-resolution transit spectra. The idea consists in exploiting the presence of multiple water bands from the optical to the near-infrared. We have produced a set of models of a typical hot Jupiter spanning various conditions of temperature and aerosol coverage. We demonstrate that comparing the CCFs of individual water bands for the models constrains the presence and the properties of the aerosol layers. The contrast difference between the CCFs of two bands can reach ~100 ppm, which could be readily detectable with current or upcoming high-resolution stabilized spectrographs spanning a wide spectral range, such as ESPRESSO, CARMENES, HARPS-N+GIANO, HARPS+NIRPS, SPIRou, or CRIRES+.

Paper Currency Image Features Extraction and Recognition Based on Gabor Filter

The paper currency image recognition method based on Gabor filter set is discussed in this paper. According to the paper currency image features, the suitable parameters of Gabor filter set are selected for the extraction of paper currency characteristics, the multi-scale and multi-directional texture characteristics of paper currency image are gotten; then the texture images are meshed, and the row and column projection sum of grid pixels' average grey are calculated, finally, the template match method based on grid projection characteristics is used for paper currency recognition. Experiments show that, this method has strong anti-interference ability, it can raise the recognition rate of old or dirty paper currency greatly, and it costs little time.

Confidence Based Hierarchical Online Ensemble Visual Tracking

In order to adapt to the target appearance changes during visual tracking, feature model needs to be updated by online learning. However, online adaptive methods suffer from the drifting problem: error data are used for updating and will finally lead to tracking failure. In this paper, we propose a novel hierarchical online ensemble tracking method. Optical flow tracker is employed to predict the rough location. Online learning classifier is employed to detect the target. Template match is used to filter the data for updating. All these parts are combined together hierarchically by their confidence to ensure the stability of online learning and tracking. In contrast to the individual online learning and semi-supervised online learning, our method show more adaptability and stability. We demonstrate the performance on several public video sequences, which shows the improvement of our method over other online tracking approaches.

Real-Time Video Stabilization Based on Smoothing Feature Trajectories

In this paper, a real-time video stabilization algorithm based on smoothing feature trajectories is proposed. For each input frame, our approach generates multiple feature trajectories by performing inter-frame template match and optical flow. A Kalman filter is then performed to smooth these feature trajectories. Finally, at the stage of image composition, the motion consistency of the feature trajectory is considered for achieving a visually plausible stabilized video. The proposed method can offer real-time video stabilization and its removed the delays for caching coming images. Experiments show that our approach can offer real-time stabilizing for videos with various complicated scenes.