With the development of solar radio spectrometer, a large number of observational data has been obtained and the manual detection is difficult to reach the research needs. An automatic detection method of solar radio burst using kmeans clustering was presented in this paper. K-means clustering is introduced to classify the burst points in solar radio spectrum, and it can do better in high spectral and time resolution spectrometer. The experimental results show that the proposed method is effective.
With the development of solar radio spectrometer, it is difficult to process a large number of observed data quickly by manual detection method. An automatic detection method of solar radio burst based on Otsu binarization is proposed in this paper. In this method, channel normalization is used to denoise the original solar radio image. Through experimental comparison, Otsu method is selected as a binary method of solar radio spectrum, and Open and Close operations are used to smooth the binary image. Experiments show that the proposed method for automatic detection of solar radio bursts is effective
In recent several years, object tracking based on convolution neural network has gained more and more attention. The initialization and update of convolution filters can directly affect the precision of object tracking effective. In this paper, a novel object tracking via an enhanced online convolution neural network without offline training is proposed, which initializes the convolution filters by a k-means++ algorithm and updates the filters by an error back-propagation. The comparative experiments of 7 trackers on 15 challenging sequences showed that our tracker can perform better than other trackers in terms of AUC and precision.
Feature extraction is very important for robust and real-time tracking. Compressive sensing provided a technical support for real-time feature extraction. However, all existing compressive tracking were based on compressed Haar-like feature, and how to compress many more excellent high-dimensional features is worth researching. In this paper, a novel compressed normalized block difference feature (CNBD) was proposed. For resisting noise effectively in a highdimensional normalized pixel difference feature (NPD), a normalized block difference feature extends two pixels in the original formula of NPD to two blocks. A CNBD feature can be obtained by compressing a normalized block difference feature based on compressive sensing theory, with the sparse random Gaussian matrix as the measurement matrix. The comparative experiments of 7 trackers on 20 challenging sequences showed that the tracker based on CNBD feature can perform better than other trackers, especially than FCT tracker based on compressed Haar-like feature, in terms of AUC, SR and Precision.
Both robustness and real-time are very important for the application of object tracking under a real environment. The focused trackers based on deep learning are difficult to satisfy with the real-time of tracking. Compressive sensing provided a technical support for real-time tracking. In this paper, an object can be tracked via a multi-block local binary pattern feature. The feature vector was extracted based on the multi-block local binary pattern feature, which was compressed via a sparse random Gaussian matrix as the measurement matrix. The experiments showed that the proposed tracker ran in real-time and outperformed the existed compressive trackers based on Haar-like feature on many challenging video sequences in terms of accuracy and robustness.
Moving object detection is the fundamental task in machine vision applications. However, moving cast shadows detection is one of the major concerns for accurate video segmentation. Since detected moving object areas are often contain shadow points, errors in measurements, localization, segmentation, classification and tracking may arise from this. A novel shadow elimination algorithm is proposed in this paper. A set of suspected moving object area are detected by the adaptive Gaussian approach. A model is established based on shadow optical properties analysis. And shadow regions are discriminated from the set of moving pixels by using the properties of brightness, chromaticity and texture in sequence.