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.
Face detection is one of the important topics in computer vision research and is the basis of many applications. A face detection algorithm based on improved Multi-Task Convolution Neural Network (MTCNN) is proposed in this paper. To increase the accuracy of eye location in complex situations, this method improves the network structure of MTCNN, builds a neural network model based on MTCNN using TensorFlow, and cascades an eye regression network. The Face-Net neural network model was used for training, and the obtained training model was used for detection. Experiments have shown that the accuracy on the LFW dataset is 0.9963 and the accuracy on the YouTube Faces DB dataset is 0.9512.
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
The performance of vector graphics render has always been one of the key elements in mobile devices and the most important step to improve the performance is to enhance the efficiency of polygon fill algorithms. In this paper, we proposed a new and more efficient polygon fill algorithm based on the scan line algorithm and Grid Fill Algorithm (GFA). First, we elaborated the GFA through solid fill. Second, we described the techniques for implementing antialiasing and self-intersection polygon fill with GFA. Then, we discussed the implementation of GFA based on the gradient fill. Generally, compared to other fill algorithms, GFA has better performance and achieves faster fill speed, which is specifically consistent with the inherent characteristics of mobile devices. Experimental results show that better fill effects can be achieved by using GFA.
Proc. SPIE. 9875, Eighth International Conference on Machine Vision (ICMV 2015)
KEYWORDS: Light sources, Detection and tracking algorithms, Optical properties, Cameras, Image segmentation, Video, Chromium, Machine vision, Scene classification, Probability theory, Light, RGB color model
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.