This paper presents an effective method to recognize mechanical sphygmomanometer dial value based on Hough transform. The edges of image are detected by using the canny operator on the binarized image. Geometric features in the image are obtained based on Hough transform, which includes the center of the dial and the vertex of the pointer. After connecting the center of the dial and the vertex of the pointer, a pointer line segment is obtained, and then an accurate dial value is effectively computed according to the deflection angle of the line segment. Experiments show that the method in this paper has wide applicability and can be used to display the waveform of numerical changes more accurately in the video.
OSVOS is one of the best algorithms in video semantic segmentation of single-target. CBAM module is proposed in object detection and classification. This module can improve the performance of the network without adding extra computation. In view of this, this paper proposes an end-to-end network AttnVSS based on attention and VGG-16. CBAM is embedded in the shallow layer of our network. The network is first pre-trained on ImageNet, and then full connection layer is removed and fine-tuned to meet the segmentation requirements. Through the ablation experiment on DAVIS dataset, it is proved that CBAM can be embedded into semantic segmentation networks. At the same time, it can help the network to allocate "attention", focus on the most meaningful part of the input, to achieve faster and more accurate segmentation.
This paper proposes a deinterlacing algorithm based on scene change and content characteristics detection. Firstly, scene changes and video content characteristics are detected. Secondly, optimized motion estimation is performed based on scene change detection results. Thirdly, the image blocks are locally partitioned and different interpolation methods are applied. Experimental results show that the algorithm can not only improve the vertical image resolution with lower algorithm complexity, but also obtain high-quality progressive sequences for interlaced video sequences of different video content.
The intent of video stabilization to remove violent jitter in the video and reduce video distortion. Recently, many excellent video stabilization algorithms have been proposed. However, it is a question to assess the video stabilization performance objectively. In order to solve the problem, we propose a novel objective assessment method for video stabilization performance by computing the distance, distortion and similarity between the stabilized and reference frames. According to published video stabilization database, data sets are built and tested. Finally, the experimental results demonstrate the accuracy of our proposed assessment method and the results are consistent with subjective human eyes.
In order to eliminate the effect of edge aliasing and blurring caused by deinterlacing algorithm based on traditional edge detection, this paper proposes a novel edge direction determination method. On the basic of the preliminary direction judgment obtained from the absolute difference, the reliability judgment of the direction is added. The correlation between the vertical direction of the current pixel and the pixel mean of the remaining four directions is used to obtain a direction determination criterion, and then interpolation is performed by means of median filtering. The experimental results indicate that the PSNR and visual quality of the proposed algorithm outperforms other existing methods, whether its objective evaluation or subjective evaluation.
The basic idea of motion estimation is to divide each frame of an image sequence into a number of non-overlapped blocks, with the assumption that all pixels in a block have the same amount of displacement, then give each block a specificity reference frame. In the specified search range, a block that is most similar to the current block is considered to be a matching block according to a certain matching rule. There are many methods for searching for matching blocks, such as Full Search, Three Step Search, New Three Step Search, Four Step Search, and Diamond Search. These methods effectively improve the search accuracy and make the motion vectors obtained from motion estimation more accurate. This article mainly introduced the basic principles, implementation techniques, advantages and disadvantages, and the optimal and worst search complexity of various algorithms under different search windows of these algorithms. Finally, these algorithms are used to perform motion estimation, and the same method of motion compensation is performed to experimentally illustrate the applicable scenarios of various algorithms.
This paper proposes a digital video stabilization algorithm based on block motion estimation and iterative optimization of global motion parameters (GMP). In the algorithm, a block matching algorithm based on rotation invariant feature is used to estimate the local true motion vectors of the center of all the blocks considered. Using these local motion vectors and their corresponding block positions, a linear system is constructed, and the GMPs are generated by solving them by repeated least squares method. Then, an iterative optimization algorithm is used to improve the overall motion parameters. The experimental results show that, compared with the ever presented digital image stabilization based on circular block matching technique, it can produce more accurate GMP and output more clear and stable image sequences.