Algorithm of 3D Virtual Reconstruction of Ancient Buildings in Qing Dynasty Based on Image Sequence

Aiming at the problem of low accuracy and poor integrity of traditional Qing Dynasty ancient architecture 3D virtual reconstruction algorithm, a 3D virtual reconstruction algorithm of Qing Dynasty ancient architecture based on image sequence is proposed. Acquire the sequence images of ancient buildings in the Qing Dynasty through the pinhole camera model, analyze the projective space and reconstruction space of the sequence images, redefine the similarity measurement coefficient according to the improved 2DPCA-SIFT feature matching algorithm, match the feature points of the ancient architecture images in the Qing Dynasty, and use random sampling to be consistent. The algorithm solves the basic matrix, removes the interference error in the image reconstruction process, and realizes the design of the three-dimensional reconstruction algorithm through image sequence fusion. The experimental results show that, compared with the existing methods, the completeness of the three-dimensional virtual reconstruction 3D model of ancient Qing Dynasty buildings constructed by the designed algorithm is 87.26% on average, and the completeness and accuracy of the 3D model construction of the subparts of the ancient Qing Dynasty buildings of this method are better. The height of the building fully shows that the designed building has good performance in the construction of the three-dimensional model of ancient buildings in the Qing Dynasty.

Image Life Trails Based On Contrast Reduction Models For Face Counter-Spoofing

Natural face images are both content and context-rich, in the sense that they carry significant immersive information via depth cues embedded in the form of self-shadows or a space varying blur. Images of planar face prints, on the other hand, tend to have lower contrast and also suppressed depth cues. In this work, a solution is proposed, to detect planar print spoofing by enhancing self-shadow patterns present in face images. This process is facilitated and siphoned via the application of a non-linear iterative functional map, which is used to produce a contrast reductionist image sequence, termed as an image life trail. Subsequent images in this trail tend to have lower contrast in relation to the previous iteration. Differences taken across this image sequence help in bringing out the self-shadows already present in the original image. On a client specific mode, when the subjects and faces are registered, secondary statistics which capture the prominence of self-shadow information, indicate that planar print-images tend to have highly suppressed self-shadows when compared with natural face images. An elaborate tuning procedure, based on a reduced set of training images was developed to first identify the optimal parameter set and then adapt the feature-vectors so that the error-rates were minimized for a specific dataset. Overall mean error rate for the calibration-set (reduced CASIA dataset) was found to be 0.267% and the error rates for other datasets such OULU-NPU and CASIA-SURF were 0.17% and 0.73% respectively

Research on Multimodal Music Emotion Recognition Method Based on Image Sequence

The work of music performance system is to control the light change by identifying the emotional elements of music. Therefore, once the identification error occurs, it will not be able to create a good stage effect. Therefore, a multimodal music emotion recognition method based on image sequence is studied. The emotional characteristics of music are analyzed, including acoustic characteristics, melody characteristics, and audio characteristics, and the feature vector is constructed. The recognition and classification model based on neural network is trained, the weight and threshold of each layer are adjusted, and then the feature vector is input into the trained model to realize the intelligent recognition and classification of multimodal music emotion. The threshold of the starting point range of a specific humming note is given by the center clipping method, which is used to eliminate the low amplitude part of the humming note signal, extract the short-time spectral structure features and envelope features of the pitch, and complete the multimodal music emotion recognition. The results show that the calculated kappa coefficient k is greater than 0.75, which shows that the recognition and classification results are in good agreement with the actual results, and the classification and recognition accuracy is high.

Spatiotemporal distribution of the temperature field inside the thin heated foil during impacting liquid spray

The spatiotemporal distribution of the temperature inside a constantan foil during impacting spray is resolved experimentally in the present work. The received infrared image sequence will be used to find the local and average heat transfer coefficient of the foil. In the future, the results obtained will be used to calculate the heat flux in the region of the contact line of each drop.

Automatic Registration of Mobile Mapping System Lidar Points and Panoramic-Image Sequences by Relative Orientation Model

To register mobile mapping system (MMS) lidar points and panoramic-image sequences, a relative orientation model of panoramic images (PROM) is proposed. The PROM is suitable for cases in which attitude or orientation parameters are unknown in the panoramic-image sequence. First, feature points are extracted and matched from panoramic-image pairs using the SURF algorithm. Second, these matched feature points are used to solve the relative attitude parameters in the PROM. Then, combining the PROM with the absolute position and attitude parameters of the initial panoramic image, the MMS lidar points and panoramic-image sequence are registered. Finally, the registration accuracy of the PROM method is assessed using corresponding points manually selected from the MMSlidar points and panoramic-image sequence. The results show that three types of MMSdata sources are registered accurately based on the proposed registration method. Our method transforms the registration of panoramic images and lidar points into image feature-point matching, which is suitable for diverse road scenes compared with existing methods.

Sports Intelligent Assistance System Based on Deep Learning

Traditional sports aid systems analyze sports data via sensors and other types of equipment and can support athletes with retrospective analysis, but they require several sensors and have limited data. This paper examines a sports aid system that uses deep learning to recognize, review, and analyze behaviors through video acquisition and intelligent video sequence processing. This paper’s primary research is as follows: (1) With an eye on the motion assistance system’s application scenarios, the network topology and implementation details of the two-stage Faster R-CNN and the single-stage YOLOv3 target detection algorithms are investigated. Additionally, training procedures are used to enhance the algorithm’s detection performance and training speed. (2) To address the issue of target detection techniques’ low detection performance in complicated backgrounds, an improved scheme from Faster R-CNN is proposed. To begin, a new approach replaces the VGG-16 network in the previous algorithm with a ResNet-101 network. Second, an expansion plan for the dataset is provided. (3) To address the short duration of action video and the high correlation of image sequence data, we present an action recognition method based on LSTM. To begin, we will present a motion decomposition scheme and evaluation index based on the key transaction frame in order to simplify the motion analysis procedure. Second, the spatial features of the frame images are extracted using a convolutional neural network. Besides, the spatial and temporal aspects of the image sequence are fused using a two-layer bidirectional LSTM network. The algorithm suggested in this research has been validated using a golf experiment, and the results are favorable.