Estimating the Parameters of a Stochastic Geometrical Model for Multiphase Flow Images Using Local Measures

This paper presents a new method for estimating the parameters of a stochastic geometric model for multiphase flow image processing using local measures. Local measures differ from global measures in that they are only based on a small part of a binary image and consequently provide different information of certain properties such as area and perimeter. Since local measures have been shown to be helpful in estimating the typical grain elongation ratio of a homogeneous Boolean model, the objective of this study was to use these local measures to statistically infer the parameters of a more complex non-Boolean model from a sample of observations. An optimization algorithm is used to minimize a cost function based on the likelihood of a probability densityof local measurements. The performance of the model is analysed using numerical experiments and real observations. The errors relative to real images of most of the properties of the model-generated images are less than 2%. The covariance and particle size distribution are also calculated and compared.

An Online Arrangement Method of Difficult Actions in Competitive Aerobics Based on Multimedia Technology

In accordance with the development trend of competitive aerobics’ arrangement structure, this paper studies the online arrangement method of difficult actions in competitive aerobics based on multimedia technology to improve the arrangement effect. RGB image, optical flow image, and corrected optical flow image are taken as the input modes of difficult action recognition network in competitive aerobics video based on top-down feature fusion. The key frames of input modes in competitive aerobics video are extracted by using the key frame extraction method based on subshot segmentation of a double-threshold sliding window and fully connected graph. Through forward propagation, the score vector of video relative to all categories is obtained, and the probability score of probability distribution is obtained after normalization. The human action recognition in competitive aerobics video is completed, and the online arrangement of difficult action in competitive aerobics is realized based on this. The experimental results show that this method has a high accuracy in identifying difficult actions in competitive aerobics video; the online arrangement of difficult actions in competitive aerobics has obvious advantages, meets the needs of users, and has strong practicability.

Huntington's disease treatment with memantine prevented the progression of chorea movement

We have recorded the Huntington’s disease patient’s gait videos at the5 years. It’s treatment with memantine prevented the progression of Clinical symptom. It is recognized to be improved blood flow image in front-temporal lobe of IMP-SPECT.

Multi-scale joint feature network for micro-expression recognition

Micro-expression recognition is a substantive cross-study of psychology and computer science, and it has a wide range of applications (e.g., psychological and clinical diagnosis, emotional analysis, criminal investigation, etc.). However, the subtle and diverse changes in facial muscles make it difficult for existing methods to extract effective features, which limits the improvement of micro-expression recognition accuracy. Therefore, we propose a multi-scale joint feature network based on optical flow images for micro-expression recognition. First, we generate an optical flow image that reflects subtle facial motion information. The optical flow image is then fed into the multi-scale joint network for feature extraction and classification. The proposed joint feature module (JFM) integrates features from different layers, which is beneficial for the capture of micro-expression features with different amplitudes. To improve the recognition ability of the model, we also adopt a strategy for fusing the feature prediction results of the three JFMs with the backbone network. Our experimental results show that our method is superior to state-of-the-art methods on three benchmark datasets (SMIC, CASME II, and SAMM) and a combined dataset (3DB).

Analysis of Gait Flow Image and Gait Gaussian Image Using Extension Neural Network for Gait Recognition

This paper proposes a new technique to recognize human gait by combining model free feature extraction approaches and a classifier. Gait flow image (GFI) and gait Gaussian image (GGI) are the two feature extraction techniques used in combination with ENN. GFI is a gait period based technique, uses optical flow features. So it directly focuses on dynamic part of human gait. GGI is another gait period based technique, computed by applying Gaussian membership function on human silhouettes. Next, ENN has been used as a classifier which combines the extension theory and neural networks. All the study has been done on CASIA-A and OU-ISIR treadmill B databases. The results derived using ENN are compared with SVM (support vector machines) and NN (Nearest neighbor) classifiers. ENN proved to give good accuracy and less iteration as compared to other traditional methods.