Diyuan Guan

Gait recognition is a distinctive biometric technique that can identify pedestrians by their walking patterns from considerable distances. A critical challenge in gait recognition lies in effectively acquiring discriminative spatial-temporal representations from silhouettes that exhibit invariance to disturbances. In this paper, we present a novel gait recognition network by aggregating features in the spatial-temporal and view domains, which consists of two-path spatial-temporal feature fusion module and view embedding module. Specifically, two-path spatial-temporal feature fusion module firstly utilizes multi-scale feature extraction (MSFE) to enrich the input features with multiple convolution kernels of various sizes. Then, frame-level spatial feature extraction (FLSFE) and multi-scale temporal feature extraction (MSTFE) are parallelly constructed to capture spatial and temporal gait features of different granularities and these features are fused together to obtain muti-scale spatial-temporal features. FLSFE is designed to extract both global and local gait features by employing a specially designed residual operation. Simultaneously, MSTFE is applied to adaptively interact multi-scale temporal features and produce suitable motion representations in temporal domain. Taking into account the view information, we introduce a view embedding module to reduce the impact of differing viewpoints. Through the extensive experimentation over CASIA-B and OU-MVLP datasets, the proposed method has achieved superior performance to the other state-of-the-art gait recognition approaches.

To facilitate measuring crystal sizes during batch crystallization at high-solid concentrations by using an invasive imaging system, an in situ imaging measurement strategy based on edge detection and superpixel is proposed for the ambiguous boundary problem of large amounts of crystals. Firstly, an image filtering is employed to cope with image degradation caused by noise disturbance and suspension turbulence in the crystallizer. Subsequently, an image segmentation method is developed by utilizing improved edge detection and superpixel, which can be easily performed for crystal extraction. Accordingly, crystal size measurement can be developed for evaluation of the crystal size distribution. The experiment results on α-form L-glutamic acid present the effectiveness of the proposed method.