Leaf recognition has been an important research field of image recognition in the recent past. However, traditional leaf recognition methods can be easily affected by environments and cannot realize multi-leaf recognition under a complex background in real time. In this work, we present a real-time leaf recognition method based on image segmentation and feature recognition. First, we denoise the input of a leaf image, performing a leaf segmentation with an improved FCN network model, and then optimize the contour edge with a CRF algorithm to get a leaf segmentation image. Second, we extract the content features of the segmented leaf image with an Inception-V2 network model to get a feature map of the leaf image. Third, we input the feature map into an RPN network to obtain a set of regional candidate frames and then integrate the feature map and the information of candidate frames in a RoI Pooling layer, which can extract the feature map of a candidate frame area and scale it to a fixed-size feature map. Finally, we send the feature map to a fully connected layer to classify each preselection box content through the calculation of preselection feature maps, and then obtain the final accurate position of the prediction box by utilizing a bounding box regression. The experimental results show that the proposed method can achieve multi-leaf recognitions with high accuracy and fast speed under complex environments in real time.

To address the issues of low efficiency in manual terrain feature map annotating and poor realism in terrain elevation map generation, this paper proposes a terrain elevation map generation method based on self-attention mechanism and multifeature sketch. Firstly, the proposed method extracts features from a terrain elevation map using an adaptive feature enhancement method. Afterwards, our method adds a self-attention mechanism to the generator and discriminator of conditional generative adversarial network to capture the global spatial features and generates a realistic terrain elevation map. Finally, a level of detail method is used to visualize the three-dimensional terrain, and an interactive terrain editing tool for roaming interaction is implemented. Experimental data show that the proposed method performs well in subjective visual performance and objective criteria and has obvious advantages over other current typical methods.

Music-driven automatic dance movement generation has become a hot research topic in the field of computer vision and internet of things in the recent past. To address the problems of increasing loss of Chinese folk dance culture, high cost of manual choreography methods and requirements for professional background, this paper proposes an automatic generation method for folk dance movements. Firstly, the proposed method collects paired folk music and dance videos to construct a synchronized folk music–dance dataset, extracting music and dance features using a feature extraction tool and a multi-scale fusion high-resolution network, respectively. Afterward, a sequence-to-sequence network model is constructed and then trained based on music features and dance features to synthesize rhythmically matched dance sequences for new music clips. Finally, an easy-to-use and effective automatic folk dance choreography method is implemented. Experimental data show that the proposed method performs well in automatic folk dance generation and the generated dances have folk characteristics and match the rhythm of the given music.