2 results about "Scale transformation" patented technology

Embodiments of the present application disclose a tongue feature recognition method and device, electronic equipment and a storage medium. The method comprises: acquiring a tongue image; performing multi-scale operation on the tongue image to determine at least two scale transformation images; inputting the at least two scale transformation images into a recognition model to output target feature information corresponding to the tongue image through the recognition model; and the recognition model is a cross-network model composed of a segmented convolutional neural network (Seg-CNN) structure and a point convolutional neural network (Point-CNN) structure. In this way, when analyzing the tongue image, the multi-scale operation is performed on the tongue image, and the network model composed of the Seg-CNN structure and the Point-CNN structure is used, so that the global feature information with points, lines and surfaces can be recognized, thereby the crack feature in the tongue image can be clearly recognized, and the recognition accuracy of the crack feature is improved.

This invention discloses a behavior recognition method based on spatiotemporal scale transformation, comprising: detecting a target using a target detector to obtain corresponding target bounding box regions; performing spatial multi-scale transformation on each target region to obtain multi-scale target regions; fusing features of the multi-scale target regions to obtain fused features; and classifying and recognizing behaviors based on the fused features. This invention combines target detection with behavior recognition, enabling the behavior recognition model to effectively focus on the regions where behaviors actually occur. Addressing the problem of inconsistent target spatial scales, this invention utilizes upsampling and downsampling to achieve scale uniformity, better ensuring the scale consistency of targets. To address the temporal differences between different samples, this method proposes a multi-frame-rate sampling operation to fuse features from multiple time scales, enabling effective feature extraction for behaviors of different durations.