SCN: A Novel Shape Classification Algorithm Based on Convolutional Neural Network

Shape classification and matching is an important branch of computer vision. It is widely used in image retrieval and target tracking. Shape context method, curvature scale space (CSS) operator and its improvement have been the main algorithms of shape matching and classification. The shape classification network (SCN) algorithm is proposed inspired by LeNet5 basic network structure. Then, the network structure of SCN is introduced and analyzed in detail, and the specific parameters of the network structure are explained. In the experimental part, SCN is used to perform classification tasks on three shape datasets, and the advantages and limitations of our algorithm are analyzed in detail according to the experimental results. SCN performs better than many traditional shape classification algorithms. Accordingly, a practical example is given to show that SCN can save computing resources.

Automatic segmentation algorithm for breast cell image based on multi-scale CNN and CSS corner detection

As the shapes of breast cell are diverse and there is adherent between cells, fast and accurate segmentation for breast cell remains a challenging task. In this paper, an automatic segmentation algorithm for breast cell image is proposed, which focuses on the segmentation of adherent cells. First of all, breast cell image enhancement is carried out by the staining regularization. Then, the cells and background are separated by Multi-scale Convolutional Neural Network (CNN) to obtain the initial segmentation results. Finally, the Curvature Scale Space (CSS) corner detection is used to segment adherent cells. Experimental results show that the proposed algorithm can achieve 93.01% accuracy, 93.93% sensitivity and 95.69% specificity. Compared with other segmentation algorithms of breast cell, the proposed algorithm can not only solve the difficulty of segmenting adherent cells, but also improve the segmentation accuracy of adherent cells.

Measurement of surface parameters of three-dimensional braided composite preform based on curvature scale space corner detector

Surface braiding angle and pitch length are two important parameters for characterizing and evaluating the performance of three-dimensional braided composite preforms. In this paper a new method based on an improved curvature scale space corner detector with adaptive threshold is proposed for measuring these parameters, with applications to three-dimensional, four-directional carbon-fiber braided composite preforms. First, the original image is acquired using a system employing ‘dome light source + CCD camera + circular polarizing filter’. Second, the original image is processed using Lab transform and BM3D (block-matching and 3D filter). Third, the corners are detected using an improved curvature scale space corner detector with adaptive threshold. Finally, the pitch lengths and surface braiding angles are measured from the detected corners. Experimental results show that the proposed method can achieve automatic measurement of the pitch length and surface braiding angle with smaller average errors relative to manual measurements compared with alternative schemes.