To solve the low accuracy and high complexity in feature extraction of stereoscopic vision depth map, a feature extraction algorithm based on Principal Component Analysis and Histogram of Oriented Depth Gradient (PCA-HODG) was proposed. Firstly, disparity computation and depth map extraction were executed for binocular stereoscopic vision image to obtain high quality depth map; secondly, edge detection and gradient calculation of depth map within fixed size window were performed, then the features of region shape histograms were acquired and quantified. Meanwhile, dimensionality reduction by Principal Component Analysis (PCA) was implemented; finally, to realize the accuracy and completeness of feature extraction from depth map, a detection method of sliding window was used for the feature extraction of whole depth map and the dimensionality reduction was implemented once again. In the experiment of feature matching and classification, for the frames of test sequence Street, the average classification accuracy rate of the proposed algorithm increased by 1.15% when compared with the Range-Sample Depth Feature (RSDF) algorithm; while for Tanks, Tunnel, Temple, the average classification accuracy rate increased by 0.69%, 1.95%, 0.49% respectively when compared with the Geodesic Invariant Feature (GIF) algorithm. At the same time, the average running time decreased by 71.65%, 78.05%, 80.06% respectively compared with the Histogram of Oriented Depth (HOD), RSDF, GIF algorithm. The experimental results show that the proposed algorithm can not only detect and extract features of depth map more accurately, but also reduce the running time greatly by dimensionality reduction. Moreover, the proposed algorithm also has better robustness.

For balancing transparency and robustness of watermark, this paper proposed wavelet-domain digital watermarking method based on Fruit Fly Optimization Algorithm (FOA). The algorithm used Discrete Wavelet Transform (DWT) by FOA to watermarking technology and solved the contradiction between transparency and robustness in the watermark by swarm intelligence algorithm. In order to protect the copyright information of digital image, the selected original image was decomposed through a two-dimensional discrete wavelet transform, and watermark image through Arnold transformation was better embedded into wavelet coefficients of vertical sub-band, which guaranteed image quality. In the optimization process, the scaling factor was continuously being trained and updated by FOA. In addition, a new algorithm framework was proposed, which evaluated the scaling factor by prediction feasibility of DWT domain. The experimental results show that, the proposed algorithm has higher transparency and robustness against attacks, with watermarking similarity above 0.95, and 10% higher under geometric attacks such as rotation and shearing compared to some existing watermarking methods based on swarm intelligence.