Image stitching based on a global alignment model is widely used in computer vision. However, the resulting stitched image may look blurry or ghosted due to parallax. To solve this problem, we propose a parallax-tolerant image stitching method based on nonrigid warping in this paper. Given a group of putative feature correspondences between overlapping images, we first use a semiparametric function fitting, which introduces a motion coherence constraint to remove outliers. Then, the input images are warped according to a nonrigid warp model based on Gaussian radial basis functions. The nonrigid warping is a kind of elastic deformation that is flexible and smooth enough to eliminate moderate parallax errors. This leads to high-precision alignment in the overlapped region. For the nonoverlapping region, we use a rigid similarity model to reduce distortion. Through effective transition, the nonrigid warping of the overlapped region and the rigid warping of the nonoverlapping region can be used jointly. Our method can obtain more accurate local alignment while maintaining the overall shape of the image. Experimental results on several challenging data sets for urban scene show that the proposed approach is better than state-of-the-art approaches in both qualitative and quantitative indicators.

Considering that the traditional machine learning algorithm mainly relies on manual feature extraction for image classification, the classification results are often not ideal. This paper proposes a front-end optimization method based on multi-feature fusion. Extracting image features uses fusion mode of Scale-Invariant Feature Transform (SIFT) and Histogram of Oriented Gradient (HOG) and forms a new feature set HOG-SIFT. Classification results are obtained by training with Support Vector Machine (SVM). Results show that the fusion of new features has a higher precision and recall than single feature extraction.

The increasing concern with false information has stimulated research in joint Fact Extraction and VERification (FEVER). Now we propose a system by deep learning which can help people identify the authenticity of most claims as well as providing evidences selected from knowledge source like Wikipedia. In this paper, we examine how to use deep learning method to improve the performance of the automatic fact verification system. Firstly, the inverted index of the knowledge base is established by using a Python package named Whoosh. Secondly, the claim is regularized by the Named Entity Recognition (NER) tool, and the most relevant documents are filtered based on the relevance ranking algorithm. Thirdly, top 20 relevant sentences for each claim are filtered by word embeddings. Finally, the effectiveness of each sentence and the label of claim is judged based on the two-level pre-training model. Our approach achieved a 0.89 document.

Large radiometric and geometric distortion in multi-source images leads to fewer matching points with high matching blunder ratios, and global geometric relationship models between multi-sensor images are inexplicit. Thus, traditional matching blunder detection methods cannot work effectively. To address this problem, we propose two matching blunder detection methods based on graph theory. The proposed methods can build statistically significant clusters in the case of few matching points with high matching blunder ratios, and use local geometric similarity constraints to detect matching blunders when the global geometric relationship is not explicit. The first method (named the complete graph-based method) uses clusters constructed by matched triangles in complete graphs to encode the local geometric similarity of images, and it can detect matching blunders effectively without considering the global geometric relationship. The second method uses the triangular irregular network (TIN) graph to approximate a complete graph to reduce to computational complexity of the first method. We name this the TIN graph-based method. Experiments show that the two graph-based methods outperform the classical random sample consensus (RANSAC)-based method in recognition rate, false rate, number of remaining matching point pairs, dispersion, positional accuracy in simulated and real data (image pairs from Gaofen1, near infrared ray of Gaofen1, Gaofen2, panchromatic Landsat, Ziyuan3, Jilin1and unmanned aerial vehicle). Notably, in most cases, the mean false rates of RANSAC, the complete graph-based method and the TIN graph-based method in simulated data experiments are 0.50, 0.26 and 0.14, respectively. In addition, the mean positional accuracy (RMSE measured in units of pixels) of the three methods is 2.6, 1.4 and 1.5 in real data experiments, respectively. Furthermore, when matching blunder ratio is no higher than 50%, the computation time of the TIN graph-based method is nearly equal to that of the RANSAC-based method, and roughly 2 to 40 times less than that of the complete graph-based method.