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Large components docking is an important step in industrial manufacturing. During the docking process, the relative pose of joining components needs real-time and accurate acquisition. Existing methods rely on expensive and complex optical instruments. Photogrammetry has the advantages of low cost and fast measurement speed, but its measurement accuracy decreases sharply along with the increase of the measurement range. This paper proposes a two-stage binocular vision which consists of two sets of binocular vision systems with different accuracy levels in the same coordinate system. Binocular system with corresponding structural parameters is designed for pose measurement at corresponding stages to solve the contradiction between the range and accuracy in measurement. A triangulation and spatial plane fitting method is proposed to calculate relative poses without introducing coordinate transformation errors. We adopt a novel 3D optimization method to further improve the accuracy. Experiment results show that this method can meet the measurement range and accuracy requirements for large components docking. Compared with traditional combined vision measurement station based on multi-vision sensor, the proposed method reduces coordinate transformation errors and overcomes the contradiction between the measurement range and accuracy, which can improve the accuracy and meet the requirements of engineering application.

Training a good dictionary is the key to a successful image fusion method of sparse representation based models. In this paper, we propose a novel dictionary learning scheme for medical image fusion. First, we reinforce the weak information of images by extracting and adding their multi-layer details to generate the informative patches. Meanwhile, we introduce a simple and effective multi-scale sampling to implement a multi-scale representation of patches while reducing the computational cost. Second, we design a neighborhood energy metric and a multi-scale spatial frequency metric for clustering the image patches with a similar brightness and detail information into each respective patch group. Then, we train the energy sub-dictionary and detail sub-dictionary, respectively by K-SVD. Finally, we combine the sub-dictionaries to construct a final, complete, compact and informative dictionary. As a main contribution, the proposed online dictionary learning can not only obtain an informative as well as compact dictionary, but can also address the defects, such as superfluous patch issues and low computation efficiency, in traditional dictionary learning algorithms. The experimental results show that our algorithm is superior to some state-of-the-art dictionary learning based techniques in both subjective visual effects and objective evaluation criteria.

Differential expression (DE) analysis is a necessary step in the analysis of single-cell RNA sequencing (scRNA-seq) and spatially resolved transcriptomics (SRT) data. Unlike traditional bulk RNA-seq, DE analysis for scRNA-seq or SRT data has unique characteristics that may contribute to the difficulty of detecting DE genes. However, the plethora of DE tools that work with various assumptions makes it difficult to choose an appropriate one. Furthermore, a comprehensive review on detecting DE genes for scRNA-seq data or SRT data from multi-condition, multi-sample experimental designs is lacking. To bridge such a gap, here, we first focus on the challenges of DE detection, then highlight potential opportunities that facilitate further progress in scRNA-seq or SRT analysis, and finally provide insights and guidance in selecting appropriate DE tools or developing new computational DE methods.