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Due to the limitations on the depth of field of high-resolution fluorescence microscope, it is difficult to obtain an image with all objects in focus. The existing image fusion methods suffer from blocking effects or out-of-focus fluorescence. The proposed multi-focus image fusion method based on local maximum luminosity, intensity variance and the information filling method can reconstruct the all-in-focus image. Moreover, the depth of tissue's surface can be estimated to reconstruct the 3D surface model. [ABSTRACT FROM AUTHOR]

High-speed motions performed by industrial machines can induce severe vibrations that degrade the positioning accuracy and efficiency. To address this issue, this paper proposes a novel motion profile design method utilizing a sinusoidal jerk model to generate fast and smooth motions with low vibrations. The expressions for the acceleration, velocity, and displacement were obtained through successive integrations of the continuous jerk profile. A minimum-time solution with actuator limits was formulated based on an analysis of the critical constraint conditions. Differing from previous studies, the current study introduces an analytical optimization procedure for the profile parameters to minimize both the motion duration and excitation frequency contents corresponding to the system pole. By examining the correlation between the input motion profiles and system responses, the conditions for vibration elimination were identified, highlighting the significance of specific time intervals in controlling the vibration amplitude. Numerical and experimental studies were conducted to validate the effectiveness of the proposed method. The comparative results illustrate that this method outperforms existing baseline techniques in terms of smoothness and vibration attenuation. The residual-vibration level and settling time are significantly reduced with the optimized sinusoidal jerk profile, even in the presence of modeling errors, contributing to higher productivity. [ABSTRACT FROM AUTHOR]

Holistic scene understanding is a challenging problem in computer vision. Most recent researches in this field were focusing on the object detection, the semantic segmentation and the relationship detection tasks. The attribute can provide meaningful information for the object instance, thus the object instance can be expressed more detail in the scene understanding. However, most researches in this field have been limited to several special conditions. Such as, several researches were just focusing on the attribute of special object class, because their solutions were aimed at a limited-scenarios, their methods are hardly to generalize in other scenarios. We also find that most of the research for multi-attribute detection task were only regarding each attribute as binary class and simply use the multi-binary-classifier method for the attribute detection. But these strategies above not consider the relation between each pair of the attributes, they will fall into trouble in the "imperfect" attribute dataset (which is labeled with the missing and incomplete annotations), and they will have low performance in the long-tail attribute class (which has lower rank of annotation and more missing labels). In this paper, we focus on the multi-attribute detection for a variant of object classes and take the relation between attributes into consideration. We propose a GRU-based model to detect a variable-length attribute sequence with a customized loss compute method to solve the "imperfect" attribute dataset problem. Furthermore, we perform ablative studies to prove the effectiveness of each part of our method. Finally, we compare our model with several existed multi-attribute detection methods on VG (Visual Genome) and CUB200 bird datasets to prove the superior performance of the proposed model. [ABSTRACT FROM AUTHOR]

Detecting the marking characters of industrial metal parts remains challenging due to low visual contrast, uneven illumination, corroded surfaces, and cluttered background of metal part images. Affected by these factors, bounding boxes generated by most existing methods could not locate low-contrast text areas very well. In this paper, we propose a refined feature-attentive network (RFN) to solve the inaccurate localization problem. Specifically, we first design a parallel feature integration mechanism to construct an adaptive feature representation from multi-resolution features, which enhances the perception of multi-scale texts at each scale-specific level to generate a high-quality attention map. Then, an attentive proposal refinement module is developed by the attention map to rectify the location deviation of candidate boxes. Besides, a re-scoring mechanism is designed to select text boxes with the best rectified location. To promote the research towards industrial scene text detection, we contribute two industrial scene text datasets, including a total of 102156 images and 1948809 text instances with various character structures and metal parts. Extensive experiments on our dataset and four public datasets demonstrate that our proposed method achieves the state-of-the-art performance. Both code and dataset are available at: https://github.com/TongkunGuan/RFN. [ABSTRACT FROM AUTHOR]

Potential scaffolds for repair of the female pelvic floor require new materials and fabrication by novel methods to improve cellular infiltration. An 'ideal' engineered scaffold for pelvic-floor tissue should mimic the three-dimensional (3D) network of the extracellular matrix (ECM), which possesses intricate macro- and nano-architecture. In this study, a series of blended poly(l-lactide-co-ecaprolactone) P(LLA-CL)/thermoplastic polyurethane (TPU) microyarn/microfibrous scaffolds were produced with different weight ratios via dynamic liquid electrospinning and electrospinning. Both biopolymers were dissolved in 1,1,1,3,3,3-hexafluoro-2-propanol (HFIP). Our data showed the mean diameter of microyarn scaffolds to be significantly larger than that of microfibers. Microyarn scaffolds possessed large pore sizes and high porosity. There was no significant difference between the mechanical properties of microyarn and microfibrous scaffolds. Fourier-transform infrared spectroscopy suggested that intermolecular bonds were not present between the molecules of TPU and P(LLA-CL). Morphologic observations using scanning electron microscopy and inverted fluorescence microscopy showed that adipose-derived stem cells labeled with enhanced green fluorescent protein could grow well along or within blend microyarns and migrate within the novel 3D scaffolds. Hematoxylin and eosin staining demonstrated that cell infiltration on microyarn scaffolds was significantly enhanced. The CCK-8 assay showed that microyarns could significantly facilitate cell proliferation compared with microfibrous scaffolds. These results suggested that blend microyarns of P(LLA-CL)/TPU designed to mimic the ECM for female pelvic-floor tissue may be excellent macroporous scaffolds for tissue repair.