Sentence examples for multimodal figures from inspiring English sources
Thus, for a better use of multimodal figures in document classification or retrieval tasks, as well as for providing the evidence source for derived assertions, it is important to automatically segment multimodal figures into subfigures and panels.
The performance our our segmentation system is summarize in Table 3. Performance of our system for the subtask of segmenting unimodal panels from multimodal figures.
The use of these multimodal figures is a common practice in bioscience, as experimental results are graphically validated via multiple methodologies or procedures.
As a result, classical image segmentation techniques based on low-level image features, such as edges or color, are not directly applicable to robustly partition multimodal figures into single modal panels.
In this paper, we present an approach for identifying and segmenting unimodal panels from a multimodal figure.
In this paper, we describe a robust solution for automatically identifying and segmenting unimodal panels from a multimodal figure.
Figure 10 Results for multimodal image pairs: RF-FA, AF-color, and color-SLO.
IES's multimodal analysis figure out the dynamic of the neurovascular unit considering the interactions between the neuronal, vascular networks and the extra cellular space occurring around the IES.
Further inspection of the cluster plot for rs136617760 revealed that in addition to the 93 samples that were not called, there was an excess of B alleles (alternative base forms at a specific genomic position), as well as a number of multimodal clusters (Supplementary Figure 4).
In the multimodal images in Figures 1(a), 1 e), and 1(f), the CARS signal is displayed in red and in Figure 1(b) the single channel information is provided; in the intact spinal cord and distant to the lesion site, alternating white matter tracts (bright CARS signal) and gray matter (low CARS signal) can be recognized in the longitudinal sections.
The non-uniform distribution of points degrades the efficient number of the scale invariance features, too. Figure 3 shows a non-uniform distribution of the standard SIFT features for multimodal retinal image pairs in different octaves and scale layers.
