Sentence examples for multiple image sequences from inspiring English sources
This section looks at the segmentation of multiple image sequences, in which all process steps are considered: image segmentation, blob computation, optical flow computation followed by segmentation, cluster recognition, and finally, cluster-blob association.
In this paper, we consider multiple image sequences over a wide SR range, from mid-level SR (see Fig. 5a) to extremely low-level SR (see Fig. 5d); the advanced techniques do not work consistently well on image sequences with wide SR ranges, but the NCC-based technique works stably for these image sequences.
When we generate image sequences with different TRs by a process of selection of specific frame intervals, multiple image sequences can then be generated from a single image sequence because the selected frames must be different and are dependent on the starting frames.
The first step is the detection of multiple planes in image sequences.
The technique is applicable to imaging any cyclically moving structure and operates on multiple, spatially overlapping tiled image sequences (each sequence acquired sequentially at a given spatial location) and effectively decouples the (rigid) spatial alignment and (non-rigid) temporal registration problems.
A set of image sequences from multiple views are applied to evaluate performance.
Will the recognition results of such approaches be generally improved when using multiple images or video sequences?
To test this, we extend the formulation of a probabilistic appearance-based face recognition approach (which was originally defined to do recognition from a single still) to work with multiple images and video sequences.
We have termed this pulse sequence single excitation multiple image RARE (SEMI-RARE).
This paper presents a robust approach to detect multiple moving targets from aerial infrared (IR) image sequences.
Some researchers use multiple-view videos [23 25], although single-view image sequences are more generic and easy to acquire.
