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We are interested in developing a pool of no-reference (NR) metrics to automatically assess the performance of the algorithms composing the image generation pipeline of digital cameras.
This paper presents the DCT-CNR (Discrete Cosine Transform-Chroma Noise Reduction), an efficient chroma noise reduction algorithm based on soft-thresholding on DCT data and designed to be integrated in the Image Generation Pipeline (IGP) [20], which allows any image system to yield the final color image starting from sensor data.
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The model generation pipeline first involved the construction of a geometrically faithful representation of the ventricular geometry, obtained by segmentation of the MR dataset followed by finite-element mesh generation of the segmented voxel image stack.
These processes consist of image generation; image maintenance; image inspection; and image transformation.
Hydraulic capsule pipelines (HCPs) are the third generation pipelines transporting hollow containers, known as capsules.
Tissue segmentation was performed with an automatic image analysis pipeline.
Figure 2 A block diagram of the image processing pipeline.
First, red-eye candidates are extracted from the input image by using an image filtering pipeline.
Images acquired from the Genome Analyzer were processed through the bundled image extraction pipeline (Illumina).
The image analysis pipeline consists of several steps.
Image analysis was carried out using the Freesurfer image analysis pipeline (version 5.1.0).
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