Table of Contents
What is image fusion techniques?
Image fusion techniques allow the integration of different information sources. The fused image can have complementary spatial and spectral resolution characteristics. However, the standard image fusion techniques can distort the spectral information of the multispectral data while merging.
What is feature fusion in image processing?
The feature fusion technique is widely used in many areas, e.g., image processing and classification. Feature fusion attempts to extract the most discriminative information from several input features and eliminate redundant information.
What is pixel level image fusion?
Pixel-level image fusion is designed to combine multiple input images into a fused image, which is expected to be more informative for human or machine perception as compared to any of the input images.
What is spatial domain fusion?
The performance of image fusion is based on two domains, spatial domain, and frequency domain, it is also known as time domain and spectral domain respectively. The spatial domain contains Intensity Hue Saturation (IHS) [10], Averaging [9] , Maximum Selection and Principal Component Analysis (PCA) techniques.
What is multimodal medical image?
Multimodal medical imaging is a research field that consists in the development of robust algorithms that can enable the fusion of image information acquired by different sets of modalities. In this paper, a novel multimodal medical image fusion algorithm is proposed for a wide range of medical diagnostic problems.
What is pixel classification?
Semantic segmentation, also known as pixel-based classification, is an important task where classification of each pixel belongs to a particular class. In GIS, you can use segmentation for land cover classification or for extracting roads or buildings from satellite imagery.
What is pixel level?
Pixel-level prediction enables visual understanding at finer granularity, such as segmenting all the persons and vehicles and estimating their 3D shapes as well as distances from the camera.
What are the four categories of digital image processing?
For discussion purposes, most of the common image processing functions available in image analysis systems can be categorized into the following four categories:
- Preprocessing.
- Image Enhancement.
- Image Transformation.
- Image Classification and Analysis.
What is multimodal MRI?
Multimodal magnetic resonance imaging: The coordinated use of multiple, mutually informative probes to understand brain structure and function – PMC.
What is semantic segmentation in image processing?
Semantic segmentation is a deep learning algorithm that associates a label or category with every pixel in an image. It is used to recognize a collection of pixels that form distinct categories.
What is focal loss in semantic segmentation?
As I wrote in the last article of this series, focal loss is a more focused cross entropy loss. In semantic segmentation problems, focal loss can help the model focus on pixels that have not been well trained yet, which is more effective and purposeful than cross entropy loss.
What is pixel labeling?
Label-Pixels is a tool for semantic segmentation of remote sensing images using fully convolutional networks (FCNs), designed for extracting the road network from remote sensing imagery and it can be used in other applications applications to label every pixel in the image ( Semantic segmentation).
What is BW image?
The names black-and-white, B&W, monochrome or monochromatic are often used for this concept, but may also designate any images that have only one sample per pixel, such as grayscale images. In Photoshop parlance, a binary image is the same as an image in “Bitmap” mode.