What does LBOM mean in UNCLASSIFIED
LBOM stands for Local Binary Orientation Module. It is a texture descriptor that is used to characterize the local image structure. LBOM is based on the assumption that the local image structure can be well represented using a set of binary strings. These binary strings are generated by comparing the intensity of each pixel in a small neighborhood with the intensity of the center pixel.
LBOM meaning in Unclassified in Miscellaneous
LBOM mostly used in an acronym Unclassified in Category Miscellaneous that means Local Binary Orientation Module
Shorthand: LBOM,
Full Form: Local Binary Orientation Module
For more information of "Local Binary Orientation Module", see the section below.
How does LBOM work?
LBOM works by dividing the image into a grid of small blocks. For each block, a histogram of the binary strings is computed. This histogram is then used to represent the texture of the block. The LBOM descriptor is a concatenation of the histograms of all the blocks in the image.
Advantages of LBOM
- Robustness to noise: LBOM is robust to noise because it is based on binary strings, which are less affected by noise than continuous values.
- Computational efficiency: LBOM is computationally efficient because it can be computed using a simple set of operations.
- Discriminative power: LBOM has a high discriminative power, which means that it can be used to effectively distinguish between different textures.
Applications of LBOM
LBOM has been used in a variety of applications, including:
- Texture classification: LBOM can be used to classify different types of textures, such as brick, wood, and fabric.
- Object recognition: LBOM can be used to recognize objects in images.
- Image retrieval: LBOM can be used to retrieve images from a database that are similar to a query image.
Essential Questions and Answers on Local Binary Orientation Module in "MISCELLANEOUS»UNFILED"
What is a Local Binary Orientation Module (LBOM)?
An LBOM is a computer vision technique used to describe the local patterns of an image. It assigns a binary code to each pixel in the image based on the orientation of its neighbors, creating a unique representation of the image's local structure.
How are LBOMs generated?
LBOMs are generated by comparing the intensity of a pixel with the intensity of its neighbors in a circular neighborhood. If the neighbor's intensity is greater than or equal to the center pixel, a '1' is assigned to the corresponding bit in the binary code. Otherwise, a '0' is assigned. The resulting binary code represents the local orientation pattern around the center pixel.
What are the applications of LBOMs?
LBOMs are widely used in computer vision applications, including:
- Texture classification
- Object recognition
- Image retrieval
- Motion tracking
What are the advantages of using LBOMs?
LBOMs offer several advantages:
- They are robust to noise and illumination changes
- They can capture local image structure effectively
- They are computationally efficient to generate
What are the limitations of LBOMs?
LBOMs have some limitations:
- They may not be suitable for images with fine details
- They can be sensitive to changes in image scale
- They may not perform well in the presence of occlusions
How are LBOMs different from other image descriptors?
LBOMs differ from other image descriptors such as SIFT and HOG in the following ways:
- LBOMs are based on local binary patterns, while SIFT and HOG use gradient information.
- LBOMs are more computationally efficient to generate than SIFT and HOG.
- LBOMs are less robust to scale changes than SIFT and HOG.
Final Words: LBOM is a powerful texture descriptor that is robust to noise, computationally efficient, and has a high discriminative power. It has been used in a variety of applications, including texture classification, object recognition, and image retrieval.