Intel corporation (20240320953). METHODS AND APPARATUS FOR EXPLAINABLE MULTI-SCALE GAUSSIAN MIXTURE MODEL DISTANCE simplified abstract

METHODS AND APPARATUS FOR EXPLAINABLE MULTI-SCALE GAUSSIAN MIXTURE MODEL DISTANCE

Organization Name

intel corporation

Inventor(s)

Anthony Rhodes of Portland OR (US)

Ilke Demir of Hermosa Beach CA (US)

Yali Bian of Fremont CA (US)

METHODS AND APPARATUS FOR EXPLAINABLE MULTI-SCALE GAUSSIAN MIXTURE MODEL DISTANCE - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240320953 titled 'METHODS AND APPARATUS FOR EXPLAINABLE MULTI-SCALE GAUSSIAN MIXTURE MODEL DISTANCE

Simplified Explanation: The patent application describes an apparatus that can compare two saliency maps associated with an image dataset by analyzing their pixel-level intensity and spatial properties.

• Key Features and Innovation:

   - Accesses and encodes pixel-level intensity of two saliency maps
   - Generates a saliency comparison metric based on the intensity
   - Compares spatial properties of the saliency maps using the metric

• Potential Applications:

   - Image processing and analysis
   - Computer vision applications
   - Visual attention modeling

• Problems Solved:

   - Efficient comparison of saliency maps
   - Enhanced understanding of visual attention mechanisms
   - Improved image analysis techniques

• Benefits:

   - Better insights into image saliency
   - Enhanced image processing capabilities
   - Advanced computer vision algorithms

• Commercial Applications:

   - Image recognition software
   - Visual search engines
   - Medical imaging analysis tools

• Prior Art:

   - Researchers in the field of computer vision and image processing
   - Academic studies on saliency mapping techniques

• Frequently Updated Research:

   - Latest advancements in computer vision algorithms
   - New developments in image analysis technologies

Questions about Saliency Map Comparison: 1. How does the apparatus encode the pixel-level intensity of the saliency maps? 2. What are the potential real-world applications of comparing saliency maps in image datasets?

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Original Abstract Submitted

an example apparatus includes interface circuitry, machine-readable instructions, and at least one processor circuit to be programmed by the machine-readable instructions to access a first saliency map and a second saliency map associated with an image dataset, encode pixel-level intensity of the first saliency map, encode pixel-level intensity of the second saliency map, generate a saliency comparison metric based on the pixel-level intensity of the first saliency map and the pixel-level intensity of the second saliency map, and compare spatial properties of the first saliency map and the second saliency map using the saliency comparison metric.