Canon kabushiki kaisha (20240095934). IMAGE PROCESSING APPARATUS, TRAINING APPARATUS, IMAGE PROCESSING METHOD, TRAINING METHOD, AND NON-TRANSITORY COMPUTER-READABLE MEDIUM simplified abstract
Contents
- 1 IMAGE PROCESSING APPARATUS, TRAINING APPARATUS, IMAGE PROCESSING METHOD, TRAINING METHOD, AND NON-TRANSITORY COMPUTER-READABLE MEDIUM
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 IMAGE PROCESSING APPARATUS, TRAINING APPARATUS, IMAGE PROCESSING METHOD, TRAINING METHOD, AND NON-TRANSITORY COMPUTER-READABLE MEDIUM - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Unanswered Questions
- 1.11 Original Abstract Submitted
IMAGE PROCESSING APPARATUS, TRAINING APPARATUS, IMAGE PROCESSING METHOD, TRAINING METHOD, AND NON-TRANSITORY COMPUTER-READABLE MEDIUM
Organization Name
Inventor(s)
IMAGE PROCESSING APPARATUS, TRAINING APPARATUS, IMAGE PROCESSING METHOD, TRAINING METHOD, AND NON-TRANSITORY COMPUTER-READABLE MEDIUM - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240095934 titled 'IMAGE PROCESSING APPARATUS, TRAINING APPARATUS, IMAGE PROCESSING METHOD, TRAINING METHOD, AND NON-TRANSITORY COMPUTER-READABLE MEDIUM
Simplified Explanation
The image processing apparatus described in the patent application searches for a tracking target in an image by acquiring features of the target image and the tracking target at different times, generating weights for these features, and detecting the tracking target based on the correlation between the features.
- The image processing apparatus searches for a tracking target in an image by analyzing features of the target image and the tracking target at different times.
- Weights are generated for the features of the tracking target based on the comparison between the target image and the tracking target images.
- The tracking target is detected in the image based on the correlation between the features of the target image and the tracking target, weighted according to the generated weights.
Potential Applications
This technology can be applied in various fields such as surveillance, object tracking in videos, augmented reality, and autonomous vehicles.
Problems Solved
This technology solves the problem of accurately tracking objects in images or videos, even when there are changes in lighting conditions, background clutter, or occlusions.
Benefits
The benefits of this technology include improved accuracy in object tracking, robustness to changes in the environment, and efficient processing of images for tracking applications.
Potential Commercial Applications
Potential commercial applications of this technology include security systems, video analysis software, robotics, and computer vision systems for industrial automation.
Possible Prior Art
Prior art in image processing and object tracking technologies may include methods using machine learning algorithms, template matching, and optical flow techniques.
Unanswered Questions
How does the image processing apparatus handle occlusions in the tracking target?
The patent abstract does not provide specific details on how occlusions in the tracking target are addressed during the tracking process.
What is the computational complexity of the image processing algorithm described in the patent application?
The abstract does not mention the computational requirements or efficiency of the image processing algorithm, which could be crucial for real-time applications.
Original Abstract Submitted
an image processing apparatus is provided. the image processing apparatus searches for a tracking target from a target image. a feature of the target image and a feature of a first image of the tracking target at a first time are acquired. a weight of a feature for each of a plurality of positions of the first image of the tracking target is generated on the basis of the first image of the tracking target and a second image of the tracking target at a second time. the tracking target is detected from the target image on the basis of a correlation between a feature of the target image and a feature of a first image of the tracking target weighted on the basis of the weight.
