Automated Image Path Generation Using Feature Point Regression
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Solution Overview
Problem
Conventional methods for providing users with a sequence of images are manual and labor-intensive, making it difficult to efficiently view a large number of images.
Innovation Solution
An information processing device and method that sets a path connecting characteristic image portions by using metadata about detected feature points, including determining regression curves and adjusting based on feature point scores and distances, to generate efficient image viewing paths for still and moving images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual methods are used to create image sequences, then users can view images in a structured manner, but the data creation process becomes troublesome and time-consuming
Solution Approach 1:
The system automatically detects feature points in images and generates viewing paths without requiring manual intervention. The path generation unit creates sequences by autonomously analyzing image metadata and feature point information, allowing the system to serve itself rather than relying on manual data creation by users.
Solution Approach 2:
The patent replaces manual mechanical operations with automated computational processes. Instead of manually selecting and sequencing images, the system uses a path generation unit that automatically processes image metadata, detects feature points, and computes optimal viewing paths through algorithmic analysis.
2Quantity of substance
If a large number of images are provided to users, then more content is available for viewing, but it becomes difficult to efficiently view characteristic images
Solution Approach 1:
The system extracts and highlights characteristic images by detecting feature points and determining their significance scores. Instead of presenting all images equally, the path generation unit identifies and extracts only the most characteristic images that contain meaningful feature points, filtering out redundant content to improve viewing efficiency.
Solution Approach 2:
The patent applies different treatment to different images based on their local characteristics. Images containing significant feature points are selected for inclusion in the viewing path, while images without characteristic features are excluded. This local quality approach ensures that only meaningful content is presented, maintaining high viewing efficiency even with large image collections.
3Reliability
If manual path creation is used, then precise control over image sequences is achieved, but the process becomes complex and difficult to maintain
Solution Approach 1:
The patent replaces complex manual path creation processes with automated computational algorithms. The path generation unit uses algorithmic methods to analyze image metadata, detect feature points, and compute viewing paths automatically, eliminating the need for manual intervention while maintaining or improving path accuracy through systematic processing.
Data Source
AI summary
The present technology relates to an information processing device, an information processing method, and a program that are designed to enable easy generation of a path for successively displaying characteristic images. The information processing device includes a setting unit that sets a path for connecting characteristic portions in at least one image by referring to metadata including at least information about feature points detected from the image. The setting unit sets the path by determining a regression curve, using the feature points. In a case where the feature points include a feature point at a distance equal to or longer than a predetermined threshold value from the regression curve, the setting unit redetermines the regression curve after removing a feature point detected from an image including a feature point having a low score among the feature points. The present technology can be applied to information processing devices that process still images and moving images.