Adaptive Image Capture for Detailed Object Identification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems struggle with efficient and timely identification of detailed types of articles, particularly when labels are positioned differently and in varying formats, leading to complex algorithms and increased processing time.
Innovation Solution
An information processing apparatus that discriminates approximate types from a first captured image, sets image capturing conditions based on the approximate type, and identifies detailed types from a second captured image under optimized conditions, using a combination of image processing units and learned models to facilitate accurate and efficient reading of identification information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single complex algorithm is used to identify detailed types from varied label positions and formats, then identification accuracy can be maintained, but processing time increases and algorithm complexity increases
Solution Approach 1:
The identification process is divided into two segments: first capturing approximate type information from the entire article image, then capturing detailed type information from specific label regions. This segmentation allows each stage to use simpler, targeted processing rather than one complex algorithm analyzing the entire image, reducing overall processing time while maintaining accuracy.
Solution Approach 2:
The system performs preliminary capture of approximate type information before detailed identification. By first determining the general category from the full article image, the system can then focus subsequent detailed identification efforts on relevant regions and formats, preparing the identification process in advance and reducing total processing time.
2Adaptability or versatility
If a single complex algorithm is used to handle all label variations, then comprehensive identification can be achieved, but algorithm complexity increases
Solution Approach 1:
The identification system is segmented into approximate type identification and detailed type identification modules. Each module handles specific aspects of label variation with dedicated, simpler algorithms rather than one complex universal algorithm, reducing overall system complexity while maintaining versatility.
Solution Approach 2:
The system captures more image data than immediately necessary (excessive action) by taking a full article image for approximate identification, then performs partial processing on specific regions for detailed identification. This approach handles label variations effectively by focusing computational resources only where needed rather than processing entire images uniformly.
3Measurement precision
If detailed identification is performed on all articles with the same complexity, then accuracy is maintained, but processing time increases
Solution Approach 1:
The system performs preliminary capture and approximate identification before detailed identification. This preliminary stage prepares by determining article categories and guiding subsequent detailed processing, ensuring accurate identification while avoiding unnecessary complex processing on articles where it isn't needed, thus improving overall productivity.
Solution Approach 2:
The system performs detailed identification selectively based on approximate results rather than applying the same complex processing to all articles uniformly. This partial action approach maintains accuracy for articles requiring detailed identification while skipping unnecessary processing for others, improving processing efficiency.
Data Source
AI summary
There is provided with an information processing apparatus. An approximate discrimination unit discriminates an approximate type of an object from a first captured image obtained by capturing the object to which identification information is added. A setting unit sets, based on the approximate type of the object, an image capturing condition for capturing an image to obtain the identification information. A detail discrimination unit identifies the identification information from a second captured image obtained by capturing the object under the image capturing condition and discriminates a detailed type of the object based on a result of the identification.