Contextual Image Mesh Illuminance Adjustment

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Solution Overview

Problem

Current image processing techniques require significant manual effort and resources to generate realistic image variations that incorporate contextual features like depth and illumination, often resulting in artificial-looking outputs due to the lack of automation in contextual feature incorporation.

Innovation Solution

A contextual image modeling application generates a rectangular mesh from an input image, determines illuminance values based on directional derivatives, adjusts mesh dimensions accordingly, and combines these adjustments with a synthetic image component to create a contextual composite image that accurately represents shading and reflectance, thereby enhancing the realism of image variations.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual image editing techniques are used to generate image variations, then contextual accuracy can be maintained, but time consumption and resource usage increase significantly

Engineering Contradiction:

Improvecontextual accuracyVSAvoidtime consumption

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs automatic mesh generation, illuminance calculation, and image component synthesis without requiring manual intervention. The contextual image modeling application autonomously processes the input image, generates the rectangular mesh structure, calculates illuminance values based on directional derivatives, and synthesizes the final image component, thereby eliminating the need for manual image editing while maintaining contextual accuracy

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces manual mechanical image editing processes with automated computational methods. Instead of manually identifying objects and mapping synthetic variations, the system uses algorithms to automatically generate rectangular meshes, compute illuminance values through directional derivatives, and synthesize image components that preserve contextual features like shading and depth

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Productivity

If automated image variation techniques are used, then time and resources are reduced, but contextual features like shading and illumination are lost

Engineering Contradiction:

Improveprocessing efficiencyVSAvoidcontextual feature accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The system applies different processing characteristics to different regions of the image by dividing it into a rectangular mesh. Each rectangle receives localized illuminance calculations based on its specific directional derivatives, allowing the system to preserve local contextual features like shading and illumination variations while maintaining overall processing automation and efficiency

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The patent transforms the input image through systematic parameter changes including generating illuminance values from directional derivatives, adjusting mesh rectangle dimensions based on illuminance variations, and synthesizing image components that incorporate these parameter changes to preserve contextual features such as shading, depth, and illumination in the automated output

Inventive Principle:

Principle #35Parameter changes

3Measurement precision

If complex mesh adjustment processes are applied to preserve contextual features, then image realism improves, but computational complexity increases

Engineering Contradiction:

Improveimage realismVSAvoidcomputational complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system segments the image into a rectangular mesh structure, dividing the complex task of preserving contextual features into manageable discrete rectangles. This segmentation allows for systematic processing of illuminance values and dimensional adjustments for each rectangle independently, reducing overall computational complexity while maintaining image realism through localized processing

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS11354828B2Generating contextualized image variants of multiple component images

Publication Date: 2022.06.07 ADOBE INC

AI summary

In some embodiments, contextual image variations are generated for an input image. For example, a contextual composite image depicting a variation is generated based on a input image and a synthetic image component. The synthetic image component includes contextual features of a target object from the input image, such as shading, illumination, or depth that are depicted on the target object. The synthetic image component also includes a pattern from an additional image, such as a fabric pattern. In some cases, a mesh is determined for the target object. Illuminance values are determined for each mesh block. An adjusted mesh is determined based on the illuminance values. The synthetic image component is based on a combination of the adjusted mesh and the pattern from the additional image, such as a depiction of the fabric pattern with stretching, folding, or other contextual features from the target image.