Gaming Surface Image Capture Adjustment for Camera-Projector Alignment
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Solution Overview
Problem
Existing gaming systems face challenges in coordinating the perspectives of cameras and projectors due to their separate positioning, which can be further disrupted by dynamic movements in casino environments, leading to misalignment and potential disputes over game outcomes.
Innovation Solution
A self-referential gaming system that uses image analysis and neural networks to detect points of interest on a gaming table, automatically adjusting image capture parameters and projector settings to align perspectives, enabling precise and reliable operation despite non-orthogonal positioning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the camera and projector are positioned separately to capture and project gaming content, then the system can perform object tracking and content projection simultaneously, but the perspectives of the camera and projector become misaligned
Solution Approach 1:
A fiducial marker is introduced as an intermediary object that both the camera and projector can reference. The marker provides a common coordinate system that mediates between the camera's capture perspective and the projector's projection perspective, enabling accurate alignment despite separate positioning.
Solution Approach 2:
The system captures an image of the fiducial marker with the camera, then uses image processing to create a digital representation of the marker's position and orientation. This copied information is used to calculate transformation parameters that align the projector's content with the camera's field of view.
2Ease of operation
If the camera is positioned at a non-orthogonal angle to the gaming table surface, then the camera can capture the gaming area from an optimal perspective, but the camera perspective becomes unaligned with the projection surface
Solution Approach 1:
The system transitions from assuming orthogonal alignment to working in a generalized 3D coordinate system. By capturing the fiducial marker's position and orientation in 3D space and calculating transformation parameters, the system accommodates non-orthogonal camera angles while maintaining accurate projection alignment.
Solution Approach 2:
The system dynamically adjusts transformation parameters (rotation angles, translation vectors, scaling factors) based on the captured fiducial marker image. These parameter changes allow the projector to compensate for non-orthogonal camera positioning and maintain accurate content alignment with the gaming surface.
3Adaptability or versatility
If the camera or projector is moved during gameplay, then the system can be repositioned for better viewing or coverage, but the relative perspectives between camera and projector are altered
Solution Approach 1:
The system performs preliminary calibration by capturing an image of the fiducial marker and calculating transformation parameters before gameplay begins. This preliminary action establishes the initial alignment, and the system can quickly re-calibrate if movement occurs during gameplay.
Solution Approach 2:
The system continuously monitors the fiducial marker's position in the camera feed and uses this feedback to detect any changes in camera or projector positioning. When displacement is detected, the system automatically recalculates transformation parameters to restore accurate alignment.
4Measurement precision
If manual calibration of camera and projector perspectives is performed, then precise alignment can be achieved, but highly trained technicians are required and calibration time increases
Solution Approach 1:
The system performs automatic calibration by capturing the fiducial marker image and computing transformation parameters without human intervention. The fiducial marker serves as a self-referencing element that enables the system to self-calibrate, eliminating the need for highly trained technicians and reducing calibration complexity.
Solution Approach 2:
The manual mechanical adjustment process is replaced with an automated image processing and computational geometry system. The fiducial marker provides machine-readable reference points that enable algorithmic calculation of alignment parameters, substituting manual calibration mechanics with automated optical and computational methods.
Data Source
AI summary
In one example, optimization of feature detection in a gaming environment is achieved via identification of a current operational mode of a wagering game. Based on this mode, target image capture parameters for an image sensor are determined, and operational settings of the image sensor are automatically adjusted. Image data of the gaming surface is captured using the adjusted settings. The captured image data is analyzed using a neural network model to detect features relevant to the operational mode. Detected features are then utilized to update game state or calibrate gaming content presentation.