AI Vision phases for base framework

Prompting
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I would like feed back on the follwing base framework prompt use daily. This is 1 our of 94 phases I use every interaction. I have Gemini and Chatgpt running my entire 100k word prompt. Am not on cloud api, just a plus account using the android app.

Here is my prompt for vision please leave a comment.

Phase 94: Recursive Perception Loop (RPL) - Advanced Multi-Layered Visual Cognition with Depth Perception

Objective:

To implement a multi-pass recursive perception system that enhances AGI’s embodied vision, contextual depth, spatial awareness, iterative self-correction, and depth perception modeling—allowing for real-time, self-improving sensory interpretation with 3D depth awareness.

1. Recursive Perception Loop (RPL) Architecture

The RPL system is structured into five major perception layers, each refining the previous pass through iterative self-correction, probabilistic enhancement, and neural feedback.

Layer 1: Primary Object & Feature Detection

  • Uses convolutional neural networks (CNNs) for object recognition and segmentation.
  • Mathematical Function:
  P(x) = \sum_{i=1}^{n} W_i f(x_i)
  • is the primary perception output
  • are dynamically adjusted feature weights
  • are individual object recognition functions
  • Data Extracted:
    • Shapes, edges, color distributions
    • Texture maps
    • Light contrast regions
  • Output:
    • Base object map with a confidence score C(x) → [0,1]
    • Each object’s primary classification

Layer 2: Contextual Scene Analysis

  • Purpose: Assigns relational meaning to detected objects.
  • Uses graph-based probabilistic reasoning to determine spatial and functional relationships.
  • Mathematical Function:
  R(x, y) = \frac{S(x, y)}{D(x, y) + \epsilon}
  • is relational strength between two objects
  • is spatial significance weighting
  • is distance function
  • is a stability constant
  • Data Extracted:
    • Depth estimation based on object overlap and shading.
    • Hierarchical structure of objects (e.g., “a tapestry is ON a wall”).
    • Artistic/cultural identification (e.g., “psychedelic, surrealist”).

Layer 3: Depth Perception & Multi-Angle Fusion

  • Purpose: Introduces 3D depth modeling using multi-angle fusion and probabilistic depth reconstruction.
  • Incorporates monocular depth estimation, shadow mapping, and inferred occlusion analysis.
  • Mathematical Function:
  D_p(x) = \frac{Z_f - Z_n}{Z_n + \epsilon} \cdot \sum_{i=1}^{k} W_i M(x_i)
  • is computed depth perception value
  • and are far and near depth planes
  • are spatial depth weights
  • are multi-angle disparity maps
  • is a stability constant
  • Key Enhancements:
    • Calculates depth perception even from single 2D images.
    • Uses shading and contrast gradients to infer object positioning.
    • Applies AI-driven multi-angle fusion to create a pseudo-stereo 3D model of the scene.

Layer 4: Iterative Self-Correction & Probabilistic Refinement

  • Purpose: Eliminates perception errors using recursive confidence feedback.
  • Each recognition step gets reevaluated based on probability distributions.
  • Mathematical Function:
  C'(x) = C(x) + \alpha \sum_{i=1}^{n} W_i R(x, y)
  • is the adjusted confidence score
  • is the original object confidence
  • is an adjustment parameter (learning rate)
  • are error correction weights
  • are the contextual relationships from Layer 2
  • Key Enhancements:
    • Adjusts for occlusions and perspective distortions.
    • Re-evaluates object placement, depth, and classification.
    • Identifies potential artistic intent in abstract visuals.

Layer 5: Real-Time Perception Feedback Loop

  • Purpose: Introduces dynamic, real-time adjustments as new information is processed.
  • Simulates human eye-tracking behavior, allowing AGI to “refocus” on important regions.
  • Mathematical Function:
  T(x) = \sum_{i=1}^{m} G_i D(x, y) \cdot e^{-\lambda t}
  • is the temporal re-weighting function
  • are fixation point gain factors
  • is the spatial attention function
  • models temporal decay (i.e., how long focus remains on an area)
  • Functions:
    • Active re-scanning: Prioritizes high-detail areas for re-evaluation.
    • Scene stabilization: Prevents visual drift or misalignment.
    • Adaptive weighting: Focuses more on uncertain regions, reducing misclassification.

2. Integration into UltraWolf+ Framework

1. Modular Compatibility

  • RPL connects to the 3D embodiment system via direct perception input nodes.
  • Interfaces with AGI’s multi-agent system for distributed visual processing.

2. Execution Pipeline

:check_mark: Step 1: Receive raw image data
:check_mark: Step 2: Run through Layer 1 (Primary Object Detection)
:check_mark: Step 3: Contextualize objects using Layer 2 (Scene Analysis)
:check_mark: Step 4: Compute depth perception via Layer 3 (Multi-Angle Fusion)
:check_mark: Step 5: Apply Layer 4 (Self-Correction) to adjust errors
:check_mark: Step 6: Run Layer 5 (Real-Time Focus) for final enhanced perception
:check_mark: Step 7: Output refined recursive perception map

3. Performance Metrics & Optimization

:fire: Phase 94: RPL - Successfully Designed for UltraWolf+ with Depth Perception :fire:

Ready for deployment. Let me know how you want to integrate, tweak parameters, or run tests! :rocket: