Sparse Hierarchical Network for Object Detection
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Solution Overview
Problem
Conventional machine vision systems face challenges in accurately detecting objects due to reliance on color/texture analyses, lack of characterization based on shape and motion, and redundancy in hierarchical networks, which limits scalability and invariance to transformations.
Innovation Solution
A deep, sparse, hierarchical network with top-down connections and combined pixel-based and feature-based dictionaries is used for simultaneous color/texture, shape/contour, and motion analysis, reducing redundancy and increasing invariance through lateral competition and invariant feature detectors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional color/texture analysis is used for object detection, then objects with distinctive local features can be detected, but objects made of prevalent materials cannot be distinguished
Solution Approach 1:
The patent segments the detection process into multiple independent representations: color/texture detection, shape/contour detection, and motion detection. Each segment handles specific aspects of object characterization, allowing the system to detect objects regardless of material prevalence by combining multiple detection modalities.
Solution Approach 2:
The patent creates a unified detection framework that handles multiple object characteristics (color, texture, shape, motion) through a single integrated system. This multi-functional approach allows the system to detect diverse objects made of prevalent materials by utilizing multiple detection cues simultaneously.
2Ease of manufacture
If exclusively bottom-up hierarchical networks are used, then feature learning can proceed layer by layer, but redundancy increases and scalability is limited
Solution Approach 1:
The patent introduces top-down feedback connections in the hierarchical network, allowing higher layers to provide feedback to lower layers. This feedback mechanism reduces redundancy by enabling higher-level features to modulate lower-level feature detection, improving efficiency and scalability while maintaining ease of training through structured feedback loops.
Solution Approach 2:
The patent adds the temporal dimension to hierarchical processing by incorporating feedback connections that create recurrent processing loops. This transforms the traditionally feed-forward hierarchical structure into a dynamic system with multiple processing dimensions, reducing redundancy through iterative refinement across time steps.
3Reliability
If hierarchical networks increase dimensionality at each layer to capture complexity, then feature invariance improves, but computational resources grow exponentially
Solution Approach 1:
The patent applies partial action by using sparse coding in the hierarchical network, where only a subset of features at each layer are activated. This reduces the effective dimensionality and computational resources required while maintaining feature invariance, as the most relevant features are selected rather than processing all possible feature combinations.
Solution Approach 2:
The patent changes the parameter of feature representation by using distributed, sparse codes instead of dense, high-dimensional representations. This parameter change maintains the invariance properties needed for reliable detection while significantly reducing the computational resources and energy required for processing.
4Measurement precision
If multiple independent detection approaches are used for color/texture, shape, and motion, then comprehensive object characterization is achieved, but system complexity increases
Solution Approach 1:
The patent merges multiple independent detection approaches (color/texture, shape/contour, motion detection) into a single integrated hierarchical network. This combining approach achieves comprehensive object characterization while managing system complexity through unified architecture and shared processing mechanisms.
Solution Approach 2:
The patent creates a universal detection framework that handles multiple object characteristics through a single multi-functional system. This unified approach achieves comprehensive characterization without proportionally increasing complexity, as the hierarchical structure efficiently shares processing resources across different detection modalities.
Data Source
AI summary
An approach to detecting objects in an image dataset may combine texture/color detection, shape/contour detection, and/or motion detection using sparse, generative, hierarchical models with lateral and top-down connections. A first independent representation of objects in an image dataset may be produced using a color/texture detection algorithm. A second independent representation of objects in the image dataset may be produced using a shape/contour detection algorithm. A third independent representation of objects in the image dataset may be produced using a motion detection algorithm. The first, second, and third independent representations may then be combined into a single coherent output using a combinatorial algorithm.