# Exploitation of Image Statistics with Sparse Coding in the Case of Stereo Vision

arXiv: https://arxiv.org/abs/2101.09710 · HTML: https://mlsanity.com/paper/2101.09710/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 115 references that are in the map.
- First posted on arXiv 24 Jan 2021.

## Builds on (most cited first, 6 of 6)

- [FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks](https://mlsanity.com/paper/1612.01925/) (2016)
- [Deep Sparse Coding for Invariant Multimodal Halle Berry Neurons](https://mlsanity.com/paper/1711.07998/) (2017)
- [Sparse Coding by Spiking Neural Networks: Convergence Theory and Computational Results](https://mlsanity.com/paper/1705.05475/) (2017)
- [Sparse Coding on Stereo Video for Object Detection](https://mlsanity.com/paper/1705.07144/) (2017)
- [Replicating Kernels with a Short Stride Allows Sparse Reconstructions with Fewer Independent Kernels](https://mlsanity.com/paper/1406.4205/) (2014)
- [Sparse Coding Predicts Optic Flow Specificities of Zebrafish Pretectal Neurons](https://mlsanity.com/paper/1805.01277/) (2018)

## Built on by (most cited first, 1 of 1)

- [WARP-LCA: Efficient Convolutional Sparse Coding with Locally Competitive Algorithm](https://mlsanity.com/paper/2410.18794/) (2024)

## Abstract (arXiv)

The sparse coding algorithm has served as a model for early processing in mammalian vision. It has been assumed that the brain uses sparse coding to exploit statistical properties of the sensory stream. We hypothesize that sparse coding discovers patterns from the data set, which can be used to estimate a set of stimulus parameters by simple readout. In this study, we chose a model of stereo vision to test our hypothesis. We used the Locally Competitive Algorithm (LCA), followed by a na"ive Bayes classifier, to infer stereo disparity. From the results we report three observations. First, disparity inference was successful with this naturalistic processing pipeline. Second, an expanded, highly redundant representation is required to robustly identify the input patterns. Third, the inference error can be predicted from the number of active coefficients in the LCA representation. We conclude that sparse coding can generate a suitable general representation for subsequent inference tasks. Keywords: Sparse coding; Locally Competitive Algorithm (LCA); Efficient coding; Compact code; Probabilistic inference; Stereo vision