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Estimating disparity with confidence from energy neurons

Neural Information Processing Systems

Binocular fusion takes place over a limited region smaller than one degree of visual angle (Panum's fusional area), which is on the order of the range of preferred disparities measured in populations of disparity-tuned neurons in the visual cortex. However, the actual range of binocular disparities encountered in natural scenes ranges over tens of degrees. This discrepancy suggests that there must be a mechanism for detecting whether the stimulus disparity is either inside or outside of the range of the preferred disparities in the population. Here, we present a statistical framework to derive feature in a population of V1 disparity neuron to determine the stimulus disparity within the preferred disparity range of the neural population. When optimized for natural images, it yields a feature that can be explained by the normalization which is a common model in V1 neurons. We further makes use of the feature to estimate the disparity in natural images. Our proposed model generates more correct estimates than coarse-to-fine multiple scales approaches and it can also identify regions with occlusion. The approach suggests another critical role for normalization in robust disparity estimation.


A Simple and Fast Neural Network Approach to Stereovision

Neural Information Processing Systems

A neural network approach to stereovision is presented based on aliasing effects of simple disparity estimators and a fast coherencedetection scheme.Within a single network structure, a dense disparity map with an associated validation map and, additionally, the fused cyclopean view of the scene are available. The network operations are based on simple, biological plausible circuitry; the algorithm is fully parallel and non-iterative. 1 Introduction Humans experience the three-dimensional world not as it is seen by either their left or right eye, but from a position of a virtual cyclopean eye, located in the middle between the two real eye positions. The different perspectives between the left and right eyes cause slight relative displacements of objects in the two retinal images (disparities), which make a simple superposition of both images without diplopia impossible. Proper fusion of the retinal images into the cyclopean view requires the registration of both images to a common coordinate system, which in turn requires calculation of disparities for all image areas which are to be fused.


A Simple and Fast Neural Network Approach to Stereovision

Neural Information Processing Systems

A neural network approach to stereovision is presented based on aliasing effects of simple disparity estimators and a fast coherencedetection scheme. Within a single network structure, a dense disparity map with an associated validation map and, additionally, the fused cyclopean view of the scene are available. The network operations are based on simple, biological plausible circuitry; the algorithm is fully parallel and non-iterative.


A Simple and Fast Neural Network Approach to Stereovision

Neural Information Processing Systems

A neural network approach to stereovision is presented based on aliasing effects of simple disparity estimators and a fast coherencedetection scheme. Within a single network structure, a dense disparity map with an associated validation map and, additionally, the fused cyclopean view of the scene are available. The network operations are based on simple, biological plausible circuitry; the algorithm is fully parallel and non-iterative.


Does mitigating ML's impact disparity require treatment disparity?

Neural Information Processing Systems

Following precedent in employment discrimination law, two notions of disparity are widely-discussed in papers on fairness and ML. Algorithms exhibit treatment disparity if they formally treat members of protected subgroups differently; algorithms exhibit impact disparity when outcomes differ across subgroups (even unintentionally). Naturally, we can achieve impact parity through purposeful treatment disparity. One line of papers aims to reconcile the two parities proposing disparate learning processes (DLPs). Here, the sensitive feature is used during training but a group-blind classifier is produced.