Can Peripheral Representations Improve Clutter Metrics on Complex Scenes?
Authors: Arturo Deza, Miguel Eckstein
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Previous studies have proposed image-based clutter measures that correlate with human search times and/or eye movements. Here, we introduce a new foveated clutter model to predict the detrimental effects in target search utilizing a forced fixation search task. We show that Foveated Feature Congestion (FFC) clutter scores (r(44) = 0.82 0.04, p < 0.0001) correlate better with target detection (hit rate) than regular Feature Congestion (r(44) = 0.19 0.13, p = 0.0774) in forced fixation search; and we extend foveation to other clutter models showing stronger correlations in all cases. |
| Researcher Affiliation | Academia | Arturo Deza Dynamical Neuroscience Institute for Collaborative Biotechnologies UC Santa Barbara, CA, USA deza@dyns.ucsb.edu Miguel P. Eckstein Psychological and Brain Sciences Institute for Collaborative Biotechnologies UC Santa Barbara, CA, USA eckstein@psych.ucsb.edu |
| Pseudocode | Yes | Algorithm 1: Computation of Peripheral Integration Feature Congestion (PIFC) Coefficient |
| Open Source Code | Yes | Code for building peripheral representations is available1. 1Piranhas Toolkit: https://github.com/Arturo Deza/Piranhas |
| Open Datasets | No | The paper describes creating its own stimuli (videos and clips) for the experiment, but does not provide concrete access information (link, DOI, citation) for this custom dataset or any other publicly available dataset used for training or evaluation in a machine learning context. |
| Dataset Splits | No | The paper describes an experiment with human subjects and data collection, but does not specify training, validation, or test dataset splits in the context of machine learning model development and evaluation. |
| Hardware Specification | No | The paper describes the 'Apparatus' for the human psychophysical experiment, including the Eye Link 1000 system and LCD screen specifications, but it does not provide details on the computational hardware (e.g., GPU/CPU models) used to run their models or process data. |
| Software Dependencies | No | The paper mentions the 'Piranhas Toolkit' for creating peripheral architecture and discusses 'Feature Congestion', but it does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | The parameters we used match a V1 architecture with a scale of s = 0.25 , a visual radius of er = 24 deg, a fovea of 2 deg, with e0 = 0.25 deg 2, and t0 = 1/2. For our analysis, we only used the low zoom and 100 ms time condition since there was less ceiling effects across all eccentricities. |