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@@ -171,7 +171,7 @@ <h1 class="title is-1 publication-title"><span style="color: rgb(192, 0, 0)">PIX
  <h2 class="title is-3">Abstract</h2>
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- Accurate feature detection is fundamental for var- ious computer vision tasks including autonomous robotics, 3D reconstruction, medical imaging, and remote sensing. Despite advancements in enhancing the robustness of visual features, no existing method measures the utility of visual information be- fore processing by specific feature-type algorithms. To address this gap, we introduce PIXER and the concept of “Featureness”, which reflects the inherent interest and reliability of visual information for robust recognition independent of any specific feature type. Leveraging a generalization on Bayesian learning, our approach quantifies both the probability and uncertainty of a pixel’s contribution to robust visual utility in a single- shot process, avoiding costly operations such as Monte Carlo sampling, and permitting customizable featureness definitions adaptable to a wide range of applications. We evaluate PIXER on visual odometry with featureness selectivity, achieving an av- erage of 31% improvement in RMSE trajectory with 49% fewer features.
+ Accurate feature detection is fundamental for various computer vision tasks including autonomous robotics, 3D reconstruction, medical imaging, and remote sensing. Despite advancements in enhancing the robustness of visual features, no existing method measures the utility of visual information be- fore processing by specific feature-type algorithms. To address this gap, we introduce PIXER and the concept of “Featureness”, which reflects the inherent interest and reliability of visual information for robust recognition independent of any specific feature type. Leveraging a generalization on Bayesian learning, our approach quantifies both the probability and uncertainty of a pixel's contribution to robust visual utility in a single- shot process, avoiding costly operations such as Monte Carlo sampling, and permitting customizable featureness definitions adaptable to a wide range of applications. We evaluate PIXER on visual-odometry with featureness selectivity, achieving an average of 31% improvement in RMSE trajectory with 49% fewer features.
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  <div class="column"><h2 class="title is-3">The Davis Dataset</h2>
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- We evaluate PIXER aided visual odometry on a custom dataset, named "Davis", collected using a ZED 2i camera + Mosaic X5 GNSS on a Boston Dynamics Spot Quadruped. Results in Table I show superior estimation performance with mean RMSE improvement of 34% and mean feature reduction of 41%.
+ We evaluate PIXER aided visual odometry on a custom dataset, named "Davis", collected using a ZED 2i camera + Mosaic X5 GNSS on a Boston Dynamics Spot Quadruped. Results in Table below show superior estimation performance with mean RMSE improvement of 34% and mean feature reduction of 41%.
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