Multiscale Fields of Patterns
Authors: Pedro Felzenszwalb, John G Oberlin
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the approach with two example applications. One involves contour detection. The other involves binary segmentation. We evaluated our models and algorithms on two different applications. For a quantitative evaluation we compute precision-recall curves for the different models by thresholding the estimated contour maps at different values. |
| Researcher Affiliation | Academia | Pedro F. Felzenszwalb Brown University Providence, RI 02906 pff@brown.edu John G. Oberlin Brown University Providence, RI 02906 john oberlin@brown.edu |
| Pseudocode | No | The paper describes algorithms in text (e.g., Section 3) but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | The BSD [12, 2] contains images of natural scenes and manual segmentations of the most salient objects in those images. For this experiment we obtained binary images from the Swedish Leaf Dataset [18]. |
| Dataset Splits | No | The paper specifies training and test sets ('Our training and test sets each have 200 examples.' and 'We used 50 examples for training and 25 examples for testing.') but does not explicitly mention or detail a separate validation dataset split. |
| Hardware Specification | No | Training each model took 2 days on a 20-core machine. Inference on each image took 20 minutes on an 8-core machine. This specifies CPU core counts but lacks specific CPU models, GPU details, or memory specifications. |
| Software Dependencies | No | The paper does not provide specific software names with version numbers for dependencies used in the experiments. |
| Experiment Setup | Yes | To generate the observations y we used µ0 = 150, µ1 = 100 and σy = 40 in Equation (10). During training and testing we used the band sampler with h = 3 rows. To generate the observations y we used µ0 = 150, µ1 = 100 and σy = 100 in Equation (10). |