Identification of Log Characteristics in Computed Tomography Images Using Back-Propagation Neural Networks with the Resilient Back-Propagation Training Algorithm and Textural Analysis: Preliminary Results
Bibliographic record
Abstract
This research addressed the feasibility of identifying internal log characteristics in computed tomography (CT) images of sugar maple and black spruce logs by means of back-propagation (BP) neural networks with a resilient BP training algorithm. Five CT images were randomly sampled from each log. Three of the images were used to develop the corresponding classifier, and the remaining two images were used for validation. The image features that were used in the classifier were gray-level values, textual, and distance features. The important part of the classifier topology, ie the hidden node number, was determined based on the performance indicators: overall accuracy, mean square error, training iteration number, and training time. For the training images, the classifiers produced class accuracies for heartwood, sapwood, bark, and knots of 99.3, 100, 96.7, and 97.9%, respectively, for the sugar maple log; and 99.7, 95.3, 98.4, and 93.2%, respectively, for the black spruce log. Overall accuracies were 98.5% for sugar maple and 96.6% for black spruce, respectively. High overall accuracies were also achieved with the validation images of both species. The results also suggest that using textural information as the inputs can improve the classification accuracy. Moreover, the resilient BP training algorithm made BP artificial neural networks converge faster compared with the steepest gradient descent with momentum algorithm. This study indicates that the developed BP neural networks may be applicable to identify the internal log characteristics in the CT images of sugar maple and black spruce logs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".