Multi-Focus Fusion Technique on Low-Cost Camera Images for Canola Phenotyping
Why this work is in the frame
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Bibliographic record
Abstract
To meet the high demand for supporting and accelerating progress in the breeding of novel traits, plant scientists and breeders have to measure a large number of plants and their characteristics accurately. Imaging methodologies are being deployed to acquire data for quantitative studies of complex traits. Images are not always good quality, in particular, they are obtained from the field. Image fusion techniques can be helpful for plant breeders with more comfortable access plant characteristics by improving the definition and resolution of color images. In this work, the multi-focus images were loaded and then the similarity of visual saliency, gradient, and color distortion were measured to obtain weight maps. The maps were refined by a modified guided filter before the images were reconstructed. Canola images were obtained by a custom built mobile platform for field phenotyping and were used for testing in public databases. The proposed method was also tested against the five common image fusion methods in terms of quality and speed. Experimental results show good re-constructed images subjectively and objectively performed by the proposed technique. The findings contribute to a new multi-focus image fusion that exhibits a competitive performance and outperforms some other state-of-the-art methods based on the visual saliency maps and gradient domain fast guided filter. The proposed fusing technique can be extended to other fields, such as remote sensing and medical image fusion applications.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it