Partial Erosion-Based Feature Extraction Approach for Plastic Bottle Shape Classification
Why this work is in the frame
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Bibliographic record
Abstract
In order to utilize or to extract the shape information of objects in an image for recognition, classification or retrieval, a method for representing a shape is needed. In this paper, a work on representing plastic bottle shape using morphological based approach for an automated classification is reported. Morphological operations are used to describe the structure or form of an image. There are three primary morphological functions: erosion, dilation, and hit-or-miss. By using the two-dimensional description of plastic bottle silhouettes, we perform edge detection of the object silhouette followed by the erosion process. This work will compare two versions of erosion which are regular erosion, the matlab function imerode and the improved version of erosion which is called partial erosion. The erosion technique involves defining a set of flat and linear structuring elements and specifying the angle at 1o apart to obtain the maximum number of elements of 180o degrees. This is followed by a normalization procedure in which we divide the sum pixel value after erosion by the sum pixel of the whole silhouette. The normalized values are grouped into histograms of 9 bins and find the maximum number of the 9 histogram bin of sum pixel value (9HbSPV) obtained forms a set of feature set and is then used as inputs to train a neural network for plastic bottle shape classification. Both feature sets from the two types of erosion were tested on their uniqueness to represent the shape. Results obtained showed that the proposed feature extraction method can be applied to discriminate plastic bottles according to shape, either slim or broad bottles, efficiently.
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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 | 0.001 | 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