Novel Filtering Methods for Image and Video Processing Applications
Bibliographic record
Abstract
During the last few years, digital filtering methods for image/video processing applications have reached a satisfactory level. However, their performance degrades in the presence of noise, trend, motion, shape deformation, intensity inhomogeneity, shadows, or low image quality, to name a few. To cope with these challenges, this dissertation presents novel filtering methods for image/video processing applications that outperform the existing and state-of-the-art methods. The dissertation starts by introducing a novel trend filtering method that transforms the inter-frame registration problem into low complexity trend filtering problem. In the proposed method, Laplacian eigenmaps in conjunction with the modified empirical mode decomposition has been used to suppress the noise artifacts and the trend term. In multi-dimensional signals, the trend term is often referred to as non-uniform illumination or global intensity inhomogeneity. This dissertation presents a new filtering method for estimating the global intensity inhomogeneity in two dimensional and volume images. Global intensity inhomogeneity often arises due to the imperfections of data acquisition device, direction of source light, and properties of the subject under study. The proposed method generates a high-pass filter based on the grey-weighted distance transform of the frequency content of an image/volume. It provides an accurate estimation of global intensity inhomogeneity without any parameter tweaking, which makes it applicable to many imaging modalities. The dissertation also presents a filtering methodology to cope with local intensity inhomogeneity that gives rise to shadow artifacts. These artifacts appear as sharp discontinuities and are often corrected at different scales and orientations. The proposed method makes use of decimation-free directional filter bank to suppress the local intensity inhomogeneity and shadow artifacts irrespective of scale and orientation. In addition to intensity inhomogeneity correction, the dissertation also presents a filtering method that utilizes the Gabor filter bank to generate rotation invariant feature codes. The effectiveness of the proposed method has been evaluated in both identification and verification modes for fingerprint recognition. The uniqueness of the presented filtering methods lies in the fact that they are essentially parameter free and can easily be scaled to higher dimensions. This makes them applicable to many different image/video processing applications with least of effort from the end user, i.e., eliminating the user biases.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".