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The optimal scale level of complex steerable pyramid for phase-based motion estimation under different motion ranges and target sizes

2025· article· en· W4409053916 on OpenAlex
Shin'ichiro AI, Chuan‐Zhi Dong, Qipei Mei

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMeasurement · 2025
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Vision and Imaging
Canadian institutionsUniversity of Alberta
FundersChina Scholarship Council
KeywordsScale (ratio)Motion (physics)Pyramid (geometry)Phase (matter)Artificial intelligenceComputer scienceComputer visionMathematicsPhysicsGeometryGeographyCartography

Abstract

fetched live from OpenAlex

• The scale level of the complex steerable pyramid (CSP) is found to significantly affect the measurement performance of phase-based motion estimation. Furthermore, there exists an optimal CSP scale level for the displacement estimation of a certain motion video, and the reason for its existence is also explained. • The optimal scale level of CSP is found to be positively correlated with the motion region, interpreted as the motion range and target size in motion video. Based on the relationships, a 2D contour map of the optimal scale level in relation to motion range ratio and target size ratio is proposed. • A new measurement strategy is proposed to optimize the measurement performance of phase-based method based on the inherent properties of the motion video (target size and motion range) without the need for ground-truth motion. In recent years, the phase-based motion estimation method has received significant attention in the field of vision-based motion estimation due to its robustness under illumination variation and high subpixel accuracy. The complex steerable pyramid (CSP) is widely adopted to generate phase of frames for motion estimation. The scale level of CSP is a significant influential parameter that affects the displacement measurement performance. However, systematic study regarding the optimal scale level of CSP in relation to motion video’s properties (motion range and target size) has not been carried out. Understanding this relationship could be helpful to identify the optimal scale level based on these properties, rather than simply comparing measurement results with the ground truth. In our work, a series of numerical motion videos with different properties are employed to find the optimal scale levels of CSP for motion estimation. Based on the phase-based motion estimation results, we found that the optimal scale level has clearly positive relationships with both the motion range and target size of motion video. Leveraging these relationships, a measurement strategy is proposed to automatically select the optimal scale level without the need to know ground truth of motion. The proposed strategy is further verified through a series of laboratory experiments, including shaker tests on a column and impact tests on a four-floor frame structure, using phased-based method alongside conventional sensors such as laser displacement sensor and accelerometers.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.849
Threshold uncertainty score0.289

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.136
GPT teacher head0.338
Teacher spread0.202 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it