A simple and fast text localization algorithm for indoor mobile robot navigation
Bibliographic record
Abstract
The automatic detection of Region of Interests (ROI) is an active research area in the design of machine vision systems. By using bottom-up image processing algorithms to predict human eye fixations or focus of attention and extract the relevant embedded information content in images has been widely applied in this area, especially in mobile robot navigation. Text that appears in images contains large quantities of useful information. Further more, many potential landmarks in mobile robot navigation contain text, such as nameplates, information signs and hence scene text is an important feature to be extracted. In this paper, we propose a simple and fast text localization algorithm based on a zero-crossing operator, which can effectively detect text-based features in an indoor environment for mobile robot navigation. This method is based on the idea that high local spatial variance is one of the distinguishing characteristics of text. Text in images has distinct intensity/color differences relative to its neighbourhood background and appears in clusters with uniform inter-character distance. If we compute the spatial variance along the text line we can get a large value, while the spatial variance in the background is fairly low. Experimental results show that calculating the spatial variance to detect text-based landmarks in real-time is an effective and efficient method for mobile robot navigation.
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How this classification was reachedexpand
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 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".