Line & Sphere Detectors

The archived Python experiments use local receptive-field geometry. Chords spanning a circular field provide intensity profiles in many orientations. Bilinear samples allow arbitrary chord directions; low-intensity statistics and response accumulation provide line hypotheses.

Two investigated variants

One uses sampled chord profiles and rankings to accumulate dark-line responses. Another explores multi-channel smoothing of intensity sequences. They are experimental techniques and should not be confused with the fully separate project for vectorizing gradient lines.

Evidence boundary: source code is preserved; benchmark precision/recall and comparisons with conventional detectors are not available. The inspected chord-selection implementation also contains a discrepancy between the intended top-k selection and the final iteration.

The two archived algorithm variants are documented separately from the sphere detector. They analyze directional responses in local neighborhoods and build line hypotheses through explicit geometric criteria, without turning the line detector into a claimed implementation of the helix method.

Original publication: “A heuristic real-world sphere detection algorithm based on spiral approximation of gradient representations.” The detector was developed for noisy, partially occluded scenes, including apples whose colors can be difficult to distinguish from surrounding leaves.

Main idea: localize receptive fields, construct an enhanced grayscale gradient representation, group selected gradient vectors into helix candidates, fit a three-dimensional spiral to each candidate, apply supporting and rejecting hypotheses, and assign a detection weight. The paper describes the useful signature as a double helix or a partial helix for incomplete observations.

1. Build receptive-field / ROI representation.
2. Compute and enhance gradients from a grayscale image.
3. Filter and group gradient vectors into helix-like structures.
4. Fit candidate gradient groups with a 3D helix.
5. Apply heuristic constraints and competing hypotheses.
6. Evaluate the resulting prediction and possible sphere location.

Claims and evidence boundary

The paper argues that the representation reduces dependence on color masks and object size and improves resistance to clutter. Its illustrations document qualitative detection outcomes. They are not a standardized benchmark, an independent accuracy assessment, or evidence of a production deployment.

This method uses spiral/helix fitting to gradient representations. It is not the same as simple circular contour matching, and it is independent of the directional line-detector experiments described above.

The shared engineering pattern is to choose observations that survive confusing color, partial visibility or noise, then construct and evaluate explicit geometric hypotheses. This is a direction of specialized algorithm design, not one universal line-and-sphere model.