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REVIEW 2 major objections 1 minor 8 references

R-C-P Method: An Autonomous Volume Calculation Method Using Image Processing and Machine Vision

T0 review · 2 major / 1 minor · reviewed 2026-05-24 · grok-4.3

Challenge this review Download PDF Read on arXiv ↗

Pith's one-line read The R-C-P method derives real object dimensions and volumes from two 2D camera images using pixel counts and known distance.

desk verdict This is a basic known-distance scaling method with edge detection for rectangles; the equations are standard and the experiments supply no error metrics or robustness checks. read the letter →

arxiv 2308.10058 v2 submitted 2023-08-19 cs.CV

MA Muktadir , Sydney Parker , Sun Yi This is my paper
classification cs.CV
keywords imageprocessingmachinevisionvolumecalculationedgedetectionautonomoussystemsR-C-Pmethod2Dcamerasrectangularobjects
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper develops the R-C-P method to obtain volumetric information for rectangular objects in real time with standard 2D cameras rather than depth sensors such as LiDAR. Images from two cameras undergo edge detection to extract row, column, and pixel data, which equations then convert into physical surface dimensions once the camera-to-object distance is supplied. The approach targets autonomous systems that need situation awareness in settings where complex sensor setups are impractical. It additionally flags discontinuous edges that may signal volume changes. Experimental illustrations show how the derived equations produce actual object measurements from the image data.

What carries the argument

The R-C-P (row-column-pixel) method, which counts image pixels along detected edges from two cameras and scales those counts by known camera distance to obtain physical measurements.

What would settle it

Run the system on a rectangular block of known physical size at a measured fixed distance, then compare the R-C-P output dimensions against the block's true measurements; mismatch beyond sensor noise would refute the method.

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Extended reading notes

Core claim

The R-C-P method processes dual-camera images through edge detection to count rows, columns, and pixels, then applies distance-scaled equations to compute actual surface dimensions and detect volume discontinuities without requiring 3D depth hardware.

Load-bearing premise

Edge detection must reliably mark the true boundaries of rectangular objects and the camera-to-object distance must be known accurately and stay constant.

Editorial extensions

If this is right

  • Real-time surface area and volume values become available from ordinary 2D camera pairs.
  • Discontinuous edges or volume shifts are flagged in addition to steady dimensions.
  • The supplied equations turn pixel counts directly into physical units when distance is given.
  • Operation is possible in environments where LiDAR hardware is unsuitable.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The method could support continuous monitoring if distance is tracked over time rather than assumed fixed.
  • Hardware simplification in robotics might follow if 2D cameras replace dedicated depth units for rectangular targets.
  • Extension to irregular shapes would require new edge-to-volume mapping rules beyond the current rectangular focus.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

2 major / 1 minor

Summary. The manuscript proposes the R-C-P (row-column-pixel) method, which uses two 2D cameras, image processing, and edge detection to compute real-time dimensions, surface areas, and volumes of rectangular objects as an alternative to LiDAR in constrained environments such as space. The central claim is that the derived equations, when supplied with camera-to-object distance, directly yield actual object dimensions and can detect discontinuous edges or volumes; experimental results are presented solely for illustration.

Significance. If validated with quantitative error bounds, the approach could provide a low-cost, hardware-light alternative for dimension measurement in autonomous systems where depth sensors are impractical. The use of standard 2D cameras and explicit geometric scaling from known distance is a straightforward idea that aligns with existing machine-vision practice, but the current lack of any reported accuracy metrics prevents evaluation of whether the method offers a meaningful advance.

major comments (2)
  1. [Abstract] Abstract: the claim that 'the vision system provides the dimensions of actual objects' using the R-C-P equations and given distance information is presented without any error analysis, ground-truth comparisons, pixel-to-mm statistics, or sensitivity tests to distance error or edge jitter, leaving the central claim unverified.
  2. [Abstract] Abstract and experimental description: the manuscript states that experiments are 'for illustration' only and supplies no handling of perspective distortion, non-constant distance across the object face, or robustness of edge detection under varying lighting/texture, all of which are load-bearing assumptions for the geometric scaling claim.
minor comments (1)
  1. The expansion of the R-C-P acronym and the precise mapping from row/column/pixel features to the derived equations should be stated explicitly in the introduction or methods section for clarity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed review and constructive comments on our manuscript. The feedback correctly identifies that the work presents a geometric method with illustrative examples rather than a fully validated measurement system. We address each major comment below, indicating planned revisions where appropriate.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the claim that 'the vision system provides the dimensions of actual objects' using the R-C-P equations and given distance information is presented without any error analysis, ground-truth comparisons, pixel-to-mm statistics, or sensitivity tests to distance error or edge jitter, leaving the central claim unverified.

    Authors: We agree that the abstract phrasing implies a stronger validation than the manuscript delivers. The R-C-P equations are derived under the assumption of known constant distance and perfect edge detection; the experiments serve only to demonstrate the equations in a controlled setting. We will revise the abstract to state that the method yields computed dimensions from the given inputs and will add a dedicated limitations subsection discussing error sources (distance uncertainty, pixel quantization, edge jitter) without claiming empirical accuracy bounds, as no ground-truth dataset was collected for that purpose. revision: partial

  2. Referee: [Abstract] Abstract and experimental description: the manuscript states that experiments are 'for illustration' only and supplies no handling of perspective distortion, non-constant distance across the object face, or robustness of edge detection under varying lighting/texture, all of which are load-bearing assumptions for the geometric scaling claim.

    Authors: The manuscript explicitly labels the experiments as illustrative and assumes a fixed camera-to-object distance with frontal rectangular faces. We will expand the method section to list these assumptions explicitly and add a short paragraph on conditions under which the scaling would degrade (perspective effects, non-planar faces, lighting changes affecting edge detection). No new experiments or robustness tests will be added, as that would exceed the scope of the current illustrative study. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity; derivation is direct geometric scaling from known inputs

full rationale

The paper derives the R-C-P method from standard image processing, edge detection, and geometric scaling using supplied distance to camera as an explicit input. No equations reduce outputs to fitted parameters, self-citations, or ansatzes imported from prior work by the same authors. The central claim is presented as a first-principles calculation rather than a statistical fit or renamed empirical pattern. Experiments are described only as illustrative, with no load-bearing predictions that collapse to the inputs by construction. This is the common case of a self-contained geometric method.

Assumptions & free parameters 1 free parameters · 1 assumptions · 0 invented entities

The central claim rests on the assumptions that objects are rectangular, edges are detectable without occlusion or lighting issues, and camera-to-object distance is provided as an external input. No free parameters are fitted inside the method itself.

free parameters (1)
  • camera-to-object distance
    Required external input used to scale pixel measurements to physical units; not derived by the method.
assumptions (1)
  • domain assumption Object is rectangular and its edges are reliably detectable by standard image processing
    Invoked to allow row-column-pixel counting to correspond to physical dimensions.

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Cite this review

Pith. "Pith review of R-C-P Method: An Autonomous Volume Calculation Method Using Image Processing and Machine Vision." pith.science (2026). https://pith.science/paper/2308.10058

@misc{pith2026230810058,
  author       = {Pith},
  title        = {Pith review of: R-C-P Method: An Autonomous Volume Calculation Method Using Image Processing and Machine Vision},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2308.10058}},
  note         = {Machine review of arXiv:2308.10058}
}
read the original abstract

Machine vision and image processing are often used with sensors for situation awareness in autonomous systems, from industrial robots to self-driving cars. The 3D depth sensors, such as LiDAR (Light Detection and Ranging), Radar, are great invention for autonomous systems. Due to the complexity of the setup, LiDAR may not be suitable for some operational environments, for example, a space environment. This study was motivated by a desire to get real-time volumetric and change information with multiple 2D cameras instead of a depth camera. Two cameras were used to measure the dimensions of a rectangular object in real-time. The R-C-P (row-column-pixel) method is developed using image processing and edge detection. In addition to the surface areas, the R-C-P method also detects discontinuous edges or volumes. Lastly, experimental work is presented for illustration of the R-C-P method, which provides the equations for calculating surface area dimensions. Using the equations with given distance information between the object and the camera, the vision system provides the dimensions of actual objects.

Figures

Figures reproduced from arXiv: 2308.10058 by the authors.

Figure 1
Figure 1. Method flow chart. 2.1. Experimental setup To obtain the pictures of the two surfaces of a rectangular object, two cameras have been considered [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Experimental setup. 2.2. RGB Data and data conversion. RGB data capture is the first step after setting up the two cameras from two sides of a rectangular object. After that process, both pictures from Cam-1 and Cam-2 were converted to GRAY scale images. These GRAY scale images are further processed to find the edges. 2.2.1.RGB to GRAY scale RGB image(s) consists of three colors: red, green, and blue ( [PITH_FULL_I… view at source ↗
Figure 4
Figure 4. [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗
Figures from the paper (10 more)
Figure 3
Figure 3. Figure 3: Left to right, RGB pixels are represented as Red, Green, and Blue [PITH_FULL_IMAGE:figures/full_fig_p004_3.png]
Figure 4
Figure 4. Figure 4: The RGB image (left) and the GRAY scale image (right). 2.2.2. Edge detection The edges of images refer to pixels whose GRAY levels change suddenly, which is the most basic feature of an image. Based on GRAY discontinuous points, edge detection is a basic method for rec…
Figure 6
Figure 6. Figure 6: R-C-P step 1. In the second step, the R-C-P method considers each white pixel in column directions and searches for any other white pixel in its parallel (column) direction, and if it detects, then the algorithm converts the black pixel (0) into a white pixel between t…
Figure 7
Figure 7. Figure 7: R-C-P method step 2. In [PITH_FULL_IMAGE:figures/full_fig_p006_7.png]
Figure 8
Figure 8. Figure 8: R-C-P method output [PITH_FULL_IMAGE:figures/full_fig_p006_8.png]
Figure 9
Figure 9. Figure 9: The figure shows the setup that includes a camera and a scale that is used to measure the distance from the surface to the camera face. The objective of this experiment was to calculate and analyze the surface area of the object by varying the distance from the object'…
Figure 10
Figure 10. Figure 10: The RGB image, edge, and R-C-P methods detected surfaces (left to right). Canny edge detection faces challenges in accurately detecting edges due to various factors, including the presence of noise, blurry images, complex background patterns, and images with low contr…
Figure 11
Figure 11. Figure 11: R-C-P method output (right), incomplete edge detection (middle), and RGB image (left). All the objects are placed in the same place by keeping the same distance for the camera surface and applying the method discussed in the previous section from RGB to find the lengt…
Figure 12
Figure 12. Figure 12: Distance versus ratio (D/G/J). Object -1 Object -2 Object -3 A B C D E F G H I J x (mm) width/row (mm) R-C-P method (mm) D= B/C width/row (mm) R-C-P method (mm) G=E/F width/row (mm) R-C-P method (mm) J=H/I 395 55.00 6.87 8.00 90.00 15.85 5.68 92.00 17.16 5.36 445 55.0…
Figure 13
Figure 13. Figure 13: Distance versus ratio (T/Q/N). To find the relationship between the actual and calculated dimension after applying the R-C-P needs further anal￾ysis. Based on the data from [PITH_FULL_IMAGE:figures/full_fig_p010_13.png]

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Reference graph

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