Stagnant zone segmentation with U-net

Abstract: Silo discharging and monitoring the process for
      industrial or research application depend on computerized
      segmentation of different parts of images such as stagnant and
      flowing zones which is the toughest task. X-ray Computed
      Tomography (CT) is one of a powerful non-destructive technique
      for cross-sectional images of a 3D object based on X-ray
      absorption. CT is the most proficient for investigating different
      granular flow phenomena and segmentation of the stagnant zone
      as compared to other imaging techniques. In any case, manual
      segmentation is tiresome and erroneous for further investigations.
      Hence, automatic and precise strategies are required. In the
      present work, a U-net architecture is used for segmenting the
      stagnant zone during silo discharging process. This proposed
      image segmentation method provides fast and effective outcomes
      by exploiting a convolutional neural networks technique with an
      accuracy of 97 percent

Reviews and author responses:

- This paper simply proposes to use UNet for the segmentation of stagnant zones in X-ray CTs. While the applicability of this model may represent an advance in the particular field of the authors, the technical contribution of this paper is far from the level expected in this conference. 

- As the paper reads, the main contribution of the paper is the modified version of UNet 'proposed' by the authors, which major modification consists on replacing SGD by Adam. Nevertheless, this cannot be considered a contribution, as changing the optimizer in a deep model is a marginal change, from a methodological point of view. 

- Overall, the quality of the paper is below the standards of ICLR (content, technical contribution, length).

- The submission is not anonymized (authors included their names and affiliations). 

I support desk rejection since violating double blind rule, wrong format and insufficient length.

                                                                                                                                                                                                                                                                                              
                                                                                                                                                                                                                                                                                              


This paper propose a modified U-net architecture to segment the stagnant zone during silo discharging process. It lacks novelty and the improvement is marginal.  More importantly than all of that, this paper violates the double blind review rule and is same with [1]. So I think this paper is not suitable for acception.

[1]Waktola S, Grudzien K, Babout L. Stagnant zone segmentation with U-net[C]//2019 IEEE Second International Conference on Artificial Intelligence and Knowledge Engineering (AIKE). IEEE, 2019: 277-280.


This paper contains authors name and affiliation, and violates the double blind review rule. 

Meta-review: The paper proposed U-net for segmentation of stagnant zones in computed tomography. Technical contribution of the paper is severely limited, and is not of the quality expected of publications in this venue. The paper is not anonymized and violates the double blind review rule. I'm thus recommending rejection.

Decision: reject