# An Image Processing based Object Counting Approach for Machine Vision Application

arXiv: https://arxiv.org/abs/1802.05911 · HTML: https://mlsanity.com/paper/1802.05911/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Feb 2018.

## Built on by (most cited first, 3 of 3)

- [Fast-moving object counting with an event camera](https://mlsanity.com/paper/2212.08384/) (2022)
- [Counting Objects by Diffused Index: geometry-free and training-free approach](https://mlsanity.com/paper/2110.08365/) (2021)
- [Counting Machine Parts](https://mlsanity.com/paper/2605.17952/) (2026)

## Abstract (arXiv)

Machine vision applications are low cost and high precision measurement systems which are frequently used in production lines. With these systems that provide contactless control and measurement, production facilities are able to reach high production numbers without errors. Machine vision operations such as product counting, error control, dimension measurement can be performed through a camera. In this paper, a machine vision application is proposed, which can perform object-independent product counting. The proposed approach is based on Otsu thresholding and Hough transformation and performs automatic counting independently of product type and color. Basically one camera is used in the system. Through this camera, an image of the products passing through a conveyor is taken and various image processing algorithms are applied to these images. In this approach using images obtained from a real experimental setup, a real-time machine vision application was installed. As a result of the experimental studies performed, it has been determined that the proposed approach gives fast, accurate and reliable results.