Difficulty in detecting objects with similar colors

Oct 13, 2025 by thesweetdesigns.com 96 views

Introduction

The process of calculating objects in digital images is a crucial task that is often carried out by humans. However, as the number of objects to be calculated increases, this process becomes more time-consuming and requires more energy. Therefore, it is essential to develop a system that can calculate objects from the image quickly and accurately. With such a system, efficiency in the use of resources and time can be maintained.

In this effort, a combination of the Threshold OTSU method and the modified Boundary Fill algorithm is used to calculate the number of objects in digital images. The Threshold OTSU method functions to automatically find the right threshold to separate objects from the background, making the calculation process more efficient. Meanwhile, the Boundary Fill algorithm, which serves to color an area in a digital image, has been modified to allow the process of calculation and selection of objects when coloring. This modification aims to avoid repetition in object detection.

The Threshold OTSU Method

The Threshold OTSU method is a widely used technique in image processing that functions to automatically find the right threshold to separate objects from the background. This method works by calculating the image histogram and finding a point where the variance between classes (objects and background) is maximum, thus separating the two classes effectively. The use of the Threshold OTSU method provides excellence in terms of efficiency, mainly because of its ability to determine the threshold value automatically. This eliminates the need for manual adjustments that can take time and often inaccurate.

The Threshold OTSU method has several advantages, including:

Efficiency: The Threshold OTSU method is able to determine the threshold value automatically, eliminating the need for manual adjustments.
Accuracy: The method works by calculating the image histogram and finding a point where the variance between classes is maximum, thus separating the two classes effectively.
Simplicity: The Threshold OTSU method is a simple and widely used technique in image processing.

However, the Threshold OTSU method also has some limitations, including:

Difficulty in detecting objects with similar colors: The Threshold OTSU method may have difficulty in detecting objects with similar colors, as it relies on the variance between classes to separate objects from the background.
Difficulty in detecting objects that are too close to each other: The Threshold OTSU method may also have difficulty in detecting objects that are too close to each other, as it relies on the image histogram to separate objects from the background.

The Modified Boundary Fill Algorithm

The Boundary Fill algorithm is a widely used technique in image processing that serves to color an area in a digital image. However, in this effort, the Boundary Fill algorithm has been modified to allow the process of calculation and selection of objects when coloring. This modification aims to avoid repetition in object detection.

The modified Boundary Fill algorithm offers a more sophisticated approach in the area coloring process. This modification allows the system to check whether the object that has been detected previously has been calculated. This not only accelerates the process, but also reduces the possibility of errors in the calculation of objects.

The modified Boundary Fill algorithm has several advantages, including:

Acceleration of the process: The modified Boundary Fill algorithm accelerates the process of calculation and selection of objects when coloring.
Reduction of errors: The modification reduces the possibility of errors in the calculation of objects.
Sophistication: The modified Boundary Fill algorithm offers a more sophisticated approach in the area coloring process.

However, the modified Boundary Fill algorithm also has some limitations, including:

Difficulty in detecting objects with similar colors: The modified Boundary Fill algorithm may have difficulty in detecting objects with similar colors, as it relies on the image histogram to separate objects from the background.
Difficulty in detecting objects that are too close to each other: The modified Boundary Fill algorithm may also have difficulty in detecting objects that are too close to each other, as it relies on the image histogram to separate objects from the background.

Challenges and Future Directions

The system developed by this method is able to detect and calculate the object well in accordance with the specified limits. However, this system still faces several challenges, including:

Difficulty in detecting objects that are too close to each other: The system has difficulty in detecting objects that are too close to each other.
Difficulty in detecting objects with colors that are not in accordance with the specified limits: The system has difficulty in detecting objects with colors that are not in accordance with the specified limits.

To overcome these challenges, additional image processing techniques can be applied, such as increasing contrast or the use of more complex segmentation techniques. This will allow the system to distinguish objects that are better close together and increase accuracy in the calculation.

By continuing to develop and adjust to the methods and algorithms used, it is hoped that this system can improve the ability to detect and calculate objects in various applications, ranging from safety supervision to environmental data analysis. The implementation of the right system will not only increase efficiency, but also make a significant contribution in the field of digital image processing.

Conclusion

In conclusion, the Threshold OTSU method and the modified Boundary Fill algorithm are two widely used techniques in image processing that have been combined to calculate the number of objects in digital images. The Threshold OTSU method functions to automatically find the right threshold to separate objects from the background, while the modified Boundary Fill algorithm offers a more sophisticated approach in the area coloring process. However, the system still faces several challenges, including difficulty in detecting objects that are too close to each other and objects with colors that are not in accordance with the specified limits. By continuing to develop and adjust to the methods and algorithms used, it is hoped that this system can improve the ability to detect and calculate objects in various applications.

Q: What is the Threshold Otsu method?

A: The Threshold Otsu method is a widely used technique in image processing that functions to automatically find the right threshold to separate objects from the background. This method works by calculating the image histogram and finding a point where the variance between classes (objects and background) is maximum, thus separating the two classes effectively.

Q: What is the modified Boundary Fill algorithm?

A: The Boundary Fill algorithm is a widely used technique in image processing that serves to color an area in a digital image. However, in this effort, the Boundary Fill algorithm has been modified to allow the process of calculation and selection of objects when coloring. This modification aims to avoid repetition in object detection.

Q: What are the advantages of using the Threshold Otsu method?

A: The Threshold Otsu method has several advantages, including efficiency, accuracy, and simplicity. It is able to determine the threshold value automatically, eliminating the need for manual adjustments, and works by calculating the image histogram and finding a point where the variance between classes is maximum, thus separating the two classes effectively.

Q: What are the limitations of using the Threshold Otsu method?

A: The Threshold Otsu method has several limitations, including difficulty in detecting objects with similar colors and difficulty in detecting objects that are too close to each other.

Q: What are the advantages of using the modified Boundary Fill algorithm?

A: The modified Boundary Fill algorithm has several advantages, including acceleration of the process, reduction of errors, and sophistication. It accelerates the process of calculation and selection of objects when coloring, reduces the possibility of errors in the calculation of objects, and offers a more sophisticated approach in the area coloring process.

Q: What are the limitations of using the modified Boundary Fill algorithm?

A: The modified Boundary Fill algorithm has several limitations, including difficulty in detecting objects with similar colors and difficulty in detecting objects that are too close to each other.

Q: What are the challenges faced by the system developed by this method?

A: The system developed by this method faces several challenges, including difficulty in detecting objects that are too close to each other and difficulty in detecting objects with colors that are not in accordance with the specified limits.

Q: How can the challenges faced by the system be overcome?

A: The challenges faced by the system can be overcome by applying additional image processing techniques, such as increasing contrast or the use of more complex segmentation techniques. This will allow the system to distinguish objects that are better close together and increase accuracy in the calculation.

Q: What are the potential applications of the system developed by this method?

A: The system developed by this method has several potential applications, including safety supervision, environmental data analysis, and other fields where digital image processing is used.

Q: What is the significance of the system developed by this method?

A: The system developed by this method is significant because it can improve the ability to detect and calculate objects in various applications, increasing efficiency and making a significant contribution in the field of digital image processing.

Q: What are the future directions of the research?

A: The future directions of the research include continuing to develop and adjust to the methods and algorithms used, improving the ability to detect and calculate objects in various applications, and exploring new applications of the system developed by this method.