Image Recognition for Robots

This assignment, titled 'Image Recognition for Robots', delves into the application of image recognition technology within robotic systems. It emphasizes the challenges of accurate image interpretation even for human observers, highlighting the need for advanced AI-powered solutions. The paper proposes an idea for implementing intelligent image recognition in robots, emphasizing its potential in commercial, security, and forensic applications. Furthermore, it explores the role of deep learning-based computer vision techniques in object detection within images, providing a deeper understanding of machine learning concepts.

Running head: RDIT - IDEATION
SIT740 Research and Development in Information Technology
Pass Task 8.1: Ideation
Student Name:
University Name:

1RDIT - IDEATION
Recognition of Image and Machine Learning
The advancement in context to the Artificial Intelligence has opened new areas for study and
ability to operate in robotics. From the rising demand for robotics, this study is focused on
technique that can be used by robots or machine for recognition of images. The recognition of
images to an accurate level is sometime even difficult for the human eye. Hence, in this paper an
idea has been decided for implementing the intelligence in robots to reorganize images with
accuracy. The idea will help to design advanced systems that could be used in commercial
purposes as well as security purposes. The proper recognition of images can also help in forensic
investigations as a deep learning based computer vision technique. This means that the robots
will also support detecting of objects within an image and help to gain insight into the concept of
machine learning.

2RDIT - IDEATION
Bibliography
Wu, R., Yan, S., Shan, Y., Dang, Q. and Sun, G., 2015. Deep image: Scaling up image
recognition. arXiv preprint arXiv:1501.02876, 7(8).
Zawbaa, H.M., Abbass, M., Hazman, M. and Hassenian, A.E., 2014, November. Automatic fruit
image recognition system based on shape and color features. In International Conference on
Advanced Machine Learning Technologies and Applications (pp. 278-290). Springer, Cham.