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Detection of Partially Occluded Area in Images Using Image Segmentation Technique

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Fourth Congress on Intelligent Systems (CIS 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 868))

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Abstract

Computer vision is subfield of artificial intelligence. Detection of occluded area in images is one of the challenging tasks in the computer vision. In this paper, we concentrate on occluded area detection in images using image segmentation techniques. Occlusion is nothing but one object in image is hidden by another object. Detection of occluded area in images using image segmentation techniques. Image segmentation is the process of dividing the images into different regions based on the characteristics of the pixels in the original image and reduces the complexity of analysis. When determining the number of objects in a scene, instance segmentation is an excellent choice. But in semantic segmentation algorithms if all that is required is to group items that belong to the same class. Image segmentation has many applications in a number of fields, including gaming, robotics, autonomous vehicles, agriculture, object detection, and pedestrian detection. In this research work, we concentrate on detection of occluded area in images using Mask R-CNN. The experiment will show better segmentation results corresponding to the manually labeled occluded area.

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Author information

Authors and Affiliations

  1. Jawaharlal Technological University, Anantapur, India

    Jyothsna Cherapanamjeri

  2. Sri Vidyanikethan Engineering College, Tirupathi, India

    B. Narendra Kumar Rao

Authors
  1. Jyothsna Cherapanamjeri
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  2. B. Narendra Kumar Rao
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Corresponding author

Correspondence to Jyothsna Cherapanamjeri .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Christ (Deemed to be University), Bengaluru, India

    Sandeep Kumar

  2. Department of Computer Science and Engineering, Christ (Deemed to be University), Bengaluru, India

    Balachandran K.

  3. School of Civil, Environmental and Architectural Engineering, Anam-ro, Anam-dong, Korea University, Seoul, Korea (Republic of)

    Joong Hoon Kim

  4. South Asian University, New Delhi, Delhi, India

    Jagdish Chand Bansal

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Cherapanamjeri, J., Narendra Kumar Rao, B. (2024). Detection of Partially Occluded Area in Images Using Image Segmentation Technique. In: Kumar, S., K., B., Kim, J.H., Bansal, J.C. (eds) Fourth Congress on Intelligent Systems. CIS 2023. Lecture Notes in Networks and Systems, vol 868. Springer, Singapore. https://doi.org/10.1007/978-981-99-9037-5_17

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