Project Background:
Image segmentation involves refining the understanding of visual content within images by dissecting them into semantically meaningful regions. Image segmentation is a crucial task within computer vision, aiming to partition an image into distinct segments or regions based on various attributes such as color, texture, or object boundaries. This field has witnessed significant advancements largely propelled by the emergence of deep learning techniques and the availability of large-scale annotated datasets. Key objectives typically include enhancing segmentation accuracy, improving the efficiency of segmentation algorithms for real-time applications, and extending capabilities to handle diverse image types and contexts.
Problem Statement
- Improve the accuracy of semantic segmentation algorithms to ensure precise delineation of object boundaries and semantic regions within images.
- Enhancing the efficiency to enable real-time processing of high-resolution images is crucial for applications like autonomous driving.
- Develop methods to differentiate individual objects of the same class within an image, facilitating more detailed understanding and interaction with the scene.
- Ensure robustness of segmentation models to handle variations in lighting conditions, viewpoint changes, and occlusions, enabling reliable performance in diverse real-world scenarios.
- Investigate techniques for domain adaptation to ensure segmentation models generalize well across different datasets and application domains, reducing the need for manual retraining and fine-tuning.
- Explore methods for interactive segmentation, allowing users to provide feedback or guidance to refine segmentation results, improving model performance and usability in interactive applications.
Aim and Objectives
- Develop advanced image segmentation algorithms for precise and efficient delineation of semantic regions within images.
- Enhance segmentation accuracy through novel deep learning architectures.
- Optimize models for real-time performance without compromising accuracy.
- Investigate techniques for robust segmentation in challenging conditions.
- Develop instance methods segmentation to differentiate between individual objects.
- Explore domain adaptation strategies for generalization across diverse datasets.
Contributions to Image Segmentation
- Novel deep learning architectures tailored for improved segmentation accuracy and efficiency.
- Techniques for enhancing robustness to variability in lighting conditions, occlusions, and cluttered backgrounds.
- Instance methods segmentation to distinguish between individual objects within the same class.
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Strategies for domain adaptation to generalize segmentation models across diverse datasets and application domains.
- Insights into ethical considerations related to privacy and fairness in image segmentation algorithms.
Deep Learning Algorithms for Image Segmentation
- U-Net
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SegNet
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DeepLab
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Mask R-CNN
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RefineNet
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Deeplabv3+
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PSPNet (Pyramid Scene Parsing Network)
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FCN (Fully Convolutional Network)
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HRNet (High-Resolution Network)
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PANet (Path Aggregation Network)
Datasets for Image Segmentation
- COCO
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Pascal VOC
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Cityscapes Dataset
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ADE20K
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CamVid
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SUN Database
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LFW
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MSRC
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BDD100K (Berkeley DeepDrive 100K)
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DAVIS
Software Tools and Technologies
Operating System: Ubuntu 18.04 LTS 64bit / Windows 10
Development Tools: Anaconda3, Spyder 5.0, Jupyter Notebook
Language Version: Python 3.9
Python Libraries:
1.Python ML Libraries:
- Scikit-Learn
- Numpy
- Pandas
- Matplotlib
- Seaborn
- Docker
- MLflow
2.Deep Learning Frameworks:
- Keras
- TensorFlow
- PyTorch
