As identification of object or segment the object from video is one the complex problem in computer vision. Now a day's deep learning based methods providing a good result in these type of task. As video is very diverse in nature is fundamental approach (without any type of learning) compete with the DL based recent model and provide better segmentation accuracy
What is your call? Please Provide your expert comment and some solution.
The use of deep learning (DL) based methods has indeed revolutionized the field of computer vision and has led to significant improvements in object segmentation tasks. DL models are able to learn and extract relevant features from images and videos and can adapt to different types of data and environmental conditions. However, traditional or fundamental approaches to object segmentation may still have advantages in certain scenarios.
Fundamental approaches to object segmentation, such as thresholding or clustering, rely on simple rules or algorithms to identify and extract objects from images or videos. While these methods may not be as accurate as DL-based models in some cases, they can be faster and less computationally intensive. Furthermore, they may be more suitable for certain types of data or applications, such as low-resolution or noisy images, or real-time processing.
In summary, both DL-based and fundamental approaches have their strengths and weaknesses, and the choice of method depends on the specific task and the available resources. In some cases, a combination of both approaches may be the most effective solution. For example, DL-based models can be used to pre-process data and extract relevant features, which can then be fed into fundamental algorithms for further processing and refinement.
Ya that's fine. But my question is fundamental approach alone sufficient to produce better segmentation mask if you are focusing on accurate detection of object shape ( not bounding box) and the shape and size of object is not similar in different frames, means it has frequent changes?
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