Segment Anything Model Demonstrates Promising Zero-Shot Segmentation of Eye Features in Virtual Reality Setups

Kernkonzepte

The Segment Anything Model (SAM) can effectively segment the pupil and iris in eye images captured in virtual reality setups, but struggles to accurately segment the sclera without extensive manual guidance.

Zusammenfassung

This study evaluates the performance of the Segment Anything Model (SAM) in segmenting key eye features - the pupil, iris, and sclera - from eye images captured in virtual reality (VR) setups.
The authors utilized two publicly available datasets, OpenEDS2019 and OpenEDS2020, which contain eye images recorded using VR head-mounted displays. They assessed SAM's zero-shot segmentation capabilities using various prompting strategies, including automatic "everything" mode, single/multiple point prompts, bounding box prompts, and combinations thereof.
The results show that SAM performs exceptionally well in segmenting the pupil, achieving an Intersection over Union (IoU) score of over 93% in both datasets. For the iris, SAM also demonstrated strong performance, reaching an IoU of over 86% when using a combination of bounding box and point prompts.
However, SAM struggled to accurately segment the sclera, with its best IoU score being only around 62%. The authors attribute this to the sclera's low contrast and blurred edges with the iris, as well as non-uniform illumination and shadows from the eyelids, which pose significant challenges for the model.
Overall, the study suggests that SAM has promising zero-shot capabilities for segmenting certain eye features, particularly the pupil, which is a crucial component in many gaze estimation pipelines. The authors recommend further research to explore fine-tuning SAM on eye image datasets or developing a bespoke foundation model tailored for eye tracking applications.

Statistiken

The pupil segmentation achieved an IoU of 93.34% in the OpenEDS2020 dataset using the BBOXP4 prompting strategy.
The iris segmentation achieved an IoU of 86.63% in the OpenEDS2019 dataset using the BBOXP1-1 prompting strategy.
The sclera segmentation achieved an IoU of 62.19% in the OpenEDS2019 dataset using the BBOXP4-4 prompting strategy.

Zitate

"SAM exhibits strong capabilities in pupil segmentation which can be used in pupil detection pipelines, a crucial element in numerous gaze-tracking frameworks."
"SAM struggles to recognize the sclera as a distinct object and requires extensive guidance from an annotator through the use of manual prompts."

Wichtige Erkenntnisse aus

Zero-Shot Segmentation of Eye Features Using the Segment Anything Model (SAM)

by Virm... um arxiv.org 04-09-2024

https://arxiv.org/pdf/2311.08077.pdf

Tiefere Fragen

How could the performance of SAM be further improved for segmenting the iris and sclera in eye images?

To enhance SAM's performance in segmenting the iris and sclera in eye images, several strategies can be implemented:

Fine-tuning on Eye Image Datasets: Fine-tuning SAM on a small set of eye images could potentially improve its performance by adapting the model to the specific characteristics of eye features.

Augmentation Techniques: Implementing advanced image augmentation techniques, such as rotation, scaling, and flipping, can help SAM learn variations in eye images, leading to more robust segmentation results.

Text Prompting Integration: Integrating text prompts into SAM could simplify the annotation process for users without technical expertise, providing more precise guidance for segmenting intricate features like the iris and sclera.

Refinement of Prompting Strategies: Continuously refining and optimizing the prompting strategies used with SAM, such as combining bounding box prompts with point prompts, can help provide clearer guidance for segmenting complex eye features.

Development of Specialized Foundation Models: Developing a foundation model specifically trained on comprehensive eye image datasets, similar to MedSAM for medical images, could unlock new possibilities for automating eye tracking data annotation and improve segmentation accuracy for iris and sclera features.

How could the performance of SAM be further improved for segmenting the iris and sclera in eye images?

To enhance SAM's performance in segmenting the iris and sclera in eye images, several strategies can be implemented:

Fine-tuning on Eye Image Datasets: Fine-tuning SAM on a small set of eye images could potentially improve its performance by adapting the model to the specific characteristics of eye features.

Augmentation Techniques: Implementing advanced image augmentation techniques, such as rotation, scaling, and flipping, can help SAM learn variations in eye images, leading to more robust segmentation results.

Text Prompting Integration: Integrating text prompts into SAM could simplify the annotation process for users without technical expertise, providing more precise guidance for segmenting intricate features like the iris and sclera.

Refinement of Prompting Strategies: Continuously refining and optimizing the prompting strategies used with SAM, such as combining bounding box prompts with point prompts, can help provide clearer guidance for segmenting complex eye features.

Development of Specialized Foundation Models: Developing a foundation model specifically trained on comprehensive eye image datasets, similar to MedSAM for medical images, could unlock new possibilities for automating eye tracking data annotation and improve segmentation accuracy for iris and sclera features.

How could the performance of SAM be further improved for segmenting the iris and sclera in eye images?

To enhance SAM's performance in segmenting the iris and sclera in eye images, several strategies can be implemented:

Fine-tuning on Eye Image Datasets: Fine-tuning SAM on a small set of eye images could potentially improve its performance by adapting the model to the specific characteristics of eye features.

Augmentation Techniques: Implementing advanced image augmentation techniques, such as rotation, scaling, and flipping, can help SAM learn variations in eye images, leading to more robust segmentation results.

Text Prompting Integration: Integrating text prompts into SAM could simplify the annotation process for users without technical expertise, providing more precise guidance for segmenting intricate features like the iris and sclera.

Refinement of Prompting Strategies: Continuously refining and optimizing the prompting strategies used with SAM, such as combining bounding box prompts with point prompts, can help provide clearer guidance for segmenting complex eye features.

Development of Specialized Foundation Models: Developing a foundation model specifically trained on comprehensive eye image datasets, similar to MedSAM for medical images, could unlock new possibilities for automating eye tracking data annotation and improve segmentation accuracy for iris and sclera features.