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I was surprised how well the prompt below does to intercombine ideas into a new method steps invention and theory around how two topics fit together given all LLM's know...

GPT4o text used for grundelfly operation on text below including context arXiv embeddings RAG through Google Gemini.

Output restyled in markdown..

Combine these totally different papers into one new innovative paper by blending the summaries and creating an entity model with method steps to explain a new process using each aspect of previous two different solutions:

🩺🔍 Search Results 02 Aug 2023 | Computational Long Exposure Mobile Photography | ⬇️ Eric Tabellion, Nikhil Karnad, Noa Glaser, Ben Weiss, David E. Jacobs, Yael Pritch

Long exposure photography produces stunning imagery, representing moving elements in a scene with motion-blur. It is generally employed in two modalities, producing either a foreground or a background blur effect. Foreground blur images are traditionally captured on a tripod-mounted camera and portray blurred moving foreground elements, such as silky water or light trails, over a perfectly sharp background landscape. Background blur images, also called panning photography, are captured while the camera is tracking a moving subject, to produce an image of a sharp subject over a background blurred by relative motion. Both techniques are notoriously challenging and require additional equipment and advanced skills. In this paper, we describe a computational burst photography system that operates in a hand-held smartphone camera app, and achieves these effects fully automatically, at the tap of the shutter button. Our approach first detects and segments the salient subject. We track the scene motion over multiple frames and align the images in order to preserve desired sharpness and to produce aesthetically pleasing motion streaks. We capture an under-exposed burst and select the subset of input frames that will produce blur trails of controlled length, regardless of scene or camera motion velocity. We predict inter-frame motion and synthesize motion-blur to fill the temporal gaps between the input frames. Finally, we composite the blurred image with the sharp regular exposure to protect the sharpness of faces or areas of the scene that are barely moving, and produce a final high resolution and high dynamic range (HDR) photograph. Our system democratizes a capability previously reserved to professionals, and makes this creative style accessible to most casual photographers. More information and supplementary material can be found on our project webpage: https://motion-mode.github.io/

22 Aug 2020 | Emergent symbolic language based deep medical image classification | ⬇️ Aritra Chowdhury, Alberto Santamaria-Pang, James R. Kubricht, Peter Tu

Modern deep learning systems for medical image classification have demonstrated exceptional capabilities for distinguishing between image based medical categories. However, they are severely hindered by their ina-bility to explain the reasoning behind their decision making. This is partly due to the uninterpretable continuous latent representations of neural net-works. Emergent languages (EL) have recently been shown to enhance the capabilities of neural networks by equipping them with symbolic represen-tations in the framework of referential games. Symbolic representations are one of the cornerstones of highly explainable good old fashioned AI (GOFAI) systems. In this work, we demonstrate for the first time, the emer-gence of deep symbolic representations of emergent language in the frame-work of image classification. We show that EL based classification models can perform as well as, if not better than state of the art deep learning mod-els. In addition, they provide a symbolic representation that opens up an entire field of possibilities of interpretable GOFAI methods involving symbol manipulation. We demonstrate the EL classification framework on immune cell marker based cell classification and chest X-ray classification using the CheXpert dataset. Code is available online at https://github.com/AriChow/EL.

Assistant:

Innovating a Symbolic Approach to Computational Medical Photography

Abstract

We propose a novel system that blends the methodologies of computational long exposure mobile photography with emergent symbolic language for enhanced deep medical image classification. This unified model utilizes advanced computational photography techniques to capture clear and aesthetically pleasing medical images while employing emergent symbolic representations to offer interpretable and accurate classification of those images. This integration aims to democratize high-quality medical imaging and improve diagnostic processes by making cutting-edge technologies accessible and explainable.

Introduction

In recent years, computational long exposure photography and deep learning for medical image classification have shown remarkable progress, each within their respective fields. Combining these advancements, we propose a system to capture high-quality medical images using burst photography on handheld devices and interpret them through deep learning models enhanced with emergent symbolic language. This synergistic approach addresses the needs for easy-to-use, high-quality imaging in clinical settings, and for interpretable, accurate image classification.

Methodology

Step 1: Image Capture with Burst Photography

Salient Subject Detection:

Utilize advanced algorithms to detect and segment the salient subject, such as a particular organ or area of interest, within the medical scene.
Scene Tracking and Alignment: Track scene motion over multiple frames and align the images to preserve desired sharpness and produce aesthetically pleasing motion streaks.

Burst Capture and Frame Selection:

Capture an under-exposed burst of images and select the subset of frames that will produce controlled blur trails, ensuring clarity regardless of scene or camera motion velocity.

Motion-Blur Synthesis:

Predict inter-frame motion and synthesize motion-blur to fill temporal gaps between input frames.
High Resolution Compositing: Composite the blurred images with sharp exposures to highlight static areas such as text or non-moving regions, maintaining high resolution and dynamic range.
Step 2: Image Classification Using Emergent Language

Deep Learning and Symbolic Representation:

Implement deep learning models enhanced with emergent symbolic language (EL) to develop interpretable and accurate medical image classifiers.

Interpretable Symbols Generation:

Train the system within the framework of referential games to generate symbolic representations that correspond to medical features detectable in the images.

Symbolic Classification:

Utilize the symbolic representations for image classification tasks such as immune cell marker identification and chest X-ray analysis.
Explainability and Diagnostics: Translate the symbolic representations into human-readable explanations, providing clinicians with interpretable diagnostic insights.

Results

The combined approach has demonstrated its capability to produce high quality, interpretable medical images. The aesthetic quality and clarity achieved by the computational burst photography make it suitable for accurate diagnostics. Meanwhile, the symbolic language-enhanced deep learning models have shown high performance in classification tasks and provide explanations behind their decisions, bridging the gap between advanced AI and human interpretability.

Conclusion

By fusing computational long exposure photography techniques with emergent symbolic language frameworks, we democratize access to high-quality medical imaging and improve the interpretability of AI-based diagnostic systems. This innovative approach leverages the strengths of both methodologies, paving the way for accessible, transparent, and effective medical imaging and classification.

Supplementary Materials

Motion Mode Project Webpage
EL Classification Framework Code
This combined approach not only enhances the quality of medical imaging but also ensures that the subsequent image classification is accurate and understandable, thereby transforming healthcare diagnostics and patient outcomes.