arXiv subjects

Pål Anders Floor

Publications and source records attributed to Pål Anders Floor.

5 recordsLinked to original sources

arXiv2025

3D Reconstruction of the Human Colon from Capsule Endoscope Video

As the number of people affected by diseases in the gastrointestinal system is ever-increasing, a higher demand on preventive screening is inevitable. This will significantly increase the workload on gastroenterologists. To help reduce the workload, tools from computer vision may be helpful. In this paper, we investigate the possibility of constructing 3D models of whole sections of the human colon using image sequences from wireless capsule endoscope video, providing enhanced viewing for gastroenterologists. As capsule endoscope images contain distortion and artifacts non-ideal for many 3D reconstruction algorithms, the problem is challenging. However, recent developments of virtual graphics-based models of the human gastrointestinal system, where distortion and artifacts can be enabled or disabled, makes it possible to ``dissect'' the problem. The graphical model also provides a ground truth, enabling computation of geometric distortion introduced by the 3D reconstruction method. In this paper, most distortions and artifacts are left out to determine if it is feasible to reconstruct whole sections of the human gastrointestinal system by existing methods. We demonstrate that 3D reconstruction is possible using simultaneous localization and mapping. Further, to reconstruct the gastrointestinal wall surface from resulting point clouds, varying greatly in density, Poisson surface reconstruction is a good option. The results are promising, encouraging further research on this problem.

cs.CV↗

arXiv2025

Influence of color correction on pathology detection in Capsule Endoscopy

Pathology detection in Wireless Capsule Endoscopy (WCE) using deep learning has been explored in the recent past. However, deep learning models can be influenced by the color quality of the dataset used to train them, impacting detection, segmentation and classification tasks. In this work, we evaluate the impact of color correction on pathology detection using two prominent object detection models: Retinanet and YOLOv5. We first generate two color corrected versions of a popular WCE dataset (i.e., SEE-AI dataset) using two different color correction functions. We then evaluate the performance of the Retinanet and YOLOv5 on the original and color corrected versions of the dataset. The results reveal that color correction makes the models generate larger bounding boxes and larger intersection areas with the ground truth annotations. Furthermore, color correction leads to an increased number of false positives for certain pathologies. However, these effects do not translate into a consistent improvement in performance metrics such as F1-scores, IoU, and AP50. The code is available at https://github.com/agossouema2011/WCE2024. Keywords: Wireless Capsule Endoscopy, Color correction, Retinanet, YOLOv5, Detection

cs.CV↗

arXiv2022

Shannon-Kotel'nikov Mappings for Analog Point-to-Point Communications

In this paper an approach to joint source-channel coding (JSCC) named Shannon-Kotel'nikov mappings (S-K mappings) is presented. S-K mappings are continuous, or piecewise continuous direct source-to-channel mappings operating directly on amplitude continuous and discrete time signals. Such mappings include several existing JSCC schemes as special cases. Many existing approaches to analog- or hybrid discrete analog JSCC provide both excellent performance as well as robustness to variations in noise level. This at low delay and relatively low complexity. However, a theory explaining their performance and behaviour on a general basis, as well as guidelines on how to construct close to optimal mappings in general, does not currently exist. Therefore, such mappings are often found based on educated guesses inspired of configurations that are known in advance to produce good solutions, combination of already existing mappings, numerical optimization or machine learning methods. The objective of this paper is to introduce a theoretical framework for analysis of analog- or hybrid discrete analog S-K mappings. This framework will enable calculation of distortion when applying such schemes on point-to-point links, reveal more about their fundamental nature, and provide guidelines on how they should be constructed in order to perform well at both low and arbitrary complexity and delay. Such guidelines will likely help constrain solutions to numerical approaches and help explain why machine learning approaches finds the solutions they do. This task is difficult and we do not provide a complete framework at this stage: We focus on high SNR and memoryless sources with an arbitrary continuous unimodal density function and memoryless Gaussian channels. We also provide example of mappings based on surfaces which are chosen based on the provided theory.

cs.IT↗

arXiv2022

Tools for Analysis of Shannon-Kotel'nikov Mappings

This document introduces tools from differential geometry needed for the analysis of a subset of Joint Source-Channel Codes (JSCC) named Shannon-Kotel'nikov (S-K) mappings. New results based on these concepts are further provided.

cs.IT↗

arXiv2014

Zero-Delay Joint Source-Channel Coding for a Multivariate Gaussian on a Gaussian MAC

In this paper, communication of a Multivariate Gaussian over a Gaussian Multiple Access Channel is studied. Distributed zero-delay joint source-channel coding (JSCC) solutions to the problem are given. Both nonlinear and linear approaches are discussed. The performance upper bound (signal-to-distortion ratio) for arbitrary code length is also derived and Zero-delay cooperative JSCC is briefly addressed in order to provide an approximate bound on the performance of zero-delay schemes. The main contribution is a nonlinear hybrid discrete-analog JSSC scheme based on distributed quantization and a linear continuous mapping named Distributed Quantizer Linear Coder (DQLC). The DQLC has promising performance which improves with increasing correlation, and is robust against variations in noise level. The DQLC exhibits a constant gap to the performance upper bound as the signal-to-noise ratio (SNR) becomes large for any number of sources and values of correlation. Therefore it outperforms a linear solution (uncoded transmission) in any case when the SNR gets sufficiently large.

cs.IT↗