Hasil untuk "eess.IV"

Roberto Gutierrez Beraldo, Ricardo Suyama

In this book, written in Portuguese, we discuss what ill-posed problems are and how the regularization method is used to solve them. In the form of questions and answers, we reflect on the origins and future of regularization, relating the similarities and differences of its meaning in different areas, including inverse problems, statistics, machine learning, and deep learning.

Asahi Miyazaki, Tsuyoshi Okita

Masked image modeling is one of the most poplular objectives of training. Recently, the SparK model has been proposed with superior performance among self-supervised learning models. This paper proposes a new mask pattern for this SparK model, proposing it as the Mesh Mask-ed SparK model. We report the effect of the mask pattern used for image masking in pre-training on performance.

Ziyang Liu, Xingchen Xiao, Yueyang Xu

3D-display technology has been a promising emerging area with potential to be the core of next-generation display technology. When directly observing unprocessed images and text through a naked-eye 3D display device, severe distortion and jaggedness will be displayed, which will make the display effect much worse. In this work, we try to settle down such degradation with spatial and frequency processing, furthermore, we make efforts to extract degenerate function of columnar lens array thus fundamentally eliminating degradation.

Jaeho Kim, Daniel David, Ana Vizitiv

This paper evaluates Tucker decomposition and Singular Value Decomposition (SVD) for compressing neuroimaging data. Tucker decomposition preserves multi-dimensional relationships, achieving superior reconstruction fidelity and perceptual similarity. SVD excels in extreme compression but sacrifices fidelity. The results highlight Tucker decomposition's suitability for applications requiring the preservation of structural and temporal relationships.

Gaocheng Ma, Yinfeng Chai, Tianhao Jiang et al.

Image compression has been the subject of extensive research for several decades, resulting in the development of well-known standards such as JPEG, JPEG2000, and H.264/AVC. However, recent advancements in deep learning have led to the emergence of learned image compression methods that offer significant improvements in coding efficiency compared to traditional codecs. These learned compression techniques have shown noticeable gains and even outperformed traditional schemes

Wei-Hsiang Shen, Meng-Lin Li

Deep learning has shown great success in high-level image analysis problems; yet its efficacy relies on the quality and diversity of the training data. In this work, we introduce a copypaste image augmentation for ultrasound images. The Poisson image editing technique is used to generate realistic and seamless boundary transitions around the pasted image. Results showed that the proposed image augmentation technique improves training performance in terms of higher objective metrics and more stable training results.

Yongsong Huang, Shinichiro Omachi

The ability of generative models to accurately fit data distributions has resulted in their widespread adoption and success in fields such as computer vision and natural language processing. In this chapter, we provide a brief overview of the application of generative models in the domain of infrared (IR) image super-resolution, including a discussion of the various challenges and adversarial training methods employed. We propose potential areas for further investigation and advancement in the application of generative models for IR image super-resolution.

Manuel Cossio

Computational pathology is a field that has complemented various subspecialties of diagnostic pathology over the last few years. In this article a brief analyzis the different applications in nephrology is developed. To begin, an overview of the different forms of image production is provided. To continue, the most frequent applications of computer vision models, the salient features of the different clinical applications, and the data protection considerations encountered are described. To finish the development, I delve into the interpretability of these applications, expanding in depth on the three dimensions of this area.

Suman Kunwar

Interests in digital image processing are growing enormously in recent decades. As a result, different data compression techniques have been proposed which are concerned mostly with the minimization of information used for the representation of images. With the advances of deep neural networks, image compression can be achieved to a higher degree. This paper describes an overview of JPEG Compression, Discrete Fourier Transform (DFT), Convolutional Neural Network (CNN), quality metrics to measure the performance of image compression and discuss the advancement of deep learning for image compression mostly focused on JPEG, and suggests that adaptation of model improve the compression.

David Barina

In recent years, a bag with image and video compression formats has been torn. However, most of them are focused on lossy compression and only marginally support the lossless mode. In this paper, I will focus on lossless formats and the critical question: "Which one is the most efficient?" It turned out that FLIF is currently the most efficient format for lossless image compression. This finding is in contrast to that FLIF developers stopped its development in favor of JPEG XL.

L. Yaroslavsky

The article addresses the problem of image sampling with minimal possible sampling rates and reviews the recent advances in sampling theory and methods: modern formulations of the sampling theorems, potentials and limitations of Compressed sensing methods and a practical method of image sampling and reconstruction with sampling rates close to the theoretical minimum.

Gajendra Singh Solanki

Any practical imaging system, be it reflection, refraction, or diffraction-based, is basically band limited and therefore is bound to be affected by airy pattern noise. Apodization of the passband is most often applied as a preferred method to get rid of these unwanted artifacts. But it comes at a cost of losing resolution as higher frequencies are tapered in apodization. Here, we propose and demonstrate a novel technique for discriminating the airy lobes based on their phase. We could thus eliminate the dominant noisy lobes without compromising with the resolution of the imaging system.

A. C. Bovik

In this rather informal paper and talk I will discuss my own experiences, feelings, and evolution as an Image Processing and Digital Video educator trying to navigate the Deep Learning revolution. I will discuss my own ups and downs of trying to deal with extremely rapid technological changes, and how I have reacted to, and dealt with consequent dramatic changes in the relevance of the topics I've taught for three decades. I have arranged the discussion in terms of the stages, over time, of my progression dealing with these sea changes.

Paul Kirkland

The Dynamic Vision Sensor (DVS) has many attributes that allow it to be well suited to the task for UAV Detection. This paper is the first to look at exploiting the features of an Event Camera solely for Drone Detection while combining it with a Spiking Neural Network (SNN) trained using the unsupervised approach of Spike-Time-Dependent Plasticity (STDP) for feature and pattern recognition for detection. Highlighting the key features and current drawbacks with the technology while comparing real and simulated data to show how future devices could overcome these drawbacks to help tackle this current problem.

Mikhail Chekanov, Oleg Shipitko

We present the collections of images of the same rotating plastic object made in X-ray and visible spectra. Both parts of the dataset contain 400 images. The images are maid every 0.5 degrees of the object axial rotation. The collection of images is designed for evaluation of the performance of circular motion estimation algorithms as well as for the study of X-ray nature influence on the image analysis algorithms such as keypoints detection and description. The dataset is available at https://github.com/Visillect/xvcm-dataset.

Andréanne Lemay

Organ localization can be challenging considering the heterogeneity of medical images and the biological diversity from one individual to another. The contribution of this paper is to overview the performance of the object detection model, YOLOv3, on kidney localization in 2D and in 3D from CT scans. The model obtained a 0.851 Dice score in 2D and 0.742 in 3D. The SSD, a similar state-of-the-art object detection model, showed similar scores on the test set. YOLOv3 and SSD demonstrated the ability to detect kidneys on a wide variety of CT scans including patients suffering from different renal conditions.

Ruitao Xie, Libo Liu, Jingxin Liu et al.

We present a summary of transfer learning based methods for several challenging myopic fundus image analysis tasks including classification of pathological and non-pathological myopia,localisation of fovea,and segmentation of optic disc.By adapting existing popular deep learning architectures,our proposed methods have achieved 1st and 2nd place in several tasks at the Pathologic Myopia Challenge held at ISBI2019.

Nick Pawlowski, Ben Glocker

Deep learning builds the foundation for many medical image analysis tasks where neuralnetworks are often designed to have a large receptive field to incorporate long spatialdependencies. Recent work has shown that large receptive fields are not always necessaryfor computer vision tasks on natural images. We explore whether this translates to certainmedical imaging tasks such as age and sex prediction from a T1-weighted brain MRI scans.

Chetan Sai Tutika, Charan Vallapaneni, Karthikeyan B

This paper aims to develop a robust and flexible algorithm for vacant parking space detections using the image processing capabilities of OpenCV. It removes the need for independent sensors to detect a car and instead, uses real-time images derived from various sources and servers to consider a group of slots together. This greatly decreases the expenses required to design an efficient parking system and increases the flexibility of the operation. This method includes the use of a portable processing system with recognition algorithm and has the option of extracting and importing images to the specified servers. The results can be viewed on a custom website with the option to reserve the particular empty slots and GPS navigations to the selected slots.

Enrico Celeghini

We propose an approach to image processing related to algebraic operators acting in the space of images. In view of the interest in the applications in optics and computer science, mathematical aspects of the paper have been simplified as much as possible. Underlying theory, related to rigged Hilbert spaces and Lie algebras, is discussed elsewhere