Top Research Papers on Computer Vision

This undergraduate textbook-reference comprehensively examines computer vision techniques, analysis, and real-world applications in which they are used.

The development and refining of deep neural networks that can now accurately identify objects in images and remember past surgical events has sparked a surge in the applications of CV to analyze intraoperative video and has allowed for the accurate identification of surgical phases (steps) and instruments across a variety of procedures.

The intersection of vision transformers and medical images is investigated, an overview of various ViT based frameworks that are being used by different researchers to decipher the obstacles in medical computer vision is proffered, and the pointers to possible solutions for future direction are deliberated.

A comprehensive review of the state of the art of computer vision techniques and their applications in manufacturing industries, including the most common methods, including feature detection, recognition, segmentation, and three-dimensional modeling.

In this work, we study a complete framework of Video Compressive Sensing (VCS), from capturing a sequence of video frames in one single compressed measurement to reconstructing the original frames. To our best knowledge, we present the first end-to-end sampling and recovery network built upon video Transformers, widely explored in vision related tasks, to capture long-range spatio-temporal relations. Our proposed Video Transformer for Snapshot Compressive Imaging recovery (ViT-SCI) is based on Spatio-temporal Convolutional Multi-Head Attention (ST-ConvMHA) which is an extended version of the f...

A comprehensive survey of more than 200 major fashion-related works covering four main aspects for enabling intelligent fashion and highlighting promising directions for future research.

Deep learning algorithms have brought a revolution to the computer vision community by introducing non-traditional and efficient solutions to several image-related problems that had long remained unclear or difficult to solve.

During last few years the computer applications have gone dramatic transformation from simple data processing to machine learning, thanks to the availability and accessibility of huge volume of data collected through sensors and internet. The idea of machine learning demonstrates and propagates the facts that computer has the ability to improve itself with the passage of time. The western countries have shown great interest on the topic of machine learning, computer vision, and pattern recognition via organizing conferences, workshops, collective discussion, experimentation, and real life impl...

This comprehensive review of modern computing systems looks ahead to the future of research in the field, highlighting key challenges and emerging trends, and underscoring their importance in cost-effectively driving technological progress.

In this paper, an in-depth analysis promotes a broad vision for bringing Serverless to the Edge Computing and issues major challenges for serverless to be met before entering Edge computing.

Focusing on the models of attention mechanisms commonly used in computer vision, their categorizations, principles, and outlook are summarized in this overview.

This special issue covers a wide range of topics from the area of Computer Vision, Pattern Recognition, and Machine Learning.This breadth of scope is reflected by the papers included in this special issue, which touch topics including geometric Computer Vision, medical image processing, physical scene understanding, and interpretability of deep neural networks.This special issue consists of extended versions of the best papers originally presented at the 42nd German Conference on Pattern Recognition (DAGM GCPR 2020), held virtually between September 28th and October 1st, 2020.This special issu...

The objective is to provide an overview as well as a critical analysis of the status of GAN research in terms of relevant progress toward critical computer vision application requirements and discuss the most compelling applications in computer vision.

This survey provides a comprehensive review of various attention mechanisms in computer vision and categorize them according to approach, such as channel attention, spatial attention, temporal attention, and branch attention.

The correlated optoelectronic characteristics of multi-terminal mixed-dimensional graphene–germanium heterostructure devices can be used for the accurate detection and robust tracking of dim targets.

This article provides an in-depth and practical review of tensors and tensor methods in the context of representation learning and deep learning, with a particular focus on visual data analysis and computer vision applications.

This paper proposes a two-tier edge computing based model that takes into account of both the limited computing capability in cloudlets and the unstable network condition to the TMC, and shows that the proposed hybrid edge-cloud solution outperforms both the cloud-only and edge-only solutions.

Fractional calculus is an abstract idea exploring interpretations of differentiation having non-integer order. For a very long time, it was considered as a topic of mere theoretical interest. However, the introduction of several useful definitions of fractional derivatives has extended its domain to applications. Supported by computational power and algorithmic representations, fractional calculus has emerged as a multifarious domain. It has been found that the fractional derivatives are capable of incorporating memory into the system and thus suitable to improve the performance of locality-aw...

This review extensively reviews 200+ papers of plant phenotyping in the light of its technical evolution, spanning over twenty years, including imaging technologies, plant datasets, and state-of-the-art phenotypesing methods.

Recent progress in the development of modern computer vision techniques—powered by deep learning—for medical applications, focusing on medical imaging, medical video, and clinical deployment is surveyed.

Over the last years deep learning methods have been shown to outperform previous state-of-the-art machine learning techniques in several fields, with computer vision being one of the most prominent cases. This review paper provides a brief overview of some of the most significant deep learning schemes used in computer vision problems, that is, Convolutional Neural Networks, Deep Boltzmann Machines and Deep Belief Networks, and Stacked Denoising Autoencoders. A brief account of their history, structure, advantages, and limitations is given, followed by a description of their applications in var...

This research aims to guide practitioners to successfully find suitable approaches for a particular project, with a focus on state-of-the-art methods in a typical vision-based scheme.

Automated visual understanding of our diverse and open world demands computer vision models to generalize well with minimal customization for specific tasks, similar to human vision. Computer vision foundation models, which are trained on diverse, large-scale dataset and can be adapted to a wide range of downstream tasks, are critical for this mission to solve real-world computer vision applications. While existing vision foundation models such as CLIP, ALIGN, and Wu Dao 2.0 focus mainly on mapping images and textual representations to a cross-modal shared representation, we introduce a new co...

Most animal species on Earth are insects, and recent reports suggest that their abundance is in drastic decline. Although these reports come from a wide range of insect taxa and regions, the evidence to assess the extent of the phenomenon is sparse. Insect populations are challenging to study, and most monitoring methods are labor intensive and inefficient. Advances in computer vision and deep learning provide potential new solutions to this global challenge. Cameras and other sensors can effectively, continuously, and noninvasively perform entomological observations throughout diurnal and sea...

An elementary understanding of CNN components and their functions, including input layers, convolution layers, pooling layers, activation functions, batch normalization, dropout, fully connected layers, and output layers are presented.

Automating the process of construction progress monitoring through computer vision can enable effective control of projects. Systematic classification of available methods and technologies is necessary to structure this complex, multi-stage process. Using the PRISMA framework, relevant studies in the area were identified. The various concepts, tools, technologies, and algorithms reported by these studies were iteratively categorised, developing an integrated process framework for Computer-Vision-Based Construction Progress Monitoring (CV-CPM). This framework comprises: data acquisition and 3D-...

This work provides a detailed review of recent and state-of-the-art research advances of deep reinforcement learning in computer vision, and proposes a categorization ofDeep reinforcement learning methodologies and discusses their advantages and limitations.

This review elaborates the two aspects of using traditional image-processing methods and deep learning-based methods to solve weed detection problems and provides an overview of various methods for weed detection in recent years.

In this system, a retinomorphic photomemristor array, working as dynamic vision reservoir, embeds past motion frames as hidden states into the present frame through inherent dynamic memory, which facilitates accurate recognition of past and prediction of future motions with machine learning algorithms.

This survey paper is intended to provide a preliminary review of the available literature on the computer vision efforts against COVID-19 pandemic, and to make it available to computer vision researchers to save precious time.

This work automatically extracts body poses and movement kinematics from the videos of at-risk infants and calculates how much they deviate from a group of healthy infants using Naïve Gaussian Bayesian Surprise.

Monitoring water uptake in cementitious systems is crucial to assess their durability against corrosion, salt attack, and freeze-thaw damage. However, gauging absorption currently relies on labor-intensive and infrequent weight measurements, as outlined in ASTM C1585. To address this issue, we introduce a custom computer vision model trained on 6234 images, consisting of 4000 real and 2234 synthetic, that automatically detects the water level in prismatic samples absorbing water. This model provides accurate and frequent estimations of water penetration values every minute. After training the ...

Chromosomes of all species studied so far display a variety of higher-order organisational features, such as self-interacting domains or loops. These structures, which are often associated to biological functions, form distinct, visible patterns on genome-wide contact maps generated by chromosome conformation capture approaches such as Hi-C. Here we present Chromosight, an algorithm inspired from computer vision that can detect patterns in contact maps. Chromosight has greater sensitivity than existing methods on synthetic simulated data, while being faster and applicable to any type of genome...

Current CV techniques that have been applied to minimally invasive surgery and their clinical applications are reviewed, and the challenges and obstacles that remain to be overcome for broader implementation and adoption of CV in surgery are discussed.

This paper performs a cross-sectional model-based quantitative census covering factors such as age, gender, NSFW content scoring, class- wise accuracy, human-cardinality-analysis, and the semanticity of the image class information in order to statistically investigate the extent and subtleties of ethical transgressions.

How computer vision techniques can help transportation systems to become smarter is shown by presenting a holistic picture of the literature on different CV applications in the ITS context by bringing together research from various sources.

Renewable energy forecasting is crucial for integrating variable energy sources into the grid. It allows power systems to address the intermittency of the energy supply at different spatiotemporal scales. To anticipate the future impact of cloud displacements on the energy generated by solar facilities, conventional modeling methods rely on numerical weather prediction or physical models, which have difficulties in assimilating cloud information and learning systematic biases. Augmenting computer vision with machine learning overcomes some of these limitations by fusing real-time cloud cover o...

This overview surveys CV methods for numerically encoding the visual information contained in a microstructural image using either feature-based representations or convolutional neural network layers, which then provides input to supervised or unsupervised ML algorithms that find associations and trends in the high-dimensional image representation.

Recent years have witnessed enormous progress in AI-related fields such as computer vision, machine learning, and autonomous vehicles. As with any rapidly growing field, it becomes increasingly difficult to stay up-to-date or enter the field as a beginner. While several survey papers on particular sub-problems have appeared, no comprehensive survey on problems, datasets, and methods in computer vision for autonomous vehicles has been published. This monograph attempts to narrow this gap by providing a survey on the state-of-the-art datasets and techniques. Our survey includes both the historic...

An overview of HAR applications in sports primarily based on Computer Vision as the main contribution is presented, along with popular publicly available datasets for this purpose, including actions of everyday activities.