Academic Articles & Research Papers

A coarse to fine network for fast and accurate object detection in high‐resolution images

Abstract Because of the popularisation of high‐resolution images, detecting objects in these images quickly and accurately has attracted increasing attention in recent studies. Current convolutional neural networks (CNN)‐based detection met...

MaskDis R‐CNN: An instance segmentation algorithm with adversarial network for herd pigs

Abstract The current instance segmentation method can achieve satisfactory results in common scenarios. However, under the overlap or partial occlusion between targets caused by the complex scenes, accurate segmentation of pigs remains a ch...

TSER: A Two-Stage Character Segmentation Network With Two-Stream Attention and Edge Refinement

Segmenting characters in an image is a classic yet challenging task in computer vision. Correctly determining boundaries of adhesive characters with various scales and shapes is essential for character segmentation, especially for separatin...

Adversarial Attack on Object Detection via Object Feature-Wise Attention and Perturbation Extraction

Deep neural networks are commonly used in computer vision tasks, but they are vulnerable to adversarial samples, resulting in poor recognition accuracy. Although traditional algorithms that craft adversarial samples have been effective in a...

Deep Learning and Bidirectional Optical Flow Based Viewport Predictions for 360° Video Coding

The rapid development of virtual reality applications continues to urge better compression of 360° videos owing to the large volume of content. These videos are typically converted to 2-D formats using various projection techniques i...

Deep-Learning based Melanoma Detection using Cloud Approach

The aim of computer vision techniquesand deep learning in the era of digitalization is to derive valuable insights from them and generate novel understanding. This makes it possible to employ imaging to quickly diagnose and treat a variety ...

A Comprehensive Joint Learning System to Detect Skin Cancer

Skin, the body’s biggest organ and a barrier against heat, light, damage, and infection can be affected by many diseases. However, a correct diagnosis can lead to proper treatment. Skin diseases must be identified early to reduce ski...

Convolutional-de-convolutional neural networks for recognition of surgical workflow

Computer-assisted surgery (CAS) has occupied an important position in modern surgery, further stimulating the progress of methodology and technology. In recent years, a large number of computer vision-based methods have been widely used in ...

Approaches to Improve the Quality of Person Re-Identification for Practical Use

The idea of the person re-identification (Re-ID) task is to find the person depicted in the query image among other images obtained from different cameras. Algorithms solving this task have important practical applications, such as illegal ...

A Lightweight Reconstruction Model via a Neural Network for a Video Super-Resolution Model

Super-resolution in image and video processing has been a challenge in computer vision, with its progression creating substantial societal ramifications. More specifically, video super-resolution methodologies aim to restore spatial details...

A Taxonomy of Deep Convolutional Neural Nets for Computer Vision

Traditional architectures for solving computer vision problems and the degree of success they enjoyed have been heavily reliant on hand-crafted features. However, of late, deep learning techniques have offered a compelling alternative -- th...

A Novel Mixed-Precision Quantization Approach for CNNs

Model size and inference speed have brought about major challenges for the deployment of Convolutional Neural Networks (CNNs) in many applications. An effective approach to address this issue is model quantization, which achieves network co...

Computer Vision and Pattern Recognition 2020

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 geome...

Enhancing Analysis of Diadochokinetic Speech Using Deep Neural Networks.

Hardware and Software Optimizations for Accelerating Deep Neural Networks: Survey of Current Trends, Challenges, and the Road Ahead

Currently, Machine Learning (ML) is becoming ubiquitous in everyday life. Deep Learning (DL) is already present in many applications ranging from computer vision for medicine to autonomous driving of modern cars as well as other sectors in ...

Deep Learning-Based Eye-Writing Recognition with Improved Preprocessing and Data Augmentation Techniques

Eye-tracking technology enables communication for individuals with muscle control difficulties, making it a valuable assistive tool. Traditional systems rely on electrooculography (EOG) or infrared devices, which are accurate but costly and...

Integrating Computer Vision: Advancements in Fire and Smoke Detection Using CNNs

Conventional fire detection systems, which depend on smoke and fire sensors, frequently have problems with calibration, susceptibility to environmental changes, and the requirement for ongoing maintenance. These flaws can result in unfavora...

Data Efficient Vision Transformer Based Model for Brain Tumor Classification in MRI

Accurate detection of brain tumors at early stages is crucial for clinical diagnosis and treatment planning. Magnetic Resonance Imaging (MRI) is the most popular method due to its soft-tissue contrast and spatial resolution. However, manual...

Real time distracted driver detection using Xception architecture and Raspberry Pi

Researchers are concentrating on developing technologies to identify and caution drivers against driving while distracted because it is a major cause of traffic accidents. According to the National Highway Traffic Safety Administrator's rep...

DAPS3D: Domain Adaptive Projective Segmentation of 3D LiDAR Point Clouds

LiDARs are one of the key sources of reliable environmental ranging information for autonomous vehicles. However, segmentation of 3D scene elements (roads, buildings, people, cars, etc.) based on LiDAR point clouds has limitations. On the o...