Academic Articles & Research Papers

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...

A Systematic YOLO-Specific Model Selection for Mechanical Fault Identification in High-Voltage Insulators

Regular monitoring of outdoor insulators is crucial to ensure the reliable functioning of the power grid. With recent progress in computer vision technologies, traditional manual and expensive visual inspections can now be replaced by autom...

Implementation and Design of a Smart Pharmacy Automation System Using Raspberry Pi

Manual medication dispensing in pharmacies remains prone to human errors and is time-intensive, particularly under high patient loads, while existing commercial automation solutions are cost-prohibitive for small to medium-sized pharmacy se...

The NWRD Dataset: An Open-Source Annotated Segmentation Dataset of Diseased Wheat Crop

Wheat stripe rust disease (WRD) is extremely detrimental to wheat crop health, and it severely affects the crop yield, increasing the risk of food insecurity. Manual inspection by trained personnel is carried out to inspect the disease spre...

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...

Soft–Hard Attention U-Net Model and Benchmark Dataset for Multiscale Image Shadow Removal

Effective shadow removal is pivotal in enhancing visual image quality in applications such as computer vision and digital photography. During the last decades physics-based and machine learning methodologies have been proposed; however, mos...

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...

A human activity recognition method based on Vision Transformer

Abstract Human activity recognition has a wide range of applications in various fields, such as video surveillance, virtual reality and human–computer intelligent interaction. It has emerged as a significant research area in computer vision...

An accurate, efficient, and accessible AI-powered solution for wildlife re-identification in conservation

Abstract Accurate wildlife re-identification is critical for a wide range of ecological studies, including density estimation via capture–recapture, demographic analyses, and behavioral research. We pr esent GIRAFFE (Generalized Image-based...

Discriminant Affinity Matrix for Deterministic Motion Trajectory Segmentation

We present a novel algorithm (DAM) for deterministic motion trajectory segmentation by using epipolar geometry and adaptive kernel-scale voting. DAM is based on geometric models and exploits the information derived from superpixels to deter...

Review of Weed Detection Methods Based on Computer Vision

Weeds are one of the most important factors affecting agricultural production. The waste and pollution of farmland ecological environment caused by full-coverage chemical herbicide spraying are becoming increasingly evident. With the contin...

A survey of public datasets for computer vision tasks in precision agriculture

Computer vision technologies have attracted significant interest in precision agriculture in recent years. At the core of robotics and artificial intelligence, computer vision enables various tasks from planting to harvesting in the crop pr...

Mammography datasets and artificial intelligence for breast cancer detection: challenges, bias, and future directions.

Artifact segmentation using the U-Net architecture for powder X-ray diffraction images

Modern synchrotron X-ray facilities generate X-ray diffraction (XRD) image data at rates that far exceed the capacity of manual analysis, yet critical processing steps such as artifact removal rely on human intervention. Rule-based algorith...

Multistage Convolutional Neural Network With Deformable Attention for Word-Level Offline Text-Independent Writer Identification

Offline writer identification is an essential component of biometric and forensic security fields, focusing on identifying the authors of handwritten documents in physical form. Historically, this approach has focused on text-dependent iden...

A comprehensive analytical assessment of dino-based architectures for sustainable automated potato inspection in the Al Kharj Region

Abstract Self-supervised learning (SSL) has emerged as a pivotal paradigm in computer vision, particularly for applications where labeled data are limited, costly, or labor-intensive to obtain. This paradigm is especially relevant to sustai...

Learning by Watching: A Review of Video-Based Learning Approaches for Robot Manipulation

Robot learning of manipulation skills is hindered by the scarcity of diverse, unbiased datasets. While curated datasets can help, challenges remain regarding generalizability and real-world transfer. Meanwhile, large-scale ‘in-the-wi...

SAIN:structure-aware image inpainting for large missing areas

Abstract Image inpainting is crucial for restoring missing or corrupted regions in images, with diverse applications in computer vision and digital content creation. Existing deep learning-based methods, however, often struggle with large o...

Discriminative feature encoding for intrinsic image decomposition

Abstract Intrinsic image decomposition is an important and long-standing computer vision problem. Given an input image, recovering the physical scene properties is ill-posed. Several physically motivated priors have been used to restrict th...

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 ...