In the era of smart cities and AIoT infrastructure, deploying efficient machine learning models on resource-constrained edge devices has become critical for urban utility management. This paper evaluates the effectiveness of various data augmentation techniques in enhancing the performance of machine learning models on these devices despite their limited computational resources. Our study utilizes three datasets of digit images: one from Kaggle, one from SCUT, and a proprietary dataset. We tested ranges of parameters for data augmentation, including noise, brightness, contrast, and geometric transformations, to assess their impact on model accuracy. The findings indicate that while augmentation generally improves model performance, an optimal range exists beyond which accuracy may decline due to overfitting. This paper describes this standardized approach to parameter testing that contributes to developing more efficient and accurate edge-based machine learning applications.
Texture classification is a critical task with applications spanning various domains, from facial recognition to cancer detection in medical images. In traditional approaches, the application’s success heavily depends on the feature extraction and classification stages. Over the years, numerous feature extraction methods have been proposed, with non-handcrafted approaches consistently outperforming handcrafted ones. This paper investigates static and dynamic selection techniques of classifiers trained on features extracted from non-handcrafted architectures. The experiments were conducted on two challenging benchmarks widely used for texture classification evaluation: the FMD dataset and the Describable Texture Dataset (DTD). We first evaluated individual features and found that Visual Transformers (ViT) performed exceptionally well compared to other architectures. However, the significantly higher Oracle accuracy for the ensemble of classifiers suggests room for improvement in investigations concerning classifier combination and selection techniques. We observed enhanced performance when combining the top-performing individual classifiers by applying static combination methods such as sum, product, and max rules. Dynamic classifier selection techniques have not yielded improvements in the rates. The best performance was achieved using a static combination of classifiers through sum and product rules. In the FMD database, the F1-score was 93.2
In this paper, we propose a novel method for rotation invariant pattern recognition. We perform adaptive denoising to the input pattern images. If the noise level is above a threshold, we perform block matching and 3D filtering (BM3D) to reduce noise from the noisy pattern images. We do not conduct denoising otherwise. We extract ridgelet-Fourier features from the denoised pattern and classify the unknown pattern to one of the known classes with the nearest neighbor classifier. Experiments demonstrate that our new method achieves perfect classification rate (100
Classifying gravitational wave signals is an essential task in analyzing data from space collected by advanced tools such as an interferometer. In this paper, we present a new architecture of a convolutional neural network that classifies gravitational wave spectrograms into a selected class. For this purpose, a novel attention module based on the fuzzy controller architecture was proposed. The mechanism is based on the generation of matrices: query Q, keys K, and values V, where the first two are fuzzified by a Gaussian function and subjected to fuzzy inference. The inference results are sharpened and multiplied by values in V. This solution allows the use of the idea of a fuzzy controller to analyze features in neural networks. The model was tested and analyzed in terms of different evaluation metrics that show that this model can reach higher results than the state-of-the-art.
In this article, the authors address classification as a process that can be automated. The primary focus is the analysis of dermatological skin changes by leveraging expert knowledge and factor analysis based on structural geometric patterns associated with skin diseases. Furthermore, the study explores the feasibility of remote classification using a hierarchical model designed to optimize computational efficiency. Additionally, the classification process incorporates rejected principal components to identify relevant factors accurately.
Weeding is a critical, yet labor intensive task in agriculture, where manual methods are inefficient and unsustainable for large-scale farming. To address this challenge, we propose two complementary components aimed at enhancing precision agriculture: (1) a custom designed autonomous robotic platform for efficient field navigation and monitoring, and (2) a novel unsupervised domain adaptation (UDA) framework for robust crop and weed segmentation. The proposed UDA framework integrates two key modules, the contrastive learning module (CLM) for improved feature alignment and the enhanced fast fourier transform module (EFFT) for stylistic adaptation designed to mitigate domain gaps between source and target datasets. Extensive experiments on diverse agricultural datasets, including UAV-Bonn, UAV-Zurich, Sunflower, and Sugarbeet, demonstrate that the proposed method achieves significant improvements in mean Intersection over Union (mIoU), outperforming the MIC baseline by 15.52
The conventional approach to semantic segmentation necessitates training models on extensive datasets, a process that is often resource-intensive and time-consuming. Few-shot learning methods, by contrast, employ previously trained models to rapidly adapt to novel, unseen classes. These methods utilize a limited set of k samples to establish prototypes representing the novel class, guiding the model's predictions and facilitating iterative weight adjustments in alignment with this foundational structure. In this study, we build on these strengths, augmenting the proposed system with multiple neural architectures incorporating attention modules. Specifically, we employ a 1-shot learning strategy across.k different models (with k = 3 in our experiments), whose aggregated results enable a comprehensive representation of the novel class's features with minimal data support. The conducted experiments have shown that a properly selected consensus method can have a positive impact on the obtained segmentation results.
In recent years, text-to-image generation models have gained immense popularity and widespread use. The increasing availability of computational resources has accelerated the development of more sophisticated methods, but this rapid growth has introduced a challenge: comparing models to determine which is best suited for a given task. Currently, this problem is often addressed through manual evaluations, where humans assess and rank model outputs. However, this approach is inefficient and time-consuming, requiring extensive human input and subjective judgment. With the exponential growth of text-to-image models, manually assessing each model's output quality has become a Sisyphean task. Automated image quality assessment (IQA) models offer a promising alternative, enabling us to reduce reliance on subjective human evaluations and instead use predicted values as metrics that indicate how well a generated image may appeal to users. In this paper, we extend our previous research on predicting the Mean Opinion Score (MOS) for image quality and propose a novel, efficient method for evaluating the quality of text-to-image generation models. Our approach uses a ConvNeXt-based architecture, representing an upgrade to previous solutions, and provides robust and innovative metrics applicable to a wide range of text-to-image generation tasks. This model improves the speed and reliability of quality assessments, offering a scalable solution to meet the growing demand for automated evaluation in the text-to-image generation space.
Automated medical image classification is essential to improve diagnostic precision, reduce the burden on clinicians, and accelerate disease detection. This study evaluates the performance of various convolutional neural network (CNN) architectures in small-scale segmented medical image datasets. Models were trained from scratch without pre-trained weights, using deterministic augmentation pipelines to ensure reproducibility. Xception achieved the highest accuracy of 96.73
Unmanned aerial vehicles (UAVs), also known as drones, have become a popular research aspect in recent digital forensics. Many papers deal with various approaches connected with UAVs, including identification by flight logs, detecting a mobile device used to pilot a UAV, or different methods for managing their transmission. In this paper, we deal with the problem of linking mobile devices with a drone that it was flown by. More precisely, we consider, whether it is possible to identify which mobile device was used to pilot a particular drone, based on flight logs that are stored in the mobile device's internal memory after each flight. We propose a method based on a transformer that predicts the mobile device that was used to pilot a drone. Experimental evaluation shows that it is possible to determine that a drone was piloted with a particular mobile device based on the log file. The evaluation is performed on real data, acquired from a popular UAV and several modern mobile devices used to pilot them.
In this work, we focus on detecting small road objects in fisheye cameras using the FishEye8K dataset. The images within the FishEye8K dataset pose significant challenges due to heavy distortion and blurring. We first thoroughly analyze the dataset and then design a data augmentation pipeline tailored specifically to simulate the characteristics of FishEye8K images in other datasets. Our approach involves using fisheye distortion alongside several pixel-level transformations, which we apply to other traffic-oriented datasets like VisDrone, UAVDT, and WoodScape. Additionally, we employ GAN-based data augmentation techniques to transform the original dataset, simulating multiple weather and lighting conditions. Finally, we conduct a comprehensive analysis to assess the suitability of typical small object detection methods for this particular problem domain. Our method was developed for AiCityChallenge2024, and we achieved an F1 score of 58.2 https://github.com/deepdrivepl/aicity24-DDPL ).
In this article, we propose a method for comparing arbitrary sets of point clouds based on pattern descriptors. The method is specifically designed to process large outdoor 3D laser scans characterized by highly variable point density, which changes with distance from the scan center. To generate pattern descriptors, we utilize a modified encoder block from a Vision in Transformer model, trained with a differential loss function. The model is trained on scanner-specific data, enabling it to generalize to any scans without requiring retraining.
People with the autism spectrum have special needs when it comes to using computers. Therefore designers of graphical interfaces dedicated to such people should bear in mind the limitations faced by them. In this paper, the system, which supports designers by automatically evaluating the accessibility of graphical interfaces, is proposed. Image processing methods are used to extract features, on which the introduced fit scale allowing for evaluation of interface adaptation to special preferences is based. The presented approach facilitates the creation of interfaces intelligible for autistic people and raises awareness about their needs. The fit scale values were calculated for several game interfaces and the obtained results have been compared with a visual analysis performed by an expert working with autistic people.
This research enhances Fusarium Head Blight (FHB) detection in hyperspectral images using a Deep Convolutional Neural Network (DCNN) with Principal Component Analysis (PCA) and a Spectral Attention Module (SAM). By reducing spectral dimensionality with PCA and applying channel attention, the model improves feature representation. Tested on the AI for Agriculture 2024 dataset, it achieved 97.06
Aircraft recognition is an extremely important task in air defense. In the existing literature, there exist only a few works on this topic. As a result, there is an urgent need to tackle this problem. In this paper we propose a novel method for aircraft recognition by polarizing the aircraft, extracting the Fourier features along the angle direction, and computing the dual-tree complex wavelet transform (DTCWT) features along the radial direction. We normalize the aircraft so that it is translation and scale invariant. Rotation invariance is achieved by taking the spectrum of the Fourier coefficients. We choose all except the finest scale of DTCWT coefficients and low frequency Fourier coefficients to classify the unknown aircraft because they are more robust to noise. Experimental results demonstrate that our new method is better than the Fourier-wavelet descriptor for all testing cases for a combination of scaling factors and rotation angles and a combination of noise levels and rotation angles, respectively.
Effective human resource management requires continuous monitoring of workforce dynamics, including role transitions, promotions, and structural changes within an organization. This paper presents a solution based on recurrent neural networks (RNN), utilizing LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Unit) architectures to analyze sequential data derived from employee interactions in a large organizational environment. The research was conducted using a text-based dataset of approximately 184 GB, encompassing various communication formats from emails and meeting transcripts to team discussions while incorporating organizational hierarchy context. The proposed model detects significant personnel events, such as changes in supervisors, promotions, or positional shifts. The analysis considers 16 features describing relationships between employees and their organizational surroundings. The use of LSTM and GRU architectures enabled the capture of complex temporal dependencies and accurate classification of career-related behavioral patterns. Designed for near real-time operation, the system supports the rapid identification of potential anomalies and assists managerial decision-making. This approach may be applied in both private and public sector institutions, wherever workforce management and information security are of strategic importance.
Bottleneck analysis in software development highlights inefficiencies that hinder team productivity. This study uses Process Mining techniques to identify and address bottlenecks in workflows by analyzing event logs from IDEs and task management systems. The approach involves process discovery, conformance checking, and predictive modeling to detect inefficiencies and deviations. A predictive model was developed to anticipate future bottlenecks, allowing for targeted interventions. Major bottlenecks included delays in communication, redundant task cycles, and misallocated resources. Workflow adjustments, such as process redesign and task redistribution, led to a 38
Large visual models have recently made considerable progress in Text-to-Video generation thanks to the development of foundation models and multi-modal alignment techniques, making video generation more and more realistic. Current approaches predominantly rely on adapting image-based diffusion models via spatiotemporal attention, but this generally leads to temporal inconsistency and increasing model complexity. This inconsistency is mainly related to the fact those approaches are founded on models that were originally designed for image generation, thus, they do not consider implicitly the spatiotemporal aspect of videos. In this paper, we introduce Swin-Editor, an efficient approach of video editing from text-instruction that expands a diffusion-based Text-to-Image model into Text-to-Video. Specifically, our focus lies in enhancing the visual quality of the generated videos by incorporating a spatiotemporally factorized video prediction mechanism in the diffusion model. Additionally, to reduce computational complexity and memory requirements, the proposed model includes a Vector Quantized Variational Autoencoder module, intended to quantize and compress the spatiotemporal latent features. The proposed architecture produces a good compromise between multiple evaluation metrics against state-of-the-art models in various scenarios. Project page: Swin-Editor.
Content-based image retrieval (CBIR) is one of the basic tasks of computer vision. Numerous studies have been conducted, leading to many groundbreaking methods based on deep neural networks and even more recently on vision transformers (ViT). In this article, we propose a new CBIR method based on the original self-distilled with no labels semantic features (DINO), obtained using ViT, and then additionally compressed using the principal and neighbourhood component analysis. We show highly accurate results on non trivial datasets such as Caltech-256, as well as on histopathological scans such as Kather and BreaKHis. Our method freely compares with the best CBIR approaches while having very compact image representations.
Linear classifiers are widely used in machine learning due to their simplicity and computational efficiency. However, existing methods face limitations including sensitivity to outliers, restrictive assumptions, and computational instability. To address these challenges, we propose the Elliptic Separator (ES), a novel linear classifier based on geometric principles. The method employs Principal Component Analysis, affine transformations, and ellipsoid fitting to transform the feature space, enabling the determination of a maximum-margin boundary between clusters. The classification task is reduced to minimizing the distance between the origin and an ellipse, which simplifies computation by solving polynomial equations. Numerical experiments on 2D binary datasets demonstrate that the Elliptic Separator outperforms traditional linear classifiers, offering a stable and efficient alternative. Future extensions aim to generalize the approach to higher dimensions and evaluate its performance on real-world data.
