1,720,981 research outputs found
Supervised Anomaly Detection with Highly Imbalanced Datasets Using Capsule Networks
No full textDetecting anomalous patterns in data is a relevant task in many practical applications, such as defective items detection in industrial inspection systems, cancer identification in medical images, or attacker detection in network intrusion detection systems. This paper focuses on detection of anomalous images, this is images that visually deviate from a reference set of regular data. While anomaly detection has been widely studied in the context of classical machine learning, the application of modern deep learning techniques in this field is still limited. We here propose a capsule-based network for anomaly detection in an extremely imbalanced fully supervised context: we assume that anomaly samples are available, but their amount is limited if compared to regular data. By using a variant of the standard CapsNet architecture, we achieved state-of-the-art results on the MNIST, F-MNIST and K-MNIST datasets
A Neural Network for Image Anomaly Detection with Deep Pyramidal Representations and Dynamic Routing
Full text linkImage anomaly detection is an application-driven problem where the aim is to identify novel samples, which differ significantly from the normal ones. We here propose Pyramidal Image Anomaly DEtector (PIADE), a deep reconstruction-based pyramidal approach, in which image features are extracted at different scale levels to better catch the peculiarities that could help to discriminate between normal and anomalous data. The features are dynamically routed to a reconstruction layer and anomalies can be identified by comparing the input image with its reconstruction. Unlike similar approaches, the comparison is done by using structural similarity and perceptual loss rather than trivial pixel-by-pixel comparison. The proposed method performed at par or better than the state-of-the-art methods when tested on publicly available datasets such as CIFAR10, COIL-100 and MVTec
An ensemble feature method for food classification
No full textIn the last years, several works on automatic image-based food recognition have been proposed, often based on texture feature extraction and classification. However, there is still a lack of proper comparisons to evaluate which approaches are better suited for this specific task. In this work, we adopt a Random Forest classifier to measure the performances of different texture filter banks and feature encoding techniques on three different food image datasets. Comparative results are given to show the performance of each considered approach, as well as to compare the proposed Random Forest classifiers with other feature-based state-of-the-art solutions
Drone swarm patrolling with uneven coverage requirements
No full textSwarms of drones are being more and more used in many practical scenarios, such as surveillance, environmental monitoring, search and rescue in hardly-accessible areas and so on. While a single drone can be guided by a human operator, the deployment of a swarm of multiple drones requires proper algorithms for automatic task-oriented control. In this study, the authors focus on visual coverage optimisation with drone-mounted camera sensors. In particular, they consider the specific case in which the coverage requirements are uneven, meaning that different parts of the environment have different coverage priorities. They model these coverage requirements with relevance maps and propose a deep reinforcement learning algorithm to guide the swarm. This study first defines a proper learning model for a single drone, and then extends it to the case of multiple drones both with greedy and cooperative strategies. Experimental results show the performance of the proposed method, also compared with a standard patrolling algorithm
Image Anomaly Detection by Aggregating Deep Pyramidal Representations
No full textAnomaly detection consists in identifying, within a dataset, those samples that significantly differ from the majority of the data, representing the normal class. It has many practical applications, e.g. ranging from defective product detection in industrial systems to medical imaging. This paper focuses on image anomaly detection using a deep neural network with multiple pyramid levels to analyze the image features at different scales. We propose a network based on encoding-decoding scheme, using a standard convolutional autoencoders, trained on normal data only in order to build a model of normality. Anomalies can be detected by the inability of the network to reconstruct its input. Experimental results show a good accuracy on MNIST, FMNIST and the recent MVTec Anomaly Detection dataset
A time-series classification approach to shallow web traffic de-anonymization
Full text linkWeb traffic analysis and classification has been extensively studied, both with classical and deep learning techniques. Many of these systems analyse the entire packet to perform the classification task. Due to the increase of encrypted traffic in recent years, this approach has become problematic. Moreover, few works focus on the classification of the users themselves, also called web traffic de-anonymization. In the present paper we address this problem by proposing an approach focused on a shallow, temporal analysis of web traffic data packets. We show that it is possible to identify the users of a network just by analyzing their navigation patterns and without accessing the content of the TCP packets. Finally, we propose a comparison between the performance of our approach and a more classical feed forward neural network architecture to showcase the informational power of temporal data in this context
VT-ADL: A Vision Transformer Network for Image Anomaly Detection and Localization
No full textWe present a transformer-based image anomaly detection and localization network. Our proposed model is a combination of a reconstruction-based approach and patch embedding. The use of transformer networks helps preserving the spatial information of the embedded patches, which is later processed by a Gaussian mixture density network to localize the anomalous areas. In addition, we also publish BTAD, a real-world industrial anomaly dataset. Our results are compared with other state-of-the-art algorithms using publicly available datasets like MNIST and MVTec
Exploring few-shot text line segmentation approaches in challenging ancient manuscripts
Full text linkText line segmentation is a critical component of document layout analysis, particularly for ancient handwritten manuscripts. Its primary goal is to accurately extract individual text lines, a step that significantly influences subsequent tasks such as optical character recognition, text transcription, and information extraction. However, segmenting text lines in historical manuscripts is particularly challenging due to irregular handwriting, faded ink, and complex layouts with overlapping lines and non-linear text flows. Additionally, the limited availability of large annotated datasets makes fully supervised learning approaches impractical for these documents. In this paper, we explore the applicability of three prominent semantic segmentation models when applied in a few-shot learning setting, using only a small number of labeled examples per manuscript. Our results demonstrate the challenges of addressing text line segmentation in the context of scarce labeled data. This provides a promising avenue for future research in document analysis for historical manuscripts
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