Zobrazeno 1 - 10
of 45
pro vyhledávání: '"Akçay, Samet"'
Divide and Conquer: High-Resolution Industrial Anomaly Detection via Memory Efficient Tiled Ensemble
Industrial anomaly detection is an important task within computer vision with a wide range of practical use cases. The small size of anomalous regions in many real-world datasets necessitates processing the images at a high resolution. This frequentl
Externí odkaz:
http://arxiv.org/abs/2403.04932
Recent advances in visual anomaly detection research have seen AUROC and AUPRO scores on public benchmark datasets such as MVTec and VisA converge towards perfect recall, giving the impression that these benchmarks are near-solved. However, high AURO
Externí odkaz:
http://arxiv.org/abs/2401.01984
This paper introduces anomalib, a novel library for unsupervised anomaly detection and localization. With reproducibility and modularity in mind, this open-source library provides algorithms from the literature and a set of tools to design custom ano
Externí odkaz:
http://arxiv.org/abs/2202.08341
Publikováno v:
IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2021
Screening cluttered and occluded contraband items from baggage X-ray scans is a cumbersome task even for the expert security staff. This paper presents a novel strategy that extends a conventional encoder-decoder architecture to perform instance-awar
Externí odkaz:
http://arxiv.org/abs/2201.02560
Automated systems designed for screening contraband items from the X-ray imagery are still facing difficulties with high clutter, concealment, and extreme occlusion. In this paper, we addressed this challenge using a novel multi-scale contour instanc
Externí odkaz:
http://arxiv.org/abs/2108.09603
Identifying potential threats concealed within the baggage is of prime concern for the security staff. Many researchers have developed frameworks that can detect baggage threats from X-ray scans. However, to the best of our knowledge, all of these fr
Externí odkaz:
http://arxiv.org/abs/2107.07333
Publikováno v:
In International Journal of Gastronomy and Food Science March 2024 35
Robust semantic scene segmentation for automotive applications is a challenging problem in two key aspects: (1) labelling every individual scene pixel and (2) performing this task under unstable weather and illumination changes (e.g., foggy weather),
Externí odkaz:
http://arxiv.org/abs/2012.05320
Automotive scene understanding under adverse weather conditions raises a realistic and challenging problem attributable to poor outdoor scene visibility (e.g. foggy weather). However, because most contemporary scene understanding approaches are appli
Externí odkaz:
http://arxiv.org/abs/2012.05304
Detecting baggage threats is one of the most difficult tasks, even for expert officers. Many researchers have developed computer-aided screening systems to recognize these threats from the baggage X-ray scans. However, all of these frameworks are lim
Externí odkaz:
http://arxiv.org/abs/2009.13158