869 citations · 1.3k across the 21 of their papers we have counts for
23 papers
Vision-Based 6-DoF Grasp Pose Estimation for Robot Cloth Unfolding
Domen Tabernik, Peter Nimac, Jan Jerićević +2
Cloth manipulation is a challenging task due to the deformable and high-dimensional nature of cloth, which leads to complex interaction dynamics and perceptual ambiguity arising fr…
Evaluation of Monocular SLAM Systems on High-Altitude Nadir UAV Footage
Gašper Spagnolo, Matej Dobrevski, Danijel Skočaj
Aerial nadir video combines weak geometric constraints with severe perceptual aliasing, making it a difficult regime for monocular SLAM. We benchmark five monocular SLAM systems on…
ISOPoT: Imaging Sonar Odometry by Point Tracking
Jaša Samec, Vid Rijavec, Marko Peljhan +4
Reliable navigation in underwater environments remains a key challenge in marine robotics. In such scenarios, forward-looking sonars are a natural choice for long-range perception,…
AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors
Matic Fučka, Vitjan Zavrtanik, Danijel Skočaj
Zero-shot anomaly detection aims to detect and localise abnormal regions in the image without access to any in-domain training images. While recent approaches leverage vision-langu…
SALAD -- Semantics-Aware Logical Anomaly Detection
Matic Fučka, Vitjan Zavrtanik, Danijel Skočaj
Recent surface anomaly detection methods excel at identifying structural anomalies, such as dents and scratches, but struggle with logical anomalies, such as irregular or missing o…
No Label Left Behind: A Unified Surface Defect Detection Model for all Supervision Regimes
Blaž Rolih, Matic Fučka, Danijel Skočaj
Surface defect detection is a critical task across numerous industries, aimed at efficiently identifying and localising imperfections or irregularities on manufactured components.…
A Contrastive Learning-Guided Confident Meta-learning for Zero Shot Anomaly Detection
Muhammad Aqeel, Danijel Skocaj, Marco Cristani +1
Industrial and medical anomaly detection faces critical challenges from data scarcity and prohibitive annotation costs, particularly in evolving manufacturing and healthcare settin…
Patherea: Cell Detection and Classification for the 2020s
Dejan Štepec, Maja Jerše, Snežana Đokić +3
We present Patherea, a unified framework for point-based cell detection and classification that enables the development and fair evaluation of state-of-the-art methods. To support…
Dense Center-Direction Regression for Object Counting and Localization with Point Supervision
Domen Tabernik, Jon Muhovič, Danijel Skočaj
Object counting and localization problems are commonly addressed with point supervised learning, which allows the use of less labor-intensive point annotations. However, learning b…
Center Direction Network for Grasping Point Localization on Cloths
Domen Tabernik, Jon Muhovič, Matej Urbas +1
Object grasping is a fundamental challenge in robotics and computer vision, critical for advancing robotic manipulation capabilities. Deformable objects, like fabrics and cloths, p…
SuperSimpleNet: Unifying Unsupervised and Supervised Learning for Fast and Reliable Surface Defect Detection
Blaž Rolih, Matic Fučka, Danijel Skočaj
The aim of surface defect detection is to identify and localise abnormal regions on the surfaces of captured objects, a task that's increasingly demanded across various industries.…
Cheating Depth: Enhancing 3D Surface Anomaly Detection via Depth Simulation
Vitjan Zavrtanik, Matej Kristan, Danijel Skočaj
RGB-based surface anomaly detection methods have advanced significantly. However, certain surface anomalies remain practically invisible in RGB alone, necessitating the incorporati…
TransFusion -- A Transparency-Based Diffusion Model for Anomaly Detection
Matic Fučka, Vitjan Zavrtanik, Danijel Skočaj
Surface anomaly detection is a vital component in manufacturing inspection. Current discriminative methods follow a two-stage architecture composed of a reconstructive network foll…
DSR -- A dual subspace re-projection network for surface anomaly detection
Vitjan Zavrtanik, Matej Kristan, Danijel Skočaj
The state-of-the-art in discriminative unsupervised surface anomaly detection relies on external datasets for synthesizing anomaly-augmented training images. Such approaches are pr…
Segmentation of Multiple Myeloma Plasma Cells in Microscopy Images with Noisy Labels
Álvaro García Faura, Dejan Štepec, Tomaž Martinčič +1
A key component towards an improved and fast cancer diagnosis is the development of computer-assisted tools. In this article, we present the solution that won the SegPC-2021 compet…
DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detection
Vitjan Zavrtanik, Matej Kristan, Danijel Skočaj
Visual surface anomaly detection aims to detect local image regions that significantly deviate from normal appearance. Recent surface anomaly detection methods rely on generative m…
Automated System for Ship Detection from Medium Resolution Satellite Optical Imagery
Dejan Stepec, Tomaz Martincic, Danijel Skocaj
In this paper, we present a ship detection pipeline for low-cost medium resolution satellite optical imagery obtained from ESA Sentinel-2 and Planet Labs Dove constellations. This…
Image Synthesis as a Pretext for Unsupervised Histopathological Diagnosis
Dejan Stepec, Danijel Skocaj
Anomaly detection in visual data refers to the problem of differentiating abnormal appearances from normal cases. Supervised approaches have been successfully applied to different…
Unsupervised Detection of Cancerous Regions in Histology Imagery using Image-to-Image Translation
Dejan Stepec, Danijel Skocaj
Detection of visual anomalies refers to the problem of finding patterns in different imaging data that do not conform to the expected visual appearance and is a widely studied prob…
Mixed supervision for surface-defect detection: from weakly to fully supervised learning
Jakob Božič, Domen Tabernik, Danijel Skočaj
Deep-learning methods have recently started being employed for addressing surface-defect detection problems in industrial quality control. However, with a large amount of data need…
End-to-end training of a two-stage neural network for defect detection
Jakob Božič, Domen Tabernik, Danijel Skočaj
Segmentation-based, two-stage neural network has shown excellent results in the surface defect detection, enabling the network to learn from a relatively small number of samples. I…
Deep Learning for Large-Scale Traffic-Sign Detection and Recognition
Domen Tabernik, Danijel Skočaj
Automatic detection and recognition of traffic signs plays a crucial role in management of the traffic-sign inventory. It provides accurate and timely way to manage traffic-sign in…
Segmentation-Based Deep-Learning Approach for Surface-Defect Detection
Domen Tabernik, Samo Šela, Jure Skvarč +1
Automated surface-anomaly detection using machine learning has become an interesting and promising area of research, with a very high and direct impact on the application domain of…