Search Results for author:
Found 19 papers, 4 papers with code
Autonomous Hyperspectral Characterisation Station: Robotically Assisted Characterisation of Polymer Degradation
This paper addresses the gap between the capabilities and utilisation of robotics and automation in laboratory settings and builds upon the concept of Self Driving Labs (SDL).
Enhanced Multi-Target Tracking in Dynamic Environments: Distributed Control Methods Within the Random Finite Set Framework
Tracking multiple targets in dynamic environments using distributed sensor networks is a challenging problem that has received significant attention in recent years.
Single Domain Generalization via Normalised Cross-correlation Based Convolutions
This approach is invariant to both affine shifts and changes in energy within a local feature patch and eliminates the need for commonly used non-linear activation functions.
IT-RUDA: Information Theory Assisted Robust Unsupervised Domain Adaptation
Distribution shift between train (source) and test (target) datasets is a common problem encountered in machine learning applications.
Distributed Complementary Fusion for Connected Vehicles
We present a random finite set-based method for achieving comprehensive situation awareness by each vehicle in a distributed vehicle network.
Interaction-Aware Labeled Multi-Bernoulli Filter
But in many real-world applications, target objects interact with one another and the environment.
ITSA: An Information-Theoretic Approach to Automatic Shortcut Avoidance and Domain Generalization in Stereo Matching Networks
State-of-the-art stereo matching networks trained only on synthetic data often fail to generalize to more challenging real data domains.
Maximum Consensus by Weighted Influences of Monotone Boolean Functions
In particular, we study endowing the Boolean cube with the Bernoulli measure and performing biased (as opposed to uniform) sampling.
Anomaly Detection of Defect using Energy of Point Pattern Features within Random Finite Set Framework
Experimental results show the outstanding performance of our proposed approach compared to the state-of-the-art methods, and the proposed RFS energy outperforms the state-of-the-art in the few shot learning settings.
Ranked #63 on
Anomaly Detection
on MVTec AD
Achieving Domain Robustness in Stereo Matching Networks by Removing Shortcut Learning
We provide evidence that demonstrates that learning of features in the synthetic domain by a stereo matching network is heavily influenced by two "shortcuts" presented in the synthetic data: (1) identical local statistics (RGB colour features) between matching pixels in the synthetic stereo images and (2) lack of realism in synthetic textures on 3D objects simulated in game engines.
Consensus Maximisation Using Influences of Monotone Boolean Functions
Consensus maximisation (MaxCon), which is widely used for robust fitting in computer vision, aims to find the largest subset of data that fits the model within some tolerance level.
Evaluation of Point Pattern Features for Anomaly Detection of Defect within Random Finite Set Framework
The results show that using point pattern features, such as SIFT as data points for random finite set-based anomaly detection achieves the most consistent defect detection accuracy on the MVTec-AD dataset.
Rotation Averaging with Attention Graph Neural Networks
In this paper we propose a single-stage graph neural network that can robustly perform rotation averaging in the presence of noise and outliers.
Adjusting Bias in Long Range Stereo Matching: A semantics guided approach
Consequently, the learning algorithms often produce unreliable depth estimates of foreground objects, particularly at large distances~($>50$m).
Robust Object Classification Approach using Spherical Harmonics
In this paper, we present a robust spherical harmonics approach for the classification of point cloud-based objects.
Multiple Instance-Based Video Anomaly Detection using Deep Temporal Encoding-Decoding
The proposed approach uses both abnormal and normal video clips during the training phase which is developed in the multiple instance framework where we treat video as a bag and video clips as instances in the bag.
Anomaly Detection In Surveillance Videos
Multiple Instance Learning
+1
Monotone Boolean Functions, Feasibility/Infeasibility, LP-type problems and MaxCon
This paper outlines connections between Monotone Boolean Functions, LP-Type problems and the Maximum Consensus Problem.
Effective Sampling: Fast Segmentation Using Robust Geometric Model Fitting
In this paper, we propose an effective sampling method to obtain a highly accurate approximation of the full graph required to solve multi-structural model fitting problems in computer vision.
Labeled Multi-Bernoulli Tracking for Industrial Mobile Platform Safety
This paper presents a track-before-detect labeled multi-Bernoulli filter tailored for industrial mobile platform safety applications.
