323 citations · 925 across the 7 of their papers we have counts for
6 papers · 1 filter
Dynamic Amelioration of Resolution Mismatches for Local Feature Based Identity Inference
Yongkang Wong, Conrad Sanderson, Sandra Mau +1
While existing face recognition systems based on local features are robust to issues such as misalignment, they can exhibit accuracy degradation when comparing images of differing…
Shadow Detection: A Survey and Comparative Evaluation of Recent Methods
Andres Sanin, Conrad Sanderson, Brian C. Lovell
This paper presents a survey and a comparative evaluation of recent techniques for moving cast shadow detection. We identify shadow removal as a critical step for improving object…
Improved Anomaly Detection in Crowded Scenes via Cell-based Analysis of Foreground Speed, Size and Texture
Vikas Reddy, Conrad Sanderson, Brian C. Lovell
A robust and efficient anomaly detection technique is proposed, capable of dealing with crowded scenes where traditional tracking based approaches tend to fail. Initial foreground…
Improved Foreground Detection via Block-based Classifier Cascade with Probabilistic Decision Integration
Vikas Reddy, Conrad Sanderson, Brian C. Lovell
Background subtraction is a fundamental low-level processing task in numerous computer vision applications. The vast majority of algorithms process images on a pixel-by-pixel basis…
Combined Learning of Salient Local Descriptors and Distance Metrics for Image Set Face Verification
Conrad Sanderson, Mehrtash T. Harandi, Yongkang Wong +1
In contrast to comparing faces via single exemplars, matching sets of face images increases robustness and discrimination performance. Recent image set matching approaches typicall…
A Low-Complexity Algorithm for Static Background Estimation from Cluttered Image Sequences in Surveillance Contexts
Vikas Reddy, Conrad Sanderson, Brian C. Lovell
For the purposes of foreground estimation, the true background model is unavailable in many practical circumstances and needs to be estimated from cluttered image sequences. We pro…