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Found 7 papers, 0 papers with code
Anomaly Detection in Satellite Videos using Diffusion Models
The definition of anomaly detection is the identification of an unexpected event.
Attacks as Defenses: Designing Robust Audio CAPTCHAs Using Attacks on Automatic Speech Recognition Systems
We begin by comparing 20 recent attack papers, classifying and measuring their suitability to serve as the basis of new "robust to transcription" but "easy for humans to understand" CAPTCHAs.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Advances in Deep Learning for Hyperspectral Image Analysis--Addressing Challenges Arising in Practical Imaging Scenarios
Deep neural networks have proven to be very effective for computer vision tasks, such as image classification, object detection, and semantic segmentation -- these are primarily applied to color imagery and video.
Sparse Representation-Based Classification: Orthogonal Least Squares or Orthogonal Matching Pursuit?
Based on these developments, a sparse representation-based classification (SRC) has been proposed for a variety of classification and related tasks, including face recognition.
Composite Kernel Local Angular Discriminant Analysis for Multi-Sensor Geospatial Image Analysis
With the emergence of passive and active optical sensors available for geospatial imaging, information fusion across sensors is becoming ever more important.
Person Re-identification with Hyperspectral Multi-Camera Systems --- A Pilot Study
Specifically, we assert that by accurately characterizing the unique spectral signature for each person's skin, hyperspectral imagery can provide very useful descriptors (e. g. spectral signatures from skin pixels) for re-identification.
Spatial Context based Angular Information Preserving Projection for Hyperspectral Image Classification
Additionally, we also propose a sparse representation based classifier which is optimized to exploit spatial information during classification - we hence assert that our proposed projection is particularly suitable for classifiers where local similarity and spatial context are both important.
