The RX anomaly detector is well known for its unsupervised ability to detect anomalies in hyperspectral images (HSI). However, the RX method assumes the data is uncorrelated and homogeneous, both of which are not inherent in HSI data. To defeat the correlation and homogeneity, a new method dubbed Iterative Linear RX is proposed. Rather than the test pixel being inside a window used by RX, Itera...

Evaluation of hydrocarbon reservoir requires classification of petrophysical properties from available dataset. However, characterization of reservoir attributes is difficult due to the nonlinear and heterogeneous nature of the subsurface physical properties. In this context, present study proposes a generalized one class classification framework based on Support Vector Data Description (SVDD) ...

In this paper, we present a novel anomaly detection framework which integrates motion and appearance cues to detect abnormal objects and behaviors in video. For motion anomaly detection, we employ statistical histograms to model the normal motion distributions and propose a notion of “cut-bin” in histograms to distinguish unusual motions. For appearance anomaly detection, we develop a novel sch...

We present a method to solve the inverse problem in pulsed photothermal radiometry sPPTRd that exploits advantages of truncated singular value decomposition sT-SVDd while imposing a non-negativity constraint to the solution. The presented method is a hybrid in the sense that it expresses the solution vector as a linear superposition of right singular vectors, but with a non-negative constraint ...

The Varroa destructor mite is one of the most dangerous Honey Bee (Apis mellifera) parasites worldwide and bee colonies have to be regularly monitored in order control its spread. In this paper we present an object detector based method for health state monitoring colonies. This has potential online measurement processing. our experiment, compare YOLO SSD detectors along with Deep SVDD anomaly ...

Multi-category classification is an on going research topic in image acquisition and processing for numerous applications. In this paper, a novel approach called margin and domain integrated classifier (MDIC) is addressed. It merges the conventional support vector machine (SVM) and support vector domain description (SVDD) classifiers, and handles multi-class problems as a combination of several...

This paper explores support vectors as a tool for vocabulary acquisition in robots. The intention is to investigate the language grounding process at the single-word stage. A social language grounding scenario is designed, where a robotic agent is taught the names of the objects by a human instructor. The agent grounds the names of these objects by associating them with their respective sensor-...

To improve the efficiency and usability of adaptive anomaly detection system, we propose a new framework based on Support Vector Data Description (SVDD) method. This framework includes two main techniques: online change detection and unsupervised anomaly detection. The first one enables automatically obtain model training data by measuring and distinguishing change caused by intensive attacks f...

The increasing interest in Support Vector Machines (SVMs) over the past 15 years is described. Methods are illustrated using simulated case studies, and 4 experimental case studies, namely mass spectrometry for studying pollution, near infrared analysis of food, thermal analysis of polymers and UV/visible spectroscopy of polyaromatic hydrocarbons. The basis of SVMs as two-class classifiers is s...

Novelty detection methods have been frequently applied in medical diagnosis, fault detection, network security and the discovery of new species. Among them, Support Vector Data Description (SVDD) has received considerable attention for its comprehensivedescription ability which covers the target data. Additionally, the Multiple Kernel Learning (MKL) technique has been extensively applied in mac...