in this research, the framework is presented for unsupervised change detection using multitemporal sar images based on integration clustering and level set methods. spatial correlation between pixels were considered by using contextual information. also as proposed method was used integration of gustafson-kessel clustering techniques (gkc) and level set methods for change detection. using clust...

A test statistic for equality of two covariance matrices following the complex Wishart distribution has previously been used in new algorithms for change detection, edge detection and segmentation in polarimetric SAR images. Previously, the results for change detection and edge detection have been quantitatively evaluated. This paper deals with the evaluation of segmentation. A segmentation per...

Stream mining is a challenging problem that has attracted considerable attention in the last decade. As a result there are numerous algorithms for mining data streams, from summarizing and analyzing, to change and anomaly detection. However, most research focuses on proposing, adapting or improving algorithms and studying their computational performance. For a practitioner of stream mining, the...

Data stored in a data warehouse must be kept consistent and up-to-date with respect to the underlying information sources. By providing the capability to identify, categorize and detect changes in these sources, only the modified data needs to be transfered and entered into the warehouse. Another alternative, periodically reloading from scratch, is obviously inefficient. When the schema of an i...

Scene change detection plays an important role in a number of video applications, including video indexing, searching, browsing, semantic features extraction, and, in general, pre-processing and post-processing operations. Several scene change detection methods have been proposed in different coding standards. Most of them use fixed thresholds for the similarity metrics to determine if there wa...

The great presumption of change detection has led to rapid development of diverse change detection algorithms. Unsupervised change detection has a vital role in a wide variety of applications like remote sensing, motion detection, environmental monitoring, medical diagnosis, damage assessment, agricultural surveys, surveillance etc. In this paper a systematic survey of the commonly used methodo...

In this paper, we present an object detection and tracking algorithm for low-frame-rate applications. We extend the standard mean-shift technique such that it is not limited within a single kernel but uses multiple kernels centered around high motion areas obtained by change detection. We also improve the convergence properties of the mean-shift by integrating two additional likelihood terms. O...

Anomaly detection in graph streams requires both the discovery of normative subgraph patterns in the stream and then the identification of subgraphs that are unexpected deviations from the normative patterns. Both of these processes are computationally complex, and therefore we should only update them when necessary. We present an approach based on a change detection metric used for graph sampl...