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Intelligent surveillance aims at conceiving reliable and efficient systems that are able to detect and track moving objects in complicated real world scenes. This paper proposes an innovative 3D stationary wavelet-based motion detection technique that fuses spatial and temporal analysis in a single 3D transform. This single transform is composed of applying a 2D transform in the spatial domain followed by 1D transform in the time domain. The results of the proposed technique are compared favorably with those of the recently used stationary wavelet-based technique. In addition of being accurate and has reasonable complexity of O(N2 log N), the proposed technique is robust to real world scene variations, including nonuniform and time-varying illumination.

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Recently, transformation‐based methods have been widely used in many computer vision areas because of their powerful representation ability. One of the most widely used transforms is the wavelet transform that has proved to be very useful in many applications. In this study, a new method for human action representation and description is proposed. This method combines the advantages of local and global descriptions. The method works by fusing the Hu invariant moments as global descriptors with a new local descriptor that is based on three‐dimensional stationary wavelet transform and the concept of local binary patterns. The performance of the new method was examined in two different ways. The first one is by fusing the proposed directional global and local features in one feature vector, while the other is using the features of different directional bands separately to train multiple classifiers and then using a voting scheme to vote for the best match. The performance of the proposed method is verified using standard datasets, achieving high accuracy in comparison with state‐of‐the‐art methods. In addition, the proposed method is proved to be robust to the changes in lighting and scale variations, but it exhibits limitations towards dynamic backgrounds.

Background: Three-Dimensional visualization of brain tumors is very useful in both diagnosis and treatment stages of brain cancer. Discussion: It helps the oncologist/neurosurgeon to take the best decision in Radiotherapy and/or surgical resection techniques. 3D visualization involves two main steps; tumor segmentation and 3D modeling. Conclusion: In this article, we illustrate the most widely used segmentation and 3D modeling techniques for brain tumors visualization. We also survey the public databases available for evaluation of the mentioned techniques.