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Surveillance videos taken in unconstrained environments can be tampered with due to different environmental factors and malicious human activities. They often blur the video content and introduce difficulty in identifying the events in the scene. The problem is particularly acute for smart surveillance systems that need to make real-time decisions based on the video. Automatic detection of the blur anomalies in the video is crucial to these systems. In this research, a learning-based approach for camera blur detection is proposed.

AHT2D dataset is composed of Handwritten Arabic letters with diacritics. In this dataset, we have 28 letter classes according to the number of Arabic letters. Each class contains a multiple letter form. We have different letter images from different sources such as the internet, our writers, etc. The AHT2D dataset includes only isolated letters. In addition, this dataset contains different writing styles, orientations, colors, thicknesses, sizes, and backgrounds, which makes it a very large and rich dataset.

WITH the advancement in sensor technology, huge amounts of data are being collected from various satellites. Hence, the task of target-based data retrieval and acquisition has become exceedingly challenging. Existing satellites essentially scan a vast overlapping region of the Earth using various sensing techniques, like multi-spectral, hyperspectral, Synthetic Aperture Radar (SAR), video, and compressed sensing, to name a few.

ALL-IDB (Acute Lymphoblastic Leukemia) Image Database for Image Processing

ALL-IDB dataset comprises of two subsets among them one subset has 260 segmented lymphocytes of them 130 belongs to the leukaemia and the remaining 130 belongs to the non leukaemuia class it requires only classification. second subset has around 108 non segmented blood images that belongs to the leukaemia and non leukaemia groups thus requires segmentation and classification.