Recently several papers have appeared in the literature which propose pseudo-dynamic features for automatic static handwritten signature verification based on the use of gray level values from signature stroke pixels. Good results have been obtained using rotation invariant uniform local binary patterns LBP plus LBP and statistical measures from gray level co-occurrence matrices (GLCM) with MCYT and GPDS offline signature corpuses. In these studies the corpuses contain signatures written on a uniform white “nondistorting” background, however the gray level distribution of signature strokes changes when it is written on a complex background, such as a check or an invoice. The aim of this paper is to measure gray level features robustness when it is distorted by a complex background and also to propose more stable features. A set of different checks and invoices with varying background complexity is blended with the MCYT and GPDS signatures. The blending model is based on multiplication. The signature models are trained with genuine signatures on white background and tested with other genuine and forgeries mixed with different backgrounds. Results show that a basic version of local binary patterns (LBP) or local derivative and directional patterns are more robust than rotation invariant uniform LBP or GLCM features to the gray level distortion when using a support vector machine with histogram oriented kernels as a classifier.

Classification of videos based on its content is one of the challenging and significant research problems. In this paper, a simple and efficient model is proposed for classification of sports videos using deep learned convolution neural networks. In the proposed research, the gray scale variants of image frames are employed for classification process through convolution technique at varied levels of abstraction by adapting it through a sequence of hidden layers. The image frames considered for classification are obtained after the duplicate frame elimination and each frame is further rescaled to dimension 120x240. The sports videos categories used for experimentation include badminton, football, cricket and tennis which are downloaded from various sources of google and YouTube. The classification in the proposed method is performed with Deep Convolution Neural Networks (DCNN) with around 20 filters each of size 5x5 with around stride length of2 and its outcomes are compared with Local Binary Patterns (LBP), Bag of Words Features (BWF) technique. The SURF features are extracted from the BWF technique and further 80% of strongest feature points are employed for clustering the image frames using K-Means clustering technique with an average accuracy achieved of about 87% in classification. The LBF technique had produced an average accuracy of 73% in differentiating one image frame to other whereas the DCNN had shown a promising outcome with accuracy of about 91% in case of 40% training and 60% test datasets, 99% accuracy in case of 60% training an 40% test datasets. The results depict that the proposed method outperforms the image processing-based techniques LBP and BWF.

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ABSTILlCTWith the increased use of composites and sensitive Electronic System on board a fighter aircraft, there is a necessity to design the aircrajl structure and system to withstand lightning strike-both direct and indirect. A comprehensive Lightning Test Facility has been established in Bangalore by ADA and IISc as part of Light Combat Aircmjl Programme for carrying out various Lightning Tests as per MIL-Standards. This paper gives a brief description of the Facility and its Capabilities.The effects of lightning on aircraft arc broadly classified into Direct effects and Indirect effects. The Direct effects are the physical damages caused at the lightning attachment and hang-on locations. These include local burn-throughs. tufting of non-metallic composites, splinterin~shattering, fuel ignition, puncture of electrical insulation, etc. The Indirect 1.81-900652-0-3/97 Rs.40.00 0 1 997 SEMCEI

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