Mahesh Saptharishi

Discriminative Focus of Attention for Real-Time Object Detection in Video

We propose a novel object detection approach that combines the discriminative power of object category classifiers with a simple pixel level focus of attention mechanism. The pixel-level foreground/background detectors evolve to classify each pixel as either being part of an object of interest or noise. Unlike background subtraction algorithms, the decision of what is foreground is influenced by object level knowledge rather than it being an outlier to a background distribution. The approach outperforms many background subtraction techniques in challenging scenarios. Combined with the proposed focus of attention mechanism, a robust object classifier(capable of classifying known objects or rejecting noise) runs in real-time while processing 1920x1080 videos on an off-the-shelf DSP.

Multi-operand block-floating point arithmetic for image processing

We extend the application of block-floating point arrays to multi-operand algebraic expressions consisting of additions and multiplications. The proposed method enables automatic runtime calculation of binary shifts of array elements. The shifts are computed for all elementary operations in an expression using a dataflow graph. The method attempts to preserve accuracy across the entire expression while avoiding overflow. A variety of common computer vision and image processing operations can be efficiently realized in fixed-point processors using the proposed technique. It eliminates the need to hand-craft block-floating point implementations for each new operation or processor. The result is a reduction in development time and the likelihood of errors.