1. A block-based method for calculating a set of feature values for use in generic object detection, comprising acts of: selecting a class of objects to be identified in an image, where the class of objects is associated with a set of feature kernels and corresponding feature values; dividing the image into blocks, where each block is associated with image data describing that block of the image, where each feature value is determined by correlating a feature kernel with the image data from the corresponding block of the image, and where feature kernels may overlap such that a block of the image is shared by multiple feature kernels; calculating a set of block sums for each block by: correlating the image data corresponding to each block with a feature kernel to generate correlated image data; and summing the correlated image data to yield the set of block sums; and calculating the set of feature values by: outputting the blocks sums to a set of feature bins at each of a set of clock cycles according to a clock cycle matrix, where the number of feature bins corresponds to the number of feature values; and accumulating and summing the block sums corresponding to each feature value, whereby the set of feature values are calculated.