In this paper, we introduce an FPGA implementation for correcting radial distortion which is non-linear and non-uniform, and inherently observed in images taken by wide angle lenses. In the implementation, the correction is performed in on-the-fly manner by employing a parallel architecture which focuses on efficient manipulation
of look-up table (LUT) for coordinate translation: LUT decomposition and single-LUT-multiple-access method. 2D LUT is decomposed into three 1D LUTs to reduce the resource usage. The strategy of single-LUT-multipleaccess is inspired by the fact that there exists spatial and temporal proximity among the LUT accesses, even the nature of the mapping is non-linear and non-uniform. In addition, a way to eliminate redundancy, which occurs as the backward mappings and the interpolations are overlapping, is incorporated into the implementation. The series of effort aims to alleviate problems observed in conventional FPGA implementations of image handling
algorithms, which are parallelization of function blocks for higher throughput and minimization of the number of access to off-chip memory. As the result, the corrected image to a distorted input frame can be stored within a vertical blank interval, with less usage of hardware resources and without unnecessary access to off-chip memory.
The digit string recognition differs from that of isolated digits because it requires segmentation of a given string into individual digits. However, a proper segmentation requires a priori knowledge of the patterns that form meaningful units, which implies recognition capability. Therefore segmentation and recognition are not different things, rather one thing composed of two procedures with mutual dependencies. In this paper, we propose a new approach to segment the unconstrained handwritten numeral strings without the explicit guessing of break points. To segment the string of digits naturally, we adopt the concept of continuation and introduce the technique of subgraph matching to predefined prototypes. This approach makes an explicit segmentation unnecessary because it does not guess the possible break positions and also it possible to recognize a digit even if strokes not belonging to digit are attached to it. The correct segmentation rate of our method for 20 handwritten numerical strings belonging to NIST database is 97.5%.
In this paper, we are proposing an efficient method of classifying form that is applicable in real life. Our method identifies a small number of matching areas by their distinctive images with respect to their layout structure and then form classification is performed by matching only these local regions partially. The partial matching method can overcomes the problems caused by the lengthy computation time and low recognition rate. The process is summarized as follows. First, each image of the form is partitioned into rectangular local regions along specific locations of horizontal and vertical lines of the forms. Next, the disparity in each local region of the comparing form images is defined and measured. The penalty for each local area is computed by using the pre-printed text, filled-in data, and the size of a partitioned local area to prevent extracting erroneous lines. The disparity and penalty are considered to compute the score to select matching areas. Genetic Algorithm will also be applied to select the best regions of matching. Our approach of searching and matching only a small number of structurally distinctive local regions would reduce the processing time and yield a high rate of classification.
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