Click on the title to browse this book

Bilinear interpolation is often used to improve image quality after performing spatial transformation operations such as digital zooming or rotation. In the traditional case where the input coordinates appear in a raster-based fashion, the required pixel values can be obtained from the previous calculation, the frame buffer and a single line cache. This paper presents a novel approach to performing real-time bilinear interpolation that is useful in applications such as lens distortion correction where the input coordinates follow a curved path that spans multiple rows. To help retrieve the required pixels in a single clock cycle under imposed data bandwidth constraints a unique caching system has been devised. In the event that constraints make it impossible to obtain the four required pixel values, the approach performs a modified three-point interpolation. An example field programmable gate array implementation of the bilinear interpolation method used in conjunction with a lens distortion correction algorithm has been successfully completed.

A real-time image filtering technique is proposed which could result in faster implementation for fingerprint image enhancement. One major hurdle associated with fingerprint filtering techniques is the expensive nature of their hardware implementations. To circumvent this, a modified anisotropic Gaussian filter is efficiently adopted in hardware by decomposing the filter into two orthogonal Gaussians and an oriented line Gaussian. An architecture is developed for dynamically controlling the orientation of the line Gaussian filter. To further improve the performance of the filter, the input image is homogenized by a local image normalization. In the proposed structure, for a middle-range reconfigurable FPGA, both parallel compute-intensive and real-time demands were achieved. We manage to efficiently speed up the image-processing time and improve the resource utilization of the FPGA. Test results show an improved speed for its hardware architecture while maintaining reasonable enhancement benchmarks.

The classic connected components labelling algorithm requires two passes through an image. This paper presents an algorithm that allows the connected components to be analysed in a single pass by gathering data on the regions as they are built. This avoids the need for buffering the image, making it ideally suited for processing streamed images on an FPGA or other embedded system with limited memory. An FPGA-based implementation is described, emphasising the modifications made to the algorithm to enable it to satisfy timing constraints.

Classical connected components labelling algorithms are unsuitable for real-time processing of streamed images on an FPGA because they require two passes through the image. Recently, a single-pass algorithm was proposed that avoided the need to buffer an intermediate image. In this paper, a new single pass algorithm is described that is a considerable improvement over the existing algorithms. The new algorithm reassigns and reuses labels each row to minimise the size of both the equivalence and region data tables. The optimised single-pass algorithm reduces the worst case memory requirement by over 100 times that of the original algorithm (for measuring region area), and reduces the latency to only 1 row.

Range imaging is a technology that utilizes an amplitude-modulated light source and gain-modulated image sensor to simultaneously produce distance and intensity data for all pixels of the sensor. The precision of such a system is, in part, dependent on the modulation frequency. There is typically a tradeoff between precision and maximum unambiguous range. Research has shown that, by taking two measurements at different modulation frequencies, the unambiguous range can be extended without compromising distance precision. In this paper, we present an efficient method for combining two distance measurements obtained using different modulation frequencies. The behavior of the method in the presence of noise has been investigated to determine the expected error rate. In addition, we make use of the signal amplitude to improve the precision of the combined distance measurement. Simulated results compare well to actual data obtained using a system based on the PMD19k range image sensor.