This PDF file contains the editorial “Editorial” for JEI Vol. 4 Issue 03

Three-dimensional interpolation is suitable for many kinds of color space transformations. We examine and analyze several linear interpolation schemes—some standard, some known, and one novel. An interpolation algorithm design is divided into three parts: packing (filling the space of the input variable with sample points), extraction (selecting from the constellation of sample points those appropriate to the interpolation of a specific input point), and calculation (using the extracted values and the input point to determine the interpolated approximation to the output point). We focus on regular (periodic) packing schemes. Seven principles govern the design of linear interpolation algorithms: 1) Each sample point should be used as a vertex of as many polyhedra as possible; 2) the polyhedra should completely fill the space; 3) polyhedra that share any part of a face must share the entire face; 4) the polyhedra used should have the fewest vertices possible; 5) polyhedra should be small; 6) in the absence of information about cuivature anisotropy, polyhedra should be close to regular in shape; and 7) polyhedra should be of similar size. A test for interpolation algorithm performance in performing actual color space conversions is described, and results are given for an example color space conversion using several linear interpolation methods. The extractions from cubic, body-centered-cubic, and face-centered-cubic lattices are described and analyzed. The results confirm Kanamori's claims for the accuracy of PRISM interpolation; it comes close to the accuracy of trilinear interpolation with roughly three-quarters the computations. The results show that tetrahedral interpolation, with close to half the computational cost of trilinear interpolation, is capable of providing better accuracy. Of the tetrahedral interpolation techniques, one diagonal extraction from cubic packing is useful as a general-purpose color space interpolator.

We present a cryptographic scheme for encrypting 2-D gray scale images by using a large family of fractals. This scheme is based on a transposition of the image elements implemented by a generator of 2-D hierarchical scanning patterns producing a large subset of the (n²)! possible orders defined on a 2-D image of n × n elements. Each pattern defines a distinct order of pixels and can be described by an expression, which is considered as the key of the transposition. This transposition cipher can easily be combined with various substitution ciphers, producing efficient product ciphers operating on pictorial data. Two such ciphers are constructed and their effects on real gray value images are shown. Encryption and decryption algorithms are derived from a parallel algorithm implementing the creation of the family of scanning patterns.

A real-time image processing system is presented. The system consists, mainly, of a 2-D convolver appilcation-specific integrated circuit (ASIC) that performs 2-D convolution, and a pixel image sensor capable of capturing an image of 256×256 resolution. The ASIC can convolve the pixel image data (8 bit/pixel) present within a video window with a set of programmable coefficients that can be loaded by the microprocessor, which is responsible for the proper flow of image information within the system. The image simulated results by the system are presented for a number of convolution masks.

The realization of a TV-interlaced (TVI) to HDTV-interlaced (HDI) real-time format converter for studio applications is described. The conversion is performed by motion-compensated 3-D interpolation. The estimation of motion is based on hierarchical block matching. Reliability checking of motion vectors is applied to achieve high picture quality. Furthermore, various picture classification algorithms are utilized to improve the reliability of motion vectors. This format converter has been developed using specially designed VLSI chips, digital signal processors, and field-programmable gate arrays for the reduction of hardware. The special VLSI chips have been developed using semicustom and full-custom design techniques. Besides employment within the format converter they are suitable for various applications in video processing.

Normalized cross-correlation (NCC) measure has often been used for image matching due to its invariance under changes in image bias and gain. We address the problem of using it for pattern matching in practical imaging systems. It presents an empirical relationship between the contrast level in an image and its best-matched NCC. It further derives and confirms this relationship theoretically. A novel, adaptive method of adjusting the best-matched NCC based on the established relationship is subsequently presented to alleviate the problem of pattern matching in practical imaging systems. Experimental results on real scenes of both low and high contrast images are finally presented to demonstrate the usefulness of the proposed method.

Restoration of subtractive noise on a binary image by a single morphological operation, closing, is analyzed. Restoration by closing alone is appropriate under particular explicitly defined random noise models, based respectively on erosion, independent pixel subtractive noise, and independent pixel subtractive noise followed by dilation. Since in general it is not possible to perfectly restore subtractive noise, we use the Hausdorff metric to measure the residual error in restoration. This metric is an appropriate one because of its geometric interpretation in terms of set coverings. We describe a best first search procedure to find a structuring element for closing that is optimal in the sense of minimizing the mean Hausdorff error. The search procedure's utility function is based on the calculation of certain probabilities related to the noise model, namely the probability of one set being the subset of another set and some related probabilities. We describe how a bound on these probabilities can be efficiently computed to speed up the search process.

We propose an improved thinning algorithm. Basically, the algorithm uses a 3×3 window to accommodate an eight-neighbor skeleton in each thinning iteration. This algorithm overcomes many thinning problems, such as Y-shaped distortions, spiky skeletons, and skeleton shortening, and thus preserves precise features of digital character patterns. By using this thinning algorithm, better structural features of a character pattern can be provided to an optical character recognition system, such that accurate recognition results can be achieved.

A simple but comprehensive prototype model developed to automate the inspection of wedge bonds in the IC assembly process is described. The defects associated with the bond quality are classified into four categories: size, shape, position, and dimension. The bond is inspected sequentially for each category of defects and is rejected without further processing when any defect is detected. The procedure adopted in the prototype is as follows. A global thresholding technique automatically binarizes the intensity image of the bond. Simple features such as the pixel count, minimum enclosing rectangle, centroid, and median are used to verify the specifications related to the size and shape. An intelligent scanning technique inspects the position-related specifications in addition to identifying the wire and tall positions of the bond. The dimensions of the bond are determined using projection information. Most importantly, this model is capable of determining the wire position, which is useful in inspecting wire-related defects. Experiments conducted on actual sample ICs have shown a 100% success rate.

This PDF file contains the editorial “Book Rvw: An Introduction to Electronic Imaging for Photographers," by Adrian Davies and Phil Fennessy for JEI Vol. 4 Issue 03