Wesley E. Snyder

Application of affine-invariant Fourier descriptors to recognition of 3-D objects

The method of Fourier descriptors is extended to produce a set of normalized coefficients which are invariant under any affine transformation (translation, rotation, scaling, and shearing). The method is based on a parameterized boundary description which is transformed to the Fourier domain and normalized there to eliminate dependencies on the affine transformation and on the starting point. Invariance to affine transforms allows considerable robustness when applied to images of objects which rotate in all three dimensions, as is demonstrated by processing silhouettes of aircraft maneuvering in three-space. >

Demosaicking methods for Bayer color arrays

Digital Still Color Cameras sample the color spectrum using a monolithic array of color filters overlaid on a charge coupled device array such that each pixel samples only one color band. The resulting mosaic of color samples is processed to produce a high resolution color image such that the values of the color bands not sampled at a certain location are estimated from its neighbors. This process is often referred to as demosaicking. This paper introduces and compares a few commonly used demosaicking methods using error metrics like mean squared error in the RGB color space and perceived error in the CIELAB color space.

Color image processing pipeline

Digital still color cameras (DSCs) have gained significant popularity in recent years, with projected sales in the order of 44 million units by the year 2005. Such an explosive demand calls for an understanding of the processing involved and the implementation issues, bearing in mind the otherwise difficult problems these cameras solve. This article presents an overview of the image processing pipeline, first from a signal processing perspective and later from an implementation perspective, along with the tradeoffs involved.

Optimization of functions with many minima

A numerical method for finding the global minimum of nonconvex functions is presented. The method is based on the principles of simulated annealing, but handles continuously valued variables in a natural way. The method is completely general, and optimizes functions of up to 30 variables. Several examples are presented. A general-purpose program, INTEROPT, is described, which finds the minimum of arbitrary functions, with user-friendly, quasi-natural-language input. >

A unified solution to coverage and search in explored and unexplored terrains using indirect control

An algorithm which solves the coverage and search problems in either explored or unexplored terrains is described. The coverage problem requires that the robot pass over all parts of the terrain that are free of obstacles. The search problem requires that the robot seek a specified target in a given terrain. The target need not be present in the terrain; in that case, the algorithm will realize the fact after a thorough search. In an explored terrain problem, a model of the terrain is available indicating which regions of the terrain are traversable. In an unexplored terrain problem, such a model is not available, thus forcing the robot to build the model by the use of sensors. It is proved that the algorithm guarantees complete coverage and a thorough search if physically possible. Due to its use of indirect control, the algorithm is not complicated and is thus simple to implement.<<ETX>>

Optimal thresholding—a new approach

Abstract Finding the optimal threshold(s) for an image with a multimodal histogram is described in well-known literature as a problem in fitting a sum of Gaussians to the histogram. This fitting problem is shown experimentally to be a nonlinear minimization with local minima. A new minimization technique, tree annealing, is presented which finds the global minimum. Experimental results for histograms with two and three modes are presented.

Band selection using independent component analysis for hyperspectral image processing

Although hyperspectral images provide abundant information about objects, their high dimensionality also substantially increases computational burden. Dimensionality reduction offers one approach to Hyperspectral Image (HSI) analysis. Currently, there are two methods to reduce the dimension, band selection and feature extraction. In this paper, we present a band selection method based on Independent Component Analysis (ICA). This method, instead of transforming the original hyperspectral images, evaluates the weight matrix to observe how each band contributes to the ICA unmixing procedure. It compares the average absolute weight coefficients of individual spectral bands and selects bands that contain more information. As a significant benefit, the ICA-based band selection retains most physical features of the spectral profiles given only the observations of hyperspectral images. We compare this method with ICA transformation and Principal Component Analysis (PCA) transformation on classification accuracy. The experimental results show that ICA-based band selection is more effective in dimensionality reduction for HSI analysis.

Binary Tree-based Generic Demosaicking Algorithm for Multispectral Filter Arrays

In this paper, we extend the idea of using mosaicked color filter array (CFA) in color imaging, which has been widely adopted in the digital color camera industry, to the use of multispectral filter array (MSFA) in multispectral imaging. The filter array technique can help reduce the cost, achieve exact registration, and improve the robustness of the imaging system. However, the extension from CFA to MSFA is not straightforward. First, most CFAs only deal with a few bands (3 or 4) within the narrow visual spectral region, while the design of MSFA needs to handle the arrangement of multiple bands (more than 3) across a much wider spectral range. Second, most existing CFA demosaicking algorithms assume the fixed Bayer CFA and are confined to properties only existed in the color domain. Therefore, they cannot be directly applied to multispectral demosaicking. The main challenges faced in multispectral demosaicking is how to design a generic algorithm that can handle the more diversified MSFA patterns, and how to improve performance with a coarser spatial resolution and a less degree of spectral correlation. In this paper, we present a binary tree based generic demosaicking method. Two metrics are used to evaluate the generic algorithm, including the root mean-square error (RMSE) for reconstruction performance and the classification accuracy for target discrimination performance. Experimental results show that the demosaicked images present low RMSE (less than 7) and comparable classification performance as original images. These results support that MSFA technique can be applied to multispectral imaging with unique advantages

Restoration of piecewise-constant images by mean-field annealing

An algorithm is described that removes the noise from images without causing blurring or other distortions of edges. The problem of noise removal is posed as a restoration of an uncorrupted image, given additive noise. The restoration problem is solved by using a new minimization strategy called mean-field annealing (MFA). An a priori statistical model of the image is chosen that drives the minimization toward solutions that are locally homogeneous. The strategy for MFA is derived, and the resulting algorithm is discussed. Applications of the algorithm to both synthetic images and real images are presented.

Content-based image retrieval in picture archiving and communications systems.

We propose the concept of content-based image retrieval (CBIR) and demonstrate its potential use in picture archival and communication system (PACS). We address the importance of image retrieval in PACS and highlight the drawbacks existing in traditional textual-based retrieval. We use a digital mammogram database as our testing data to illustrate the idea of CBIR, where retrieval is carried out based on object shape, size, and brightness histogram. With a user-supplied query image, the system can find images with similar characteristics from the archive, and return them along with the corresponding ancillary data, which may provide a valuable reference for radiologists in a new case study. Furthermore, CBIR can perform like a consultant in emergencies when radiologists are not available. We also show that content-based retrieval is a more natural approach to man-machine communication.