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An approach to automatic target cueing (ATC) in hyperspectral images, referred to as K-means reclustering, is introduced. The objective is to extract spatial clusters of spectrally related pixels having specified and distinctive spatial characteristics. K-means reclustering has three steps: spectral cluster initialization, spectral clustering and spatial re-clustering, plus an optional dimensionality reduction step. It provides an alternative to classical ATC algorithms based on anomaly detection, in which pixels are classified as type anomaly or background clutter. K-means reclustering is used to cue targets of various sizes in AVIRIS imagery. Statistical performance and computational complexity are evaluated experimentally as a function of the designated number of spectral classes (K) and the initially specified spectral cluster centers.

In previous work, we reported on the benefits of noise reduction prior to coding of very high quality images. Perceptual transparency can be achieved with a significant improvement in compression as compared to error free codes. In this paper, we examine the benefits of preprocessing when the quality requirements are not very high, and perceptible distortion results. The use of data dependent anisotropic diffusion that maintains image structure, edges, and transitions in luminance or color is beneficial in controlling the spatial distribution of errors introduced by coding. Thus, the merit of preprocessing is for the control of coding errors. In this preliminary study, we only consider preprocessing prior to the use of the standard JPEG and MPEG coding techniques.© (1995) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

We have recently proposed the use of geometry in image processing by representing an image as a surface in 3-space. The linear variations in intensity (edges) were shown to have a nondivergent surface normal. Exploiting this feature we introduced a nonlinear adaptive filter that only averages the divergence in the direction of the surface normal. This led to an inhomogeneous diffusion (ID) that averages the mean curvature of the surface, rendering edges invariant while removing noise. This mean curvature diffusion (MCD) when applied to an isolated edge imbedded in additive Gaussian noise results in complete noise removal and edge enhancement with the edge location left intact. In this paper we introduce a new filter that will render corners (two intersecting edges), as well as edges, invariant to the diffusion process. Because many edges in images are not isolated the corner model better represents the image than the edge model. For this reason, this new filtering technique, while encompassing MCD, also outperforms it when applied to images. Many applications will benefit from this geometrical interpretation of image processing, and those discussed in this paper include image noise removal, edge and/or corner detection and enhancement, and perceptually transparent coding.

In the perceptually transparent coding of images, we use representation and quantization strategies that exploit properties of human perception to obtain an approximate digital image indistinguishable from the original. This image is then encoded in an error free manner. The resulting coders have better performance than error free coding for a comparable quality. Further, by considering changes to images that do not produce perceptible distortion, we identify image characteristics onerous for the encoder, but perceptually unimportant. Once such characteristic is the typical noise level, often imperceptible, encountered in still images. Thus, we consider adaptive noise removal to improve coder performance, without perceptible degradation of quality. In this paper, several elements contribute to coding efficiency while preserving image quality: adaptive noise removal, additive decomposition of the image with a high activity remainder, coarse quantization of the remainder, progressive representation of the remainder, using bilinear or directional interpolation methods, and efficient encoding of the sparse remainder. The overall coding performance improvement due to noise removal and the use of a progressive code is about 18%, as compared to our previous results for perceptually transparent coders. The compression ratio for a set of nine test images is 3.72 for no perceptible loss of quality.

A number of new approaches to image coding are being developed to meet the increasing need for a broader range of quality and usage environments for image compression. Several of these new approaches, such as subband coders, are intended to provide higher quality images at the same bit rate as compared to the JPEG standard, because they are not subject to end of block artifacts, or because they are inherently better attuned to the image representation that occurs in the peripheral visual system. Still, in the absence of a pertinent quality criterion, the quality and performance of subband coders, or wavelet coders, can be mediocre. We have developed over the past few years the elements of a methodology applicable to this problem. We reported last year at the SPIE in San Diego, a Comparison of Coding Techniques based on a new Picture Quality Scale (PQS). In that work, we were able to rate coders designed by any criteria, on the basis of performance and quality. The problem that we are now considering is to design the coding technique so as to provide a better quality or a lower bit rate. The image quality, as evaluated by PQS, depends on a combination of several objective distortion factors, which can be identified with perceived coding artifacts, but the design of coders using all the factors is much too complex for an analytical approach. We make use instead of two design methodologies. The first one is to optimize the design of an existing subband coder using PQS as a distortion metric. The second one makes use of a methodology for the design of linear filters based on properties of human perception that we have developed previously and that may provide a tractable design method.

The encoding of Super High Definition Images presents new problems with regard to the effect of noise on the quality of images and on coding performance. Although the information content of images decreases with increasing resolution, the noise introduced in the image acquisition or scanning process, remains at a high level, independently of resolution. Although this noise may not be perceptible in the original image, it will effect the quality of the encoded image, if the encoding process introduces correlation and structure in the coded noise. Further, the coder performance will be affected by the noise, even if the noise is not perceived. Therefore, there is a need to reduce the noise by pre-processing the SHD image, so as to maintain image quality and improve the encoding process. The reduction of noise cannot be performed by low pass filtering operations that will degrade image quality. We are applying to this problem image analysis for adaptive noise removal. We discuss first the information theoretic issues on the effect of noise on coders. We then consider adaptive noise removal techniques to the perceptually transparent and very high quality coding of still SHD images.© (1992) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

An application of neural networks is the recognition of objects under translation, rotation, and scale change. Most existing networks for invariant object recognition require a huge number of connections and/or processing units. In this paper, we propose a new connectionist model for invariant object recognition for binary images with a reasonable network size. The network consists of five stages. The first stage shifts the object so that the centroid of the object coincides with the center of the image plane. The second stage is a variation of the polar- coordinate transformation used to obtain two N-dimensional representations of the input object. In this stage, the 0 axis is represented by the positions of the output units; therefore, any rotation of the original object becomes a cyclic shift of the output values of this stage. The third stage is a variation of the rapid transform, which provides invariant representations of cyclic-shift inputs. The next stage normalizes the outputs of the rapid transform to obtain scale invariance. The final stage is a nearest neighbor classifier. We tested the performance of the network for character recognition and good results were obtained with only one pattern per class in training.

Image understanding is a broad field of image processing where the goal is to classify the elements of a scene. In this paper we describe an approach to image understanding based on the matching of structure graphs. The structure graph of the input image is composed of `nodes' (primitives extracted from the image, e.g., regions, line segments) and `edges' (relationships between primitives in the image). The goal of our algorithm is to find the best match between this graph and a prototype graph, representing the knowledge about the expected scene. We formulate the graph matching problem as a consistent labeling problem, where the nodes of the prototype graph are considered labels. We then search for a labeling of the input structure graph that is optimal in the sense that the nodes and edges of the input graph are consistent with the labels and relationships represented in the prototype graph. A `quality of fit' measurement is derived for the matching, and a genetic algorithm is used to find the optimal solution. The advantages of this method of inexact (or fuzzy) matching include its graceful degradation (robustness) in the presence of noise and image deformation, its parallelism, and its adaptability to a variety of domains. We complete this work with the discussion of experimental results.© (1992) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

In the encoding of high quality images beyond current standards, a reexamination of issues in the representation, processing and encoding problems is needed. The fundamental reason for that change of emphasis is because the image representation, sampling density, color and motion parameters are no longer given by accepted practices or standards and, thus, require study. Some basic issues that should be reconsidered are as follows

We have developed over the past few years new linear filtering methods for images which incorporate the use of properties of human vision in design. These methods apply to noise removal, image enhancement and image restoration. One interesting extension of our work is to the use of the anisotropy of vision to achieve better visual quality. In this paper we propose combining several of these processing tasks at once, by taking advantage of visual perception and of the image content information that can be estimated directly from the image. We present a brief overview of the image filtering techniques using properties of vision that we have developed and discuss the basis on which several processing tasks can be combined for the same image. We discuss briefly the approach taken to the implementation of such multistep algorithms on dedicated pipeline image processors.