Showing papers on "Image processing published in 1989"

Multifrequency channel decompositions of images and wavelet models

Abstract: The author reviews recent multichannel models developed in psychophysiology, computer vision, and image processing. In psychophysiology, multichannel models have been particularly successful in explaining some low-level processing in the visual cortex. The expansion of a function into several frequency channels provides a representation which is intermediate between a spatial and a Fourier representation. The author describes the mathematical properties of such decompositions and introduces the wavelet transform. He reviews the classical multiresolution pyramidal transforms developed in computer vision and shows how they relate to the decomposition of an image into a wavelet orthonormal basis. He discusses the properties of the zero crossings of multifrequency channels. Zero-crossing representations are particularly well adapted for pattern recognition in computer vision. >

Two-Dimensional Signal and Image Processing

Abstract: New to P-H Signal Processing Series (Alan Oppenheim, Series Ed) this text covers the principles and applications of "multidimensional" and "image" digital signal processing. For Sr/grad level courses in image processing in EE departments.

Mapping small elevation changes over large areas: Differential radar interferometry

Abstract: A technique is described, based on synthetic aperture radar (SAR) interferometry, which uses SAR images for measuring very small (1 cm or less) surface motions with good resolution (10 m) over swaths of up to 50 km. The method was applied to a Seasat data set of an imaging site in Imperial Valley, California, where motion effects were observed that were identified with movements due to the expansion of water-absorbing clays. The technique can be used for accurate measurements of many geophysical phenomena, including swelling and buckling in fault zones, residual displacements from seismic events, and prevolcanic swelling.

Digital image correlation using Newton-Raphson method of partial differential correction

Abstract: Digital image correlation is finding wider use in the field of mechanics. One area of weakness in the current technique is the lack of available displacement gradient terms. This technique, based on a coarse-fine search method, is capable of calculating the gradients. However the speed at which it does so has prevented widespread use. Presented in this paper is the development and limited experimental verification of a method which can determine displacements and gradients using the Newton-Raphson method of partial corrections. It will be shown that this method is accurate in determining displacements and certain gradients, while using significantly less CPU time than the current coarse-fine search method.

Digital topology: introduction and survey

Abstract: Digital topology deals with the topological properties of digital images: or, more generally, of discrete arrays in two or more dimensions. It provides the theoretical foundations for important image processing operations such as connected component labeling and counting, border following, contour filling, and thinning—and their generalizations to three- (or higher-) dimensional “images.” This paper reviews the fundamental concepts of digital topology and surveys the major theoretical results in the field. A bibliography of almost 140 references is included.

A new technique for fully autonomous and efficient 3D robotics hand/eye calibration

Abstract: The authors describe a novel technique for computing position and orientation of a camera relative to the last joint of a robot manipulator in an eye-on-hand configuration. It takes only about 100+64N arithmetic operations to compute the hand/eye relationship after the robot finishes the movement, and incurs only additional 64 arithmetic operations for each additional station. The robot makes a series of automatically planned movements with a camera rigidly mounted at the gripper. At the end of each move, it takes a total of 90 ms to grab an image, extract image feature coordinates, and perform camera extrinsic calibration. After the robot finishes all the movements, it takes only a few milliseconds to do the calibration. A series of generic geometric properties or lemmas are presented, leading to the derivation of the final algorithms, which are aimed at simplicity, efficiency, and accuracy while giving ample geometric and algebraic insights. Critical factors influencing the accuracy are analyzed, and procedures for improving accuracy are introduced. Test results of both simulation and real experiments on an IBM Cartesian robot are reported and analyzed. >

Analytic 3D image reconstruction using all detected events

Abstract: The authors present the results of testing an algorithm for three-dimensional image reconstruction that uses all gamma-ray coincidence events detected by a PET (positron emission tomography) volume imaging scanner. By using two iterations of an analytic filter-backprojection method, the algorithm is not constrained by the requirement of a spatially invariant detector point spread function, which limits normal analytic techniques. Removing this constraint allows the incorporation of all detected events, regardless of orientation, which improves the statistical quality of the final reconstructed image. >

Kalman Filter-based Algorithms for Estimating Depth from Image Sequences

Abstract: Using known camera motion to estimate depth from image sequences is an important problem in robot vision. Many applications of depth-from-motion, including navigation and manipulation, require algorithms that can estimate depth in an on-line, incremental fashion. This requires a representation that records the uncertainty in depth estimates and a mechanism that integrates new measurements with existing depth estimates to reduce the uncertainty over time. Kalman filtering provides this mechanism. Previous applications of Kalman filtering to depth-from-motion have been limited to estimating depth at the location of a sparse set of features. In this paper, we introduce a new, pixel-based (iconic) algorithm that estimates depth and depth uncertainty at each pixel and incrementally refines these estimates over time. We describe the algorithm and contrast its formulation and performance to that of a feature-based Kalman filtering algorithm. We compare the performance of the two approaches by analyzing their theoretical convergence rates, by conducting quantitative experiments with images of a flat poster, and by conducting qualitative experiments with images of a realistic outdoor-scene model. The results show that the new method is an effective way to extract depth from lateral camera translations. This approach can be extended to incorporate general motion and to integrate other sources of information, such as stereo. The algorithms we have developed, which combine Kalman filtering with iconic descriptions of depth, therefore can serve as a useful and general framework for low-level dynamic vision.

High-resolution image recovery from image-plane arrays, using convex projections.

Abstract: We consider the problem of reconstructing remotely obtained images from image-plane detector arrays. Although the individual detectors may be larger than the blur spot of the imaging optics, high-resolution reconstructions can be obtained by scanning or rotating the image with respect to the detector. As an alternative to matrix inversion or least-squares estimation [Appl. Opt. 26, 3615 (1987)], the method of convex projections is proposed. We show that readily obtained prior knowledge can be used to obtain good-quality imagery with reduced data. The effect of noise on the reconstruction process is considered.

Practical fast 1-D DCT algorithms with 11 multiplications

Abstract: A class of practical fast algorithms is introduced for the discrete cosine transform (DCT). For an 8-point DCT only 11 multiplications and 29 additions are required. A systematic approach is presented for generating the different members in this class, all having the same minimum arithmetic complexity. The structure of many of the published algorithms can be found in members of this class. An extension of the algorithm to longer transformations is presented. The resulting 16-point DCT requires only 31 multiplications and 81 additions, which is, to the authors' knowledge, less than required by previously published algorithms. >

Pattern spectrum and multiscale shape representation

Abstract: The results of a study on multiscale shape description, smoothing and representation are reported. Multiscale nonlinear smoothing filters are first developed, using morphological opening and closings. G. Matheron (1975) used openings and closings to obtain probabilistic size distributions of Euclidean-space sets (continuous binary images). These distributions are used to develop a concept of pattern spectrum (a shape-size descriptor). A pattern spectrum is introduced for continuous graytone images and arbitrary multilevel signals, as well as for discrete images, by developing a discrete-size family of patterns. Large jumps in the pattern spectrum at a certain scale indicate the existence of major (protruding or intruding) substructures of the signal at the scale. An entropy-like shape-size complexity measure is also developed based on the pattern spectrum. For shape representation, a reduced morphological skeleton transform is introduced for discrete binary and graytone images. This transform is a sequence of skeleton components (sparse images) which represent the original shape at various scales. It is shown that the partially reconstructed images from the inverse transform on subsequences of skeleton components are the openings of the image at a scale determined by the number of eliminated components; in addition, two-way correspondences are established among the degree of shape smoothing via multiscale openings or closings, the pattern spectrum zero values, and the elimination or nonexistence of skeleton components at certain scales. >

Fluorescence microscopy in three dimensions.

Abstract: The combination of the specificity provided by fluorescence microscopy and the ability to quantitatively analyze specimens in three dimensions allows the fundamental organization of cells to be probed as never before Key features in this emergent technology have been the development of a wide variety of fluorescent dyes or fluorescently labeled probes to provide the requisite specificity High-quality, cooled charge-coupled devices have recently become available Functioning as nearly ideal imagers or "electronic film," they are more sensitive than photomultipliers and provide extraordinarily accurate direct digital readout from the microscope Not only is this precision crucial for accurate quantitative imaging such as that required for the ratioing necessary to determine intracellular ion concentrations, but it also opens the way for sophisticated image processing It is important to realize that image processing isn't simply a means to improve image aesthetics, but can directly provide new, biologically important information The impact of modern video microscopy techniques (Allen, 1985; Inoue, 1986) attests to the fact that many biologically relevant phenomena take place at the limits of conventional microscopy Image processing can be used to substantially enhance the resolution and contrast obtainable in two dimensions, enabling the invisible to be seen and quantitated Cells are intrinsically three-dimensional This can simply be a nuisance because of limited depth of focus of the microscope or it could be a fundamental aspect of the problem being studied In either case, image processing techniques can be used to rapidly provide the desired representation of the data In this chapter we have discussed the nature of image formation in three dimensions and dealt with several means to remove contaminating out-of-focus information The most straightforward of these methods uses only information from adjacent focal planes to correct the central one This approach can be readily applied to virtually any problem and with most commonly available image processing hardware to provide a substantially deblurred image in almost real time In addition to covering more sophisticated algorithms where the utmost in three-dimensional imaging is required, we have developed a method for extremely rapidly and accurately producing an in-focus, high-resolution "synthetic projection" image from a thick specimen This is equivalent to that produced by a microscope having the impossible combination of a high-NA objective lens and an infinite depth of focus A variation on this method allows efficient calculation of stereo pairs(ABSTRACT TRUNCATED AT 400 WORDS)

Automatic thresholding of gray-level pictures using two-dimensional entropy

Abstract: Automatic thresholding of the gray-level values of an image is very useful in automated analysis of morphological images, and it represents the first step in many applications in image understanding. Recently it was shown that by choosing the threshold as the value that maximizes the entropy of the 1-dimensional histogram of an image, one might be able to separate, effectively, the desired objects from the background. This approach, however, does not take into consideration the spatial correlation between the pixels in an image. Thus, the performance might degrade rapidly as the spatial interaction between pixels becomes more dominant than the gray-level values. In this case, it becomes difficult to isolate the object from the background and human interference might be required. This was observed during studies that involved images of the stomach. The objective of this report is to extend the entropy-based thresholding algorithm to the 2-dimensional histogram. In this approach, the gray-level value of each pixel as well as the average value of its immediate neighborhood is studied. Thus, the threshold is a vector and has two entries: the gray level of the pixel and the average gray level of its neighborhood. The vector that maximizes the 2-dimensional entropy is used as the 2-dimensional threshold. This method was then compared to the conventional 1-dimensional entropy-based method. Several images were synthesized and others were obtained from the hospital files that represent images of the stomach of patients. It was found that the proposed approach performs better specially when the signal to noise ratio (SNR) is decreased. Both, as expected, yielded good results when the SNR was high (more than 12 dB).

Digital Image Processing and Computer Vision

Abstract: Transforms and sampling image grey-level modelling and processing fundamentals - enhancement, restoration and conversion image motion - modelling, detection and interpretation image analysis - practical image processing concerns the future. Appendices: concepts from geometry concepts from linear algebra multidimensional Fourier transforms and effects due to affine perturbations probability and random vectors discrete mathematics review LISP/PROLOG language syntax.

Merging thermal and visual images by a contrast pyramid

Abstract: Integration of images from different sensing modalities can produce information that cannot be obtained by viewing the sensor outputs separately and consecutively. This paper introduces a hierarchical image merging scheme based on a multiresolution contrast decomposition (the ratio of a low-pass pyramid). The composite images produced by this scheme preserve those details from the input images that are most relevant to visual perception. The method is tested by merging parallel registered thermal and visual images. The results show that the fused images present a more detailed representation of the depicted scene. Detection, recognition, and search tasks may therefore benefit from this new image representation.

Using a magnetometer to image a two‐dimensional current distribution

Abstract: We describe a mathematical algorithm to obtain an image of a two‐dimensional current distribution from measurements of its magnetic field. The spatial resolution of this image is determined by the signal‐to‐noise ratio of the magnetometer data and the distance between the magnetometer and the plane of the current distribution. In many cases, the quality of the image can be improved more by decreasing the current‐to‐magnetometer distance than by decreasing the noise in the magnetometer.

Image processing using light-sensitive chemical waves

Abstract: Image processing is usually concerned with the computer manipulation and analysis of pictures1. Typical procedures in computer image-processing are concerned with improvement of degraded (low-contrast or noisy) pictures, restoration and reconstruction, segmenting of pictures into parts and pattern recognition of properties of the pre-processed pictures. To solve these problems, digitized pictures are processed by local operations in a sequential manner. Here we describe a special light-sensitive chemical system, a variant of the Belousov–Zhabotinskii medium, in which chemical reaction fronts ('chemical waves') can be modified by light. Projection of a half-tone image on such a medium initiates a very complex response. We are able to demonstrate contrast modification (contrast enhancement or contrast decrease up to contrast reversal from positive to negative and vice versa), discerning of contours (for example, segmenting of pictures up to the extreme case of skeletonizing) and smoothing of partially degraded pictures.

Interactive display and analysis of 3-D medical images

Abstract: The ANALYZE software system, which permits detailed investigation and evaluation of 3-D biomedical images, is discussed. ANALYZE can be used with 3-D imaging modalities based on X-ray computed tomography, radionuclide emission tomography, ultrasound tomography, and magnetic resonance imaging. The package is unique in its synergistic integration of fully interactive modules for direct display, manipulation, and measurement of multidimensional image data. One of the most versatile and powerful capabilities in ANALYZE is image volume rendering for 3-D display. An important advantage of this technique is that it can be used to display 3-D images directly from the original data set and to provide on-the-fly combinations of selected image transformations, such as surface segmentation, cutting planes, transparency, and/or volume set operations (union, intersection, difference, etc.). The module has been optimized to be fast (interactive) without compromising image quality. The software is written entirely in C and runs on standard UNIX workstations. >

Vision method of detecting lane boundaries and obstacles

Abstract: An image processing method operates on an image from a ccd camera viewing a roadway scene in front of a vehicle to detect lane markers and determine the relationship of the vehicle to the lane. Obstacles in the lane near the vehicle are detected and a warning is given to the driver. The method uses template matching techniques or a Hough algorithm to detect the lane markers or road edges.

A new method for image segmentation

Abstract: In applications involving visual inspection, it is often required to separate objects from background, in conditions of poor and nonuniform illumination In such cases one has to rely on adaptive methods that learn the illumination from the given images and base the object/background decision on this information We here present a new method for image segmentation via adaptive thresholding The threshold surface is determined by interpolating the image gray levels at points where the gradient is high, indicating probable object edges Several methods of data interpolation to levels given at scattered points in the image plane are discussed One method is tested on several examples and the segmentation results are compared to previously proposed adaptive thresholding algorithms

Visual navigation and obstacle avoidance structured light system

Abstract: A vision system for a vehicle, such as a mobile robot (10) includes at least one radiation projector (14, 16) which projects a structured beam of radiation into the robot's environment. The structured beam of radiation (14a, 16a) preferably has a substantially planar pattern of sufficient width to encompass the immediate forward path of the robot and also to encompass laterally disposed areas in order to permit turning adjustments. The vision system further includes an imaging (12) sensor such as a CCD imaging device having a two-dimensional field of view which encompasses the immediate forward path of the robot. An image sensor processor (18) includes an image memory (18A) coupled to a device (18D) which is operable for accessing the image memory. Image processing is accomplished in part by triangulating the stored image of the structured beam pattern to derive range and bearing, relative to the robot, of an object being illuminated. A navigation control system (20) of the robot inputs data from at least the vision system and infers therefrom data relating to the configuration of the environment which lies in front of the robot. The navigation control system generates control signals which drive propulsion and steering motors in order to navigate the robot through the perceived environment.

Vision-guided servoing with feature-based trajectory generation (for robots)

Abstract: The authors present a vision module which is able to guide an eye-in-hand robot through general servoing and tracking problems using off-the-shelf image-processing equipment. The vision module uses the location of binary image features from a camera on the robot's end-effector to control the position and one degree of orientation of the robot manipulator. A unique feature-based trajectory generator provides smooth motion between the actual image features and the desired image features even with asynchronous and discontinuous vision updates. By performing the trajectory generation in image feature space, image-processing constraints such as the feature extraction time can be accounted for when determining the appropriate segmentation and acceleration times of the trajectory. Experimental results of a PUMA robot tracking objects with vision feedback are discussed. >

Hierarchy in picture segmentation: a stepwise optimization approach

Abstract: A segmentation algorithm based on sequential optimization which produces a hierarchical decomposition of the picture is presented. The decomposition is data driven with no restriction on segment shapes. It can be viewed as a tree, where the nodes correspond to picture segments and where links between nodes indicate set inclusions. Picture segmentation is first regarded as a problem of piecewise picture approximation, which consists of finding the partition with the minimum approximation error. Then, picture segmentation is presented as an hypothesis-testing process which merges only segments that belong to the same region. A hierarchical decomposition constraint is used in both cases, which results in the same stepwise optimization algorithm. At each iteration, the two most similar segments are merged by optimizing a stepwise criterion. The algorithm is used to segment a remote-sensing picture, and illustrate the hierarchical structure of the picture. >

Magnetic resonance imaging system

Abstract: A magnetic resonance imaging system includes a magnetic field applying section for applying a static field, gradient field pulses, and an RF field to an object to be examined, a signal detecting section, including a homogeneous coil and a plurality of surface coils arranged to surround a desired region of the object, for detecting magnetic resonance signals from the object through the homogeneous coil and the surface coils, and an image data processing section for obtaining an image of the desired region by synthesizing image data based on the magnetic resonance signals respectively detected by the surface coils upon weighting/adding processing. The system uses differential surface coils as the surface coils. A magnetic resonance signal is detected through the homogeneous coil within the same time interval in which magnetic resonance signals are detected through the surface coils. Weighting functions for weighting/adding processing of image data are determined on the basis of these magnetic resonance signals.

Object-background segmentation using new definitions of entropy

Abstract: The definition of Shannon's entropy in the context of information theory is critically examined and some of its applications to image processing problems are reviewed. A new definition of classical entropy based on the exponential behaviour of information-gain is proposed along with its justification. Its properties also include those of Shannon's entropy. The concept is then extended to fuzzy sets for defining a non-probabilistic entropy and to grey tone image for defining its global, local and conditional entropy. Based on those definitions, three algorithms are developed for image segmentation. The superiority of these algorithms is experimentally demonstrated for a set of images having various types of histogram.

Developments in Maximum Entropy Data Analysis

Abstract: The Bayesian derivation of “Classic” MaxEnt image processing (Skilling 1989a) shows that exp(αS(f,m)), where S(f,m) is the entropy of image f relative to model m, is the only consistent prior probability distribution for positive, additive images. In this paper the derivation of “Classic” MaxEnt is completed, showing that it leads to a natural choice for the regularising parameter α, that supersedes the traditional practice of setting x2=N. The new condition is that the dimensionless measure of structure -2αS should be equal to the number of good singular values contained in the data. The performance of this new condition is discussed with reference to image deconvolution, but leads to a reconstruction that is visually disappointing. A deeper hypothesis space is proposed that overcomes these difficulties, by allowing for spatial correlations across the image.

Anatomic segmentation and volumetric calculations in nuclear magnetic resonance imaging

Abstract: A comprehensive methodology for image segmentation is presented. Tools for differential and intensity contouring, and outline optimization are discussed, as well as the methods for automating such procedures. After segmentation, regional volumes and image intensity distributions can be determined. The methodology is applied to nuclear magnetic resonance images of the brain. Examples of the results of volumetric calculations for the cerebral cortex, white matter, cerebellum, ventricular system, and caudate nucleus are presented. An image intensity distribution is demonstrated for the cerebral cortex. >

A physical approach to color image understanding

Abstract: In this paper, we present an approach to color image understanding that can be used to segment and analyze surfaces with color variations due to highlights and shading. The work is based on a theory—the Dichromatic Reflection Model—which describes the color of the reflected light as a mixture of light from surface reflection (highlights) and body reflection (object color). In the past, we have shown how the dichromatic theory can be used to separate a color image into two intrinsic reflection images: an image of just the highlights, and the original image with the highlights removed. At that time, the algorithm could only be applied to hand-segmented images. This paper shows how the same reflection model can be used to include color image segmentation into the image analysis. The result is a color image understanding system, capable of generating physical descriptions of the reflection processes occurring in the scene. Such descriptions include the intrinsic reflection images, an image segmentation, and symbolic information about the object and highlight colors. This line of research can lead to physicsbased image understanding methods that are both more reliable and more useful than traditional methods.