Proceedings Article•
Image Processing
01 Jan 1994-
TL;DR: The main focus in MUCKE is on cleaning large scale Web image corpora and on proposing image representations which are closer to the human interpretation of images.
Abstract: MUCKE aims to mine a large volume of images, to structure them conceptually and to use this conceptual structuring in order to improve large-scale image retrieval. The last decade witnessed important progress concerning low-level image representations. However, there are a number problems which need to be solved in order to unleash the full potential of image mining in applications. The central problem with low-level representations is the mismatch between them and the human interpretation of image content. This problem can be instantiated, for instance, by the incapability of existing descriptors to capture spatial relationships between the concepts represented or by their incapability to convey an explanation of why two images are similar in a content-based image retrieval framework. We start by assessing existing local descriptors for image classification and by proposing to use co-occurrence matrices to better capture spatial relationships in images. The main focus in MUCKE is on cleaning large scale Web image corpora and on proposing image representations which are closer to the human interpretation of images. Consequently, we introduce methods which tackle these two problems and compare results to state of the art methods. Note: some aspects of this deliverable are withheld at this time as they are pending review. Please contact the authors for a preview.
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Book•
03 Oct 1988TL;DR: This chapter discusses two Dimensional Systems and Mathematical Preliminaries and their applications in Image Analysis and Computer Vision, as well as image reconstruction from Projections and image enhancement.
Abstract: Introduction. 1. Two Dimensional Systems and Mathematical Preliminaries. 2. Image Perception. 3. Image Sampling and Quantization. 4. Image Transforms. 5. Image Representation by Stochastic Models. 6. Image Enhancement. 7. Image Filtering and Restoration. 8. Image Analysis and Computer Vision. 9. Image Reconstruction From Projections. 10. Image Data Compression.
8,504 citations
TL;DR: The image coding results, calculated from actual file sizes and images reconstructed by the decoding algorithm, are either comparable to or surpass previous results obtained through much more sophisticated and computationally complex methods.
Abstract: Embedded zerotree wavelet (EZW) coding, introduced by Shapiro (see IEEE Trans. Signal Processing, vol.41, no.12, p.3445, 1993), is a very effective and computationally simple technique for image compression. We offer an alternative explanation of the principles of its operation, so that the reasons for its excellent performance can be better understood. These principles are partial ordering by magnitude with a set partitioning sorting algorithm, ordered bit plane transmission, and exploitation of self-similarity across different scales of an image wavelet transform. Moreover, we present a new and different implementation based on set partitioning in hierarchical trees (SPIHT), which provides even better performance than our previously reported extension of EZW that surpassed the performance of the original EZW. The image coding results, calculated from actual file sizes and images reconstructed by the decoding algorithm, are either comparable to or surpass previous results obtained through much more sophisticated and computationally complex methods. In addition, the new coding and decoding procedures are extremely fast, and they can be made even faster, with only small loss in performance, by omitting entropy coding of the bit stream by the arithmetic code.
5,890 citations
TL;DR: Eight constructs decellularized hearts by coronary perfusion with detergents, preserved the underlying extracellular matrix, and produced an acellular, perfusable vascular architecture, competent a cellular valves and intact chamber geometry that could generate pump function in a modified working heart preparation.
Abstract: About 3,000 individuals in the United States are awaiting a donor heart; worldwide, 22 million individuals are living with heart failure. A bioartificial heart is a theoretical alternative to transplantation or mechanical left ventricular support. Generating a bioartificial heart requires engineering of cardiac architecture, appropriate cellular constituents and pump function. We decellularized hearts by coronary perfusion with detergents, preserved the underlying extracellular matrix, and produced an acellular, perfusable vascular architecture, competent acellular valves and intact chamber geometry. To mimic cardiac cell composition, we reseeded these constructs with cardiac or endothelial cells. To establish function, we maintained eight constructs for up to 28 d by coronary perfusion in a bioreactor that simulated cardiac physiology. By day 4, we observed macroscopic contractions. By day 8, under physiological load and electrical stimulation, constructs could generate pump function (equivalent to about 2% of adult or 25% of 16-week fetal heart function) in a modified working heart preparation.
2,454 citations
01 Sep 1997
TL;DR: This paper examines automated iris recognition as a biometrically based technology for personal identification and verification from the observation that the human iris provides a particularly interesting structure on which to base a technology for noninvasive biometric assessment.
Abstract: This paper examines automated iris recognition as a biometrically based technology for personal identification and verification. The motivation for this endeavor stems from the observation that the human iris provides a particularly interesting structure on which to base a technology for noninvasive biometric assessment. In particular the biomedical literature suggests that irises are as distinct as fingerprints or patterns of retinal blood vessels. Further, since the iris is an overt body, its appearance is amenable to remote examination with the aid of a machine vision system. The body of this paper details issues in the design and operation of such systems. For the sake of illustration, extant systems are described in some amount of detail.
2,046 citations
Cites methods from "Image Processing"
...system makes us of an isotropic bandpass decomposition derived from application of Laplacian of Gaussian filters [25], [29] to the image data....
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...In practice, the filtered image is realized as a Laplacian pyramid [8], [29]....
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TL;DR: This paper identifies some promising techniques for image retrieval according to standard principles and examines implementation procedures for each technique and discusses its advantages and disadvantages.
1,910 citations
Cites background or methods from "Image Processing"
...Structural description of chromosome shape (reprinted from [14])....
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...Common invariants include (i) geometric invariants such as cross-ratio, length ratio, distance ratio, angle, area [69], triangle [70], invariants from coplanar points [14]; (ii) algebraic invariants such as determinant, eigenvalues [71], trace [14]; (iii) di<erential invariants such as curvature, torsion and Gaussian curvature....
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...Designers of shape invariants argue that although most of other shape representation techniques are invariant under similarity transformations (rotation, translation and scaling), they depend on viewpoint [14]....
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...The extracting of the convex hull can use both boundary tracing method [14] and morphological methods [11,15]....
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...Assuming the shape boundary has been represented as a shape signature z(i), the rth moment mr and central moment r can be estimated as [14]...
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References
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TL;DR: It is proposed that upon ethylene signaling, FIT is less susceptible to proteasomal degradation, presumably due to a physical interaction between FIT and EIN3/EIL1, one of the signals that triggers Fe deficiency responses at the transcriptional and posttranscriptional levels.
Abstract: Understanding the regulation of key genes involved in plant iron acquisition is of crucial importance for breeding of micronutrient-enriched crops. The basic helix-loop-helix protein FER-LIKE FE DEFICIENCY-INDUCED TRANSCRIPTION FACTOR (FIT), a central regulator of Fe acquisition in roots, is regulated by environmental cues and internal requirements for iron at the transcriptional and posttranscriptional levels. The plant stress hormone ethylene promotes iron acquisition, but the molecular basis for this remained unknown. Here, we demonstrate a direct molecular link between ethylene signaling and FIT. We identified ETHYLENE INSENSITIVE3 (EIN3) and ETHYLENE INSENSITIVE3-LIKE1 (EIL1) in a screen for direct FIT interaction partners and validated their physical interaction in planta. We demonstrate that the ein3 eil1 transcriptome was affected to a greater extent upon iron deficiency than normal iron compared with the wild type. Ethylene signaling by way of EIN3/EIL1 was required for full-level FIT accumulation. FIT levels were reduced upon application of aminoethoxyvinylglycine and in the ein3 eil1 background. MG132 could restore FIT levels. We propose that upon ethylene signaling, FIT is less susceptible to proteasomal degradation, presumably due to a physical interaction between FIT and EIN3/EIL1. Increased FIT abundance then leads to the high level of expression of genes required for Fe acquisition. This way, ethylene is one of the signals that triggers Fe deficiency responses at the transcriptional and posttranscriptional levels.
243 citations
TL;DR: It is shown that ACR3 localizes to the vacuolar membrane in gametophytes, indicating that it likely effluxes arsenite into the vacUole for sequestration and may explain arsenic tolerance in this unusual group of ferns while precluding the same trait in angiosperms.
Abstract: The fern Pteris vittata tolerates and hyperaccumulates exceptionally high levels of the toxic metalloid arsenic, and this trait appears unique to the Pteridaceae. Once taken up by the root, arsenate is reduced to arsenite as it is transported to the lamina of the frond, where it is stored in cells as free arsenite. Here, we describe the isolation and characterization of two P. vittata genes, ACR3 and ACR3;1, which encode proteins similar to the ACR3 arsenite effluxer of yeast. Pv ACR3 is able to rescue the arsenic-sensitive phenotypes of yeast deficient for ACR3. ACR3 transcripts are upregulated by arsenic in sporophyte roots and gametophytes, tissues that directly contact soil, whereas ACR3;1 expression is unaffected by arsenic. Knocking down the expression of ACR3, but not ACR3;1, in the gametophyte results in an arsenite-sensitive phenotype, indicating that ACR3 plays a necessary role in arsenic tolerance in the gametophyte. We show that ACR3 localizes to the vacuolar membrane in gametophytes, indicating that it likely effluxes arsenite into the vacuole for sequestration. Whereas single-copy ACR3 genes are present in moss, lycophytes, other ferns, and gymnosperms, none are present in angiosperms. The duplication of ACR3 in P. vittata and the loss of ACR3 in angiosperms may explain arsenic tolerance in this unusual group of ferns while precluding the same trait in angiosperms.
242 citations
TL;DR: In this paper, the authors provide a pedagogical review of the current state-of-the-art algorithms for the planted spin glass problem, with a focus on the Ising model.
Abstract: Many questions of fundamental interest in todays science can be formulated as inference problems: Some partial, or noisy, observations are performed over a set of variables and the goal is to recover, or infer, the values of the variables based on the indirect information contained in the measurements. For such problems, the central scientific questions are: Under what conditions is the information contained in the measurements sufficient for a satisfactory inference to be possible? What are the most efficient algorithms for this task? A growing body of work has shown that often we can understand and locate these fundamental barriers by thinking of them as phase transitions in the sense of statistical physics. Moreover, it turned out that we can use the gained physical insight to develop new promising algorithms. Connection between inference and statistical physics is currently witnessing an impressive renaissance and we review here the current state-of-the-art, with a pedagogical focus on the Ising model which formulated as an inference problem we call the planted spin glass. In terms of applications we review two classes of problems: (i) inference of clusters on graphs and networks, with community detection as a special case and (ii) estimating a signal from its noisy linear measurements, with compressed sensing as a case of sparse estimation. Our goal is to provide a pedagogical review for researchers in physics and other fields interested in this fascinating topic.
241 citations
TL;DR: In a contour detection task, the Canny operator augmented with the proposed suppression and post-processing step achieves better results than the traditional Canny edge detector and the SUSAN edge detector.
238 citations
Book•
03 May 2010
TL;DR: A Selected List of Books on Image Processing and Computer Vision from Year 2000.
Abstract: PART I: FUNDAMENTALS. 1 INTRODUCTION. 1.1 The World of Signals. 1.2 Digital Image Processing. 1.3 Elements of an Image Processing System. Appendix 1.A Selected List of Books on Image Processing and Computer Vision from Year 2000. References. 2 MATHEMATICAL PRELIMINARIES. 2.1 Laplace Transform. 2.2 Fourier Transform. 2.3 Z-Transform. 2.4 Cosine Transform. 2.5 Wavelet Transform. 3 IMAGE ENHANCEMENT. 3.1 Grayscale Transformation. 3.2 Piecewise Linear Transformation. 3.3 Bit Plane Slicing. 3.4 Histogram Equalization. 3.5 Histogram Specification. 3.6 Enhancement by Arithmetic Operations. 3.7 Smoothing Filter. 3.8 Sharpening Filter. 3.9 Image Blur Types and Quality Measures. 4 MATHEMATICAL MORPHOLOGY. 4.1 Binary Morphology. 4.2 Opening and Closing. 4.3 Hit-or-Miss Transform. 4.4 Grayscale Morphology. 4.5 Basic Morphological Algorithms. 4.6 Morphological Filters. 5 IMAGE SEGMENTATION. 5.1 Thresholding. 5.2 Object (Component) Labeling. 5.3 Locating Object Contours by the Snake Model. 5.4 Edge Operators. 5.5 Edge Linking by Adaptive Mathematical Morphology. 5.6 Automatic Seeded Region Growing. 5.7 A Top-Down Region Dividing Approach. 6 DISTANCE TRANSFORMATION AND SHORTEST PATH PLANNING. 6.1 General Concept. 6.2 Distance Transformation by Mathematical Morphology. 6.3 Approximation of Euclidean Distance. 6.4 Decomposition of Distance Structuring Element. 6.5 The 3D Euclidean Distance. 6.6 The Acquiring Approaches. 6.7 The Deriving Approaches. 6.8 The Shortest Path Planning. 6.9 Forward and Backward Chain Codes for Motion Planning. 6.10 A Few Examples. 7 IMAGE REPRESENTATION AND DESCRIPTION. 7.1 Run-Length Coding. 7.2 Binary Tree and Quadtree. 7.3 Contour Representation. 7.4 Skeletonization by Thinning. 7.5 Medial Axis Transformation. 7.6 Object Representation and Tolerance. 8 FEATURE EXTRACTION. 8.1 Fourier Descriptor and Moment Invariants. 8.2 Shape Number and Hierarchical Features. 8.3 Corner Detection. 8.4 Hough Transform. 8.5 Principal Component Analysis. 8.6 Linear Discriminate Analysis. 8.7 Feature Reduction in Input and Feature Spaces. 9 PATTERN RECOGNITION. 9.1 The Unsupervised Clustering Algorithm. 9.2 Bayes Classifier. 9.3 Support Vector Machine. 9.4 Neural Networks. 9.5 The Adaptive Resonance Theory Network. 9.6 Fuzzy Sets in Image Analysis. PART II: APPLICATIONS. 10 FACE IMAGE PROCESSING AND ANALYSIS. 10.1 Face and Facial Feature Extraction. 10.2 Extraction of Head and Face Boundaries and Facial Features. 10.3 Recognizing Facial Action Units. 10.4 Facial Expression Recognition in JAFFE Database. 11 DOCUMENT IMAGE PROCESSING AND CLASSIFICATION. 11.1 Block Segmentation and Classification. 11.2 Rule-Based Character Recognition System. 11.3 Logo Identification. 11.4 Fuzzy Typographical Analysis for Character Preclassification. 11.5 Fuzzy Model for Character Classification. 12 IMAGE WATERMARKING. 12.1 Watermarking Classification. 12.2 Spatial Domain Watermarking. 12.3 Frequency-Domain Watermarking. 12.4 Fragile Watermark. 12.5 Robust Watermark. 12.6 Combinational Domain Digital Watermarking. 13 IMAGE STEGANOGRAPHY. 13.1 Types of Steganography. 13.2 Applications of Steganography. 13.3 Embedding Security and Imperceptibility. 13.4 Examples of Steganography Software. 13.5 Genetic Algorithm-Based Steganography. 14 SOLAR IMAGE PROCESSING AND ANALYSIS. 14.1 Automatic Extraction of Filaments. 14.2 Solar Flare Detection. 14.3 Solar Corona Mass Ejection Detection. INDEX.
237 citations