Book•
Digital Image Processing Using MATLAB
01 Dec 2003-
TL;DR: 1. Fundamentals of Image Processing, 2. Intensity Transformations and Spatial Filtering, and 3. Frequency Domain Processing.
Abstract: 1. Introduction. 2. Fundamentals. 3. Intensity Transformations and Spatial Filtering. 4. Frequency Domain Processing. 5. Image Restoration. 6. Color Image Processing. 7. Wavelets. 8. Image Compression. 9. Morphological Image Processing. 10. Image Segmentation. 11. Representation and Description. 12. Object Recognition.
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06 Nov 2011
TL;DR: A generic framework which allows for whole image restoration using any patch based prior for which a MAP (or approximate MAP) estimate can be calculated is proposed and a generic, surprisingly simple Gaussian Mixture prior is presented, learned from a set of natural images.
Abstract: Learning good image priors is of utmost importance for the study of vision, computer vision and image processing applications. Learning priors and optimizing over whole images can lead to tremendous computational challenges. In contrast, when we work with small image patches, it is possible to learn priors and perform patch restoration very efficiently. This raises three questions - do priors that give high likelihood to the data also lead to good performance in restoration? Can we use such patch based priors to restore a full image? Can we learn better patch priors? In this work we answer these questions. We compare the likelihood of several patch models and show that priors that give high likelihood to data perform better in patch restoration. Motivated by this result, we propose a generic framework which allows for whole image restoration using any patch based prior for which a MAP (or approximate MAP) estimate can be calculated. We show how to derive an appropriate cost function, how to optimize it and how to use it to restore whole images. Finally, we present a generic, surprisingly simple Gaussian Mixture prior, learned from a set of natural images. When used with the proposed framework, this Gaussian Mixture Model outperforms all other generic prior methods for image denoising, deblurring and inpainting.
1,552 citations
TL;DR: In this article, a method for probing a target sequence of messenger ribonucleic acid molecules (mRNA's) in a fixed, permeabilized cell, including at least 30 non- overlapping probe binding regions of 15-100 nucleotides, was proposed.
Abstract: A method for probing a target sequence of messenger ribonucleic acid molecules (mRNA's) in a fixed, permeabilized cell, said target sequence including at least 30 non- overlapping probe binding regions of 15-100 nucleotides, comprising immersing said cell in an excess of at least 30 nucleic acid hybridization probes, each singly labeled with the same fluorescent label and each containing a nucleic acid sequence that is complementary to a different probe binding region of said target sequence; washing said fixed cell to remove unbound probes; and detecting fluorescence from said probes.
1,480 citations
22 Jan 2013
TL;DR: Premises of creation of Internet portal designed to provide access to participants of educational and scientific process for the joint creation, consolidation, concentration and rapid spreading of educationaland scientific information resources in its own depository are considered.
Abstract: Premises of creation of Internet portal designed to provide access to participants of educational and scientific process for the joint creation, consolidation, concentration and rapid spreading of educational and scientific information resources in its own depository are considered. CMS-based portal content management systems’ potentiality is investigated. Architecture for Internet portal of MES of Ukraine’s information resources is offered.
969 citations
Additional excerpts
...Крім того сам алгоритм опрацювання даних може вносити додаткову похибку [96-100]....
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01 Dec 2007
TL;DR: This paper employs probabilistic neural network (PNN) with image and data processing techniques to implement a general purpose automated leaf recognition for plant classification with an accuracy greater than 90%.
Abstract: In this paper, we employ probabilistic neural network (PNN) with image and data processing techniques to implement a general purpose automated leaf recognition for plant classification. 12 leaf features are extracted and orthogonalized into 5 principal variables which consist the input vector of the PNN. The PNN is trained by 1800 leaves to classify 32 kinds of plants with an accuracy greater than 90%. Compared with other approaches, our algorithm is an accurate artificial intelligence approach which is fast in execution and easy in implementation.
823 citations
Max Planck Society1, Centre national de la recherche scientifique2, University of Navarra3, Masaryk University4, Drexel University5, Zhejiang University6, Royal Institute of Technology7, Curie Institute8, French Institute for Research in Computer Science and Automation9, Pasteur Institute10, Leiden University11, Chung Yuan Christian University12
TL;DR: Although no single method performed best across all scenarios, the results revealed clear differences between the various approaches, leading to notable practical conclusions for users and developers.
Abstract: Particle tracking is of key importance for quantitative analysis of intracellular dynamic processes from time-lapse microscopy image data. Because manually detecting and following large numbers of individual particles is not feasible, automated computational methods have been developed for these tasks by many groups. Aiming to perform an objective comparison of methods, we gathered the community and organized an open competition in which participating teams applied their own methods independently to a commonly defined data set including diverse scenarios. Performance was assessed using commonly defined measures. Although no single method performed best across all scenarios, the results revealed clear differences between the various approaches, leading to notable practical conclusions for users and developers.
819 citations
Cites background or methods from "Digital Image Processing Using MATL..."
...METHOD 4: PARAMETERS The user parameters of this method (and typical values) are: Number of iterations of convolution with the Gaussian kernel (10-500)....
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... Expected number of new particles per frame (5-100)....
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