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Image processing

About: Image processing is a(n) research topic. Over the lifetime, 229986 publication(s) have been published within this topic receiving 3536925 citation(s).

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229,986 results found

Open accessJournal ArticleDOI: 10.1038/NMETH.2089
01 Jul 2012-Nature Methods
Abstract: For the past 25 years NIH Image and ImageJ software have been pioneers as open tools for the analysis of scientific images. We discuss the origins, challenges and solutions of these two programs, and how their history can serve to advise and inform other software projects.

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Topics: Image processing (54%), Bioimage informatics (52%)

34,063 Citations

Journal ArticleDOI: 10.1109/TIP.2003.819861
Abstract: Objective methods for assessing perceptual image quality traditionally attempted to quantify the visibility of errors (differences) between a distorted image and a reference image using a variety of known properties of the human visual system. Under the assumption that human visual perception is highly adapted for extracting structural information from a scene, we introduce an alternative complementary framework for quality assessment based on the degradation of structural information. As a specific example of this concept, we develop a structural similarity index and demonstrate its promise through a set of intuitive examples, as well as comparison to both subjective ratings and state-of-the-art objective methods on a database of images compressed with JPEG and JPEG2000. A MATLAB implementation of the proposed algorithm is available online at sim/lcv/ssim/.

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Topics: Image quality (61%), Subjective video quality (56%), Human visual system model (56%) ... show more

30,333 Citations

Journal ArticleDOI: 10.1145/358669.358692
Martin A. Fischler1, Robert C. Bolles1Institutions (1)
Abstract: A new paradigm, Random Sample Consensus (RANSAC), for fitting a model to experimental data is introduced. RANSAC is capable of interpreting/smoothing data containing a significant percentage of gross errors, and is thus ideally suited for applications in automated image analysis where interpretation is based on the data provided by error-prone feature detectors. A major portion of this paper describes the application of RANSAC to the Location Determination Problem (LDP): Given an image depicting a set of landmarks with known locations, determine that point in space from which the image was obtained. In response to a RANSAC requirement, new results are derived on the minimum number of landmarks needed to obtain a solution, and algorithms are presented for computing these minimum-landmark solutions in closed form. These results provide the basis for an automatic system that can solve the LDP under difficult viewing

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Topics: RANSAC (69%), Smoothing (55%), Image processing (54%) ... show more

20,503 Citations

Open accessProceedings ArticleDOI: 10.1109/CVPR.2001.990517
Paul A. Viola1, Michael JonesInstitutions (1)
01 Dec 2001-
Abstract: This paper describes a machine learning approach for visual object detection which is capable of processing images extremely rapidly and achieving high detection rates. This work is distinguished by three key contributions. The first is the introduction of a new image representation called the "integral image" which allows the features used by our detector to be computed very quickly. The second is a learning algorithm, based on AdaBoost, which selects a small number of critical visual features from a larger set and yields extremely efficient classifiers. The third contribution is a method for combining increasingly more complex classifiers in a "cascade" which allows background regions of the image to be quickly discarded while spending more computation on promising object-like regions. The cascade can be viewed as an object specific focus-of-attention mechanism which unlike previous approaches provides statistical guarantees that discarded regions are unlikely to contain the object of interest. In the domain of face detection the system yields detection rates comparable to the best previous systems. Used in real-time applications, the detector runs at 15 frames per second without resorting to image differencing or skin color detection.

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17,417 Citations

Journal ArticleDOI: 10.1016/0167-2789(92)90242-F
Abstract: A constrained optimization type of numerical algorithm for removing noise from images is presented. The total variation of the image is minimized subject to constraints involving the statistics of the noise. The constraints are imposed using Lagrange multipliers. The solution is obtained using the gradient-projection method. This amounts to solving a time dependent partial differential equation on a manifold determined by the constraints. As t---~ 0o the solution converges to a steady state which is the denoised image. The numerical algorithm is simple and relatively fast. The results appear to be state-of-the-art for very noisy images. The method is noninvasive, yielding sharp edges in the image. The technique could be interpreted as a first step of moving each level set of the image normal to itself with velocity equal to the curvature of the level set divided by the magnitude of the gradient of the image, and a second step which projects the image back onto the constraint set.

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Topics: Image processing (60%), Total variation denoising (60%), Constrained optimization (57%) ... show more

13,575 Citations

No. of papers in the topic in previous years

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Topic's top 5 most impactful authors

Tetsujiro Kondo

194 papers, 2.6K citations

Aggelos K. Katsaggelos

89 papers, 5.4K citations

Bahram Javidi

85 papers, 4.2K citations

Joonki Paik

70 papers, 986 citations

Alan C. Bovik

62 papers, 40.7K citations

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