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Feature detection (computer vision)

About: Feature detection (computer vision) is a research topic. Over the lifetime, 25605 publications have been published within this topic receiving 516757 citations.


Papers
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Proceedings ArticleDOI
Jing Huang1, S.R. Kumar1, Mandar Mitra1, Wei-Jing Zhu1, Ramin Zabih1 
17 Jun 1997
TL;DR: Experimental evidence suggests that this new image feature called the color correlogram outperforms not only the traditional color histogram method but also the recently proposed histogram refinement methods for image indexing/retrieval.
Abstract: We define a new image feature called the color correlogram and use it for image indexing and comparison. This feature distills the spatial correlation of colors, and is both effective and inexpensive for content-based image retrieval. The correlogram robustly tolerates large changes in appearance and shape caused by changes in viewing positions, camera zooms, etc. Experimental evidence suggests that this new feature outperforms not only the traditional color histogram method but also the recently proposed histogram refinement methods for image indexing/retrieval.

1,956 citations

Journal ArticleDOI
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

Journal ArticleDOI
TL;DR: DehazeNet as discussed by the authors adopts convolutional neural network-based deep architecture, whose layers are specially designed to embody the established assumptions/priors in image dehazing.
Abstract: Single image haze removal is a challenging ill-posed problem. Existing methods use various constraints/priors to get plausible dehazing solutions. The key to achieve haze removal is to estimate a medium transmission map for an input hazy image. In this paper, we propose a trainable end-to-end system called DehazeNet, for medium transmission estimation. DehazeNet takes a hazy image as input, and outputs its medium transmission map that is subsequently used to recover a haze-free image via atmospheric scattering model. DehazeNet adopts convolutional neural network-based deep architecture, whose layers are specially designed to embody the established assumptions/priors in image dehazing. Specifically, the layers of Maxout units are used for feature extraction, which can generate almost all haze-relevant features. We also propose a novel nonlinear activation function in DehazeNet, called bilateral rectified linear unit, which is able to improve the quality of recovered haze-free image. We establish connections between the components of the proposed DehazeNet and those used in existing methods. Experiments on benchmark images show that DehazeNet achieves superior performance over existing methods, yet keeps efficient and easy to use.

1,880 citations

Journal ArticleDOI
01 Aug 2008
TL;DR: Results demonstrate the new method abilities to remove the haze layer as well as provide a reliable transmission estimate which can be used for additional applications such as image refocusing and novel view synthesis.
Abstract: In this paper we present a new method for estimating the optical transmission in hazy scenes given a single input image. Based on this estimation, the scattered light is eliminated to increase scene visibility and recover haze-free scene contrasts. In this new approach we formulate a refined image formation model that accounts for surface shading in addition to the transmission function. This allows us to resolve ambiguities in the data by searching for a solution in which the resulting shading and transmission functions are locally statistically uncorrelated. A similar principle is used to estimate the color of the haze. Results demonstrate the new method abilities to remove the haze layer as well as provide a reliable transmission estimate which can be used for additional applications such as image refocusing and novel view synthesis.

1,866 citations

Proceedings ArticleDOI
29 Jul 2007
TL;DR: In this article, seam carving is used for content-aware image resizing for both reduction and expansion, where an optimal 8-connected path of pixels on a single image from top to bottom, or left to right, where optimality is defined by an image energy function.
Abstract: Effective resizing of images should not only use geometric constraints, but consider the image content as well We present a simple image operator called seam carving that supports content-aware image resizing for both reduction and expansion A seam is an optimal 8-connected path of pixels on a single image from top to bottom, or left to right, where optimality is defined by an image energy function By repeatedly carving out or inserting seams in one direction we can change the aspect ratio of an image By applying these operators in both directions we can retarget the image to a new size The selection and order of seams protect the content of the image, as defined by the energy function Seam carving can also be used for image content enhancement and object removal We support various visual saliency measures for defining the energy of an image, and can also include user input to guide the process By storing the order of seams in an image we create multi-size images, that are able to continuously change in real time to fit a given size

1,652 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202328
2022106
2021145
2020187
2019221
2018242