Entropy-Based Image Merging

German, A.; Jenkin, Michael; Lespérance, Yves

doi:10.1109/crv.2005.38

Cited by 18 publications

(12 citation statements)

References 5 publications

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“…Entropy for images is defined in [49] as a measure of information content. It is the average number of bits needed to quantize the intensities in the image defined as…”

Section: Qualitative Analysis Of Fused Imagesmentioning

confidence: 99%

Non-parametric and region-based image fusion with Bootstrap sampling

Zribi

2010

Information Fusion

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“…Entropy for images is defined in [49] as a measure of information content. It is the average number of bits needed to quantize the intensities in the image defined as…”

Section: Qualitative Analysis Of Fused Imagesmentioning

confidence: 99%

Non-parametric and region-based image fusion with Bootstrap sampling

Zribi

2010

Information Fusion

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“…Baldi and Itti [14,15] proposed a method to detect sudden changes during a video capture based on the Kullback-Liebler (KL) divergence [16] (also relative entropy). German et al [17] developed a technique to combine several images obtained under different exposure and lighting conditions into one, where the combination is directed by the entropy of each image. Kadir and Brady [18] discussed three inter-related aspects of computer vision problems, including saliency, scale selection and content description, where saliency is measured by Shannon entropy [3] of local attributes, that is the local signal complexity or unpredictability.…”

Section: Entropy-base Technologiesmentioning

confidence: 99%

Active entropy camera

Wang

2011

Machine Vision and Applications

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Automatic exposure control (AEC) is traditionally based on the sampled intensity level in an image frame. However in certain situations, such intensity-based AEC does not achieve the desired results (with a maximum entropy) as shown in several examples in this paper. In most cases, maximizing entropy is the goal of a machine-vision system. We want the imaging system that captures the most amount of information, so that as many feature points as possible are presented in the captured image. To solve this problem, we propose an entropy-based exposure control technique. This technique adjusts the camera exposure setting to maximize the amount of information captured in the image frame. The amount of information is measured by Shannon entropy. The proposed entropy calculation is accelerated by graphics processing units (GPUs) to achieve a real-time performance. Experimental results show that under a number of conditions our AEC performs better than the traditional intensity-based AEC, preserving more detail, and capturing more information.

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“…The Entropy denoted by (H) of an image is the measurement of information which is present in an image (or in a window) and is defined as following description [35]:…”

Section: The Fusion Quality Index Qw(abf)mentioning

confidence: 99%

Multi-Source Multi-Sensor Image Fusion Based on Bootstrap Approach and SEM Algorithm

Delleji¹,

Zribi²,

Hamida³

2009

TORMSJ

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Bootstrap approach and Stochastic EM algorithm combination applied for the improvement of the multisource and multi-sensor image fusion process; was presented in this research. Improvement concerned not only image quality and reducing processing execution time as mentioned in our previous Bootstrap EM algorithm (BEM), but also regarding initialization dependence as well as fixed classes' number. Such interesting fusion algorithm for multisource and multisensor image using one stochastic phase, i.e. SEM algorithm, preceded by Bootstrap procedure was successfully implemented and tested for several prototype images. Targeted images were firstly split by an unsupervised Bayesian segmentation approach in order to determine a joint region map for the fused image. The Bootstrap approach was then applied to the targeted multisource image in conjunction with the SEM algorithm, forming hence one Bootstrap SEM algorithm called BSEM. The procedure of such algorithm involved both statistical parameters' estimation from one representative Bootstrap sample of each source or sensor images.Keywords: Multi-source multi-sensor image fusion, unsupervised bayesian segmentation, bootstrap approach, sem algorithm.

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Entropy-Based Image Merging

Cited by 18 publications

References 5 publications

Non-parametric and region-based image fusion with Bootstrap sampling

Non-parametric and region-based image fusion with Bootstrap sampling

Active entropy camera

Multi-Source Multi-Sensor Image Fusion Based on Bootstrap Approach and SEM Algorithm

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