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Data-Based Choice of Histogram Bin Width

by M. P. Wand - The American Statistician , 1996
"... The most important parameter of a histogram is the bin width, since it controls the trade-off between presenting a picture with too much detail ("undersmoothing ") or too little detail ("oversmoothing") with respect to the true distribution. Despite this importance there has been ..."
Abstract - Cited by 35 (0 self) - Add to MetaCart
The most important parameter of a histogram is the bin width, since it controls the trade-off between presenting a picture with too much detail ("undersmoothing ") or too little detail ("oversmoothing") with respect to the true distribution. Despite this importance there has

IHBM: Integrated Histogram Bin Matching For Similarity Measures of Color Image Retrieval

by V. Vijaya Kumar, N. Gnaneswara Rao, A. L. Narsimha Rao, V. Venkata Krishna , 2009
"... The selection of “proper similarity measure” of color histograms is an essential consideration for the success of many methods. The Histogram Quadratic Distance Measure (HQDM) is a metric distance. Till today, this method is supposed to be the better choice, But it holds a disadvantage that it can c ..."
Abstract - Cited by 1 (0 self) - Add to MetaCart
compute the cross similarity between all elements of histograms. Therefore, computationally it is more expensive. This paper proposes a method that is known as Integrated Histogram Bin Matching (IHBM) which is also a metric method, and overcomes the disadvantages of the HQDM. The proposed IHBM first

Resolving Histogram Binning Dilemmas with Binless and Binfull Algorithms

by Abram Krislock, Nathan Krislock , 2014
"... The histogram is an analysis tool in widespread use within many sciences, with high energy physics as a prime example. However, there exists an inherent bias in the choice of binning for the histogram, with different choices potentially leading to different interpretations. This paper aims to elimin ..."
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The histogram is an analysis tool in widespread use within many sciences, with high energy physics as a prime example. However, there exists an inherent bias in the choice of binning for the histogram, with different choices potentially leading to different interpretations. This paper aims

Scale Invariant Feature Transform with Irregular Orientation Histogram Binning

by Yan Cui, Nils Hasler, Thorsten Thormählen, Hans-peter Seidel
"... Abstract. The SIFT (Scale Invariant Feature Transform) descriptor is a widely used method for matching image features. However, perfect scale invariance can not be achieved in practice because of sampling artefacts, noise in the image data, and the fact that the computational effort limits the numbe ..."
Abstract - Cited by 4 (0 self) - Add to MetaCart
the number of analyzed scale space images. In this paper we propose a modification of the descriptor’s regular grid of orientation histogram bins to an irregular grid. The irregular grid approach reduces the negative effect of scale error and significantly increases the matching precision for image features

Learning discriminative lbp-histogram bins for facial expression recognition

by Caifeng Shan, Tommaso Gritti - In Proc. British Machine Vision Conference , 2008
"... Local Binary Patterns (LBP) have been well exploited for facial image analysis recently. In the existing work, the LBP histograms are extracted from local facial regions, and used as a whole for the regional description. However, not all bins in the LBP histogram are necessary to be useful for facia ..."
Abstract - Cited by 11 (0 self) - Add to MetaCart
Local Binary Patterns (LBP) have been well exploited for facial image analysis recently. In the existing work, the LBP histograms are extracted from local facial regions, and used as a whole for the regional description. However, not all bins in the LBP histogram are necessary to be useful

STATISTICAL COMPUTING AND GRAPHICS Data-Based Choice of Histogram Bin Width

by unknown authors
"... The most important parameter of a histogram is the bin width because it controls the tradeoff between presenting a picture with too much detail ("undersmoothing") or too little detail ("oversmoothing") with respect to the true distri-bution. Despite this importance there has been ..."
Abstract - Add to MetaCart
The most important parameter of a histogram is the bin width because it controls the tradeoff between presenting a picture with too much detail ("undersmoothing") or too little detail ("oversmoothing") with respect to the true distri-bution. Despite this importance there has

Histograms of Oriented Gradients for Human Detection

by Navneet Dalal, Bill Triggs - In CVPR , 2005
"... We study the question of feature sets for robust visual object recognition, adopting linear SVM based human detection as a test case. After reviewing existing edge and gradient based descriptors, we show experimentally that grids of Histograms of Oriented Gradient (HOG) descriptors significantly out ..."
Abstract - Cited by 3735 (9 self) - Add to MetaCart
We study the question of feature sets for robust visual object recognition, adopting linear SVM based human detection as a test case. After reviewing existing edge and gradient based descriptors, we show experimentally that grids of Histograms of Oriented Gradient (HOG) descriptors significantly

Continuous image representations avoid the histogram binning problem in mutual information based image registration

by Ajit Rajwade, Arunava Banerjee - In ISBI , 2006
"... Mutual information (MI) based image-registration methods that use histograms are known to suffer from the so-called binning problem, caused by the absence of a principled technique for choosing the “optimal ” number of bins to calculate the joint or marginal distributions. In this paper, we show tha ..."
Abstract - Cited by 2 (0 self) - Add to MetaCart
Mutual information (MI) based image-registration methods that use histograms are known to suffer from the so-called binning problem, caused by the absence of a principled technique for choosing the “optimal ” number of bins to calculate the joint or marginal distributions. In this paper, we show

CONTINUOUS IMAGE REPRESENTATIONS AVOID THE HISTOGRAM BINNING PROBLEM IN MUTUAL INFORMATION BASED IMAGE REGISTRATION

by unknown authors
"... Mutual information (MI) based image-registration methods that use histograms are known to suffer from the so-called binning problem, caused by the absence of a principled technique for choosing the “optimal ” number of bins to calculate the joint or marginal distributions. In this paper, we show tha ..."
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Mutual information (MI) based image-registration methods that use histograms are known to suffer from the so-called binning problem, caused by the absence of a principled technique for choosing the “optimal ” number of bins to calculate the joint or marginal distributions. In this paper, we show

Contextualizing Histogram

by Bingbing Ni, Shuicheng Yan, Ashraf Kassim
"... In this paper, we investigate how to incorporate spatial and/or temporal contextual information into classical histogram features with the aim of boosting visual classification performance. Firstly, we show that the stationary distribution derived from the normalized histogrambin co-occurrence matri ..."
Abstract - Cited by 2 (1 self) - Add to MetaCart
-occurrence matrix characterizes the row sums of the original histogram-bin co-occurrence matrix. This underlying rationale of the histogram-bin co-occurrence features then motivates us to propose the concept of general contextualizing histogram process, which encodes the spatial and/or temporal contexts as local
Results 1 - 10 of 491