Object recognition using multidimensional receptive field histograms

Schiele, Bernt; Crowley, James L.

doi:10.1007/bfb0015571

Cited by 325 publications

(348 citation statements)

References 8 publications

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“…Several authors have improved the performance of the original color histogram matching technique by introducing measures which are less sensitive to illumination changes [9], [10], [11], [12]. Instead of using color, grayvalue descriptors can also be used for histograms [13]. Another idea is to use a collection of images and reduce them in an eigenspace.…”

Section: Existing Recognition Methodsmentioning

confidence: 99%

Local grayvalue invariants for image retrieval

Schmid¹,

Mohr²

1997

IEEE Trans. Pattern Anal. Machine Intell.

1,228

763

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Abstract-This paper addresses the problem of retrieving images from large image databases. The method is based on local grayvalue invariants which are computed at automatically detected interest points. A voting algorithm and semilocal constraints make retrieval possible. Indexing allows for efficient retrieval from a database of more than 1,000 images. Experimental results show correct retrieval in the case of partial visibility, similarity transformations, extraneous features, and small perspective deformations.

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Section: Existing Recognition Methodsmentioning

confidence: 99%

Local grayvalue invariants for image retrieval

Schmid¹,

Mohr²

1997

IEEE Trans. Pattern Anal. Machine Intell.

1,228

763

View full text Add to dashboard Cite

show abstract

“…The object detection framework by Zhang et al [24] uses the concept of spatial histograms of Local Binary Patterns. Their features measure the similarity between model and test histograms using histogram intersection [16]. However, none of these methods compare counts of individual LBP labels in two regions as we do.…”

Section: Resultsmentioning

confidence: 99%

Haar Local Binary Pattern Feature for Fast Illumination Invariant Face Detection

Marcel¹

2009

Procedings of the British Machine Vision Conference 2009

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Face detection is the first step in many visual processing systems like face recognition, emotion recognition and lip reading. In this paper, we propose a novel feature called Haar Local Binary Pattern (HLBP) feature for fast and reliable face detection, particularly in adverse imaging conditions. This binary feature compares bin values of Local Binary Pattern histograms calculated over two adjacent image subregions. These subregions are similar to those in the Haar masks, hence the name of the feature. They capture the region-specific variations of local texture patterns and are boosted using AdaBoost in a framework similar to that proposed by Viola and Jones. Preliminary results obtained on several standard databases show that it competes well with other face detection systems, especially in adverse illumination conditions.

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“…The χ 2 divergence measure [15,16] was used to find the best match. Let H T be the color histogram of the test image and and let H M be the color histogram of the model image.…”

Section: Resultsmentioning

confidence: 99%

On Determining the Color of the Illuminant Using the Dichromatic Reflection Model

Ebner

Herrmann

2005

Lecture Notes in Computer Science

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Abstract. The human visual system is able to accurately determine the color of objects irrespective of the spectral power distribution used to illuminate the scene. This ability to compute color constant descriptors is called color constancy. Many different algorithms have been proposed to solve the problem of color constancy. Usually, some assumptions have to be made in order to solve this problem. Algorithms based on the dichromatic reflection model assume that the light reflected from an object results from a combined matte and specular reflection. This assumption is used to estimate the color of the illuminant. Once the color of the illuminant is known, one can compute a color corrected image as it would appear under a canonical, i.e. white illuminant. A number of different methods can be used to estimate the illuminant from the dichromatic reflection model. We evaluate several different methods on a standard set of images. Our results indicate that the median operator is particularly useful in estimating the color of the illuminant. We also found that it is not advantageous to assume that the illuminant can be approximated by the curve of the black-body radiator.

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Object recognition using multidimensional receptive field histograms

Cited by 325 publications

References 8 publications

Local grayvalue invariants for image retrieval

Local grayvalue invariants for image retrieval

Haar Local Binary Pattern Feature for Fast Illumination Invariant Face Detection

On Determining the Color of the Illuminant Using the Dichromatic Reflection Model

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