Colour edge detection based on the fusion of hue component and principal component analysis

Tao, Linmi; Fan, Yangyu; Wang, Yi

doi:10.1049/iet-ipr.2013.0062

Cited by 26 publications

(11 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hence, corresponding acquisition channels are not independent, and the associated axis are not orthogonal. Since Carron and Lambert [28], lots of authors tried to transfer the problem in other color spaces, such as in Hue-based spaces where axis can be defined as semantically orthogonal (Lei et al [29]). Unfortunately, the color transformation is defined by rotations from the initial RGB acquisition space, that does not solve the problem of non-orthogonality, it only reduces its impact depending on the image content.…”

Section: A Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Full-Vector Gradient for Multi-Spectral or Multivariate Images

Chatoux

Richard

Lecellier

et al. 2019

IEEE Trans. on Image Process.

View full text Add to dashboard Cite

Gradient extraction is important for a lot of metrological applications such as Control Quality by Vision. In this work, we propose a full-vector gradient for multi-spectral sensors. The full-vector gradient extends Di Zenzo expression to take into account the non-orthogonality of the acquisition channels thanks to a Gram matrix. This expression is generic and independent from channel count. Results are provided for a color and a multi-spectral snapshot sensor. Then, we show the accuracy improvement of the gradient calculation by creating a dedicated objective test and from real images.

show abstract

Section: A Related Workmentioning

confidence: 99%

“…A scalar parameter a allows controlling the distribution shape, spherical when a tends to 1, or oblong when a tends to 5 (eq. (29)). This parameter is randomly defined in the experiment.…”

Section: B Color Distribution Generationmentioning

confidence: 99%

Full-Vector Gradient for Multi-Spectral or Multivariate Images

Chatoux

Richard

Lecellier

et al. 2019

IEEE Trans. on Image Process.

View full text Add to dashboard Cite

show abstract

“…As popular methods of color image edge detection usually neglect the use of hue, some edges which are caused by hue changes are missed. To solve this problem of edge detection, fusion of principal component analysis and hue component is proposed by Lei et al [5]. Tzanidou et al [6] proposed use of color information to detect torso clothes of the person carrying baggage.…”

Section: Related Workmentioning

confidence: 99%

Object Detection In Cluttered Background using Color Clusters

Gode,

Khobragade

2019

IJEAT

View full text Add to dashboard Cite

Object detection in presence of complex background and illumination variation is important image analysis problem with many applications. Most of the object detection algorithms use local image descriptors which are computed from interest points based on luminance information and neglect precious color information of an object. If appearances of the object to be detected contain multiple colors in non-homogeneous distributions then it makes it difficult to detect these objects using shape features. In this context, we propose a robust algorithm designed to detect a class of objects using a descriptor which is computed from color information of an object. Clusters are formed in Hue and Saturation (HS) color space of an object using k-means clustering and cluster analysis based on number of pixels belong to each cluster, object detection is performed. Use of clustering algorithm in color space of an object to form descriptor reduces the large dimensionality of the histogram bins in the computation. The performance of the algorithm is demonstrated by experimentation carried out on standard dataset GroZi-120. Experimental results shows that the proposed algorithm is insensitive to scaling, object rotation, illumination variations and capable of handling cluttered background effectively. Finally results shows that proposed algorithm outperforms closely related algorithm by a decisive margin.

show abstract

“…According to the imaging quality of these six principalcomponent images, one of the images was screened for further analysis [33]. e principal-component image is the linear combination of the original images at different wavelengths.…”

Section: Extraction Of Spectral Data and Data Dimensionality Reductionmentioning

confidence: 99%

Nondestructive Identification of Salmon Adulteration with Water Based on Hyperspectral Data

Zhang

Wang

Yan

et al. 2018

Journal of Food Quality

View full text Add to dashboard Cite

For the identification of salmon adulteration with water injection, a nondestructive identification method based on hyperspectral images was proposed. The hyperspectral images of salmon fillets in visible and near-infrared ranges (390–1050 nm) were obtained with a system. The original hyperspectral data were processed through the principal-component analysis (PCA). According to the image quality and PCA parameters, a second principal-component (PC2) image was selected as the feature image, and the wavelengths corresponding to the local extremum values of feature image weighting coefficients were extracted as feature wavelengths, which were 454.9, 512.3, and 569.1 nm. On this basis, the color combined with spectra at feature wavelengths, texture combined with spectra at feature wavelengths, and color-texture combined with spectra at feature wavelengths were independently set as the input, for the modeling of salmon adulteration identification based on the self-organizing feature map (SOM) network. The distances between neighboring neurons and feature weights of the models were analyzed to realize the visualization of identification results. The results showed that the SOM-based model, with texture-color combined with fusion features of spectra at feature wavelengths as the input, was evaluated to possess the best performance and identification accuracy is as high as 96.7%.

show abstract

Colour edge detection based on the fusion of hue component and principal component analysis

Cited by 26 publications

References 33 publications

Full-Vector Gradient for Multi-Spectral or Multivariate Images

Full-Vector Gradient for Multi-Spectral or Multivariate Images

Object Detection In Cluttered Background using Color Clusters

Nondestructive Identification of Salmon Adulteration with Water Based on Hyperspectral Data

Contact Info

Product

Resources

About