Lattice-Based High-Dimensional Gaussian Filtering and the Permutohedral Lattice

Baek, Jiwon; Adams, Andrew; Dolson, Jennifer

doi:10.1007/s10851-012-0379-2

Cited by 9 publications

(4 citation statements)

References 46 publications

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“…The significance is equivalent to independence, the background colour of the image is usually distributed in any direction of the image, with high spatial variance, while the foreground colour instead of more concentrated, while the spatial variance is much lower. The resulting super-pixel images have better compactness and local features, which makes the pixels clustered in a particular area of the image have higher saliency values than the pixels distributed in the entire image [31]. Therefore, the colour space distribution of the super-pixel unit is defined as the spatial variance corresponding to the unit colour value.…”

Section: Colour Space Distributionmentioning

confidence: 99%

An Automatic Cardiac Computed Tomography (Ct) Images Sequence segmentation Technique

Ahmed,

Hongsong,

Khan

et al. 2024

Preprint

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Segmentation of the Whole Cardiac CT Image Sequence is the Key to Computer-baseddiagnosis and the analysis of internal lesions in the Human Heart. According to the flow of the blood, contraction and Dilation, the cardiac CT image is prone to weak boundaries and artefacts. The technique of Segmentation for a fast, accurate, reproducible, and fully facilitate the diagnosis of Cardiovascular diseases, it is also the consuming of time, intensive of clinical staff to produce over-segmentation. Therefore, in this paper, we proposed the automatic cardiac CT images sequence segmentation technique to depend on the visual saliency method to understand the saliency of the foreground object relative to the background area used for ImageSegmentation. Using visual attention mechanism we will find the accurate location information, filtering ribs, and muscles of the heart area. The distinctness between the background and the target area is not obvious, and internal structure diversification. It can filter out most of the non-heart tissue part, which is more conducive to the doctor on the patient's heart health status observation.

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Section: Colour Space Distributionmentioning

confidence: 99%

An Automatic Cardiac Computed Tomography (Ct) Images Sequence segmentation Technique

Ahmed,

Hongsong,

Khan

et al. 2024

Preprint

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show abstract

“…Taking Equations ( 7) and ( 23) into account, it is apparent that both equations are similar, except that in the former, Gaussian weights are shifted. Baek et al [30] proved that to use shifted Gaussian kernels, it is sufficient to slice the lattice at shifted positions. Using the ImageStack library, the lattice points are ordinary position vectors without shifting and the values of ( ) are blurred in the lattice space by using position vectors in Gaussian weights.…”

Section: -4-implementation Details Of Shifted Gaussian Kernelsmentioning

confidence: 99%

Body Field: Structured Mean Field with Human Body Skeleton Model and Shifted Gaussian Edge Potentials

Ershadi-Nasab¹,

Kasaei²,

Sanaei³

et al. 2020

jist

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An efficient method for simultaneous human body part segmentation and pose estimation is introduced. A conditional random field with a fully-connected graphical model is used. Possible node (image pixel) labels comprise of the human body parts and the background. In the human body skeleton model, the spatial dependencies among body parts are encoded in the definition of pairwise energy functions according to the conditional random fields. Proper pairwise edge potentials between image pixels are defined according to the presence or absence of human body parts that are near to each other. Various Gaussian kernels in position, color, and histogram of oriented gradients spaces are used for defining the pairwise energy terms. Shifted Gaussian kernels are defined between each two body parts that are connected to each other according to the human body skeleton model. As shifted Gaussian kernels impose a high computational cost to the inference, an efficient inference process is proposed by a mean field approximation method that uses high dimensional shifted Gaussian filtering. The experimental results evaluated on the challenging KTH Football, Leeds Sports Pose, HumanEva, and Penn-Fudan datasets show that the proposed method increases the per-pixel accuracy measure for human body part segmentation and also improves the probability of correct parts metric of human body joint locations.

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“…Due to the mixture of Gaussian kernels form of the pairwise term, the updates can be computed in parallel by convolution with Gaussian kernels. This can be achieved efficiently by exploiting fast Gaussian filtering techniques, such as the permutohedral lattice-based method of [2].…”

Section: Crf Formulationmentioning

confidence: 99%

Object Co-detection via Efficient Inference in a Fully-Connected CRF

Hayder

Salzmann

2014

Computer Vision – ECCV 2014

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Object detection has seen a surge of interest in recent years, which has lead to increasingly effective techniques. These techniques, however, still mostly perform detection based on local evidence in the input image. While some progress has been made towards exploiting scene context, the resulting methods typically only consider a single image at a time. Intuitively, however, the information contained jointly in multiple images should help overcoming phenomena such as occlusion and poor resolution. In this paper, we address the co-detection problem that aims to leverage this collective power to achieve object detection simultaneously in all the images of a set. To this end, we formulate object co-detection as inference in a fully-connected CRF whose edges model the similarity between object candidates. We then learn a similarity function that allows us to efficiently perform inference in this fully-connected graph, even in the presence of many object candidates. This is in contrast with existing co-detection techniques that rely on exhaustive or greedy search, and thus do not scale well. Our experiments demonstrate the benefits of our approach on several co-detection datasets.

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Lattice-Based High-Dimensional Gaussian Filtering and the Permutohedral Lattice

Cited by 9 publications

References 46 publications

An Automatic Cardiac Computed Tomography (Ct) Images Sequence segmentation Technique

An Automatic Cardiac Computed Tomography (Ct) Images Sequence segmentation Technique

Body Field: Structured Mean Field with Human Body Skeleton Model and Shifted Gaussian Edge Potentials

Object Co-detection via Efficient Inference in a Fully-Connected CRF

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