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Bilateral filter

About: Bilateral filter is a research topic. Over the lifetime, 3500 publications have been published within this topic receiving 75582 citations.


Papers
More filters
Journal ArticleDOI
TL;DR: In this paper, two modifications in this technique are introduced, which allow further acceleration and a significant increase in quality, and they are used for image processing tasks, including noise reduction and dynamic range compression.
Abstract: Edge-preserving lowpass filters are a valuable tool in several image processing tasks, including noise reduction and dynamic range compression. A high-quality algorithm is the bilateral filter, but its computational cost is very high. A fast but approximate implementation was introduced by Durand and Dorsey. Introduced are two modifications in this technique which allow further acceleration and a significant increase in quality.

31 citations

Patent
26 Feb 2010
TL;DR: In this paper, a weight distribution function is applied to values of an image characteristic for the pixels in the group of corresponding aligned pixels, and a net weight is subsequently assigned to each of the pixels, in order to obtain a new pixel value for a corresponding new pixel in the new digital output image.
Abstract: Systems and methods are presented for generating a new digital output image by blending a plurality of digital input images capturing the same scene at different levels of exposure. Each new pixel for the new digital output image is derived from a group of corresponding aligned pixels from the digital input images. In order to determine a weight for each pixel in each group of mutually-aligned source-image pixels, a weight distribution function is applied to values of an image characteristic for the pixels in the group of corresponding aligned pixels, and a net weight is subsequently assigned to each of the pixels in the group. Pixel values of pixels in each group of mutually-aligned source-image pixels are modified based on the net weights assigned to the pixels in order to obtain a new pixel value for a corresponding new pixel in the new digital output image.

31 citations

Journal ArticleDOI
TL;DR: In this article, a semantic HOI recognition system based on multi-vision sensors is proposed, where the de-noised RGB and depth images are segmented into multiple clusters using a Simple Linear Iterative Clustering (SLIC) algorithm.
Abstract: Human-Object Interaction (HOI) recognition, due to its significance in many computer vision-based applications, requires in-depth and meaningful details from image sequences. Incorporating semantics in scene understanding has led to a deep understanding of human-centric actions. Therefore, in this research work, we propose a semantic HOI recognition system based on multi-vision sensors. In the proposed system, the de-noised RGB and depth images, via Bilateral Filtering (BLF), are segmented into multiple clusters using a Simple Linear Iterative Clustering (SLIC) algorithm. The skeleton is then extracted from segmented RGB and depth images via Euclidean Distance Transform (EDT). Human joints, extracted from the skeleton, provide the annotations for accurate pixel-level labeling. An elliptical human model is then generated via a Gaussian Mixture Model (GMM). A Conditional Random Field (CRF) model is trained to allocate a specific label to each pixel of different human body parts and an interaction object. Two semantic feature types that are extracted from each labeled body part of the human and labelled objects are: Fiducial points and 3D point cloud. Features descriptors are quantized using Fisher’s Linear Discriminant Analysis (FLDA) and classified using K-ary Tree Hashing (KATH). In experimentation phase the recognition accuracy achieved with the Sports dataset is 92.88%, with the Sun Yat-Sen University (SYSU) 3D HOI dataset is 93.5% and with the Nanyang Technological University (NTU) RGB+D dataset it is 94.16%. The proposed system is validated via extensive experimentation and should be applicable to many computer-vision based applications such as healthcare monitoring, security systems and assisted living etc.

31 citations

Journal ArticleDOI
TL;DR: The problem of segmenting the object from the background is addressed in the proposed Gaussian and Gabor Filter Approach (GGFA) for object segmentation by performing various operations like bilateral filtering, Edge detection, Clustering, and Region growing.
Abstract: The problem of segmenting the object from the background is addressed in the proposed Gaussian and Gabor Filter Approach (GGFA) for object segmentation. An improved and efficient approach based on Gaussian and Gabor Filter reads the given input image and performs filtering and smoothing operation. The region occupied by the object is extracted from the image by performing various operations like bilateral filtering, Edge detection, Clustering, and Region growing. The proposed approach experimented on standard images taken from Caltech datasets, Corel Photo CDs, and Weizmann horse datasets show significantly improved results.

31 citations

Journal ArticleDOI
TL;DR: The developed method is more efficient in removing speckle noise from the ultrasound images compared to other current methods because it is able to adapt the filtering process according to the image contents, thus avoiding the loss of any relevant structural features in the input images.
Abstract: A new method is proposed to perform selective smoothing of images affected by speckle noise.A new smoothing criterion is defined for the average smoothing filter.The convolution window of the smoothing filter is adjustable.The method is evaluated using real ultrasound medical images based on image quality metrics.The proposed method produced better results than the current methods evaluated. Ultrasound images are strongly affected by speckle noise making visual and computational analysis of the structures more difficult. Usually, the interference caused by this kind of noise reduces the efficiency of extraction and interpretation of the structural features of interest. In order to overcome this problem, a new method of selective smoothing based on average filtering and the radiation intensity of the image pixels is proposed. The main idea of this new method is to identify the pixels belonging to the borders of the structures of interest in the image, and then apply a reduced smoothing to these pixels, whilst applying more intense smoothing to the remaining pixels. Experimental tests were conducted using synthetic ultrasound images with speckle noisy added and real ultrasound images from the female pelvic cavity. The new smoothing method is able to perform selective smoothing in the input images, enhancing the transitions between the different structures presented. The results achieved are promising, as the evaluation analysis performed shows that the developed method is more efficient in removing speckle noise from the ultrasound images compared to other current methods. This improvement is because it is able to adapt the filtering process according to the image contents, thus avoiding the loss of any relevant structural features in the input images.

31 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202321
202257
2021116
2020145
2019203
2018204