Really Quick Shift: Image Segmentation on a GPU

Fulkerson, Brian; Soatto, Stefano

doi:10.1007/978-3-642-35740-4_27

Cited by 45 publications

(31 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Just as with conventional segmentation, the CUDA programming language has also recently been used for several interactive implementations. Some examples are interactive visualization and segmentation of neural processes in electron microscopy datasets (Jeong et al, 2009), real-time segmentation by clustering (Abramov et al, 2011;Fulkerson and Soatto, 2010), interactive multi-label segmentation (Santner et al, 2010) and interactive segmentation of bones by using discrete deformable models (Schmid et al, 2011) or the random walker algorithm (Top et al, 2011).…”

Section: Image Segmentationmentioning

confidence: 99%

Medical image processing on the GPU – Past, present and future

Eklund

Dufort

Forsberg

et al. 2013

Medical Image Analysis

344

198

View full text Add to dashboard Cite

Graphics processing units (GPUs) are used today in a wide range of applications, mainly because they can dramatically accelerate parallel computing, are affordable and energy efficient. In the field of medical imaging, GPUs are in some cases crucial for enabling practical use of computationally demanding algorithms. This review presents the past and present work on GPU accelerated medical image processing, and is meant to serve as an overview and introduction to existing GPU implementations. The review covers GPU acceleration of basic image processing operations (filtering, interpolation, histogram estimation and distance transforms), the most commonly used algorithms in medical imaging (image registration, image segmentation and image denoising) and algorithms that are specific to individual modalities (CT, PET, SPECT, MRI, fMRI, DTI, ultrasound, optical imaging and microscopy). The review ends by highlighting some future possibilities and challenges.

show abstract

Section: Image Segmentationmentioning

confidence: 99%

Medical image processing on the GPU – Past, present and future

Eklund

Dufort

Forsberg

et al. 2013

Medical Image Analysis

344

198

View full text Add to dashboard Cite

show abstract

“…Clustering methods join regions of a high-dimensional feature space [13], and superpixel approaches [35] form clusters that are deliberately over-segmented into more manageable regions. These approaches are good at simplifying complex images, yet they do not capture specific objects.…”

Section: Related Workmentioning

confidence: 99%

Interactive GPU active contours for segmenting inhomogeneous objects

Willcocks

Jackson

Nelson

et al. 2017

J Real-Time Image Proc

View full text Add to dashboard Cite

We present a segmentation software package primarily targeting medical and biological applications, with a high level of visual feedback and several usability enhancements over existing packages. Specifically, we provide a substantially faster GPU implementation of the local Gaussian distribution fitting energy model, which can segment inhomogeneous objects with poorly defined boundaries as often encountered in biomedical images. We also provide interactive brushes to guide the segmentation process in a semiautomated framework. The speed of our implementation allows us to visualize the active surface in real time with a built-in ray tracer, where users may halt evolution at any time step to correct implausible segmentation by painting new blocking regions or new seeds. Quantitative and qualitative validation is presented, demonstrating the practical efficacy of our interactive elements for a variety of real-world datasets.

show abstract

“…Recently, CUDA technology has been used for image segmentation and classification [19,20,21,22], 3D simulations [20,21], different mathematical computations [25,26], etc. Most of the GPGPU-based research is about the algorithm speed up and their real-time application.…”

Section: Gpu Implementationmentioning

confidence: 99%

CPU, GPU and FPGA Implementations of MALD: Ceramic Tile Surface Defects Detection Algorithm

2014

View full text Add to dashboard Cite

Original scientific paperThis paper addresses adjustments, implementation and performance comparison of the Moving Average with Local Difference (MALD) method for ceramic tile surface defects detection. Ceramic tile production process is completely autonomous, except the final stage where human eye is required for defects detection. Recent computational platform development and advances in machine vision provides us with several options for MALD algorithm implementation. In order to exploit the shortest execution time for ceramic tile production process, the MALD method is implemented on three different platforms: CPU, GPU and FPGA, and it is implemented on each platform in at least two ways. Implementations are done in MATLAB's MEX/C++, C++, CUDA/C++, VHDL and Assembly programming languages. Execution times are measured and compared for different algorithms and their implementations on different computational platforms.Key words: CUDA, FPGA, GPU, Integral Image, MALD, Ceramic Tile CPU, GPU i FPGA implementacija MALD algoritma za otkrivanje nepravilnosti na površini keramičkih pločica. U ovom radu razmatra se prilagodba, implementacija i usporedba performansi metode pomičnog usrednjavanja s lokalnom diferencijom (MALD) s primjenom u otkrivanju površinskih nedostataka na keramičkim ploči-cama. Proizvodna linija keramičkih pločica je autonomna sve do zadnje faze u kojoj je potreban ljudski vid kako bi se otkrili eventualni nedostaci na keramičkim pločicama. Nedavnim razvojem računalnih platformi i razvojem metoda računalnog vida omogućena je implementacija MALD metode na nekoliko načina. U nastojanju skraćenja vremena potrebnog za proizvodnju keramičkih pločica, MALD metoda je implementirana u trima različitim platformama: CPU (central processing unit), GPU (graphic processing unit) i FPGA (field programmable gate array), te s barem dva različita algoritma. Implementacija je izvršena sa MATLAB MEX/C++, C++, CUDA/C++, VHDL te Asembler programskim jezicima. Izmjerena vremena obrade su meusobno usporeena za različite algoritme i njihove implementacije na različitim računalnim platformama.

show abstract

Really Quick Shift: Image Segmentation on a GPU

Cited by 45 publications

References 22 publications

Medical image processing on the GPU – Past, present and future

Medical image processing on the GPU – Past, present and future

Interactive GPU active contours for segmenting inhomogeneous objects

CPU, GPU and FPGA Implementations of MALD: Ceramic Tile Surface Defects Detection Algorithm

Contact Info

Product

Resources

About