@InProceedings{6952757,
  author = {C. Graca and G. Falcao and S. Kumar and I. N. Figueiredo},
  booktitle = {2014 22nd European Signal Processing Conference (EUSIPCO)},
  title = {Cooperative use of parallel processing with time or frequency-domain filtering for shape recognition},
  year = {2014},
  pages = {2085-2089},
  abstract = {For many computer vision applications, detection of blobs and/or tubular structures in images are of great importance. In this paper, we have developed a parallel signal processing framework for speeding up the detection of blob and tubular objects in images. We identified filtering procedure as being responsible for up to 98% of the global processing time, in the used blob or tubular detector functions. We show that after a certain dimension of the filter it is beneficial to combine frequency-domain techniques with parallel processing to develop faster signal processing algorithms. The proposed framework is applied to medical wireless capsule endoscopy (WCE) images, where blob and/or tubular detectors are useful in distinguishing between abnormal and normal images.},
  keywords = {computer vision;endoscopes;filtering theory;frequency-domain analysis;medical image processing;time-domain analysis;frequency-domain filtering;time-domain filtering;shape recognition;computer vision applications;parallel signal processing framework;tubular objects;blob objects;identified filtering procedure;medical wireless capsule endoscopy images;WCE images;Graphics processing units;Frequency-domain analysis;Detectors;Filtering;Instruction sets;Biomedical imaging;Time-domain analysis;Object shape recognition;Convolution;Frequency-domain filtering;Parallel processing;Wireless capsule endoscopy},
  issn = {2076-1465},
  month = {Sep.},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2014/html/papers/1569924255.pdf},
}

{"_id":"2eWvHJ4Q9cndmY36f","bibbaseid":"graca-falcao-kumar-figueiredo-cooperativeuseofparallelprocessingwithtimeorfrequencydomainfilteringforshaperecognition-2014","authorIDs":[],"author_short":["Graca, C.","Falcao, G.","Kumar, S.","Figueiredo, I. N."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","author":[{"firstnames":["C."],"propositions":[],"lastnames":["Graca"],"suffixes":[]},{"firstnames":["G."],"propositions":[],"lastnames":["Falcao"],"suffixes":[]},{"firstnames":["S."],"propositions":[],"lastnames":["Kumar"],"suffixes":[]},{"firstnames":["I.","N."],"propositions":[],"lastnames":["Figueiredo"],"suffixes":[]}],"booktitle":"2014 22nd European Signal Processing Conference (EUSIPCO)","title":"Cooperative use of parallel processing with time or frequency-domain filtering for shape recognition","year":"2014","pages":"2085-2089","abstract":"For many computer vision applications, detection of blobs and/or tubular structures in images are of great importance. In this paper, we have developed a parallel signal processing framework for speeding up the detection of blob and tubular objects in images. We identified filtering procedure as being responsible for up to 98% of the global processing time, in the used blob or tubular detector functions. We show that after a certain dimension of the filter it is beneficial to combine frequency-domain techniques with parallel processing to develop faster signal processing algorithms. The proposed framework is applied to medical wireless capsule endoscopy (WCE) images, where blob and/or tubular detectors are useful in distinguishing between abnormal and normal images.","keywords":"computer vision;endoscopes;filtering theory;frequency-domain analysis;medical image processing;time-domain analysis;frequency-domain filtering;time-domain filtering;shape recognition;computer vision applications;parallel signal processing framework;tubular objects;blob objects;identified filtering procedure;medical wireless capsule endoscopy images;WCE images;Graphics processing units;Frequency-domain analysis;Detectors;Filtering;Instruction sets;Biomedical imaging;Time-domain analysis;Object shape recognition;Convolution;Frequency-domain filtering;Parallel processing;Wireless capsule endoscopy","issn":"2076-1465","month":"Sep.","url":"https://www.eurasip.org/proceedings/eusipco/eusipco2014/html/papers/1569924255.pdf","bibtex":"@InProceedings{6952757,\n author = {C. Graca and G. Falcao and S. Kumar and I. N. Figueiredo},\n booktitle = {2014 22nd European Signal Processing Conference (EUSIPCO)},\n title = {Cooperative use of parallel processing with time or frequency-domain filtering for shape recognition},\n year = {2014},\n pages = {2085-2089},\n abstract = {For many computer vision applications, detection of blobs and/or tubular structures in images are of great importance. In this paper, we have developed a parallel signal processing framework for speeding up the detection of blob and tubular objects in images. We identified filtering procedure as being responsible for up to 98% of the global processing time, in the used blob or tubular detector functions. We show that after a certain dimension of the filter it is beneficial to combine frequency-domain techniques with parallel processing to develop faster signal processing algorithms. The proposed framework is applied to medical wireless capsule endoscopy (WCE) images, where blob and/or tubular detectors are useful in distinguishing between abnormal and normal images.},\n keywords = {computer vision;endoscopes;filtering theory;frequency-domain analysis;medical image processing;time-domain analysis;frequency-domain filtering;time-domain filtering;shape recognition;computer vision applications;parallel signal processing framework;tubular objects;blob objects;identified filtering procedure;medical wireless capsule endoscopy images;WCE images;Graphics processing units;Frequency-domain analysis;Detectors;Filtering;Instruction sets;Biomedical imaging;Time-domain analysis;Object shape recognition;Convolution;Frequency-domain filtering;Parallel processing;Wireless capsule endoscopy},\n issn = {2076-1465},\n month = {Sep.},\n url = {https://www.eurasip.org/proceedings/eusipco/eusipco2014/html/papers/1569924255.pdf},\n}\n\n","author_short":["Graca, C.","Falcao, G.","Kumar, S.","Figueiredo, I. N."],"key":"6952757","id":"6952757","bibbaseid":"graca-falcao-kumar-figueiredo-cooperativeuseofparallelprocessingwithtimeorfrequencydomainfilteringforshaperecognition-2014","role":"author","urls":{"Paper":"https://www.eurasip.org/proceedings/eusipco/eusipco2014/html/papers/1569924255.pdf"},"keyword":["computer vision;endoscopes;filtering theory;frequency-domain analysis;medical image processing;time-domain analysis;frequency-domain filtering;time-domain filtering;shape recognition;computer vision applications;parallel signal processing framework;tubular objects;blob objects;identified filtering procedure;medical wireless capsule endoscopy images;WCE images;Graphics processing units;Frequency-domain analysis;Detectors;Filtering;Instruction sets;Biomedical imaging;Time-domain analysis;Object shape recognition;Convolution;Frequency-domain filtering;Parallel processing;Wireless capsule endoscopy"],"metadata":{"authorlinks":{}},"downloads":0},"bibtype":"inproceedings","biburl":"https://raw.githubusercontent.com/Roznn/EUSIPCO/main/eusipco2014url.bib","creationDate":"2021-02-13T17:43:41.754Z","downloads":0,"keywords":["computer vision;endoscopes;filtering theory;frequency-domain analysis;medical image processing;time-domain analysis;frequency-domain filtering;time-domain filtering;shape recognition;computer vision applications;parallel signal processing framework;tubular objects;blob objects;identified filtering procedure;medical wireless capsule endoscopy images;wce images;graphics processing units;frequency-domain analysis;detectors;filtering;instruction sets;biomedical imaging;time-domain analysis;object shape recognition;convolution;frequency-domain filtering;parallel processing;wireless capsule endoscopy"],"search_terms":["cooperative","use","parallel","processing","time","frequency","domain","filtering","shape","recognition","graca","falcao","kumar","figueiredo"],"title":"Cooperative use of parallel processing with time or frequency-domain filtering for shape recognition","year":2014,"dataSources":["A2ezyFL6GG6na7bbs","oZFG3eQZPXnykPgnE"]}