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020 _a9783319613161
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024 7 _a10.1007/978-3-319-61316-1
_2doi
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082 0 4 _a621.382
_223
245 1 0 _aBiologically Rationalized Computing Techniques For Image Processing Applications
_h[electronic resource] /
_cedited by Jude Hemanth, Valentina Emilia Balas.
250 _a1st ed. 2018.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2018.
300 _aVI, 337 p. 210 illus., 147 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
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490 1 _aLecture Notes in Computational Vision and Biomechanics,
_x2212-9413 ;
_v25
505 0 _aArtifical Bee Colony Algorithm for Classification of Semi-Urban LU/LC Features Using High Resolution Satellite Data.- Saliency Based Image Compression Using Walsh–Hadamard Transform (WHT).- Object trajectory prediction with scarce environment information.- A Two-fold Subspace Learning Based Feature Fusion Strategy for Classification of EMG and EMG spectrogram Images.- Automatic Detection of Brain Strokes in CT Images using Soft Computing Techniques.- A survey on Intelligence based biometric techniques for authentication applications.- Spatial and Spectral Quality Assessment of Fused Hyperspectral and Multispectral Data.- Deep Learning Techniques for Breast Cancer Detection using Medical Images Analysis.- A Tour towards the development of various Techniques for Paralysis Detection using Image Processing.- Chlorella - Algae Image Analysis using Artificial Neural Network and Deep Learning.- Review on Image Enhancement Techniques using Biologically Inspired Artificial Bee Colony Algorithms and its variants.- Certain Applications and Case Studies of Evolutionary Computing Techniques for Image Processing -- Histopathological Image Analysis for the Grade Identification of Tumor -- Super Resolution via Particle Swarm Optimization Variants. .
520 _aThis book introduces readers to innovative bio-inspired computing techniques for image processing applications. It demonstrates how a significant drawback of image processing – not providing the simultaneous benefits of high accuracy and less complexity – can be overcome, proposing bio-inspired methodologies to help do so.  Besides computing techniques, the book also sheds light on the various application areas related to image processing, and weighs the pros and cons of specific methodologies. Even though several such methodologies are available, most of them do not provide the simultaneous benefits of high accuracy and less complexity, which explains their low usage in connection with practical imaging applications, such as the medical scenario. Lastly, the book illustrates the methodologies in detail, making it suitable for newcomers to the field and advanced researchers alike.
650 0 _aSignal processing.
_94052
650 0 _aComputational intelligence.
_97716
650 0 _aArtificial intelligence.
_93407
650 0 _aBiomedical engineering.
_93292
650 1 4 _aSignal, Speech and Image Processing .
_931566
650 2 4 _aComputational Intelligence.
_97716
650 2 4 _aArtificial Intelligence.
_93407
650 2 4 _aBiomedical Engineering and Bioengineering.
_931842
700 1 _aHemanth, Jude.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_960409
700 1 _aBalas, Valentina Emilia.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_960410
710 2 _aSpringerLink (Online service)
_960411
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783319613154
776 0 8 _iPrinted edition:
_z9783319613178
776 0 8 _iPrinted edition:
_z9783319870502
830 0 _aLecture Notes in Computational Vision and Biomechanics,
_x2212-9413 ;
_v25
_960412
856 4 0 _uhttps://doi.org/10.1007/978-3-319-61316-1
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cEBK
999 _c80541
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