Biologically Rationalized Computing Techniques For Image Processing Applications
-15%
portes grátis
Biologically Rationalized Computing Techniques For Image Processing Applications
Hemanth, Jude; Balas, Valentina Emilia
Springer International Publishing AG
08/2017
337
Dura
Inglês
9783319613154
15 a 20 dias
This 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.
Artifical 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.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
This 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.
Artifical 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.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
