Andere Kunden interessierten sich auch für
![Statistical Shape Models for 3D Medical Image Segmentation]( "Statistical Shape Models for 3D Medical Image Segmentation")
Statistical Shape Models for 3D Medical Image Segmentation44,99 €![Image Segmentation]( "Image Segmentation")
Image Segmentation44,99 €![Model Based Color Image Segmentation Techniques]( "Model Based Color Image Segmentation Techniques")
Model Based Color Image Segmentation Techniques42,99 €![Regional appearance modeling for model-based image segmentation]( "Regional appearance modeling for model-based image segmentation")
Regional appearance modeling for model-based image segmentation44,99 €![Content Based Document Image Retrieval]( "Content Based Document Image Retrieval")
Content Based Document Image Retrieval39,99 €![Lungs Segmentation and Nodule Detection using Machine Learning]( "Lungs Segmentation and Nodule Detection using Machine Learning")
Lungs Segmentation and Nodule Detection using Machine Learning44,99 €![Image Segmentation Using Mat Lab]( "Image Segmentation Using Mat Lab")
Image Segmentation Using Mat Lab40,99 €
Image segmentation refers to a process of dividing
the image into disjoint regions that were
meaningful. This process is fundamental in computer
vision in that many applications, such as image
retrieval, visual summary, image based modeling, and
so on, can essentially benefit from it. This process
is also challenging because the segmentation is
usually subjective and the computation is highly
costly.
This book develops in turn the prior model for the
pairwise graph approaches which is defined from
multiple cues, a hyper graph based method which
models multiple wise relations among the data
points, and a tree structured graph based method
which leads to an efficient and effective solution
to the normalized cuts criterion. These approaches
are demonstrated in multiple view, interactive and
automatic image segmentation problems.
This book is suitable for students and researchers
in image processing, computer vision, pattern
recognition and machine learning.
the image into disjoint regions that were
meaningful. This process is fundamental in computer
vision in that many applications, such as image
retrieval, visual summary, image based modeling, and
so on, can essentially benefit from it. This process
is also challenging because the segmentation is
usually subjective and the computation is highly
costly.
This book develops in turn the prior model for the
pairwise graph approaches which is defined from
multiple cues, a hyper graph based method which
models multiple wise relations among the data
points, and a tree structured graph based method
which leads to an efficient and effective solution
to the normalized cuts criterion. These approaches
are demonstrated in multiple view, interactive and
automatic image segmentation problems.
This book is suitable for students and researchers
in image processing, computer vision, pattern
recognition and machine learning.