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Books > Professional & Technical > Electronics & communications engineering > Electronics engineering > Applied optics
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Image Recognition and Restoration (Hardcover)
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Image Recognition and Restoration (Hardcover)
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A biometric recognition system that uses as a biometric feature a
static digital image of the human face is developed. Detecting and
recognizing human faces in photographs and video sequences is an
increasing problem in the field of computer vision, and there are
many practical applications at present, such as surveillance,
videoconferencing, access control, etc. The objective is to return
as a result the five people in the database which most resemble the
person of the test image. The problem of face recognition can be
divided into two phases: Detection of the face within the image and
recognition. The detection phase is mainly based on the detection
of skin in the image. Subsequently, a selection of candidate skin
regions to be expensive and validated through ""maps"" of eyes and
mouth. In addition, an alternative system of face detection, if the
previous method has not detected any. This method takes the largest
region of skin found in the image and generates an ellipse with its
characteristics to return as face the part of the image that
coincides with the ellipse. In the recognition phase the areas of
the detected images are taken as persons. PCA is used to extract
the characteristics that represent the images. These
characteristics are then used to train and simulate neural
networks. With the outputs of the neural networks, the images of
the database that most closely resemble the face of the test image.
The evaluation of the implemented system shows the great influence
of the type of images used for recognition, with much better
results when the images meet certain characteristics. The framework
of this book is the digital restoration of images, that is, the
process which recovers an original image that has been degraded by
imperfections of the acquisition system: blur and noise. Restoring
this degradation is a problem poorly conditioned because the direct
investment by least squares amplifies the noise at high
frequencies. Therefore, regularization is used mathematics as a
means to include a priori information of the image that it achieves
to stabilize the solution. During the first part of the report a
review of certain state-of-the-art algorithms, which will later be
used as methods of comparison in the experiments. To solve the
problem of regularization, the restoration of images has
prerequisites. In the first place, it is necessary to behavior of
the image outside its borders, due to non-local ownership of the
convolution modeling degradation. The absence of border in the
restoration gives rise to the artifact known as boundary ringing.
In the second one, the restoration algorithms depend on a
significant number of parameters divided into three groups:
parameters with respect to the degradation, noise and the original
image. All of them need to estimate a priori sufficiently precise,
because small errors with respect to their real values produce
significant deviations in restoration results. The problem border
and sensitivity to estimates are the objectives to be resolved in
this book using iterative algorithms. The first of the algorithms
addresses the border problem starting from an image truncated in
the field of vision as real observation. To solve this no
linearity, a neural network is used that minimizes a defined cost
function mainly due to regularization by total variation, but not
including any type of information on frontiers or require prior
training. As a result, a restored image without ringing effects is
field of view and in addition the truncated borders are
reconstructed until the original size. The algorithm is based on
the technique of retro-propagation of energy, with that the network
becomes an iterative cycle of two processes: forward and backward,
which simulate a restoration and degradation for each iteration.
Following the same iterative concept of restoration-degradation, a
second algorithm in the frequency domain to reduce dependence with
respect to parameter estimates. For this, a new filter of
desensitized restoration as a result of applying an iterative
algorithm on an original filter. Studying the sensitivity
properties of this filter and establishing a criterion for the
number of iterations, we arrive at an expression for the
desensitization algorithm particularized to the Wiener and Tikhonov
filters. The results of the experiments demonstrate the good
behavior of the filter with respect to noise-dependent error,
making the estimate that is made more robustness is the one
corresponding to the parameters of the noise, although
desensitization is also extended to other estimates.
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