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A biologically inspired spiking model of visual processing for image feature detection
To enable fast reliable feature matching or tracking in scenes, features need to be discrete and meaningful, and hence edge or corner features, commonly called interest points are often used for this purpose. Experimental research has illustrated that biological vision systems use neuronal circuits to extract particular features such as edges or corners from visual scenes. Inspired by this biological behaviour, this paper proposes a biologically inspired spiking neural network for the purpose of image feature extraction. Standard digital images are processed and converted to spikes in a manner similar to the processing that transforms light into spikes in the retina. Using a hierarchical spiking network, various types of biologically inspired receptive fields are used to extract progressively complex image features. The performance of the network is assessed by examining the repeatability of extracted features with visual results presented using both synthetic and real images
Biometric Systems
Biometric authentication has been widely used for access control and security systems over the past few years. The purpose of this book is to provide the readers with life cycle of different biometric authentication systems from their design and development to qualification and final application. The major systems discussed in this book include fingerprint identification, face recognition, iris segmentation and classification, signature verification and other miscellaneous systems which describe management policies of biometrics, reliability measures, pressure based typing and signature verification, bio-chemical systems and behavioral characteristics. In summary, this book provides the students and the researchers with different approaches to develop biometric authentication systems and at the same time includes state-of-the-art approaches in their design and development. The approaches have been thoroughly tested on standard databases and in real world applications
Robust and efficient Fourier-Mellin transform approximations for invariant grey-level image description and reconstruction
International audienceThis paper addresses the gray-level image representation ability of the Fourier-Mellin Transform (FMT) for pattern recognition, reconstruction and image database retrieval. The main practical di±culty of the FMT lies in the accuracy and e±ciency of its numerical approximation and we propose three estimations of its analytical extension. Comparison of these approximations is performed from discrete and ¯nite-extent sets of Fourier- Mellin harmonics by means of experiments in: (i) image reconstruction via both visual inspection and the computation of a reconstruction error; and (ii) pattern recognition and discrimination by using a complete and convergent set of features invariant under planar similarities. Experimental results on real gray-level images show that it is possible to recover an image to within a speci¯ed degree of accuracy and to classify objects reliably even when a large set of descriptors is used. Finally, an example will be given, illustrating both theoretical and numerical results in the context of content-based image retrieval
Algorithmic issues in visual object recognition
This thesis is divided into two parts covering two aspects of
research in the area of visual object recognition.
Part I is about human detection in still images. Human
detection is a challenging computer vision task due to the wide
variability in human visual appearances and body poses. In this
part, we present several enhancements to human detection
algorithms. First, we present an extension to the integral
images framework to allow for constant time computation of
non-uniformly weighted summations over rectangular regions
using a bundle of integral images. Such computational element
is commonly used in constructing gradient-based feature
descriptors, which are the most successful in shape-based human
detection. Second, we introduce deformable features as an
alternative to the conventional static features used in
classifiers based on boosted ensembles. Deformable features can
enhance the accuracy of human detection by adapting to pose
changes that can be described as translations of body features.
Third, we present a comprehensive evaluation framework for
cascade-based human detectors. The presented framework
facilitates comparison between cascade-based detection
algorithms, provides a confidence measure for result, and
deploys a practical evaluation scenario.
Part II explores the possibilities of enhancing the speed of
core algorithms used in visual object recognition using the
computing capabilities of Graphics Processing Units (GPUs).
First, we present an implementation of Graph Cut on GPUs, which
achieves up to 4x speedup against compared to a CPU
implementation. The Graph Cut algorithm has many applications
related to visual object recognition such as segmentation and
3D point matching. Second, we present an efficient sparse
approximation of kernel matrices for GPUs that can
significantly speed up kernel based learning algorithms, which
are widely used in object detection and recognition. We present
an implementation of the Affinity Propagation clustering
algorithm based on this representation, which is about 6 times
faster than another GPU implementation based on a conventional
sparse matrix representation
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