1,721,161 research outputs found
Multiple constraints to compute optical flow
Full text linkThe computation of the optical flow field from an image sequence requires the definition of constraints on the temporal change of image features. In this paper, we consider the implications of using multiple constraints in the computational schema. In the first step, it is shown that differential constraints correspond to an implicit feature tracking. Therefore, the best results (either in terms of measurement accuracy, and speed in the computation) are obtained by selecting and applying the constraints which are best “tuned” to the particular image feature under consideration. Considering also multiple image points not only allows us to obtain a (locally) better estimate of the velocity field, but also to detect erroneous measurements due to discontinuities in the velocity field. Moreover, by hypothesizing a constant acceleration motion model, also the derivatives of the optical flow are computed. Several experiments are presented from real image sequences
8. Advanced techniques for face-based biometrics
Full text linkFace recognition is nowadays one of the most challenging biometric modalities for the identification of individuals. In the last two decades several experimental as well as commercial systems have been developed exploiting different physical properties of the face image. Either being based on processing 2D or 3D information all these methods perform a face classification of the individuals based on some relevant features extracted from the raw image data. The data acquisition, preprocessing and the feature extraction/selection are all topics of the greatest importance to design a good performing recognition system. At the same time, the right choice of the features to be used as the basis for the face representation, which must be based on the uniqueness of such features, as well as most advanced issues such as the incorporation of quality information and the cope for ageing effects, are all of paramount importance. The tutorial will consists of two sessions (half day of total duration) devoted to the description of both the basic and most advanced techniques related to face recognition. The lectures will provide a comprehensive outline of face- based biometrics, its relation to biological systems (the psychophysics of the human visual system), including the existing applications and commercial systems. The lectures will provide an in-depth analysis of the state-of-the-art algorithms for face-image analysis including: face detection and tracking, landmark localization, feature extraction, face representation and classification. The lectures will mainly explore the image processing aspects of the recognition process. As for classification, machine learning algorithms will be also presented, including kernel methods as related to learning and the approximation theory. The most relevant issues and problems will be raised, providing practical solutions and algorithms responding to them. Particular attention will be given to the most advanced and new techniques for face - - representation and classification, as well as the current approaches presented in the literature. Attention will be also given to the performance evaluation of face recognition systems providing some examples and results from recent competitions and public evaluation contests. Finally, the tutorial will present three relevant and novel issues: the use of face image sequences for exploiting the time domain, the extension to 3D face analysis, and the how to cope with ageing and data quality
Using Camera Motion to Estimate Range for Robotic Parts Manipualtion
No full textA technique is described for determining a depth map of parts in bins using optical flow derived from camera motion. Simple programmed camera motions are generated by mounting the camera on the robot end effector and directing the effector along a known path. The results achieved using two simple trajectories, where one is along the optical axis and the other is in rotation about a fixation point, are detailed. Optical flow is estimated by computing the time derivative of a sequence of images, i.e. by forming differences between two successive images and, in particular, matching between contours in images that have been generated from the zero crossings of Laplacian of Gaussian-filtered images. Once the flow field has been determined, a depth map is computed utilizing the parameters of the known camera trajectory. Empirical results are presented for a calibration object and two bins of parts; these are compared with the theoretical precision of the technique, and it is demonstrated that a ranging accuracy on the order of two parts in 100 is achievabl
Special issue on facial image analysis
No full textIt has been a privilege to organise the first workshop on Advances in Facial Image Analysis and Recognition Technology, held in conjunction with the recent European Conference on Computer Vision in Freiburg, Germany. The rich and varied contributions to the workshop and the high standard of the presentations motivated the Program Committee to propose a Special Issue of Image and Vision Computing Journal containing the best of the workshop papers
Active Tracking Strategy for Monocular Depth Inference over Multiple Frames
No full textThe extraction of depth information from a sequence of images is investigated. An algorithm that exploits the constraint imposed by active motion of the camera is described. Within this framework, in order to facilitate measurement of the navigation parameters, a constrained egomotion strategy was adopted in which the position of the fixation point is stabilized during the navigation (in an anthropomorphic fashion). This constraint reduces the dimensionality of the parameter space without increasing the complexity of the equations. A further distinctive point is the use of two sampling rates: the faster (related to the computation of the instantaneous optical flow) is fast enough to allow the local operator to sense the passing edge (or, in other words, to allow the tracking of moving contour points), while the slower (used to perform the triangulation procedure necessary to derive depth) is slow enough to provide a sufficiently large baseline for triangulation. Experimental results on real image sequences are presente
Robust multi-modal and multi-unit feature level fusion of face and iris biometrics
Full text linkMulti-biometrics has recently emerged as a mean of more robust and effcient
personal verification and identification. Exploiting information from multiple
sources at various levels i.e., feature, score, rank or decision, the false acceptance
and rejection rates can be considerably reduced. Among all, feature level fusion
is relatively an understudied problem. This paper addresses the feature level
fusion for multi-modal and multi-unit sources of information. For multi-modal
fusion the face and iris biometric traits are considered, while the multi-unit fusion
is applied to merge the data from the left and right iris images. The proposed
approach computes the SIFT features from both biometric sources, either multi-
modal or multi-unit. For each source, the extracted SIFT features are selected via
spatial sampling. Then these selected features are finally concatenated together
into a single feature super-vector using serial fusion. This concatenated feature
vector is used to perform classification.
Experimental results from face and iris standard biometric databases are
presented. The reported results clearly show the performance improvements in
classification obtained by applying feature level fusion for both multi-modal and
multi-unit biometrics in comparison to uni-modal classification and score level
fusion
Active face recognition with a hybrid approach
No full textThe automatic detection of person's identity is a very interesting issue both in social and industrial environments. In this paper a system for automatic identity recognition from face images, is presented. The proposed approach is based on an hybrid iconic approach, where a first recognition score is obtained by matching a person's face against an eigen-space obtained from an image ensemble of known individuals. The hypothesis is then verified by computing the correlation of the gray level histogram of the new face image with the histograms of the subjects in the database. A selective attentional mechanism is applied to reduce the amount of information needed to describe a database of human faces. This is accomplished both at the task level, by performing planned fixations, and at the sensor level, by adopting a space-variant sampling of the images. By using a space-variant image geometry, the size of the database is considerably reduced and consequently also the processing time for recognition
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