Fast Recognition Algorithm Research Articles

Fast recognition algorithm of underwater micro-terrain based on ultrasonic detection

An algorithm was proposed to fast recognize three types of underwater micro-terrain, i.e. the level, the gradient and the uneven. With pendulum single beam bathymeter, the hard level concrete floor, the random uneven floor and the gradient wood panel (8°) were ultrasonically detected 20 times, respectively. The results show that the algorithm is right from fact that the first clustering values of the uneven are all less than the threshold value of 60.0% that is obtained by the level and gradient samples. The algorithm based on the dynamic clustering theory can effectively eliminate the influences of the exceptional elevation values produced by the disturbances resulted from the grazing angle, the characteristic of bottom material and environmental noises, and its real-time capability is good. Thus, the algorithm provides a foundation for the next restructuring of the micro-terrain.

평판 디스플레이 비전 정렬 시스템의 기구학 및 제어

The kinematics and control problem of a visual alignment system is investigated, which plays a crucial role in the fabrication process of flat panel displays. The first solution is the inverse kinematics of a 4PPR parallel alignment mechanism. It determines the driving distance of each joint to compensate the misalignment between mask and panel. Second, an efficient vision algorithm for fast alignment mark recognition is suggested, where by extracting essential feature points to represent the geometry of a mark, the geometric template matching enables much faster object recognition comparing with the general template matching. Finally, the overall visual alignment process including the kinematic solution, vision algorithm, and joint control is implemented and experimental results are given.

Human Identification Based on Gait

Analysis of person’s behavioral and psychological features like Gait or the manner of walking, allows human identification process to be able to extract the gait information from a video sequence of different persons at a distance and recognize the specific person. This paper proposes a fast gait recognition algorithm based on the averaged silhouette of person. Three important novelties in our proposed algorithm cause that it gives a better performance than previous methods in both recognition rate and computational speed.1- frame sampling method, which causes the algorithm computational time, without any change in recognition rate, decrease to about half. 2- New method for background estimation, which gives an acceptable estimate of the background. 3- Local thresholding method, which gives more complete and more accurate binary silhouette pictures. Experimental results demonstrate that our proposed algorithm accuracy is superior to other previous methods in the gait recognition application.

Moment Invariants for the Analysis of 2D Flow Fields

We present a novel approach for analyzing two-dimensional (2D) flow field data based on the idea of invariant moments. Moment invariants have traditionally been used in computer vision applications, and we have adapted them for the purpose of interactive exploration of flow field data. The new class of moment invariants we have developed allows us to extract and visualize 2D flow patterns, invariant under translation, scaling, and rotation. With our approach one can study arbitrary flow patterns by searching a given 2D flow data set for any type of pattern as specified by a user. Further, our approach supports the computation of moments at multiple scales, facilitating fast pattern extraction and recognition. This can be done for critical point classification, but also for patterns with greater complexity. This multi-scale moment representation is also valuable for the comparative visualization of flow field data. The specific novel contributions of the work presented are the mathematical derivation of the new class of moment invariants, their analysis regarding critical point features, the efficient computation of a novel feature space representation, and based upon this the development of a fast pattern recognition algorithm for complex flow structures.

Adaptation of a fast Fourier transform‐based docking algorithm for protein design

Designing proteins with novel protein/protein binding properties can be achieved by combining the tools that have been developed independently for protein docking and protein design. We describe here the sequence-independent generation of protein dimer orientations by protein docking for use as scaffolds in protein sequence design algorithms. To dock monomers into sequence-independent dimer conformations, we use a reduced representation in which the side chains are approximated by spheres with atomic radii derived from known C2 symmetry-related homodimers. The interfaces of C2-related homodimers are usually more hydrophobic and protein core-like than the interfaces of heterodimers; we parameterize the radii for docking against this feature to capture and recreate the spatial characteristics of a hydrophobic interface. A fast Fourier transform-based geometric recognition algorithm is used for docking the reduced representation protein models. The resulting docking algorithm successfully predicted the wild-type homodimer orientations in 65 out of 121 dimer test cases. The success rate increases to approximately 70% for the subset of molecules with large surface area burial in the interface relative to their chain length. Forty-five of the predictions exhibited less than 1 A C(alpha) RMSD compared to the native X-ray structures. The reduced protein representation therefore appears to be a reasonable approximation and can be used to position protein backbones in plausible orientations for homodimer design.

Online palmprint identification

Biometrics-based personal identification is regarded as an effective method for automatically recognizing, with a high confidence, a person's identity. This paper presents a new biometric approach to online personal identification using palmprint technology. In contrast to the existing methods, our online palmprint identification system employs low-resolution palmprint images to achieve effective personal identification. The system consists of two parts: a novel device for online palmprint image acquisition and an efficient algorithm for fast palmprint recognition. A robust image coordinate system is defined to facilitate image alignment for feature extraction. In addition, a 2D Gabor phase encoding scheme is proposed for palmprint feature extraction and representation. The experimental results demonstrate the feasibility of the proposed system.

Fast recognition algorithms for classes of partial cubes

Isometric subgraphs of hypercubes, or partial cubes as they are also called, are a rich class of graphs that include median graphs, subdivision graphs of complete graphs, and classes of graphs arising in mathematical chemistry and biology. In general, one can recognize whether a graph on n vertices and m edges is a partial cube in O( mn) steps, faster recognition algorithms are only known for median graphs. This paper exhibits classes of partial cubes that are not median graphs but can be recognized in O ( m log n ) steps. On the way relevant decomposition theorems for partial cubes are derived, one of them correcting an error in a previous paper (Eur. J. Combin. 19 (1998) 677).

Combining DEKF algorithm and trace rule for fast on-line invariance extraction and recognition

A self-organizing network is used to perform invariance extraction and recognition of handwritten digits. To extract the invariance effectively, we propose to combine the trace learning rule and the on-line dual extended Kalman filter (DEKF) algorithm. Furthermore, a new activation function is suggested to replace the traditional sigmoid activation function so as to reduce the sensitivity of the extracted features to samples with large variance. Computer simulations show that both the learning speed and the recognition rate are improved using a compact network.

A fast track pattern recognition

An algorithm for fast track pattern recognition is presented. Its main features are an optimized data organization and a conformal mapping to speed up fitting procedures. The code is applied to a generic collider detector. Reconstruction time, track finding efficiencies, and momentum resolution are presented.

On the recognition of permuted Supnick and incomplete Monge matrices

Incomplete Monge matrices are a generalization of standard Monge matrices: the values of some entries are not specified and the Monge property only must hold for all specified entries. We derive several combinatorial properties of incomplete Monge matrices and prove that the problem of recognizingpermuted incomplete Monge matrices is NP-complete. For the special case of permutedSupnick matrices, we derive a fast recognition algorithm and thereby identify a special case of then-vertex travelling salesman problem which can be solved inO(n 2logn) time.

Trigger processor for the APEX positron-electron spectrometer

The APEX experiment is designed to study anomalous positron-electron pair production in heavy-ion collisions. Its trigger processor is designed to give a fast, first-level trigger which initiates data readout for interesting events. This is achieved by identifying the back-to-back 511 keV annihilation radiation from positron decay. The radiation is detected in a segmented NaI(Tl) calorimeter and the trigger processor identifies those patterns of hit detectors which are consistent with back-to-back decay. A fast pattern recognition algorithm gives triggering pulses within the 82.5 ns accelerator beam pulse period. Valid hit patterns are software-coded into memory and can be easily changed. A novel two-level discriminator, constructed from standard IC devices, provides self-vetoing to reduce background associated with cosmic ray muons.

A hybrid model for transient stability evaluation of interconnected longitudinal power systems using neural network/pattern recognition approach

A methodology for evaluation of transient stability of medium size interconnected longitudinal power systems has been developed using a hybrid neural network pattern recognition approach. Assessment of transient stability is done using a fast pattern recognition algorithm at each load level, accurately predicted by a neural network on a half-hourly basis. As opposed to the conventional approaches, this hybrid strategy can make fast decisions with less computations. >

On the use of a metric-space search algorithm (AESA) for fast DTW-based recognition of isolated words

The approximating and eliminating search algorithm (AESA) presented was recently introduced for finding nearest neighbors in metric spaces. Although the AESA was originally developed for reducing the time complexity of dynamic time-warping isolated word recognition (DTW-IWR), only rather limited experiments had been previously carried out to check its performance in this task. A set of experiments aimed at filling this gap is reported. The main results show that the important features reflected in previous simulation experiments are also true for real speech samples. With single-speaker dictionaries of up to 200 words, and for most of the different speech parameterizations, local metrics, and DTW productions tested, the AEAS consistently found the appropriate prototype while requiring only an average of 7-12 DTW computations (94-96% savings for 200 words), with a strong tendency to need fewer computations if the samples are close to their corresponding prototypes.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Testing for the consecutive ones property, interval graphs, and graph planarity using PQ-tree algorithms

A data structure called a PQ -tree is introduced. PQ -trees can be used to represent the permutations of a set U in which various subsets of U occur consecutively. Efficient algorithms are presented for manipulating PQ -trees. Algorithms using PQ -trees are then given which test for the consecutive ones property in matrices and for graph planarity. The consecutive ones test is extended to a test for interval graphs using a recently discovered fast recognition algorithm for chordal graphs. All of these algorithms require a number of steps linear in the size of their input.