412675
Linear Asymmetric Classifier for cascade detectors
The detection of faces in images is fundamentally a rare event detection problem. Cascade classifiers provide an efficient computational solution, by leveraging the asymmetry in the distribution of faces vs. non-faces. Training a cascade classifier in turn requires a solution for the following subproblems: Design a classifier for each node in the cascade with very high detection rate but only moderate false positive rate. While there are a few strategies in the literature for indirectly addressing this asymmetric node learning goal, none of them are based on a satisfactory theoretical framework. We present a mathematical characterization of the node-learning problem and describe an effective closed form approximation to the optimal solution, which we call the Linear Asymmetric Classifier (LAC). We first use AdaBoost or AsymBoost to select features, and use LAC to learn a linear discriminant function to achieve the node learning goal. Experimental results on face detection show that LAC can improve the detection performance in comparison to standard methods. We also show that Fisher Discriminant Analysis on the features selected by AdaBoost yields better performance than AdaBoost itself.
1886465
Learning a Rare Event Detection Cascade by Direct Feature Selection
Face detection is a canonical example of a rare event detection problem, in which target patterns occur with much lower frequency than non-targets. Out of millions of face-sized windows in an input image, for example, only a few will typically contain a face. Viola and Jones recently proposed a cascade architecture for face detection which successfully addresses the rare event nature of the task. A central part of their method is a feature selection algorithm based on AdaBoost. We present a novel cascade learning algorithm based on forward feature selection which is two orders of magnitude faster than the Viola-Jones approach and yields classifiers of equivalent quality. This faster method could be used for more demanding classification tasks, such as on-line learning.
15943479
Efficient face candidates selector for face detection
Abstract In this paper an efficient face candidates selector is proposed for face detection tasks in still gray level images. The proposed method acts as a selective attentional mechanism. Eye-analogue segments at a given scale are discovered by finding regions which are roughly as large as real eyes and are darker than their neighborhoods. Then a pair of eye-analogue segments are hypothesized to be eyes in a face and combined into a face candidate if their placement is consistent with the anthropological characteristic of human eyes. The proposed method is robust in that it can deal with illumination changes and moderate rotations. A subset of the FERET data set and the BioID face database are used to evaluate the proposed method. The proposed face candidates selector is successful in 98.75% and 98.6% cases, respectively.
2718321
Face recognition with one training image per person
At present there are many methods that could deal well with frontal view face recognition. However, most of them cannot work well when there is only one training image per person. In this paper, an extension of the eigenface technique, i.e. projection-combined principal component analysis, (PC)2A, is proposed. (PC)2A combines the original face image with its horizontal and vertical projections and then performs principal component analysis on the enriched version of the image. It requires less computational cost than the standard eigenface technique and experimental results show that on a gray-level frontal view face database where each person has only one training image, (PC)2A achieves 3-5% higher accuracy than the standard eigenface technique through using 10-15% fewer eigenfaces.
6077474
Multicasting in injured hypercubes using limited global information
We study the multicast problem in injured hypercubes with faulty nodes by assuming that each node in a cube has limited global information, which is captured by the safety level associated with the node. Basically, the safety level is an approximate measure of the distribution of faulty nodes in the neighborhood. We first propose a safety-level based multicasting scheme which guarantees time optimality. Then an address-sum based multicasting scheme is studied which utilizes both the distribution of destination nodes and safety levels of neighboring nodes. Simulation results show that the traffic generated in both schemes is very close to the optimal solution. Time optimality is guaranteed when the source is safe and an additional time step is required when the source is unsafe in an n-cube with up to n - 1 node faults.<<ETX>>
5124750
An Enhanced Message Exchange Mechanism in Cluster-Based Mobile Ad Hoc Networks
In mobile ad hoc networks (MANETs), networks can be partitioned into clusters. Clustering algorithms are localized algorithms that have the property of creating an upper bounded clusterheads in any networks even in the worst case. Generally, clusterheads and selected gateways form a connected dominating set (CDS) of the network. This CDS forms the backbone of the network and can be used for routings (broadcasting/multicasting/unicating). As clusterheads need to determine the selected gateways to connect their adjacent clusterheads within the coverage set, they periodically collect 2-hop and 3-hop clusterhead information. These information can be gathered when they hear their non-clusterhead neighbors sending out messages that contain neighboring clusterhead information. These extra maintenance cost can be reduced when some enhanced mechanism is applied. In this paper, an enhanced mechanism is proposed to reduce the total length of the messages when non-clusterhead nodes exchange their 1-hop and 2-hop neighboring clusterhead information. Simulation shows that over 50% of message overhead can be saved for dense networks.
11641653
Performance analysis of broadcast protocols in ad hoc networks based on self-pruning
Self-pruning is an effective method to reduce broadcast redundancy in ad hoc wireless networks. Unlike flooding, in a self-pruning broadcast protocol, a node may not forward a broadcast packet if a certain self-pruning condition is satisfied based on the neighborhood information. For each broadcasting, only a subset of nodes forwards the broadcast packet and still guarantee the complete network delivery under the ideal network situation that no packet is lost due to packet collision and node mobility. We evaluate the performance of the family of self-pruning protocols under various network situations with ns2. The objective is to observe the efficiency and reliability of these protocols as a function of network density, congestion, and mobility, and provide a guideline of implementation in the "real world". Our performance analysis reveals that the protocol reliability is barely affected by packet collision. However, most self-pruning protocols suffer from low delivery ratio in highly mobile networks. We further explore various techniques that improve the delivery ratio and show that both high efficiency and reliability can be achieved in highly mobile networks.
23437049
Fault-Tolerant Broadcasting in 2-D Wormhole-Routed Meshes
In this paper, a fault-tolerant broadcast scheme in 2-D meshes with randomly generated faults is provided. This approach is based on an early work on time-step optimal broadcasting in square-shape fault-free 2-D meshes with optimal total communication distance (TCD). An extension to any rectangular-shape fault-free 2-D meshes is first given. The fault block model is used in which all faulty nodes in the system are contained in a set of disjoint blocks. The boundary lines of blocks divide the whole mesh into a set of fault-free polygons and a sequence of rectangular fault-free regions is derived from these polygons. The broadcast process is carried out at two levels: inter-region and intra-region. In the inter-region-level broadcast, the broadcast message is sent from a given source to a special node (called eye [1]) in each rectangular fault-free region. In the intra-region-level broadcast, the extended optimal fault-free broadcast is applied. Some analytical results are given including an upper bound of TCD.
18213709
Evaluation of a fault tolerant distributed broadcast algorithm in hypercube multicomputers
This paper performs a detailed evaluation of a fault-tolerant distributed broadcasting algorithm for cube connected networks. The main areas of evaluation are the following: (1) Algorithm effectiveness in the presence of multiple faults, (2) Establishing the maximum number of link faults allowed, before the algorithm fails to guarantee 100% effectiveness. The evaluation was done to networks connected in 3-, 4-, 5-, and 6-cube configurations. The results of the simulation were analyzed to establish algorithm characteristics under multiple faults.