waashk/dblp huggingface dataset:
region based color image retrieval using curvelet transform. in this paper, a region-based spatio-temporal markov random field (stmrf) model is proposed to segment moving objects semantically. the stmrf model combines segmentation results of four successive frames and integrates the temporal continuity in the uniform energy function. the segmentation procedure is composed of two stages: one is the short-term's classification and the other is temporal integration. at the first stage, moving objects are extracted by a region-based mrf model between two frames in a frame group of four successive frames. at the second stage, the ultimate semantic object is labeled by minimization the energy function of the stmrf model. such phased segmentation process is corresponding to a multi-level simulated anneal strategy. experimental results show that the proposed algorithm can efficiently capture the motion semantic meaning of objects and accurately extract moving objects.
multicue graph mincut for image segmentation. ensemble learning algorithms train multiple component learners and then combine their predictions. in order to generate a strong ensemble, the component learners should be with high accuracy as well as high diversity. a popularly used scheme in generating accurate but diverse component learners is to perturb the training data with resampling methods, such as the bootstrap sampling used in bagging. however, such a scheme is not very effective on local learners such as nearest-neighbor classifiers because a slight change in training data can hardly result in local learners with big differences. in this paper, a new ensemble algorithm named filtered attribute subspace based bagging with injected randomness (fasbir) is proposed for building ensembles of local learners, which utilizes multimodal perturbation to help generate accurate but diverse component learners. in detail, fasbir employs the perturbation on the training data with bootstrap sampling, the perturbation on the input attributes with attribute filtering and attribute subspace selection, and the perturbation on the learning parameters with randomly configured distance metrics. a large empirical study shows that fasbir is effective in building ensembles of nearest-neighbor classifiers, whose performance is better than that of many other ensemble algorithms.
mift: a mirror reflection invariant feature descriptor. this paper presents a superposition method for constructing low-density parity-check (ldpc) codes. several classes of structured ldpc codes are constructed. codes in these classes perform well with iterative decoding, and their tanner graphs have girth at least six.
similarity scores based on background samples. harvesting the benefits of a sensor-rich world presents many data management challenges. recent advances in research and industry aim to address these challenges. with the rapidly increasing number of large-scale sensor network deployments, the vision of a worldwide sensor web is close to becoming a reality.
a robust algorithm for color correction between two stereo images. the next generation resource monitoring system in distributed computing environments should not only accurately monitor the environment but also automatically reconfigure the environment. in this paper, we present inapmon, a robust and scalable policy-based resource monitoring system in a multi- server network computer environment. it monitors the resource usage with sensors and aggregates the data in servers for reconfiguring the environment, which aims to guarantee load balance between servers and impartiality between users. this policy-based approach indeed provides a novel perspective for the design of future resource monitoring system.
semantic classification in aerial imagery by integrating appearance and height information. haussler, littlestone and warmuth (1994) described a general-purpose algorithm for learning according to the prediction model, and proved an upper bound on the probability that their algorithm makes a mistake in terms of the number of examples seen and the vapnik-chervonenkis (vc) dimension of the concept class being learned. we show that their bound is within a factor of 1+o(1) of the best possible such bound for any algorithm
extracting spatio-temporal local features considering consecutiveness of motions. for communication between the cluster head node (chn) and the distant data fusion center (dfc) with the cooperation of intra-cluster nodes in wireless sensor networks (wsns), reliable handling of packets by the cooperative node (cn) is difficult to ensure. in this paper, we propose a novel cn selection scheme based on trust for wsns. this scheme can choose one of high trust nodes to assist in the transmission of chn, which aims to improve the transmission performance over cooperative communication in the presence of potential malicious nodes. first, we establish a trust model to improve the reliability of transmission by cn in wsns, which is based on both the direct trust and the indirect trust of nodes. then the chn selects a node based on the proposed trust model to aid the chn in transmission. simulation results show that the proposed scheme can mitigate the impact of the potential malicious nodes on transmission performance and the simulation results also demonstrate the efficiency of the proposed scheme.
dense and accurate spatio-temporal multi-view stereovision. this paper proposes an integrated approach for personal name recognition (pnr) in chinese by utilizing both statistical language models and categorized linguistic knowledge. various formulas are proposed for calculating personal name credibility and context credibility for different types of personal names. experiment is conducted on large-scale corpus to evaluate the approach and the f-1 scores has reached 98.85% and 92.73% respectively in close and open test.
efficient human action detection using a transferable distance function. utilizing the ability simple in application and quick in convergence of quantum delta-potential-well-based particle swarm optimization (qdpso) algorithm and the high generalization ability of support vector machine (svm), selecting the appropriate state variables, a dynamic time-varying model has been built. using the model and algorithm to per-estimate some biochemical state variables which can not be measured on-line, and to optimise some operational variables. it is proved that the method is efficiency through the practical application of penicillin fermentation process.
a harris-like scale invariant feature detector. in this paper, we present an algorithm for computing the bounds on energy-efficiency of digital very large scale integration (vlsi) systems in the presence of deep submicron noise. the proposed algorithm is based on a soft-decision channel model of noisy vlsi systems and employs information-theoretic arguments. bounds on energy-efficiency are computed for multimodule systems, static gates, dynamic circuits and noise-tolerant dynamic circuits in 0.25-&mu;m cmos technology. as the complexity of the proposed algorithm grows linearly with the size of the system, it is suitable for computing the bounds on energy-efficiency for complex vlsi systems. a key result presented is that noise-tolerant dynamic circuits offer the best trade off between energy-efficiency and noise-immunity when compared to static and domino circuits. furthermore, employing a 16-bit noise-tolerant manchester adder in a cdma receiver, we demonstrate a 31.2%-51.4% energy reduction over conventional systems when operating in the presence of noise. in addition, we compute the lower bounds on energy dissipation for this cdma receiver and show that these lower bounds are 2.8&times; below the actual energy consumed, and that noise-tolerance reduces the gap between the lower bounds and actual energy dissipation by a factor of 1.9&times;.
accurate and efficient cost aggregation strategy for stereo correspondence based on approximated joint bilateral filtering. we investigate the problem of memory reuse in order to reduce the memory needed to store an array variable. we develop techniques that can lead to smaller memory requirements in the synthesis of dedicated processors or to more effective use by compiled code of software-controlled scratchpad memory. memory reuse is well-understood for allocating registers to hold scalar variables. its extension to arrays has been studied recently for multimedia applications, for loop parallelization, and for circuit synthesis from recurrence equations. in all such studies, the introduction of modulo operations to an otherwise affine mapping (of loop or array indices to memory locations) achieves the desired reuse. we develop here a new mathematical framework, based on critical lattices, that subsumes the previous approaches and provides new insight. we first consider the set of indices that conflict, those that cannot be mapped to the same memory cell. next, we construct the set of differences of conflicting indices. we establish a correspondence between a valid modular mapping and a strictly-admissible integer lattice-one having no nonzero element in common with the set of conflicting index differences. the memory required by an optimal modular mapping is equal to the determinant of the corresponding lattice. the memory reuse problem is thus reduced to the (still interesting and nontrivial) problem of finding a strictly admissible integer lattice-of least determinant. we then propose and analyze several practical strategies for finding strictly admissible integer lattices, either optimal or optimal up to a multiplicative factor, and, hence, memory-saving modular mappings. we explain and analyze previous approaches in terms of our new framework.
generation of an omnidirectional video without invisible areas using image inpainting. optical burst switching (obs) has been considered the most promising optical switching technology. to the best of authors' knowledge, this is the first paper that identifies the out- of-sequence delivery problem in obs networks. to minimize the impact of the out-of-sequence delivery on buffer space and resequencing latency, we propose a novel virtual burst assembly scheme to eliminate the problem. in addition to simulation results obtained from the obs-ns2 simulator, we implemented the proposed virtual burst assembly scheme in altera stratix ii fpga hardware using verilog hdl, and tested experimentally using real video. both simulation and experimental results confirm the effectiveness of the proposed scheme.
hierarchical model for joint detection and tracking of multi-target. power is a valuable resource in embedded systems as the lifetime of many such systems is constrained by their battery capacity. recent advances in processor design have added support for dynamic frequency/voltage scaling (dvs) for saving power. recent work on real-time scheduling focuses on saving power in static as well as dynamic scheduling environments by exploiting idle and slack due to early task completion for dvs of subsequent tasks. these scheduling algorithms rely on a priori knowledge of worst-case execution times (wcet) for each task. they assume that dvs has no effect on the worst-case execution cycles (wcec) of a task and scale the wcet according to the processor frequency. however, for systems with memory hierarchies, the wcec typically does not change under dvs due to frequency modulation. hence, current assumptions used by dvs schemes result in a highly exaggerated wcet. this paper contributes novel techniques for tight and flexible static timing analysis particularly well-suited for dynamic scheduling schemes. the technical contributions are as follows: (1) we assess the problem of changing execution cycles due to scaling techniques. (2) we propose a parametric approach towards bounding the wcet statically with respect to the frequency. using a parametric model, we can capture the effect of changes in frequency on the wcec and thus, accurately model the wcet over any frequency range. (3) we discuss design and implementation of the frequency-aware static timing analysis (fast) tool based on our prior experience with static timing analysis. (4) we demonstrate in experiments that our fast tool provides safe upper bounds on the wcet, which are tight. the fast tool allows us to capture the wcet of six benchmarks using equations that overestimate the wcet by less than 1%. fast equations can also be used to improve existing dvs scheduling schemes to ensure that the effect of frequency scaling on wcet is considered and that the wcet used is not exaggerated. (5) we leverage three dvs scheduling schemes by incorporating fast into them and by showing that the power consumption further decreases. to the best of our knowledge, this study of dvs effects on timing analysis is unprecedented.
a novel system for robust text location and recognition of book covers. congestion in interconnection networks due to the presence of hot spots is an important and difficult problem that occurs in parallel machines. this problem has been studied in depth and different solutions for the case of multiprocessors with shared memory have been proposed. current trends point towards the implementation of systems with physically distributed memory, either based on, message passing (multicomputers) or on a single shared memory address space (multiprocessors). our paper is developed in this context. up to now, proposals to improve the throughput of networks with hot-spots have focused on using virtual channels or adaptive algorithms. we present a novel solution based on reconfigurable networks. a reconfigurable network is one in which nodes can change their position depending on the communication pattern in order to diminish the congestion produced in the network and, therefore, increase its throughput. we studied this problem in two-dimensional k-ary n-cube networks using a deterministic routing algorithm and wormhole routing. in this paper the main features of a reconfigurable network are presented and the results obtained by simulation are shown. these results confirm that this technique is a very interesting one for systems with distributed memory, with applications to a great variety of problems
face alignment using boosting and evolutionary search. though the popular ieee 802.11 dcf is designed primarily for wireless lan (wlan) environments, today it is being widely used for wide area wireless mesh networking. the protocol parameters of ieee 802.11 such as timeout values, interframe spaces, and slot durations, which are sufficient for a general wlan environment need to be modified in order to efficiently operate in wide area wireless mesh networks. the current wide area wireless mesh network deployments use manual configuration of these parameters to the upper limit which essentially makes the networks operate at lower system efficiency. in this paper, we propose d802.11 (dynamic 802.11) which dynamically adapts the protocol parameters in order to operate at varying link distances. we present three strategies, (i) multiplicative timer back-off (mtb), (ii) additive timer back-off (atb), and (iii) link rtt memorization (lrm), to adapt the ack_timeout in d802.11 in order to provide better adaptation for varying link dimensions. through extensive simulation experiments we observed significant performance improvement for the proposed strategies. we also theoretically modeled the maximum throughput as a function of the link dimension for the proposed system. our results show that the lrm technique provides the best adaptation compared to all other schemes.
orientation and scale invariant kernel-based object tracking with probabilistic emphasizing. while there is currently great interest in the problem of providing real time services in general purpose operating systems, the issue of real time scheduling of internal operating system activities has received relatively little attention. without such real time scheduling, the system is susceptible to conditions such as receive livelock-a situation in which an operating system spends all its time processing arriving network packets, and application processes, even if scheduled with a real time scheduler, are starved. we investigate the problem of scheduling operating system activities such as network protocol processing in a proportional share manner. we describe a proportional share implementation of the freebsd operating system and demonstrate that it solves the receive livelock problem. packets are processed within the operating system only at the cumulative rate at which the destination applications are prepared to receive them. if packets arrive at a faster rate then they are discarded after consuming minimal system resources. in this manner the performance of &ldquo;well behaved&rdquo; applications is unaffected by &ldquo;misbehaving&rdquo; applications. we demonstrate this effect by running a set of multimedia applications under a variety of network conditions on a set of increasingly sophisticated proportional share implementations of freebsd and comparing their performance. this work contributes to our knowledge of the engineering of proportional share real time systems
incremental multi-view face tracking based on general view manifold. face representations based on gabor features have achieved great success in face recognition, such as elastic graph matching, gabor fisher classifier (gfc), and adaboosted gabor fisher classifier (agfc). in gfc and agfc, either down-sampled or selected gabor features are analyzed in holistic mode by a single classifier. in this paper, we propose a novel patch-based gfc (pgfc) method, in which gabor features are spatially partitioned into a number of patches, and on each patch one gfc is constructed as component classifier to form the final ensemble classifier using sum rule. the positions and sizes of the patches are learned from a training data using adaboost. experiments on two large-scale face databases (feret and cas-peal-r1) show that the proposed pgfc with only tens of patches outperforms the gfc and agfc impressively
probabilistic cascade random fields for man-made structure detection. spectrum sensing for the china digital television terrestrial broadcasting system is considered in this paper. the spectrum sensing algorithms utilize pn sequences embedded in the frame headers to perform spectrum sensing. a theoretical lower bound on the misdetection probability for each spectrum sensor presented in this paper is derived. the performances of the proposed spectrum sensing algorithms are demonstrated by computer simulation for the multipath rayleigh fading and steady state multipath rayleigh fading channels. simulation results show that the misdetection probability evaluated by computer simulations is close to the lower bound on the misdetection probability for a steady state multipath rayleigh channel. when the probability of false alarm is 0.01 and a 50 ms of sensing time is used, a misdetection probability equal to 0.1 is achieved when the signal to noise power ratio is -16 db, -18.8 db, and -18 db for modes 1, 2, and 3 in the multipath rayleigh fading channel, respectively.
people tracking and segmentation using efficient shape sequences matching. a secure image transmission mechanism is necessary when malicious intruders intend to access and modify content delivery over wireless networks. to assure data integrity, authentication is required for multimedia data delivered over wireless image sensor networks. watermarking technique is an effective vehicle to assert and assure the image data authentications. there have been recent works reported on watermarking, but few with the consideration of energy cost in terms of the data communication and processing, which is a key constraint to many embedded systems and wireless sensor networks. most watermarking systems only target at minimizing watermarked image distortion and increasing robustness at the source coding site for lossy image processing. the watermarked image distortion caused by error-prone wireless environments during transmission has not been fully considered. in this paper, an innovative energy-aware adaptive watermarking scheme for realtime image delivery is proposed in wireless multimedia sensor networks. this new scheme allocates network resource to protect the watermarked image transmission while embedding watermark coding redundancies into the images. dynamic watermark thresholds are applied to be adaptive to the network condition (packet loss ratio) and the inter-frame correlation is exploited to reduce processing delay. the simulation results show that the proposed adaptive watermark system can achieve considerable energy efficiency and assure the data integrity.
twisted cubic: degeneracy degree and relationship with general degeneracy. this paper presents an adaptive timing method for analysis of deskew signaling in high-performance integrated systems. the proposed approach models the practical deskew schemes as an adaptive system to study the performance limit of deskew signaling. we investigate the statistical properties of optimal deskew, which can be considered as the theoretical limit of deskew signaling. this approach also allows us to study the related energy efficiency issues in deskew schemes. we determine the bound on energy efficiency subject to the performance constraint on deskew signaling. simulation results show that the performance of practical deskew schemes in a 90-nm process is 23.2% away from the optimal deskew scheme, and the gap of energy efficiency is 51% above the bound.
video segmentation using iterated graph cuts based on spatio-temporal volumes. in this paper, we present a new method for texture classification which we call the regularized simultaneous autoregressive method (rsar). the regularization technique is introduced. with the technique, the new algorithm rsar outperforms the traditional algorithm in texture classification. particularly, our new algorithm is useful for extracting texture from the image which is coarse or contains too much noise.
robust real-time multiple target tracking. feature selection method based on text study is a mainstream method currently, whose research key lies in finding out one suitable feature assessment method, which can reduce the numbers of the words to be processed as less as possible in the situation of not decreasing classification precision, to improve the speed and the efficiency of classification. a new feature assessment method entropy ratio is proposed in this paper on the base of researching the classical feature assessment methods in the existing literature. this method not only considered feature classification ability, but also the feature generalization ability. it is a new and better choice to apply the centroid-based classifier to improve the effect of classification. experimental results show that the effect obtained by using this method to select features is obviously superior to the one obtained by other methods, especially when the feature selected is less.
a multi-scale bilateral structure tensor based corner detector. biological snakes' diverse locomotion modes and physiology make them supremely adapted for environment. to realize these snakes' noticeable features, we have developed a snake-like robot that has no any forward direction driving force. to enlarge the environment-adaptable ability of our robot, in this study we discuss the creeping locomotion of our snake-like robot on a slope. a computer simulator is presented for analysis of the creeping locomotion of our snake-like robot on a slope, and the environment-adaptable body shape for the creeping locomotion of the snake-like robot on slope is also derived through this simulator.