In this paper, we proposed a discrete cosine transform (DCT)-based attnuation and accentuation method to remove lighting effects on face images for faciliating face recognition task under varying lighting conditions. In the proposed method, logorithm transform is first used to convert a face image into logarithm domain. Then discrete cosine transform is applied to obtain DCT coefficients. The low-frequency DCT coefficients are attenuated since illumination variations mainly concentrate on the low-frequency band. The high-frequency coefficients are accentuated since when under poor illuminations, the high-frequency features become more important in recognition. The reconstructed log image by inverse DCT of the modified coefficients is used for the final recognition. Experiments are conducted on the Yale B database, the combination of Yale B and Extended Yale B databases and the CMU-PIE database. The proposed method does not require modeling and model fitting steps. It can be directly applied to single face image, without any prior information of 3D shape or light sources.
This paper proposes an algorithm that is based on the application of Algebraic Integer (AI) representation of numbers on
the AAN fast Inverse Discrete Cosine Transform (IDCT) algorithm. AI representation allows for maintaining an error-free
representation of IDCT until the last step of each 1-D stage of the algorithm, where a reconstruction step from the AI
domain to the fixed precision binary domain is required. This delay in introducing the rounding error prevents the
accumulation of error throughout the calculations, which leads to the reported high-accuracy results. The proposed
algorithm is simple and well suited for hardware implementation due to the absence of computationally extensive
multiplications. The obtained results confirm the high accuracy of the proposed algorithm compared to other fixed-point
implementations of IDCT.
A high-performance target may accelerate at non-uniform rates, complete sharp turns within short time periods, thrust, roll, and pitch; which may not follow a linear model. Even though the interacting multiple model (IMM) can be considered as a multimodal approach, it still requires prior knowledge about the target model. To overcome this weakness, a fuzzy logic particle filter (FLPF) is used. It is comprised of single-input single-output; which is presented by fuzzy relational equations. A canonical-rule based form is used to express each of these fuzzy relational equations. The dynamics of the high-performance target are modeled by multiple switching (jump Markov) systems. The target may follow one-out of-seven dynamic behavior model at any time in the observation period under assumption of coordinate turn model. The FLPF has the advantage that it does not require any prior knowledge of statistical models of
process as in IMM. Moreover, it does not need any maneuver detector even when tracking a high performance target; which results in less computational complexities. By using an appropriate fuzzy overlap set, only a subset of the total number of models need to be evaluated, and these will be conditioned on acceleration values close to the estimate. This reduces the computational load compared to the fuzzy IMM (FIMM) algorithm. To achieve the whole range of maneuver variables, more models can be added without increasing the computational load as the number of models evaluated is determined only by the overlap. An example is included for visualizing the effectiveness of the proposed algorithm. Simulation results showed that the FLPF has good tracking performance and less computational load compared to the FIMM when applied to systems characterized by large scan periods.
Tracking a maneuvering target weakens the performance of predictive-model-based Bayesian state estimators (Kalman Filter). Therefore, the particle is used to track maneuverable targets instead of Kalman filter and its extensions. The particle filter proved more efficiency compared to Kalman filter and its extensions, e.g. Extended Kalman Filter (EKF) and Interacting Multiple Model (IMM). Unfortunately, due to the highly uncertainty and incompleteness of the information in a highly-maneuverable target-tracking problem, the advantage of the particle filter is weakened. Both auxiliary and smoothing particle filter were proposed to overcome this problem. In this paper, we compare the performance of both auxiliary and smoothing particle filter in tracking a highly maneuverable target. We applied both algorithms to track a maneuverable target in a multiple-sensors network. Monte Carlo simulation showed that the smoothing particle filter has a better performance when compared to auxiliary particle filter in tracking a maneuvering target.
KEYWORDS: Discrete wavelet transforms, Wavelets, Prototyping, Linear filtering, Clocks, Wavelet transforms, Medical imaging, Magnetic resonance imaging, Very large scale integration, Image compression
This paper presents a novel concept for very low bit rate video codec. It uses a new hierarchical adaptive structured mesh topology. The proposed video codec can be used in wireless video applications. It uses structures to model the dynamics of the video object where the proposed the adaptive structure splitting significantly reduces the number of bits used for mesh description. Moreover, it reduces the latency of motion estimation and compensation operations. A comprehensive performance study is presented for the proposed mesh-based motion tracking and the commonly used techniques. It shows the superior of the proposed concept compare to the current MPEG techniques.
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