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This paper presents a novel method to recognize subtle emotions based on optical strain magnitude feature extraction from the temporal point of view. The common way that subtle emotions are exhibited by a person is in the form of visually observed micro-expressions, which usually occur only over a brief period of time. Optical strain allows small deformations on the face to be computed between successive...
The diffusion least mean squares (LMS) [1] algorithm gives faster convergence than the original LMS in a distributed network. Also, it outperforms other distributed LMS algorithms like spatial LMS and incremental LMS [2]. However, both LMS and diffusion-LMS are not applicable in non-linear environments where data may not be linearly separable [3]. A variant of LMS called kernel-LMS (KLMS) has been...
In this paper, we proposed a biased support vector machine (Biased-SVM) with self-constructed Universum (termed as U-BSVM) to solve the PU learning problem. We first treat the PU problem as an imbalanced binary classification problem by labeling all the unlabeled inputs as negative with noise, then inspired by the Universum-SVM (U-SVM), introduce the Universum data set which is constructed from the...
In this paper, we propose a new approach to data density estimation based on the total sum of distances from a data point, and the recently introduced Recursive Density Estimation technique. It is suitable for autonomous real-time video analytics problems, and has been specifically designed to be executed very fast; it uses integer-only arithmetic with no divisions and no floating point numbers (no...
The paper deals with production performance evaluation of the EU selected regions in the period 2000 – 2011. We estimate a translog stochastic production frontier using the true fixed-effects methods. We detect statistically significant savings with respect to the technical progress in capital input and consumption with respect to the technical progress in labor input. We evaluate the performance...
Frequency domain Normalized Convolution (NC) process is widely performed on images to retrieve and extract valuable information in noisy and distorted environment. Genetic Normalized Convolution (GNC) is carried out for features extraction in an image or features reconstructions in a distorted image. In this paper a hybrid approach is adopted where robust algorithm of convolution based on Normalized...
Interest on anomaly detection for hyperspectral images is increasingly growing the last decades due to the diversity of applications that aims for detecting small distinctive objects dispersed in a large geographic zone, without any prior knowledge about the scene. In addition to the absence of prior knowledge, many problems are particularly challenging for the anomaly detection such as the differentiation...
Image feature detection is an important part of monocular vision SLAM system. At present, detection methods can not balance these two requirements of speed and stability. The SURF algorithm in the application of the SLAM, its robustness is slightly worse, sometimes get the less number of correct matching points than usual, aiming at disadvantages of SURF algorithm in SLAM system, this paper combines...
This paper presents a new sequential clustering algorithm based on sequential hard c-means clustering. The word sequential cluster extraction means that the algorithm extract one cluster at a time. The sequential hard c-means is one of the typical and conventional sequential clustering methods. The proposed new sequential clustering algorithm is based on Dave's noise clustering approach. A characteristic...
Motion blur is a most common degradation present in the photograph captured from hand held camera. Minute visual details are lost and spreaded over pixels creating smearing impact in captured image due to motion blur. It's hazardous for machine vision application and should be corrected by any restoration methodology. As no information is available about kind of degradation and true image, restoration...
Fuzzy clustering techniques, especially Fuzzy C-Means clustering method (FCM), is a popular algorithm widely used in the images segmentation. However, as the conventional FCM doesn't optimize data in feature space and doesn't involve any spatial information, it is sensitive to the noise. In the paper, we presented a novel FCM clustering algorithm based on kernel spatial information to segment the...
As technology advances; blur in an image remains as an ever-present issue in the image processing field. A blurred image is mathematically expressed as a convolution of a blur function with a sharp image, plus noise. Removing blur from an image has been widely researched and is still important as new images are collected. Without a reference image, identifying, measuring, and removing blur from a...
Multi-frame super-resolution brings out much potential to reconstruct real high-resolution video sequences. This potential is achieved based on its capacity to combine missing information from different input low-resolution frames. Although there have been many studies in recent decades, super-resolution problems for real-world video processing still have many challenges. This is dues to two problems...
Underwater imaging is primarily focused on search and rescue, underwater mine detection, underwater cable and pipeline overhauling and underwater geological survey. Main challenge in underwater imaging is blurriness. In underwater environment blurriness is caused by many factors which includes microscopic organism, impurities and density of water which effects refractive index of water, and bokeh...
This paper presents a system which combines a zero-velocity-update-(ZUPT-)aided inertial navigation system (INS), using a foot-mounted inertial measurement unit (IMU), with opportunistic use of multi-frequency received signal strength (RSS) measurements. The system does not rely on maps or pre-collected data from surveys of the radio-frequency (RF) environment. Instead it builds its own database of...
Quantized kernel least mean square (QKLMS) algorithm is an effective up-to-date adaptive nonlinear learning algorithm which also has good performance for kernel structure growing control. It achieves good results under Gaussian noise environment. In this paper, a new algorithm, quantized kernel least mean mixed norm (QKLMMN), is proposed for adaptive nonlinear learning with non-Gaussian additive noise...
An edge detection algorithm based on improved Rotating Kernel Transformation, IRKT edge detection method (IRKTE), is proposed in this paper. The algorithm adopt the line detection approach RKT, and defines a new model of edge detection according to the direction difference between edge and smooth regions. Simultaneously, an accurate edge location approach based on edge normal direction is presented...
Since mirror-like odd and even features in face recognition reflect the symmetrical and asymmetrical image information, respectively, their proper combination can improve the recognition rates to some extent. However, the face imaging process can easily be affected by external factors and encounter the noise signal, which disturbs the effect of face recognition based on combinational mirror-like odd...
Defocus blur has been used as a cue in image splicing detection. At present, existing methods mainly rely on consistency checking of defocus kernels estimated along suspicious edges (and other reference edges if applicable). However, the texture, nearby edges, light fields as well as noises will influence the information of defocus blur at the natural edges in a certain range, resulting in inconsistent...
Acoustic radiation force impulse (ARFI) imaging and shear wave elasticity imaging (SWEI) use the dynamic response of tissue to impulsive mechanical stimulus to characterize local elasticity. A variant of conventional, multiple track location SWEI (MTL-SWEI), denoted single track location SWEI (STL-SWEI) offers the promise of creating speckle-free shear wave images. This work compares the three imaging...
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