Image Description Using a Multiplier-Less Operator

Cataloged from PDF version of article.A fast algorithm for image classification based on a computationally efficient operator forming a semigroup on real numbers is developed. The new operator does not require any multiplications. The co-difference matrix based on the new operator is defined and an image descriptor using the co-difference matrix is developed. In the proposed method, the multiplication operation of the well-known covariance method is replaced by the new operator. The proposed method is experimentally compared with the regular covariance matrix method. The proposed descriptor performs as well as the the regular covariance method without performing any multiplications. Texture recognition and licence plate identification examples are presented

Pulse Doppler Radar Target Recognition Using a Two-Stage SVM Procedure

Cataloged from PDF version of article.It is possible to detect and classify moving and stationary targets using ground surveillance pulse-Doppler radars (PDRs). A two-stage support vector machine (SVM) based target classification scheme is described here. The first stage tries to estimate the most descriptive temporal segment of the radar echo signal and the target signal is classified using the selected temporal segment in the second stage. Mel-frequency cepstral coefficients of radar echo signals are used as feature vectors in both stages. The proposed system is compared with the covariance and Gaussian mixture model (GMM) based classifiers. The effects of the window duration and number of feature parameters over classification performance are also investigated. Experimental results are presented

Projections Onto Convex Sets (POCS) Based Optimization by Lifting

Two new optimization techniques based on projections onto convex space (POCS) framework for solving convex and some non-convex optimization problems are presented. The dimension of the minimization problem is lifted by one and sets corresponding to the cost function are defined. If the cost function is a convex function in R^N the corresponding set is a convex set in R^(N+1). The iterative optimization approach starts with an arbitrary initial estimate in R^(N+1) and an orthogonal projection is performed onto one of the sets in a sequential manner at each step of the optimization problem. The method provides globally optimal solutions in total-variation, filtered variation, l1, and entropic cost functions. It is also experimentally observed that cost functions based on lp, p<1 can be handled by using the supporting hyperplane concept

Falling person detection using multisensor signal processing

Falls are one of the most important problems for frail and elderly people living independently. Early detection of falls is vital to provide a safe and active lifestyle for elderly. Sound, passive infrared (PIR) and vibration sensors can be placed in a supportive home environment to provide information about daily activities of an elderly person. In this paper, signals produced by sound, PIR and vibration sensors are simultaneously analyzed to detect falls. Hidden Markov Models are trained for regular and unusual activities of an elderly person and a pet for each sensor signal. Decisions of HMMs are fused together to reach a final decision

Pulse doppler radar target recognition using a two-stage SVM procedure

It is possible to detect and classify moving and stationary targets using ground surveillance pulse-Doppler radars (PDRs). A two-stage support vector machine (SVM) based target classification scheme is described here. The first stage tries to estimate the most descriptive temporal segment of the radar echo signal and the target signal is classified using the selected temporal segment in the second stage. Mel-frequency cepstral coefficients of radar echo signals are used as feature vectors in both stages. The proposed system is compared with the covariance and Gaussian mixture model (GMM) based classifiers. The effects of the window duration and number of feature parameters over classification performance are also investigated. Experimental results are presented. © 2006 IEEE

Classification of multichannel ECoG related to individual finger movements with redundant spatial projections

We tackle the problem of classifying multichannel electrocorticogram (ECoG) related to individual finger movements for a brain machine interface (BMI). For this particular aim we applied a recently developed hierarchical spatial projection framework of neural activity for feature extraction from ECoG. The algorithm extends the binary common spatial patterns algorithm to multiclass problem by constructing a redundant set of spatial projections that are tuned for paired and group-wise discrimination of finger movements. The groupings were constructed by merging the data of adjacent fingers and contrasting them to the rest, such as the first two fingers (thumb and index) vs. the others (middle, ring and little). We applied this framework to the BCI competition IV ECoG data recorded from three subjects. We observed that the maximum classification accuracy was obtained from the gamma frequency band (65200Hz). For this particular frequency range the average classification accuracy over three subjects was 86.3%. These results indicate that the redundant spatial projection framework can be used successfully in decoding finger movements from ECoG for BMI. © 2011 IEEE

A novel objective function minimization for sparse spatial filters

Common spatial pattern (CSP) method is widely used in brain machine interface (BMI) applications to extract features from the multichannel neural activity through a set of spatial projections. The CSP method easily overfits the data when the number of training trials is not sufficiently large and it is sensitive to daily variation of multichannel electrode placement, which limits its applicability for everyday use in BMI systems. To overcome these problems, the amount of channels that is used in projections, should be limited. We introduce a spatially sparse projection (SSP) method that exploits the unconstrained minimization of a new objective function with approximated l\ penalty. The SSP method is employed to classify the two class EEG data set. Our method outperforms the standard CSP method and provides comparable results to £o norm based solution and it is associated with less computational complexity. © 2014 IEEE

Baseline regularized sparse spatial filters

The common spatial pattern (CSP) method has large number of applications in brain machine interfaces (BMI) to extract features from the multichannel neural activity through a set of linear spatial projections. These spatial projections minimize the Rayleigh quotient (RQ) as the objective function, which is the variance ratio of the classes. The CSP method easily overfits the data when the number of training trials is not sufficiently large and it is sensitive to daily variation of multichannel electrode placement, which limits its applicability for everyday use in BMI systems. To overcome these problems, the amount of channels that is used in projections, should be limited to some adequate number. We introduce a spatially sparse projection (SSP) method that renders unconstrained minimization possible via a new objective function with an approximated ℓ1 penalty. We apply our new algorithm with a baseline regularization to the ECoG data involving finger movements to gain stability with respect to the number of sparse channels. © 2013 IEEE

Human face detection in video using edge projections

In this paper, a human face detection method in images and video is presented. After determining possible face candidate regions using color information, each region is filtered by a high-pass filter of a wavelet transform. In this way, edges of the region are highlighted, and a caricature-like representation of candidate regions is obtained. Horizontal, vertical and filter-like projections of the region are used as feature signals in dynamic programming (DP) and support vector machine (SVM) based classifiers. It turns out that the support vector machine based classifier provides better detection rates compared to dynamic programming in our simulation studies

A hybrid SVM/HMM based system for the state detection of individual finger movements from multichannel ECoG signals

A hybrid state detection algorithm is presented for the estimation of baseline and movement states which can be used to trigger a free paced neuroprostethic. The hybrid model was constructed by fusing a multiclass Support Vector Machine (SVM) with a Hidden Markov Model (HMM), where the internal hidden state observation probabilities were represented by the discriminative output of the SVM. The proposed method was applied to the multichannel Electrocorticogram (ECoG) recordings of BCI competition IV to identify the baseline and movement states while subjects were executing individual finger movements. The results are compared to regular Gaussian Mixture Model (GMM)-based HMM with the same number of states as SVM-based HMM structure. Our results indicate that the proposed hybrid state estimation method out-performs the standard HMM-based solution in all subjects studied with higher latency. The average latency of the hybrid decoder was approximately 290ms. © 2011 IEEE