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Gaillardia plants have been widely cultivated in China and have become an important component of garden landscaping. Different from the common ligulate ray floret, the cornflower-like (funnel-shaped) ray floret is a special phenotype variation in Gaillardia species. Previous studies revealed that CYC-like genes could shape the floret phenotype in Compositae. To reveal the molecular mechanism of the cornflower-like phenotype, we checked the capitulum transcriptomes of several cultivars of Gaillardia that possess different ray florets. As a result, we identified 11 CYC-like genes, of which five included complete coding region sequences. Phylogenetic analysis revealed that all five genes were CYC2-like genes. Relative expression analysis of RNA-resequencing reads, qRT-PCR comparison, and gene-silencing treatment all showed that the CYC2c gene is the main genetic mechanism responsible for the shaping of the cornflower-like ray floret phenotype in Gaillardia cultivars. This study expounded our understanding of flower morphology evolution and provides useful insights for improving Compositae breeding. [ABSTRACT FROM AUTHOR]

Recognizing human interactions is a more challenging task than recognizing single person activities and has attracted much attention of the computer vision community. This paper proposes an innovative and effective way to recognize human interactions, which incorporates the advantages of both global motion context (MC) feature and spatio-temporal (S-T) correlation of local spatio-temporal interest point feature. The MC feature is used to train a random forest where genetic algorithm (GA) is applied to the training phase to achieve a good compromise between reliability and efficiency. Besides, we propose S-T correlation-based match, where MC's structure and Needleman-Wunsch algorithm are used to calculate the spatial and temporal correlation score of two videos, respectively. Experiments on the UT-Interaction dataset show that our approaches outperform other prevalent machine learning methods, and that the combination of GA search-based random forest and S-T correlation achieves the state-of-the-art performance. [ABSTRACT FROM AUTHOR]

In this paper, we propose a two-stage sparse decomposition-based method for single-channel speech separation in time domain. First, we propose a Dictionary-updated orthogonal matching pursuit (DUOMP) algorithm which is used in both separation stages. In the proposed DUOMP algorithm, all atoms of each source-specific dictionary are updated by subtracting off the current approximation of each source to the original atoms. It is proved that the DUOMP algorithm can limit the separated sources within a region where they are uncorrelated in statistical sense more quickly. Then, we propose an adaptive dictionary generation method followed by a frame labeling method to perform a second-stage separation on the mixed frames having certain temporal structure. Experiments show that the proposed method outperforms a separation method using sparse non-negative matrix factorization (SNMF), a separation method using OMP and a source-filter-based method using pitch information in overall. Additionally, what affects the performance of the proposed method is also shown. [ABSTRACT FROM AUTHOR]

Nowadays, local features are very popular in vision-based human action recognition, especially in 'wild' or unconstrained videos. This paper proposes a novel framework that combines Fast HOG3D and self-organization feature map (SOM) network for action recognition from unconstrained videos, bypassing the demanding preprocessing such as human detection, tracking or contour extraction. The contributions of our work not only lie in creating a more compact and computational effective local feature descriptor than original HOG3D, but also lie in first successfully applying SOM to realistic action recognition task and studying its training parameters' influence. We mainly test our approach on the UCF-YouTube dataset with 11 realistic sport actions, achieving promising results that outperform local feature-based support vector machine and are comparable with bag-of-words. Experiments are also carried out on KTH and UT-Interaction datasets for comparison. Results on all the three datasets confirm that our work has comparable, if not better, performance comparing with state-of-the-art. [ABSTRACT FROM AUTHOR]

We propose a novel approach for visual tracking based on a particle swarm optimization (PSO) framework using SIFT feature points correspondence and multiple fragments in a candidate target region to cope with the problems of partial occlusions, illumination changes, and large motion changes of the tracked target. Firstly, optimal search in the successive frame tracking process is performed by the PSO algorithm, which guides all particles towards the global optima state based on a fitness function. Then, the SIFT feature information is integrated into the iterative results of PSO to acquire a more accurate tracking state. Secondly, we present an effective appearance model updating criterion, which evaluates which fragments in appearance model need updating at each frame. However, the fragments with occluded parts or low quality measure values are not updated. The method for updating appearance model is introduced to improve the tracking performance. Compared with state-of-the-art algorithms, the proposed method can still stably track the target during the course of long-term partial occlusions using superior fragments of tracked target. The experiment results demonstrate the effectiveness of our algorithm in complex environments where the target object undergoes partial occlusions and large changes in pose and illumination. [ABSTRACT FROM AUTHOR]

The Steered Response Power (SRP) method works well for sound source localization in noisy and reverberant environment. However, the large computation complexity limits its practical application. In this paper, a fast SRP search method is proposed to reduce the computational complexity using small-aperture microphone array. The proposed method inspired by the SRP spatial spectrum includes two steps: first, the proposed method estimates the azimuth of the sound source roughly and determines whether the sound source is in far field or near field; then, different fine searching operations are performed according to the sound source being in far field or near field. Experiments both in simulation environments and real environments have been performed to compare the localization accuracy and computation complexity of the proposed method with those of the conventional SRP-PHAT algorithm. The results show that, the proposed method has a comparative accuracy with the conventional SRP algorithm, and achieves a reduction of 93.62% in computation complexity compared to the conventional SRP algorithm. [ABSTRACT FROM AUTHOR]

The steered response power-phase transform (SRP-PHAT) sound source localization algorithm is robust in a real environment. However, the large computation complexity limits the practical application of SRP-PHAT. For a microphone array, each location corresponds to a set of time differences of arrival (TDOAs), and this paper collects them into a TDOA vector. Since the TDOA vectors in the adjacent regions are similar, we present a fast algorithm based on clustering search to reduce the computation complexity of SRP-PHAT. In the training stage, the K-means or Iterative Self-Organizing Data Analysis Technique (ISODATA) clustering algorithm is used to find the centroid in each cluster with similar TDOA vectors. In the procedure of sound localization, the optimal cluster is found by comparing the steered response powers (SRPs) of all centroids. The SRPs of all candidate locations in the optimal cluster are compared to localize the sound source. Experiments both in simulation environments and real environments have been performed to compare the localization accuracy and computational load of the proposed method with those of the conventional SRP-PHAT algorithm. The results show that the proposed method is able to reduce the computational load drastically and maintains almost the same localization accuracy and robustness as those of the conventional SRP-PHAT algorithm. The difference in localization performance brought by different clustering algorithms used in the training stage is trivial. [ABSTRACT FROM AUTHOR]

CAMSHIFT algorithm and Comaniciu/Meer algorithm are two fundamental frameworks of mean shift procedure for video target tracking. This paper generalizes the two well-known mean shift tracking algorithms, originally due to Bradski and Comaniciu/Meer. A new general similarity function which defines the distance between the target model and target candidate is employed to calculate the pixel weights and the target location. The target size is iteratively estimated and updated based on the zeroth order moment of the pixel weights. Then we prove that both the CAMSHIFT algorithm and the Comaniciu/Meer algorithm can be included in the generalized mean shift tracking framework. The tracking performances of three mean shift algorithms in the unified framework are shown and compared in the experimental results. [ABSTRACT FROM AUTHOR]

If a somewhat fast moving object exists in a complicated tracking environment, snake’s nodes may fall into the inaccurate local minima. We propose a mean shift snake algorithm to solve this problem. However, if the object goes beyond the limits of mean shift snake module operation in successive sequences, mean shift snake’s nodes may also fall into the local minima in their moving to the new object position. This paper presents a motion compensation strategy by using particle filter; therefore a new Particle Filter Mean Shift Snake (PFMSS) algorithm is proposed which combines particle filter with mean shift snake to fulfill the estimation of the fast moving object contour. Firstly, the fast moving object is tracked by particle filter to create a coarse position which is used to initialize the mean shift algorithm. Secondly, the whole relevant motion information is used to compensate the snake’s node positions. Finally, snake algorithm is used to extract the exact object contour and the useful information of the object is fed back. Some real world sequences are tested and the results show that the novel tracking method have a good performance with high accuracy in solving the fast moving problems in cluttered background. [ABSTRACT FROM AUTHOR]

A novel text independent speaker identification system is proposed. In the proposed system, the 12-order perceptual linear predictive cepstrum and their delta coefficients in the span of five frames are extracted from the segmented speech based on the method of pitch synchronous analysis. The Fisher ratios of the original coefficients then be calculated, and the coefficients whose Fisher ratios are bigger are selected to form the 13-dimensional feature vectors of speaker. The Gaussian mixture model is used to model the speakers. The experimental results show that the identification accuracy of the proposed system is obviously better than that of the systems based on other conventional coefficients like the linear predictive cepstral coefficients and the Mel-frequency cepstral coefficients. [ABSTRACT FROM AUTHOR]

Video object extraction is a key technology in content-based video coding. A novel video object extracting algorithm by two Dimensional (2-D) mesh-based motion analysis is proposed in this paper. Firstly, a 2-D mesh fitting the original frame image is obtained via feature detection algorithm. Then, higher order statistics motion analysis is applied on the 2-D mesh representation to get an initial motion detection mask. After post-processing, the final segmenting mask is quickly obtained. And hence the video object is effectively extracted. Experimental results show that the proposed algorithm combines the merits of mesh-based segmenting algorithms and pixel-based segmenting algorithms, and hereby achieves satisfactory subjective and objective performance while dramatically increasing the segmenting speed. [ABSTRACT FROM AUTHOR]

Two gain forms of spectral amplitude subtraction are derived theoretically without neglecting the correlation of speech and noise spectrum during the period of a frame. In the implementation, the constrained gain is expressed as a function of noncausal a priori SNR (Signal-to-Noise Ratio). Noise and noncausal a priori SNR are estimated from the multitaper spectrum of the noisy signal with algorithms modified to be suitable for the multitaper spectrum. Objective evaluations show that in case of white Gaussian noise the proposed method outperforms some methods based on LSA (Log Spectral Amplitude) in terms of MBSD (Modified Bark Spectral Distortion), segmental SNR and overall SNR, and informal listening tests show that speech reconstructed in this way has little speech distortion and musical noise is nearly inaudible even at low SNR. [ABSTRACT FROM AUTHOR]