Human beings process stereoscopic correspondence across multiple scales. However, this bio-inspiration is ignored by state-of-the-art cost aggregation methods for dense stereo correspondence. In this paper, a generic cross-scale cost aggregation framework is proposed to allow multi-scale interaction in cost aggregation. We firstly reformulate cost aggregation from a unified optimization perspective and show that different cost aggregation methods essentially differ in the choices of similarity kernels. Then, an inter-scale regularizer is introduced into optimization and solving this new optimization problem leads to the proposed framework. Since the regularization term is independent of the similarity kernel, various cost aggregation methods can be integrated into the proposed general framework. We show that the cross-scale framework is important as it effectively and efficiently expands state-of-the-art cost aggregation methods and leads to significant improvements, when evaluated on Middlebury, KITTI and New Tsukuba datasets.

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Water quality prediction has great significance for water environment protection. A water quality prediction method based on the Improved Grey Relational Analysis (IGRA) algorithm and a Long-Short Term Memory (LSTM) neural network is proposed in this paper. Firstly, considering the multivariate correlation of water quality information, IGRA, in terms of similarity and proximity, is proposed to make feature selection for water quality information. Secondly, considering the time sequence of water quality information, the water quality prediction model based on LSTM, whose inputs are the features obtained by IGRA, is established. Finally, the proposed method is applied in two actual water quality datasets: Tai Lake and Victoria Bay. Experimental results demonstrate that the proposed method can take full advantage of the multivariate correlations and time sequence of water quality information to achieve better performance on water quality prediction compared with the single feature or non-sequential prediction methods.

Automatic player detection, labeling and tracking in broadcast soccer video are significant while quite challenging tasks. In this paper, we present a solution to perform automatic multiple player detection, unsupervised labeling and efficient tracking. Players' position and scale are determined by a boosting based detector. Players' appearance models are unsupervised learned from hundreds of samples automatically collected by detection. Thereafter, these models can be utilized for player labeling (Team A, Team B and Referee). Player tracking is achieved by Markov Chain Monte Carlo (MCMC) data association. Some data driven dynamics are proposed to improve the Markov chain's efficiency. The testing results on FIFA World Cup 2006 video demonstrate that our method can reach high detection and labeling precision, and reliably tracking in cases of scenes such as multiple player occlusion, moderate camera motion and pose variation. IntroductionAutomatic player localization, labeling and tracking is critical for team tactics, player activity analysis and enjoyment in broadcast sports videos. It is quite challenging due to many difficulties such as player-to-player occlusion, similar player appearance, varying number of players, abrupt camera motion, various noises, video blur, etc.Many algorithms have been presented to deal with the multiple target tracking problem, such as particle filter [1 of these two works, a multi-camera system was used to get a stationary, high-resolution and wide-field view of soccer game. This setting ensured a reliable background subtraction can be obtained. In our application, the camera is not fixed, which results in moving background. Thus, we need robust and adaptive background modeling and effective object association technologies. In another aspect, unsupervised player labeling is preferred for its generalization ability. In this paper, we propose a solution for player detection, labeling and tracking in broadcast soccer video. The system framework is illustrated in Figure 1. The whole procedure is a two-pass video scan. In the first scan, we (1) learn video dominant color via accumulated color histograms, and (2) unsupervised learn players' appearance models over hundreds of player samples collected by a boosted player detector. In the second scan, that is the testing procedure, we first use the dominant color for playfield segmentation and view-type classification. Then we apply a boosting player detector to localize players. Afterwards, the players are labeled as Team A, Team B or Referee with prior learned models. Finally, we perform data-driven MCMC association to generate players' trajectories, in which track length, label consistency and motion consistency are used as criterions for associating observations across frames.The main contributions of our method are: (1) robust player detection achieved by background filtering and a boosted cascade detector; (2) unsupervised player appearance modeling, the referee can be identified in addition to two teams players without any ...

Fuzzy entropy and image thresholding are the most direct and effective methods for image segmentation. This paper, taking fuzzy Kapur's entropy as the optimal objective function, with modified discrete Grey wolf optimizer (GWO) as the tool, uses pseudotrapezoid-shaped to conduct fuzzy membership initialization so as to achieve image segmentation finally by means of local information aggregation. Experiment results show that the proposed fuzzy-based GWO and aggregation algorithm and fuzzy-based modified discrete GWO and aggregation (FMDGWOA) algorithm can search out the optimal thresholds effectively and accurately. In this paper, electro-magnetism optimization based on Kapur's entropy, standard GWO and fuzzy entropy-based differential evolution algorithm are experimentally compared with the proposed method, respectively. It shows that FMDGWOA enjoys obvious advantages in segmentation quality, objective function, and stability.

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