Recent advances in bottleneck studies have highlighted that different architectural adjustments at the exit may reduce the probability of clogging at the exit thereby enhancing the outflow of the individuals. However, those studies are mostly limited to the controlled experiments with non-human organisms or predictions from simulation models. Complementary data with human subjects to test the model's prediction is limited in literature. This study aims to examine the effect of different geometrical layouts at the exit towards the pedestrian flow via controlled laboratory experiments with human participants. The experimental setups involve pedestrian flow through 14 different geometrical configurations that include different exit locations and obstacles near exit under normal and slow running conditions. It was found that corner exit performed better than middle exit under same obstacle condition. Further, it was observed that the effectiveness of obstacle is sensitive to its size and distance from the exit. Thus, with careful architectural adjustment within a standard escape area, a substantial increase in outflow under normal and slow running conditions could be achieved. However, it was also observed that placing the obstacle too close to the exit can reduce outflow under both normal and slow running conditions. Moreover, we could not observe "faster-is-slower" effect under slow running condition and instead noticed "faster-is-faster" effect. In addition, the power law fitted headway distribution demonstrated that any architectural configurations that enhanced the outflow have higher exponent value compared to the other configuration that negates the outflow. The findings from this paper demonstrate that there is a scope to adjust the architectural elements to optimize the maximum outflow at the egress point. Further, the output from the experiments can be used to develop and verify mathematical models intended to simulate crowd evacuation.From the data in Table 3, we could conclude that for normal walking situation, comparing with standard design ( s J  =2.67 Ped/s/m, S.D =  0.55 Ped/s/m), the most efficient setup is #11 (Corner exit,  =60 cm, D =100 cm) with 19.4% R. E. ( s J  =3.18 Ped/s/m, S.D =  0.34 Ped/s/m), and the least efficient geometry is #2 (Middle exit,  =60 cm, D =60 cm) with -10.6% R. E. ( s J  =2.38 Ped/s/m, S.D =  0.12 Ped/s/m). For slow running condition that is more representative of emergency evacuation condition, comparing with standard design ( s J  =3.37 Ped/s/m, S.D =  0.22 Ped/s/m), the most efficient setup was also #11 with 32.7% R. E. ( s J  =4.47 Ped/s/m, S.D =  0.19 Ped/s/m), and the least one turned into #5 (Middle exit,  =100 cm, D =60 cm) with -5.6% R. E. ( s J  =3.18 Ped/s/m, S.D =  0.08 Ped/s/m).

The traditional unsupervised image segmentation methods are widely used in synthetic aperture radar (SAR) image segmentation due to the simple and convenient application process. In order to solve the time-consuming problem of the common methods, an SAR image segmentation method using region smoothing and label correction (RSLC) is proposed. In this algorithm, the image smoothing results are used to approximate the results of the spatial information polynomials of the image. Thus, the segmentation process can be realized quickly and effectively. Firstly, direction templates are used to detect the directions at different coordinates of the image, and smoothing templates are used to smooth the edge regions according to the directions. It achieves the smoothing of the edge regions and the retention of the edge information. Then the homogeneous regions are presented indirectly according to the difference of directions. The homogeneous regions are smoothed by using isotropic operators. Finally, the two regions are fused for K-means clustering. The majority voting algorithm is used to modify the clustering results, and the final segmentation results are obtained. Experimental results on simulated SAR images and real SAR images show that the proposed algorithm outperforms the other five state-of-the-art algorithms in segmentation speed and accuracy.

Although various methods can effectively segment synthetic aperture radar (SAR) images, we found that the method combining superpixel and image edge information can get better results. To solve the problem that common SAR image segmentation methods often segment pixels incorrectly in edge region, a superpixel boundary-based edge description algorithm (SpBED) is proposed. First, an edge detection method with three edge detectors is used. Therefore, accurate strong edges of SAR images can be extracted, and false edges that are easy to appear in a single detection method can be effectively eliminated. Then the weak edges of the image are extracted by superpixel generation algorithm. The extracted weak edges can supplement the edge information that is difficult to extract by edge detection. Superpixel boundaries are also used to carry the strong edges, so that the strong and weak edges can be completely represented by superpixel boundaries. Finally, boundary constraint superpixel smoothing is used to reduce the effects of noise, and k-means algorithm is performed on superpixels. Since edge information is carried by superpixels, it effectively guarantees the segmentation accuracy in edge region. Compared with seven state-of-the-art algorithms, segmentation results on simulated images and real images demonstrate the effectiveness of the proposed SpBED.

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