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Low-light image segmentation is always the difficulty of image segmentation. The low contrast and high fuzziness caused by low light make this kind of image segmentation much more difficult than general image segmentation. In order to improve the accuracy of semantic segmentation in low light environment, this paper proposed a semantic segmentation model of low light scene with filtering light style under noise guidance(SFIS) according to the characteristics of low-light image. The model comprehensively used signal-to-noise ratio as prior knowledge, and adopted different distance interaction for different noise regions in the image by guiding the self-attention operation in the long distance branch and the feature fusion of long/short distance branches. This paper also further designed an illumination filter, which was a module that further extracted the illumination style information from the overall style of the image. By alternately training the illumination filter and the semantic segmentation model, the lighting style gap between different lighting conditions was gradually reduced, so that the segmentation network could learn illumination invariant features. The proposed model outperforms the previous work on the dataset LLRGBD and achieves the best results. The mIoU on the real dataset LLRGBD-real reaches 66.8%, it shows that the proposed long and short distance branch module and the illumination filter module can effectively improve the semantic segmentation ability of the model in low light environment. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aiming at the problems of inaccurate prediction of normal video and poor ability of learning normal features in video anomaly detection, this paper proposed an anomaly detection model combining wavelet transform and encoder-decoder attention. The model introduced multi-level discrete wavelet transform, and designed a module of discrete wavelet transform fusion. The module concatenated the sub-bands obtained by decomposing video frames, and fed the result into depthwise separable convolution, and then fused with the encoder features to compensate for the high-frequency details lost in the down sampling process. The model also constructed encoder-decoder attention module. After performing difference of Gaussian operation on the encoder feature map, the attention weights were obtained along the horizontal and vertical directions respectively. And then the encoder features were aggregated according to the weights. Finally, the decoder features were associated to enhance the network’s learning of normal events. Experiments on Ped1,Ped2 and Avenue datasets show that the AUC of the proposed model is increased by 3.2%,3.1% and 2.0%.And the results indicate that the proposed model can effectively improve the abnormal detection ability. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Data Acquisition & Processing / Shu Ju Cai Ji Yu Chu Li is the property of Editorial Department of Journal of Nanjing University of Aeronautics & Astronautics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)