Vision-based traffic accident detection is one of the challenging tasks in intelligent transportation systems due to the multi-modalities of traffic accidents. The first challenging issue is about how to learn robust ...
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Vision-based traffic accident detection is one of the challenging tasks in intelligent transportation systems due to the multi-modalities of traffic accidents. The first challenging issue is about how to learn robust and discriminative spatio-temporal feature representations. Since few training samples of traffic accidents can be collected, sparse coding techniques can be used for small data case. However, most sparsecoding algorithms which use norm regularisation may not achieve enough sparsity. The second challenging issue is about the sample imbalance between traffic accidents and normal traffic such that detector would like to favour normal traffic. This study proposes a traffic accident detection method, including a self-tuning iterative hard thresholding (ST-IHT) algorithm for learning sparse spatio-temporal features and a weighted extreme learning machine (W-ELM) for detection. The ST-IHT algorithm can improve the sparsity of encoded features by solving an norm regularisation. The W-ELM can put more focus on traffic accident samples. Meanwhile, a two-point search strategy is proposed to adaptively find a candidate value of Lipschitz coefficients to improve the tuning precision. Experimental results in our collected dataset have shown that this proposed traffic accident detection algorithm outperforms other state-of-the-art methods in terms of the feature's sparsity and detection performance.
This paper presents an automatic method for brain tumour segmentation from magnetic resonance images. The method uses the feature vectors obtained by an efficient feature encoding approach which combines the advantage...
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ISBN:
(数字)9783030117269
ISBN:
(纸本)9783030117269;9783030117252
This paper presents an automatic method for brain tumour segmentation from magnetic resonance images. The method uses the feature vectors obtained by an efficient feature encoding approach which combines the advantages of the supervoxels and sparse coding techniques. Extremely Randomized Trees (ERT) algorithm is trained using these feature vectors to detect the whole tumour and for multi-label classification of abnormal tissues. A Conditional Random Field (CRF) algorithm is implemented to delimit the region where the brain tumour is located. The obtained predictions of the ERT are used to estimate probability maps. The probability maps of the images and the Euclidean distance between the feature vectors of neighbour supervoxels define the conditional random field energy function. The minimization of the energy function is performed via graph cuts. The proposed methods are evaluated on real patient data obtained from BraTS 2018 challenge. Results demonstrate that proposed method achieves a competitive performance on the validation dataset using Dice score is: 0.5719, 0.7992 and 0.6285 for enhancing tumuor, whole tumour and tumour core respectively. The achieved performance of this method on testing set using Dice score is: 0.5081, 0.7278 and 0.5778 for enhancing tumuor, whole tumour and tumour core respectively.
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