Color image splicing localization algorithm by quaternion fully convolutional networks and superpixel-enhanced pairwise conditional random field

High-precision indoor localization plays a vital role in various places. In recent years, visual inertial odometry (VIO) system has achieved outstanding progress in the field of indoor localization. However, it is easily affected by poor lighting and featureless environments. For this problem, we propose an indoor localization algorithm based on VIO system and three-dimensional (3D) map matching. The 3D map matching is to add height matching on the basis of previous two-dimensional (2D) matching so that the algorithm has more universal applicability. Firstly, the conditional random field model is established. Secondly, an indoor three-dimensional digital map is used as a priori information. Thirdly, the pose and position information output by the VIO system are used as the observation information of the conditional random field (CRF). Finally, the optimal states sequence is obtained and employed as the feedback information to correct the trajectory of VIO system. Experimental results show that our algorithm can effectively improve the positioning accuracy of VIO system in the indoor area of poor lighting and featureless.

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A Multi-Level Approach to Waste Object Segmentation

Sensors ◽

10.3390/s20143816 ◽

2020 ◽

Vol 20 (14) ◽

pp. 3816

Author(s):

Tao Wang ◽

Yuanzheng Cai ◽

Lingyu Liang ◽

Dongyi Ye

Keyword(s):

Random Field ◽

Object Segmentation ◽

Color Image ◽

Conditional Random Field ◽

Depth Image ◽

Future Research ◽

Depth Information ◽

Multi Level ◽

Multiple Levels ◽

Potential Object

We address the problem of localizing waste objects from a color image and an optional depth image, which is a key perception component for robotic interaction with such objects. Specifically, our method integrates the intensity and depth information at multiple levels of spatial granularity. Firstly, a scene-level deep network produces an initial coarse segmentation, based on which we select a few potential object regions to zoom in and perform fine segmentation. The results of the above steps are further integrated into a densely connected conditional random field that learns to respect the appearance, depth, and spatial affinities with pixel-level accuracy. In addition, we create a new RGBD waste object segmentation dataset, MJU-Waste, that is made public to facilitate future research in this area. The efficacy of our method is validated on both MJU-Waste and the Trash Annotation in Context (TACO) dataset.

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Fully convolutional networks semantic segmentation based on conditional random field optimization

Journal of Computational Methods in Sciences and Engineering ◽

10.3233/jcm-214867 ◽

2021 ◽

pp. 1-11

Author(s):

Qian Wu ◽

Jinan Gu ◽

Chen Wu ◽

Jin Li

Keyword(s):

Conditional Random Field ◽

Semantic Segmentation ◽

Target Object ◽

High Order ◽

Target Area ◽

Original Image ◽

Convolutional Networks ◽

Fully Convolutional Networks ◽

Algorithm Comparison ◽

Feature Pyramid

Each pixel can be classified in the image by the semantic segmentation. The segmentation detection results of pixel level can be got which are similar to the contour of the target object. However, the results of semantic segmentation trained by Fully convolutional networks often lead to loss of detail information. This paper proposes a CRF-FCN model based on CRF optimization. Firstly, the original image is detected based on feature pyramid networks, and the target area information is extracted, which is used to train the high-order potential function of CRF. Then, the high-order CRF is used as the back-end of the complete convolution network to optimize the semantic image segmentation. The algorithm comparison experiment shows that our algorithm makes the target details more obvious, and improves the accuracy and efficiency of semantic segmentation.

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