A Semantic Segmentation Algorithm Using FCN with Combination of BSLIC

Zhao, Wei; Zhang, Haodi; Yan, Ye; Fu, Yi; Wang, Hai

doi:10.3390/app8040500

Cited by 24 publications

(10 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the main problem is how to divide the image into many different regions. Zhao et al [31] proposed a simple linear iterative clustering method based on boundary terms (BSLIC) and semantic segmentation combined with full convolutional networks (FCN). They achieved an improvement in the segmentation algorithm by introducing superpixel semantic annotation.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Weakly-Supervised Image Semantic Segmentation Based on Superpixel Region Merging

Jiang

Tawose

Pei

et al. 2019

BDCC

View full text Add to dashboard Cite

In this paper, we propose a semantic segmentation method based on superpixel region merging and convolutional neural network (CNN), referred to as regional merging neural network (RMNN). Image annotation has always been an important role in weakly-supervised semantic segmentation. Most methods use manual labeling. In this paper, super-pixels with similar features are combined using the relationship between each pixel after super-pixel segmentation to form a plurality of super-pixel blocks. Rough predictions are generated by the fully convolutional networks (FCN) so that certain super-pixel blocks will be labeled. We perceive and find other positive areas in an iterative way through the marked areas. This reduces the feature extraction vector and reduces the data dimension due to super-pixels. The algorithm not only uses superpixel merging to narrow down the target’s range but also compensates for the lack of weakly-supervised semantic segmentation at the pixel level. In the training of the network, we use the method of region merging to improve the accuracy of contour recognition. Our extensive experiments demonstrated the effectiveness of the proposed method with the PASCAL VOC 2012 dataset. In particular, evaluation results show that the mean intersection over union (mIoU) score of our method reaches as high as 44.6%. Because the cavity convolution is in the pooled downsampling operation, it does not degrade the network’s receptive field, thereby ensuring the accuracy of image semantic segmentation. The findings of this work thus open the door to leveraging the dilated convolution to improve the recognition accuracy of small objects.

show abstract

Section: Related Workmentioning

confidence: 99%

“…mIoU is derived by calculating the average IoU. Suppose the total amount of classes is (n + 1) and p ij is the amount of pixels of class i inferred to class j. p ii represents the number of true positives, while p ij and p ji are usually interpreted as false positives and false negatives [31].…”

Section: Quantitative Analysismentioning

confidence: 99%

Weakly-Supervised Image Semantic Segmentation Based on Superpixel Region Merging

Jiang

Tawose

Pei

et al. 2019

BDCC

View full text Add to dashboard Cite

show abstract

“…However, in this paper, we estimate the appearance model of the road surface via superpixels. Superpixels are kinds of mid-level visual cues, which have been applied to image segmentation and object detection with demonstrated success [31,32]. We choose to over-segment the images into superpixels as one of the preprocessing steps in road detection due to the following reasons: Firstly, superpixels are obtained by over-segmentation of the image into atomic regions which are ideally similar in color and texture.…”

Section: Proposed Algorithmmentioning

confidence: 99%

Fusing Appearance and Prior Cues for Road Detection

Ren

Wei

et al. 2019

Applied Sciences

View full text Add to dashboard Cite

Road detection is a crucial research topic in computer vision, especially in the framework of autonomous driving and driver assistance. Moreover, it is an invaluable step for other tasks such as collision warning, vehicle detection, and pedestrian detection. Nevertheless, road detection remains challenging due to the presence of continuously changing backgrounds, varying illumination (shadows and highlights), variability of road appearance (size, shape, and color), and differently shaped objects (lane markings, vehicles, and pedestrians). In this paper, we propose an algorithm fusing appearance and prior cues for road detection. Firstly, input images are preprocessed by simple linear iterative clustering (SLIC), morphological processing, and illuminant invariant transformation to get superpixels and remove lane markings, shadows, and highlights. Then, we design a novel seed superpixels selection method and model appearance cues using the Gaussian mixture model with the selected seed superpixels. Next, we propose to construct a road geometric prior model offline, which can provide statistical descriptions and relevant information to infer the location of the road surface. Finally, a Bayesian framework is used to fuse appearance and prior cues. Experiments are carried out on the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) road benchmark where the proposed algorithm shows compelling performance and achieves state-of-the-art results among the model-based methods.

show abstract

“…One of the state-of-the-art options is a Fully Convolutional Network (FCN) that is comprised of convolutional (encoder) and deconvolutional (decoder) networks to extract and process the features consecutively, thus achieving pixel-wise predictions [16]. Additionally, prior studies [17,18] also used FCN as a framework to perform image segmentation for various applications and with different modifications in FCN architecture, which showed the importance of FCN as one of the prior arts in image segmentation. The inference model of FCN requires dynamic adjustment of neuron weights on each network layer during the supervised learning stage based on a set of input images and corresponding ground truth maps.…”

Section: Introductionmentioning

confidence: 99%

Transferable Architecture for Segmenting Maxillary Sinuses on Texture-Enhanced Occipitomental View Radiographs

et al. 2020

View full text Add to dashboard Cite

Maxillary sinuses are the most prevalent locations for paranasal infections on both children and adults. Common diagnostic material for this particular disease is through the screening of occipitomental-view skull radiography (SXR). With the growing cases on paranasal infections, expediting the diagnosis has become an important innovation aspect that could be addressed through the development of a computer-aided diagnosis system. As the preliminary stage of the development, an automatic segmentation over the maxillary sinuses is required to be developed. This study presents a computer-aided detection (CAD) module that segments maxillary sinuses from a plain SXR that has been preprocessed through the novel texture-based morphological analysis (ToMA). Later, the network model from the Transferable Fully Convolutional Network (T-FCN) performs pixel-wise segmentation of the maxillary sinuses. T-FCN is designed to be trained with multiple learning stages, which enables re-utilization of network weights to be adjusted based on newer dataset. According to the experiments, the proposed system achieved segmentation accuracy at 85.70%, with 50% faster learning time.

show abstract

A Semantic Segmentation Algorithm Using FCN with Combination of BSLIC

Cited by 24 publications

References 28 publications

Weakly-Supervised Image Semantic Segmentation Based on Superpixel Region Merging

Weakly-Supervised Image Semantic Segmentation Based on Superpixel Region Merging

Fusing Appearance and Prior Cues for Road Detection

Transferable Architecture for Segmenting Maxillary Sinuses on Texture-Enhanced Occipitomental View Radiographs

Contact Info

Product

Resources

About