Floating pollutant image target extraction algorithm based on immune extremum region

Yu, Xiao; Xi, Ye; Zhang, Shunxian

doi:10.1016/j.dsp.2022.103442

Cited by 38 publications

(11 citation statements)

References 11 publications

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“…Ref. [33] improved the water environment by combining machine learning and regional features of artificial immune algorithms to extract images of floating objects in polluted water sources, effectively detecting floating pollutants on the water surface and aiding in water resource management [34]. In medical image segmentation tasks, feature learning regression network models provided a better method for image segmentation, which can effectively solve the problem of retinal layer segmentation bias.…”

Section: Link Prediction Methodsmentioning

confidence: 99%

Sparse Subgraph Prediction Based on Adaptive Attention

Gao

et al. 2023

Applied Sciences

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Link prediction is a crucial problem in the analysis of graph-structured data, and graph neural networks (GNNs) have proven to be effective in addressing this problem. However, the computational and temporal costs associated with large-scale graphs remain a concern. This study introduces a novel method for link prediction called Sparse Subgraph Prediction Based on Adaptive Attention (SSP-AA). The method generates sparse subgraphs and utilizes Graph SAmple and aggreGatE (GraphSAGE) for prediction, aiming to reduce computation and time costs while providing a foundation for future exploration of large-scale graphs. Certain key issues in GraphSAGE are addressed by integrating an adaptive attention mechanism and a jumping knowledge module into the model. To address the issue of adaptive weight distribution in GraphSAGE, an aggregation function is employed, which is based on the attention mechanism. This modification enables the model to distribute weights adaptively among neighboring nodes, significantly improving its ability to capture node relationships. Furthermore, to tackle the common issue of over-smoothing in GNNs, a jumping knowledge module is integrated, enabling information sharing across different layers and providing the model with the flexibility to select the appropriate representation depth based on the specific situation. By enhancing the quality of node representations, SSP-AA further boosts the performance of GraphSAGE in various prediction tasks involving graph-structured data.

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Section: Link Prediction Methodsmentioning

confidence: 99%

Sparse Subgraph Prediction Based on Adaptive Attention

Gao

et al. 2023

Applied Sciences

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“…On the other hand, the product recognition problem on the market shelves is also an object detection and object recognition problem. Therefore, the recent methods [38][39][40] that have been applied and proven successful in various areas can solve the different stages of product recognition problem on the grocery shelf images. For the product detection part, methods such as the immune coordination deep network [38] and the immune extreme region-based target extraction algorithm [39] may be useful, while methods such as multiple kernel k-means [40] may also contribute to the refinement stage.…”

Section: Related Workmentioning

confidence: 99%

Development of a Hybrid Method for Multi-Stage End-to-End Recognition of Grocery Products in Shelf Images

Melek,

Battini Sonmez,

Ayral

et al. 2023

Electronics

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Product recognition on grocery shelf images is a compelling task of object detection because of the similarity between products, the presence of the different scale of product sizes, and the high number of classes, in addition to constantly renewed packaging and added new products’ difficulty in data collection. The use of conventional methods alone is not enough to solve a number of retail problems such as planogram compliance, stock tracking on shelves, and customer support. The purpose of this study is to achieve significant results using the suggested multi-stage end-to-end process, including product detection, product classification, and refinement. The comparison of different methods is provided by a traditional computer vision approach, Aggregate Channel Features (ACF) and Single-Shot Detectors (SSD) are used in the product detection stage, and Speed-up Robust Features (SURF), Binary Robust Invariant Scalable Key points (BRISK), Oriented Features from Accelerated Segment Test (FAST), Rotated Binary Robust Independent Elementary Features (BRIEF) (ORB), and hybrids of these methods are used in the product classification stage. The experimental results used the entire Grocery Products dataset and its different subsets with a different number of products and images. The best performance was achieved with the use of SSD in the product detection stage and the hybrid use of SURF, BRISK, and ORB in the product classification stage, respectively. Additionally, the proposed approach performed comparably or better than existing models.

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“…Scholars proposed a blind detection algorithm based on boundary judgment domain contrast and HVS characteristic adaptation [22]. The scheme embeds digital media into the detail subgraphs LH and HL by analyzing the boundary judgment transformation and performs the embedding intensity factor according to the boundary judgment contrast and HVS characteristics (i.e., brightness sensitivity, texture sensitivity, and contrast sensitivity) [23,24]. Experimental results show that the scheme has strong invisibility and robustness without attacks [25] but is not robust after noise, median filtering, clipping, and JPEG compression attacks [26,27].…”

Section: Related Workmentioning

confidence: 99%

Application of Image Color Gamut Boundary Judgment Algorithm in Digital Media

2022

Wireless Communications and Mobile Computing

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Image chromatic aberration evaluation is one of the research hotspots and academic difficulties in the field of color science and imaging technology. Based on the algorithm theory of image color gamut boundary judgment, this paper constructs its application model in digital media. The model uses the threshold color difference image as a reference and applies it to the evaluation experiment of color difference above the threshold based on the quantitative estimation method. During the simulation process, the improved HAD dual-color image digital boundary judgment algorithm is programmed. For small color difference data at the threshold level, the calculated color difference of the boundary judgment algorithm framework is smaller than the visual perception color difference; for the large color difference data above the threshold, the boundary judgment algorithm framework is given. The research results of the threshold color difference experiment show that the perceptible color difference threshold and acceptable color difference threshold of image color gamut observers for image color difference are 1.85 AE and 3.63 AE, respectively, which are basically twice the relationship. The visually perceptible chromatic aberration threshold and acceptable chromatic aberration threshold for lightness parameters are higher than those of chroma and hue, which shows that the boundary judgment algorithm framework has certain advantages for the evaluation performance of color digital image threshold chromatic aberration and effectively improves the algorithm robustness.

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Floating pollutant image target extraction algorithm based on immune extremum region

Cited by 38 publications

References 11 publications

Sparse Subgraph Prediction Based on Adaptive Attention

Sparse Subgraph Prediction Based on Adaptive Attention

Development of a Hybrid Method for Multi-Stage End-to-End Recognition of Grocery Products in Shelf Images

Application of Image Color Gamut Boundary Judgment Algorithm in Digital Media

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