The paper introduces the concept of simplified neutrosophic sets SNSs, which are a subclass of neutrosophic sets, and defines the operational laws of SNSs. Then, we propose some aggregation operators, including a simplified neutrosophic weighted arithmetic average operator and a simplified neutrosophic weighted geometric average operator. Based on the two aggregation operators and cosine similarity measure for SNSs, a multicriteria decision-making method is established in which the evaluation values of alternatives with respective to criteria are represented by the form of SNSs. The ranking order of alternatives is performed through the cosine similarity measure between an alternative and the ideal alternative and the best ones can be determined as well. Finally, a numerical example shows the application of the proposed method.

A multicriteria decision-making method using aggregation operators for simplified neutrosophic sets

Automated breast cancer detection and classification using ultrasound images: A survey

Correlation acts as a critical role in the tracking field, especially in recent popular Siamese-based trackers. The correlation operation is a simple fusion manner to consider the similarity between the template and the search region. However, the correlation operation itself is a local linear matching process, leading to lose semantic information and fall into local optimum easily, which may be the bottleneck of designing high-accuracy tracking algorithms. Is there any better feature fusion method than correlation? To address this issue, inspired by Transformer, this work presents a novel attention-based feature fusion network, which effectively combines the template and search region features solely using attention. Specifically, the proposed method includes an ego-context augment module based on self-attention and a cross-feature augment module based on cross-attention. Finally, we present a Transformer tracking (named TransT) method based on the Siamese-like feature extraction backbone, the designed attention-based fusion mechanism, and the classification and regression head. Experiments show that our TransT achieves very promising results on six challenging datasets, especially on large-scale LaSOT, TrackingNet, and GOT-10k benchmarks. Our tracker runs at approximatively 50 fps on GPU. Code and models are available at https://github.com/chenxin-dlut/TransT.

Transformer Tracking

The neutrosophic set (NS) can be better to express the incomplete, indeterminate and inconsistent information, and Hamacher aggregation operators extended the Algebraic and Einstein aggregation operators and the generalized aggregation operators can generalize the arithmetic, geometric, and quadratic aggregation operators. In this paper, we combined Hamacher operations and generalized aggregation operators to NS, and proposed some new aggregation operators. Firstly, we presented some new operational laws for neutrosophic numbers (NNs) based on Hamacher operations and studied their properties. Then, we proposed the generalized neutrosophic number Hamacher weighted averaging (GNNHWA) operator, generalized neutrosophic number Hamacher ordered weighted averaging (GNNHOWA) operator, and generalized neutrosophic number Hamacher hybrid averaging (GNNHHA) operator, and explored some properties of these operators and analyzed some special cases of them. Furthermore, we gave a new method based on these operators for multiple attribute group decision making problems with neutrosophic numbers, and the operational steps were illustrated in detail. Finally, an application example is given to verify the proposed method and to demonstrate its effectiveness.

Some Generalized Neutrosophic Number Hamacher Aggregation Operators and Their Application to Group Decision Making

Article history: Received 9 September 2008 Received in revised form 16 April 2009 Accepted 14 May 2009

Automated breast cancer detection and classification using ultrasound images A survey

Accurate bounding box estimation has recently attracted much attention in the tracking community because traditional multi-scale search strategies cannot estimate tight bounding boxes in many challenging scenarios involving changes to the target. A tracker capable of detecting target corners can flexibly adapt to such changes, but existing corner detection based tracking methods have not achieved adequate success. We analyze the reasons for their failure and propose a state-of-the-art tracker that performs correlation-guided attentional corner detection in two stages. First, a region of interest (RoI) is obtained by employing an efficient Siamese network to distinguish the target from the background. Second, a pixel-wise correlation-guided spatial attention module and a channel-wise correlation-guided channel attention module exploit the relationship between the target template and the RoI to highlight corner regions and enhance features of the RoI for corner detection. The correlation-guided attention modules improve the accuracy of corner detection, thus enabling accurate bounding box estimation. When trained on large-scale datasets using a novel RoI augmentation strategy, the performance of the proposed tracker, running at a high speed of 70 FPS, is comparable with that of state-of-the-art trackers in meeting five challenging performance benchmarks.

/pdf/correlation-guided-attention-for-corner-detection-based-3ozn9h5a4f.pdf

Correlation-Guided Attention for Corner Detection Based Visual Tracking

A neutrosophic set (NS), a part of neutrosphy theory, studies the origin, nature, and scope of neutralities, as well as their interactions with different ideational spectra. The neutrosophic set is a general formal framework that has been recently proposed. However, the neutrosophic set needs to be specified from a technical point of view. Now, we apply the neutrosophic set into image domain and define some concepts and operators for image denoising. The image G is transformed into NS domain, which is described using three membership sets: T, I and F. The entropy of the neutrosophic set is defined and employed to evaluate the indeterminancy. A new operation, γmedian-filtering operation, is proposed to decrease the set indeterminancy and remove noise. We have conducted experiments on a variety of noisy images using different type of noise with different levels. The experimental results demonstrate that the proposed approach can remove noise automatically and effectively. Especially, it can process not only noisy images with different levels of noise, but also images with different kinds of noise well without knowing the type of the noise, which is the most difficult task for image denoising.

/pdf/a-new-neutrosophic-approach-to-image-denoising-488clqtnpe.pdf

A New Neutrosophic Approach to Image Denoising

Breast cancer is still a serious disease in the world Early detection is very essential for breast cancer prevention and diagnosis Breast ultrasound (US) imaging has been proven to be a valuable adjunct to mammography in the detection and classification of breast lesions Because of the fuzzy and noisy nature of the US images and the low contrast between the breast cancer and tissue, it is difficult to provide an accurate and effective diagnosis This paper presents a novel algorithm based on fuzzy logic that uses both the global and local information and has the ability to enhance the fine details of the US images while avoiding noise amplification and overenhancement We normalize the images and then fuzzify the normalized images based on the maximum entropy principle Edge and textural information are extracted to describe the lesion features and the scattering phenomenon of US images and the contrast ratio measuring the degree of enhancement is computed and modified The defuzzification process is used to obtain the enhanced US images To demonstrate the performance of the proposed approach, the algorithm was tested on 86 breast US images Experimental results confirm that the proposed method can effectively enhance the details of the breast lesions without overenhancement or underenhancement

Breast ultrasound image enhancement using fuzzy logic

Structure preservation and distribution alignment in discriminative transfer subspace learning

/pdf/a-novel-hough-transform-based-on-eliminating-particle-swarm-2rr6hkt4vw.pdf

Wei Zhao

Papers

Correlation-Guided Attention for Corner Detection Based Visual Tracking

A New Neutrosophic Approach to Image Denoising

Breast ultrasound image enhancement using fuzzy logic

Structure preservation and distribution alignment in discriminative transfer subspace learning

A novel hough transform based on eliminating particle swarm optimization and its applications