scholarly journals Simple Shading Correction Method for Brightfield Whole Slide Imaging

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3084
Author(s):  
Yoon-Oh Tak ◽  
Anjin Park ◽  
Janghoon Choi ◽  
Jonghyun Eom ◽  
Hyuk-Sang Kwon ◽  
...  
Keyword(s):  
State Of The Art ◽  
Correction Method ◽  
Input Image ◽  
Image Sequences ◽  
Whole Slide Imaging ◽  
Pattern Noise ◽  
Art Methods ◽  
Previous State ◽  
Fixed Pattern Noise

Whole slide imaging (WSI) refers to the process of creating a high-resolution digital image of a whole slide. Since digital images are typically produced by stitching image sequences acquired from different fields of view, the visual quality of the images can be degraded owing to shading distortion, which produces black plaid patterns on the images. A shading correction method for brightfield WSI is presented, which is simple but robust not only against typical image artifacts caused by specks of dust and bubbles, but also against fixed-pattern noise, or spatial variations in pixel values under uniform illumination. The proposed method comprises primarily of two steps. The first step constructs candidates of a shading distortion model from a stack of input image sequences. The second step selects the optimal model from the candidates. The proposed method was compared experimentally with two previous state-of-the-art methods, regularized energy minimization (CIDRE) and background and shading correction (BaSiC) and showed better correction scores, as smooth operations and constraints were not imposed when estimating the shading distortion. The correction scores, averaged over 40 image collections, were as follows: proposed method, 0.39 ± 0.099; CIDRE method, 0.67 ± 0.047; BaSiC method, 0.55 ± 0.038. Based on the quantitative evaluations, we can confirm that the proposed method can correct not only shading distortion, but also fixed-pattern noise, compared with the two previous state-of-the-art methods.

scholarly journals Smoothing vs. sharpening of colour images: Together or separated

2017 ◽  
Vol 2 (1) ◽  
pp. 299-316 ◽  
Author(s):  
Cristina Pérez-Benito ◽  
Samuel Morillas ◽  
Cristina Jordán ◽  
J. Alberto Conejero
Keyword(s):  
Image Denoising ◽  
Gaussian Distribution ◽  
State Of The Art ◽  
Visual Quality ◽  
The State ◽  
Art Methods ◽  
Efficiency And Effectiveness

AbstractIt is still a challenge to improve the efficiency and effectiveness of image denoising and enhancement methods. There exists denoising and enhancement methods that are able to improve visual quality of images. This is usually obtained by removing noise while sharpening details and improving edges contrast. Smoothing refers to the case of denoising when noise follows a Gaussian distribution.Both operations, smoothing noise and sharpening, have an opposite nature. Therefore, there are few approaches that simultaneously respond to both goals. We will review these methods and we will also provide a detailed study of the state-of-the-art methods that attack both problems in colour images, separately.

Global Bacteria Optimization Meta-Heuristic Algorithm for Jobshop Scheduling

2010 ◽  
Vol 1 (4) ◽  
pp. 47-58 ◽  
Author(s):  
Jairo R. Montoya-Torres ◽  
Libardo S. Gómez-Vizcaíno ◽  
Elyn L. Solano-Charris ◽  
Carlos D. Paternina-Arboleda
Keyword(s):  
Heuristic Algorithm ◽  
State Of The Art ◽  
Total Tardiness ◽  
Computational Time ◽  
Heuristic Procedure ◽  
Makespan Minimization ◽  
Light Stimulation ◽  
Jobshop Scheduling ◽  
Previous State

This paper examines the problem of jobshop scheduling with either makespan minimization or total tardiness minimization, which are both known to be NP-hard. The authors propose the use of a meta-heuristic procedure inspired from bacterial phototaxis. This procedure, called Global Bacteria Optimization (GBO), emulates the reaction of some organisms (bacteria) to light stimulation. Computational experiments are performed using well-known instances from literature. Results show that the algorithm equals and even outperforms previous state-of-the-art procedures in terms of quality of solution and requires very short computational time.

scholarly journals Multi-Turn Chatbot Based on Query-Context Attentions and Dual Wasserstein Generative Adversarial Networks

2019 ◽  
Vol 9 (18) ◽  
pp. 3908 ◽  
Author(s):  
Jintae Kim ◽  
Shinhyeok Oh ◽  
Oh-Woog Kwon ◽  
Harksoo Kim
Keyword(s):  
Performance Measures ◽  
State Of The Art ◽  
Attention Mechanism ◽  
Generative Adversarial Networks ◽  
Training Method ◽  
Adversarial Networks ◽  
Proposed Model ◽  
Previous State ◽  
Vector Representations

To generate proper responses to user queries, multi-turn chatbot models should selectively consider dialogue histories. However, previous chatbot models have simply concatenated or averaged vector representations of all previous utterances without considering contextual importance. To mitigate this problem, we propose a multi-turn chatbot model in which previous utterances participate in response generation using different weights. The proposed model calculates the contextual importance of previous utterances by using an attention mechanism. In addition, we propose a training method that uses two types of Wasserstein generative adversarial networks to improve the quality of responses. In experiments with the DailyDialog dataset, the proposed model outperformed the previous state-of-the-art models based on various performance measures.

scholarly journals Coherent video generation for multiple hand-held cameras with dynamic foreground

2020 ◽  
Vol 6 (3) ◽  
pp. 291-306
Author(s):  
Fang-Lue Zhang ◽  
Connelly Barnes ◽  
Hao-Tian Zhang ◽  
Junhong Zhao ◽  
Gabriel Salas
Keyword(s):  
Video Sequence ◽  
State Of The Art ◽  
Close Attention ◽  
Cut Points ◽  
Social Events ◽  
Art Methods ◽  
Previous State ◽  
Smooth Transitions

Abstract For many social events such as public performances, multiple hand-held cameras may capture the same event. This footage is often collected by amateur cinematographers who typically have little control over the scene and may not pay close attention to the camera. For these reasons, each individually captured video may fail to cover the whole time of the event, or may lose track of interesting foreground content such as a performer. We introduce a new algorithm that can synthesize a single smooth video sequence of moving foreground objects captured by multiple hand-held cameras. This allows later viewers to gain a cohesive narrative experience that can transition between different cameras, even though the input footage may be less than ideal. We first introduce a graph-based method for selecting a good transition route. This allows us to automatically select good cut points for the hand-held videos, so that smooth transitions can be created between the resulting video shots. We also propose a method to synthesize a smooth photorealistic transition video between each pair of hand-held cameras, which preserves dynamic foreground content during this transition. Our experiments demonstrate that our method outperforms previous state-of-the-art methods, which struggle to preserve dynamic foreground content.

scholarly journals Argumentation-Based Recommendations: Fantastic Explanations and How to Find Them

2018 ◽  
Author(s):  
Antonio Rago ◽  
Oana Cocarascu ◽  
Francesca Toni
Keyword(s):  
Recommender Systems ◽  
Hybrid Method ◽  
State Of The Art ◽  
Significant Problem ◽  
Improve Quality ◽  
Weak Property ◽  
Art Methods

A significant problem of recommender systems is their inability to explain recommendations, resulting in turn in ineffective feedback from users and the inability to adapt to users’ preferences. We propose a hybrid method for calculating predicted ratings, built upon an item/aspect-based graph with users’ partially given ratings, that can be naturally used to provide explanations for recommendations, extracted from user-tailored Tripolar Argumentation Frameworks (TFs). We show that our method can be understood as a gradual semantics for TFs, exhibiting a desirable, albeit weak, property of balance. We also show experimentally that our method is competitive in generating correct predictions, compared with state-of-the-art methods, and illustrate how users can interact with the generated explanations to improve quality of recommendations.

scholarly journals CMOS Fixed Pattern Noise Removal Based on Low Rank Sparse Variational Method

2020 ◽  
Vol 10 (11) ◽  
pp. 3694
Author(s):  
Tao Zhang ◽  
Xinyang Li ◽  
Jianfeng Li ◽  
Zhi Xu
Keyword(s):  
Variational Method ◽  
Noise Removal ◽  
Correction Method ◽  
Image Sensor ◽  
Low Rank ◽  
Statistical Characteristics ◽  
Parameter Adjustment ◽  
Structure Information ◽  
Pattern Noise ◽  
Fixed Pattern Noise

Fixed pattern noise (FPN) has always been an important factor affecting the imaging quality of CMOS image sensor (CIS). However, the current scene-based FPN removal methods mostly focus on the image itself, and seldom consider the structure information of the FPN, resulting in various undesirable noise removal effects. This paper presents a scene-based FPN correction method: the low rank sparse variational method (LRSUTV). It combines not only the continuity of the image itself, but also the structural and statistical characteristics of the stripes. At the same time, the low frequency information of the image is combined to achieve adaptive adjustment of some parameters, which simplifies the process of parameter adjustment, to a certain extent. With the help of adaptive parameter adjustment strategy, LRSUTV shows good performance under different intensity of stripe noise, and has high robustness.

scholarly journals A Fixed-Pattern Noise Correction Method Based on Gray Value Compensation for TDI CMOS Image Sensor

Sensors ◽  
2015 ◽  
Vol 15 (9) ◽  
pp. 23496-23513 ◽  
Author(s):  
Zhenwang Liu ◽  
Jiangtao Xu ◽  
Xinlei Wang ◽  
Kaiming Nie ◽  
Weimin Jin
Keyword(s):  
Cmos Image Sensor ◽  
Correction Method ◽  
Image Sensor ◽  
Pattern Noise ◽  
Fixed Pattern Noise ◽  
Noise Correction

scholarly journals Statistical Scene-Based Non-Uniformity Correction Method with Interframe Registration

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5395
Author(s):  
Baolin Lv ◽  
Shoufeng Tong ◽  
Qiaoyuan Liu ◽  
Haijiang Sun
Keyword(s):  
Statistical Method ◽  
Infrared Image ◽  
Gain Coefficient ◽  
Correction Method ◽  
Combination Method ◽  
Correction Coefficient ◽  
Initial Value ◽  
Registration Method ◽  
Pattern Noise ◽  
Fixed Pattern Noise

The non-uniform response in infrared focal plane array (IRFPA) detectors inevitably produces corrupted images with a fixed-pattern noise. In this paper, we present a novel and adaptive scene-based non-uniformity correction (NUC) method called Correction method with Statistical scene-based and Interframe Registration (CSIR), which realizes low delay calculation of correction coefficient for infrared image. This method combines the statistical method and registration method to achieve a better NUC performance. Specifically, CSIR estimates the gain coefficient with statistical method to give registration method an appropriate initial value. This combination method not only reduces the need of interactive pictures, which means lower time delay, but also achieves better performance compared to the statistical method and other single registration methods. To verify this, real non-uniformity infrared image sequences collected by ourselves were used, and the advantage of CSIR was compared thoroughly on frame number (corresponding to delay time) and accuracy. The results show that the proposed method could achieve a significantly fast and reliable fixed-pattern noise reduction with the effective gain and offset.

scholarly journals Object Detection Based on Center Point Proposals

Electronics ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 2075
Author(s):  
Hao Chen ◽  
Hong Zheng
Keyword(s):  
Object Detection ◽  
Detection Method ◽  
State Of The Art ◽  
Input Image ◽  
Small Object ◽  
Average Precision ◽  
Shallow Layer ◽  
Center Point ◽  
Speed And Accuracy

Anchor-based detectors are widely adopted in object detection. To improve the accuracy of object detection, multiple anchor boxes are intensively placed on the input image, yet most of them are invalid. Although anchor-free methods can reduce the number of useless anchor boxes, the invalid ones still occupy a high proportion. On this basis, this paper proposes an object-detection method based on center point proposals to reduce the number of useless anchor boxes while improving the quality of anchor boxes, balancing the proportion of positive and negative samples. By introducing the differentiation module in the shallow layer, the new method can alleviate the problem of missing detection caused by overlapping of center points. When trained and tested on COCO (Common Objects in Context) dataset, this algorithm records an increase of about 2% in APS (Average Precision of Small Object), reaching 27.8%. The detector designed in this study outperforms most of the state-of-the-art real-time detectors in speed and accuracy trade-off, achieving the AP of 43.2 in 137 ms.

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