scholarly journals Panoptic Segmentation of Individual Pigs for Posture Recognition

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3710
Author(s):  
Johannes Brünger ◽  
Maria Gentz ◽  
Imke Traulsen ◽  
Reinhard Koch
Keyword(s):  
Semantic Segmentation ◽  
Normal Behaviour ◽  
Data Set ◽  
Detection Rates ◽  
New Approach ◽  
Behavioural Research ◽  
Posture Recognition ◽  
Recognition Systems ◽  
Bounding Boxes ◽  
Keypoint Detector

Behavioural research of pigs can be greatly simplified if automatic recognition systems are used. Systems based on computer vision in particular have the advantage that they allow an evaluation without affecting the normal behaviour of the animals. In recent years, methods based on deep learning have been introduced and have shown excellent results. Object and keypoint detector have frequently been used to detect individual animals. Despite promising results, bounding boxes and sparse keypoints do not trace the contours of the animals, resulting in a lot of information being lost. Therefore, this paper follows the relatively new approach of panoptic segmentation and aims at the pixel accurate segmentation of individual pigs. A framework consisting of a neural network for semantic segmentation as well as different network heads and postprocessing methods will be discussed. The method was tested on a data set of 1000 hand-labeled images created specifically for this experiment and achieves detection rates of around 95% (F1 score) despite disturbances such as occlusions and dirty lenses.

scholarly journals EFN: Field-Based Object Detection for Aerial Images

2020 ◽  
Vol 12 (21) ◽  
pp. 3630
Author(s):  
Jin Liu ◽  
Haokun Zheng
Keyword(s):  
Object Detection ◽  
Semantic Segmentation ◽  
Natural Images ◽  
Aerial Images ◽  
Aerial Image ◽  
Data Sets ◽  
Data Set ◽  
Bounding Box ◽  
Good Score ◽  
Bounding Boxes

Object detection and recognition in aerial and remote sensing images has become a hot topic in the field of computer vision in recent years. As these images are usually taken from a bird’s-eye view, the targets often have different shapes and are densely arranged. Therefore, using an oriented bounding box to mark the target is a mainstream choice. However, this general method is designed based on horizontal box annotation, while the improved method for detecting an oriented bounding box has a high computational complexity. In this paper, we propose a method called ellipse field network (EFN) to organically integrate semantic segmentation and object detection. It predicts the probability distribution of the target and obtains accurate oriented bounding boxes through a post-processing step. We tested our method on the HRSC2016 and DOTA data sets, achieving mAP values of 0.863 and 0.701, respectively. At the same time, we also tested the performance of EFN on natural images and obtained a mAP of 84.7 in the VOC2012 data set. These extensive experiments demonstrate that EFN can achieve state-of-the-art results in aerial image tests and can obtain a good score when considering natural images.

A New Approach for the Analysis of Mixture Toxicity Data

1992 ◽  
Vol 26 (9-11) ◽  
pp. 2345-2348 ◽  
Author(s):  
C. N. Haas
Keyword(s):  
Quantitative Analysis ◽  
Mixture Toxicity ◽  
New Method ◽  
Metal Exposure ◽  
Positive Interactions ◽  
Toxicity Data ◽  
Data Set ◽  
New Approach ◽  
Negative Interactions

A new method for the quantitative analysis of multiple toxicity data is described and illustrated using a data set on metal exposure to copepods. Positive interactions are observed for Ni-Pb and Pb-Cr, with weak negative interactions observed for Ni-Cr.

scholarly journals Approach to hand posture recognition based on hand shape features for human–robot interaction

2021 ◽  
Author(s):  
Jing Qi ◽  
Kun Xu ◽  
Xilun Ding
Keyword(s):  
Gaussian Mixture ◽  
Human Robot Interaction ◽  
Polar Coordinates ◽  
Support Vector ◽  
Hand Posture ◽  
Data Set ◽  
Hand Shape ◽  
Hand Posture Recognition ◽  
Hand Segmentation ◽  
Posture Recognition

AbstractHand segmentation is the initial step for hand posture recognition. To reduce the effect of variable illumination in hand segmentation step, a new CbCr-I component Gaussian mixture model (GMM) is proposed to detect the skin region. The hand region is selected as a region of interest from the image using the skin detection technique based on the presented CbCr-I component GMM and a new adaptive threshold. A new hand shape distribution feature described in polar coordinates is proposed to extract hand contour features to solve the false recognition problem in some shape-based methods and effectively recognize the hand posture in cases when different hand postures have the same number of outstretched fingers. A multiclass support vector machine classifier is utilized to recognize the hand posture. Experiments were carried out on our data set to verify the feasibility of the proposed method. The results showed the effectiveness of the proposed approach compared with other methods.

Detection of Ureteral Stones in Kidney Ureter Bladder Radiography: Usefulness of Digital Post-processing

2021 ◽  
Vol 17 ◽  
Author(s):  
Sang Lim Choi ◽  
Sung Bin Park ◽  
Seungwook Yang ◽  
Eun Sun Lee ◽  
Hyun Jeong Park ◽  
...  
Keyword(s):  
Detection Sensitivity ◽  
Ureteral Stones ◽  
Data Sets ◽  
Post Processing ◽  
Stone Size ◽  
Data Set ◽  
Detection Rates ◽  
Exact Test ◽  
Significant Difference ◽  
Exact Size

Purpose: Kidney, ureter, and bladder radiography (KUB) has frequently been used in suspected urolithiasis, but its performance is known to be lower than that of computed tomography (CT). This study aimed to investigate the diagnostic performance of digitally post-processed kidney ureter bladder radiography (KUB) in the detection of ureteral stones. Materials And Methods: Thirty patients who underwent digital KUB and CT were included in this retrospective study. The original digital KUB underwent post-processing that involved noise estimation, reduction, and whitening to improve the visibility of ureteral stones. Thus, 60 digital original or post-processed KUB images were obtained and ordered randomly for blinded review. After a period, a second review was performed after unblinding stone laterality. The detection rates were evaluated at both initial and second review, using CT as reference standard. The objective (size) and subjective (visibility) parameters of ureteral stones were analyzed. Fisher’s exact test was used to compare the detection sensitivity between the original and post-processed KUB data set. Visibility analysis was assessed with a paired t-test. Correlation of stone size between CT and digital KUB data sets was assessed with Pearson’s correlation test. Results: The detection rate was higher for most reviewers once stone laterality was provided and was non-significantly better for the post-processed KUB images (p > 0.05). There was no significant difference in stone size among CT and digital KUB data sets. In all reviews, visibility grade was higher in the post-processed KUB images, irrespective of whether stone laterality was provided. Conclusion: Digital post-processing of KUB yielded higher visibility of ureteral stones and could improve stone detection, especially when stone laterality was available. Thus, digitally post-processed KUB can be an excellent modality for detecting ureteral stones and measuring their exact size.

A Bifactor Multidimensional Item Response Theory Model for Differential Item Functioning Analysis on Testlet-Based Items

2011 ◽  
Vol 35 (8) ◽  
pp. 604-622 ◽  
Author(s):  
Hirotaka Fukuhara ◽  
Akihito Kamata
Keyword(s):  
Item Response Theory ◽  
Differential Item Functioning ◽  
Item Response ◽  
Estimation Method ◽  
Multidimensional Item Response Theory ◽  
Multidimensional Item Response ◽  
Response Theory ◽  
Data Set ◽  
Detection Rates ◽  
Item Functioning

A differential item functioning (DIF) detection method for testlet-based data was proposed and evaluated in this study. The proposed DIF model is an extension of a bifactor multidimensional item response theory (MIRT) model for testlets. Unlike traditional item response theory (IRT) DIF models, the proposed model takes testlet effects into account, thus estimating DIF magnitude appropriately when a test is composed of testlets. A fully Bayesian estimation method was adopted for parameter estimation. The recovery of parameters was evaluated for the proposed DIF model. Simulation results revealed that the proposed bifactor MIRT DIF model produced better estimates of DIF magnitude and higher DIF detection rates than the traditional IRT DIF model for all simulation conditions. A real data analysis was also conducted by applying the proposed DIF model to a statewide reading assessment data set.

Introducing AIDE: a Software Suite for Annotating Images with Deep and Active Learning Assistance

2021 ◽  
Author(s):  
Benjamin Kellenberger ◽  
Devis Tuia ◽  
Dan Morris
Keyword(s):  
Deep Learning ◽  
Active Learning ◽  
Expert Knowledge ◽  
Semantic Segmentation ◽  
Third Party ◽  
Learning Models ◽  
Web Browser ◽  
Web Based ◽  
Model Training ◽  
Bounding Boxes

<p>Ecological research like wildlife censuses increasingly relies on data on the scale of Terabytes. For example, modern camera trap datasets contain millions of images that require prohibitive amounts of manual labour to be annotated with species, bounding boxes, and the like. Machine learning, especially deep learning [3], could greatly accelerate this task through automated predictions, but involves expansive coding and expert knowledge.</p><p>In this abstract we present AIDE, the Annotation Interface for Data-driven Ecology [2]. In a first instance, AIDE is a web-based annotation suite for image labelling with support for concurrent access and scalability, up to the cloud. In a second instance, it tightly integrates deep learning models into the annotation process through active learning [7], where models learn from user-provided labels and in turn select the most relevant images for review from the large pool of unlabelled ones (Fig. 1). The result is a system where users only need to label what is required, which saves time and decreases errors due to fatigue.</p><p><img src="https://contentmanager.copernicus.org/fileStorageProxy.php?f=gnp.0402be60f60062057601161/sdaolpUECMynit/12UGE&app=m&a=0&c=131251398e575ac9974634bd0861fadc&ct=x&pn=gnp.elif&d=1" alt=""></p><p><em>Fig. 1: AIDE offers concurrent web image labelling support and uses annotations and deep learning models in an active learning loop.</em></p><p>AIDE includes a comprehensive set of built-in models, such as ResNet [1] for image classification, Faster R-CNN [5] and RetinaNet [4] for object detection, and U-Net [6] for semantic segmentation. All models can be customised and used without having to write a single line of code. Furthermore, AIDE accepts any third-party model with minimal implementation requirements. To complete the package, AIDE offers both user annotation and model prediction evaluation, access control, customisable model training, and more, all through the web browser.</p><p>AIDE is fully open source and available under https://github.com/microsoft/aerial_wildlife_detection.</p><p> </p><p><strong>References</strong></p>

scholarly journals Edge Prior Multilayer Segmentation Network Based on Bayesian Framework

2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Chu He ◽  
Zishan Shi ◽  
Peizhang Fang ◽  
Dehui Xiong ◽  
Bokun He ◽  
...  
Keyword(s):  
Prior Information ◽  
Semantic Segmentation ◽  
Bayesian Framework ◽  
Excellent Performance ◽  
Transform Method ◽  
Data Set ◽  
Distance Map ◽  
Constraint Network ◽  
Average Accuracy ◽  
Complex Boundaries

In recent years, methods based on neural network have achieved excellent performance for image segmentation. However, segmentation around the edge area is still unsatisfactory when dealing with complex boundaries. This paper proposes an edge prior semantic segmentation architecture based on Bayesian framework. The entire framework is composed of three network structures, a likelihood network and an edge prior network at the front, followed by a constraint network. The likelihood network produces a rough segmentation result, which is later optimized by edge prior information, including the edge map and the edge distance. For the constraint network, the modified domain transform method is proposed, in which the diffusion direction is revised through the newly defined distance map and some added constraint conditions. Experiments about the proposed approach and several contrastive methods show that our proposed method had good performance and outperformed FCN in terms of average accuracy for 0.0209 on ESAR data set.

scholarly journals Multiscale Mask R-CNN–Based Lung Tumor Detection Using PET Imaging

2019 ◽  
Vol 18 ◽  
pp. 153601211986353 ◽  
Author(s):  
Rui Zhang ◽  
Chao Cheng ◽  
Xuehua Zhao ◽  
Xuechen Li
Keyword(s):  
Pet Imaging ◽  
Lung Tumor ◽  
Training Data ◽  
Fine Tuning ◽  
Weighted Voting ◽  
Data Set ◽  
New Approach ◽  
Positron Emission ◽  
Voting Strategy ◽  
Aided Diagnosis

Positron emission tomography (PET) imaging serves as one of the most competent methods for the diagnosis of various malignancies, such as lung tumor. However, with an elevation in the utilization of PET scan, radiologists are overburdened considerably. Consequently, a new approach of “computer-aided diagnosis” is being contemplated to curtail the heavy workloads. In this article, we propose a multiscale Mask Region–Based Convolutional Neural Network (Mask R-CNN)–based method that uses PET imaging for the detection of lung tumor. First, we produced 3 models of Mask R-CNN for lung tumor candidate detection. These 3 models were generated by fine-tuning the Mask R-CNN using certain training data that consisted of images from 3 different scales. Each of the training data set included 594 slices with lung tumor. These 3 models of Mask R-CNN models were then integrated using weighted voting strategy to diminish the false-positive outcomes. A total of 134 PET slices were employed as test set in this experiment. The precision, recall, and F score values of our proposed method were 0.90, 1, and 0.95, respectively. Experimental results exhibited strong conviction about the effectiveness of this method in detecting lung tumors, along with the capability of identifying a healthy chest pattern and reducing incorrect identification of tumors to a large extent.

scholarly journals Method for Monitoring of Managing the Modernization of Regional Economy

2019 ◽  
Vol 71 ◽  
pp. 04004
Author(s):  
T. Krasnova ◽  
T. Plotnikova ◽  
A. Pozdnyakov ◽  
A. Vilgelm
Keyword(s):  
Spatial Distribution ◽  
Economic Activity ◽  
Urban Areas ◽  
Regional Economy ◽  
Regional Level ◽  
Market Potential ◽  
New Economic Geography ◽  
Local Market ◽  
Data Set ◽  
New Approach

This paper proposes a new approach for monitoring of managing the modernisation of regional economic. The model built on proposed methodology will make it possible to smooth out the influence of non-urban areas on the unevenness of economic activity in spatial development. This paper has two goals. The first is to provide a new compilation of data on spatial distribution of economic activity at the sub-regional level. This data set allows us to monitoring of different indicators within macroregions such as Siberia. The second goal is to construct an instrument that helps to overcome the endogeneity problem using new economic geography hypothesis about the mechanisms of distribution of economic activity. Section 2 describes the data and method that we have proposed, discusses the construction of the Theil indexes using these data at the sub-federal and the sub-regional level. Section 3 presents the correlations between spatial distribution of economic activity and local market potential, discusses the robustness of the results; and the last section concludes.

The HTTP Flooding Attack Detection to Secure and Safeguard Online Applications in the Cloud

2019 ◽  
Vol 10 (3) ◽  
pp. 41-58
Author(s):  
Dhanapal A ◽  
Nithyanandam P
Keyword(s):  
Linear Prediction ◽  
Denial Of Service ◽  
Attack Detection ◽  
Healthcare Applications ◽  
Normal Behaviour ◽  
Data Set ◽  
Denial Of Service Attack ◽  
Flooding Attack ◽  
Cloud Properties ◽  
Service Attack

Cloud computing is the cutting edge and has become inevitable in all forms of computing. This is due to its nature of elasticity, cost-effectiveness, availability, etc. The online applications like e-commerce, and e-healthcare applications are moving to the cloud to reduce their operational cost. These applications have the vulnerability of a HTTP flooding Distributed Denial of Service attack in the cloud. This flooding attack aims to overload the application, making it unable to process genuine requests and bring it down. So, these applications need to be secured and safeguarded against such attacks. This HTTP flooding attack is one of the key challenging issues as it shows normal behaviour with regard to all lower networking layers like TCP 3-way handshaking by mimicking genuine requests and it is even harder in the cloud due to the cloud properties. This article offers a solution for detecting a HTTP flooding attack in the cloud by using the novel TriZonal Linear Prediction (TLP) model. The solution was implemented using OpenStack and the FIFA Worldcup '98 data set for experimentation.

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