scholarly journals A Siamese Neural Network for Non-Invasive Baggage Re-Identification

2020 ◽  
Vol 6 (11) ◽  
pp. 126
Author(s):  
Pier Luigi Mazzeo ◽  
Christian Libetta ◽  
Paolo Spagnolo ◽  
Cosimo Distante
Keyword(s):  
Neural Network ◽  
State Of The Art ◽  
Conveyor Belt ◽  
Handling System ◽  
Non Invasive ◽  
Training Images ◽  
Traffic Jams ◽  
Baggage Handling

Baggage travelling on a conveyor belt in the sterile area (the rear collector located after the check-in counters) often gets stuck due to traffic jams, mainly caused by incorrect entries from the check-in counters on the collector belt. Using suitcase appearance captured on the Baggage Handling System (BHS) and airport checkpoints and their re-identification allows for us to handle baggage safer and faster. In this paper, we propose a Siamese Neural Network-based model that is able to estimate the baggage similarity: given a set of training images of the same suitcase (taken in different conditions), the network predicts whether the two input images belong to the same baggage identity. The proposed network learns discriminative features in order to measure the similarity among two different images of the same baggage identity. It can be easily applied on different pre-trained backbones. We demonstrate our model in a publicly available suitcase dataset that outperforms the leading latest state-of-the-art architecture in terms of accuracy.

scholarly journals MEYE: Web-app for translational and real-time pupillometry

2021 ◽  
Author(s):  
Raffaele Mazziotti ◽  
Fabio Carrara ◽  
Aurelia Viglione ◽  
Leonardo Lupori ◽  
Luca Lo Verde ◽  
...  
Keyword(s):  
Neural Network ◽  
Mouse Models ◽  
State Of The Art ◽  
Web Browser ◽  
Non Invasive ◽  
Temporal Specificity ◽  
On Line ◽  
Web App ◽  
Set Up ◽  
Neuropsychiatric Conditions

AbstractPupil dynamics alterations have been found in patients affected by a variety of neuropsychiatric conditions, including autism. Studies in mouse models have used pupillometry for phenotypic assessment and as a proxy for arousal. Both in mice and humans, pupillometry is non-invasive and allows for longitudinal experiments supporting temporal specificity, however its measure requires dedicated setups. Here, we introduce a Convolutional Neural Network that performs on-line pupillometry in both mice and humans in a web app format. This solution dramatically simplifies the usage of the tool for non-specialist and non-technical operators. Because a modern web browser is the only software requirement, this choice is of great interest given its easy deployment and set-up time reduction. The tested model performances indicate that the tool is sensitive enough to detect both spontaneous and evoked pupillary changes, and its output is comparable with state-of-the-art commercial devices.

scholarly journals Conveyor Belt Damage Detection with the Use of a Two-Layer Neural Network

2021 ◽  
Vol 11 (12) ◽  
pp. 5480
Author(s):  
Agata Kirjanów-Błażej ◽  
Aleksandra Rzeszowska
Keyword(s):  
Neural Network ◽  
Damage Detection ◽  
Conveyor Belt ◽  
Belt Conveyor ◽  
Wear Process ◽  
Non Invasive ◽  
Changes Over Time ◽  
Transported Material ◽  
Matlab Programming

Non-invasive conveyor belt diagnostics in damage detection allows significant reductions of the costs related to belt replacement, as well as the evaluation of belt usability and wear degree changes over time. As a result, it increases safety in the location where the belt is used. Depending on the location of a belt conveyor, its length or the type of the transported material, the belt may undergo wear at different rates, albeit the wear process itself is inevitable. This article presents an artificial intelligence-based approach to the classification of conveyor belt damage. A two-layer neural network was implemented in the MATLAB programming language, with the use of a Deep Learning Toolbox set. As a result of the optimization of the created network, the effectiveness of operation was at the level of 80%.

scholarly journals Gesture recognition system based on CNN-IndRNN and OpenBCI

2021 ◽  
Vol 336 ◽  
pp. 06003
Author(s):  
Na Wu ◽  
Hao JIN ◽  
Xiachuan Pei ◽  
Shurong Dong ◽  
Jikui Luo ◽  
...  
Keyword(s):  
Neural Network ◽  
Gesture Recognition ◽  
Recognition Accuracy ◽  
State Of The Art ◽  
Recognition System ◽  
Classification Performance ◽  
Non Invasive ◽  
Average Accuracy ◽  
Open Source Hardware ◽  
Semg Signals

Surface electromyography (sEMG), as a key technology of non-invasive muscle computer interface, is an important method of human-computer interaction. We proposed a CNN-IndRNN (Convolutional Neural Network-Independent Recurrent Neural Network) hybrid algorithm to analyse sEMG signals and classify hand gestures. Ninapro’s dataset of 10 volunteers was used to develop the model, and by using only one time-domain feature (root mean square of sEMG), an average accuracy of 87.43% on 18 gestures is achieved. The proposed algorithm obtains a state-of-the-art classification performance with a significantly reduced model. In order to verify the robustness of the CNN-IndRNN model, a compact real¬time recognition system was constructed. The system was based on open-source hardware (OpenBCI) and a custom Python-based software. Results show that the 10-subject rock-paper-scissors gesture recognition accuracy reaches 99.1%.

scholarly journals SmokeNet: Satellite Smoke Scene Detection Using Convolutional Neural Network with Spatial and Channel-Wise Attention

2019 ◽  
Vol 11 (14) ◽  
pp. 1702 ◽  
Author(s):  
Rui Ba ◽  
Chen Chen ◽  
Jing Yuan ◽  
Weiguo Song ◽  
Siuming Lo
Keyword(s):  
Neural Network ◽  
Satellite Imagery ◽  
Large Scale ◽  
State Of The Art ◽  
Kappa Coefficient ◽  
Feature Representation ◽  
Detection Methods ◽  
Smoke Detection ◽  
Training Images ◽  
Moderate Resolution Imaging Spectroradiometer

A variety of environmental analysis applications have been advanced by the use of satellite remote sensing. Smoke detection based on satellite imagery is imperative for wildfire detection and monitoring. However, the commonly used smoke detection methods mainly focus on smoke discrimination from a few specific classes, which reduces their applicability in different regions of various classes. To this end, in this paper, we present a new large-scale satellite imagery smoke detection benchmark based on Moderate Resolution Imaging Spectroradiometer (MODIS) data, namely USTC_SmokeRS, consisting of 6225 satellite images from six classes (i.e., cloud, dust, haze, land, seaside, and smoke) and covering various areas/regions over the world. To build a baseline for smoke detection in satellite imagery, we evaluate several state-of-the-art deep learning-based image classification models. Moreover, we propose a new convolution neural network (CNN) model, SmokeNet, which incorporates spatial and channel-wise attention in CNN to enhance feature representation for scene classification. The experimental results of our method using different proportions (16%, 32%, 48%, and 64%) of training images reveal that our model outperforms other approaches with higher accuracy and Kappa coefficient. Specifically, the proposed SmokeNet model trained with 64% training images achieves the best accuracy of 92.75% and Kappa coefficient of 0.9130. The model trained with 16% training images can also improve the classification accuracy and Kappa coefficient by at least 4.99% and 0.06, respectively, over the state-of-the-art models.

scholarly journals An Enhanced Image Segmentation From 3D to 2D by Using Modified Neural Network

2021 ◽  
Vol 9 (12) ◽  
pp. 1712-1716
Author(s):  
P. Pushpalatha
Keyword(s):  
Neural Network ◽  
Optical Coherence Tomography Angiography ◽  
State Of The Art ◽  
Vascular Networks ◽  
Clinical Routine ◽  
Non Invasive ◽  
Previous State ◽  
Visualization Techniques ◽  
High Pass ◽  
Data Acquisition Process

Abstract: Optical coherence tomography angiography (OCTA) is an imaging which can applied in ophthalmology to provide detailed visualization of the perfusion of vascular networks in the eye. compared to previous state of the art dye-based imaging, such as fluorescein angiography. OCTA is non-invasive, time efficient, and it allows for the examination of retinal vascular in 3D. These advantage of the technique combined with the good usability in commercial devices led to a quick adoption of the new modality in the clinical routine. However, the interpretation of OCTA data is not without problems commonly observed image artifacts and the quite involved algorithmic details of OCTA signal construction can make the clinical assessment of OCTA exams challenging. In this paper we describe the technical background of OCTA and discuss the data acquisition process, common image visualization techniques, as well as 3D to 2D projection using high pass filtering, relu function and convolution neural network (CNN) for more accuracy and segmentation results.

scholarly journals CoCoX: Generating Conceptual and Counterfactual Explanations via Fault-Lines

2020 ◽  
Vol 34 (03) ◽  
pp. 2594-2601
Author(s):  
Arjun Akula ◽  
Shuai Wang ◽  
Song-Chun Zhu
Keyword(s):  
Neural Network ◽  
State Of The Art ◽  
Input Image ◽  
Classification Model ◽  
Learning Models ◽  
Fault Line ◽  
Semantic Level ◽  
Explainable Ai ◽  
Fault Lines ◽  
Classification Category

We present CoCoX (short for Conceptual and Counterfactual Explanations), a model for explaining decisions made by a deep convolutional neural network (CNN). In Cognitive Psychology, the factors (or semantic-level features) that humans zoom in on when they imagine an alternative to a model prediction are often referred to as fault-lines. Motivated by this, our CoCoX model explains decisions made by a CNN using fault-lines. Specifically, given an input image I for which a CNN classification model M predicts class cpred, our fault-line based explanation identifies the minimal semantic-level features (e.g., stripes on zebra, pointed ears of dog), referred to as explainable concepts, that need to be added to or deleted from I in order to alter the classification category of I by M to another specified class calt. We argue that, due to the conceptual and counterfactual nature of fault-lines, our CoCoX explanations are practical and more natural for both expert and non-expert users to understand the internal workings of complex deep learning models. Extensive quantitative and qualitative experiments verify our hypotheses, showing that CoCoX significantly outperforms the state-of-the-art explainable AI models. Our implementation is available at https://github.com/arjunakula/CoCoX

scholarly journals Image Restoration by Learning Morphological Opening-Closing Network

2020 ◽  
Vol 4 (1) ◽  
pp. 87-107
Author(s):  
Ranjan Mondal ◽  
Moni Shankar Dey ◽  
Bhabatosh Chanda
Keyword(s):  
Neural Network ◽  
Image Restoration ◽  
State Of The Art ◽  
Source Code ◽  
Back Propagation ◽  
Image Features ◽  
Main Difficulty ◽  
The Right ◽  
Right Order ◽  
Morphological Opening

AbstractMathematical morphology is a powerful tool for image processing tasks. The main difficulty in designing mathematical morphological algorithm is deciding the order of operators/filters and the corresponding structuring elements (SEs). In this work, we develop morphological network composed of alternate sequences of dilation and erosion layers, which depending on learned SEs, may form opening or closing layers. These layers in the right order along with linear combination (of their outputs) are useful in extracting image features and processing them. Structuring elements in the network are learned by back-propagation method guided by minimization of the loss function. Efficacy of the proposed network is established by applying it to two interesting image restoration problems, namely de-raining and de-hazing. Results are comparable to that of many state-of-the-art algorithms for most of the images. It is also worth mentioning that the number of network parameters to handle is much less than that of popular convolutional neural network for similar tasks. The source code can be found here https://github.com/ranjanZ/Mophological-Opening-Closing-Net

scholarly journals SketchGNN: Semantic Sketch Segmentation with Graph Neural Networks

2021 ◽  
Vol 40 (3) ◽  
pp. 1-13
Author(s):  
Lumin Yang ◽  
Jiajie Zhuang ◽  
Hongbo Fu ◽  
Xiangzhi Wei ◽  
Kun Zhou ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Large Scale ◽  
State Of The Art ◽  
Semantic Segmentation ◽  
Structure Information ◽  
Graph Neural Networks ◽  
Node Labels ◽  
Point Level

We introduce SketchGNN , a convolutional graph neural network for semantic segmentation and labeling of freehand vector sketches. We treat an input stroke-based sketch as a graph with nodes representing the sampled points along input strokes and edges encoding the stroke structure information. To predict the per-node labels, our SketchGNN uses graph convolution and a static-dynamic branching network architecture to extract the features at three levels, i.e., point-level, stroke-level, and sketch-level. SketchGNN significantly improves the accuracy of the state-of-the-art methods for semantic sketch segmentation (by 11.2% in the pixel-based metric and 18.2% in the component-based metric over a large-scale challenging SPG dataset) and has magnitudes fewer parameters than both image-based and sequence-based methods.

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