scholarly journals Quantifying Vision through Language Demonstrates that Visionary Ideas Come from the Periphery

2021 ◽  
Author(s):  
Paul Vicinanza ◽  
Amir Goldberg ◽  
Sameer Srivastava
Keyword(s):  
Deep Learning ◽  
Technical Innovation ◽  
The World ◽  
Novel Method

Where do visionary ideas come from? Although the products of vision as manifested in technical innovation are readily observed, the ideas that eventually change the world are often obscured. Here we develop a novel method that uses deep learning to identify visionary ideas from the language used by individuals and groups. Quantifying vision this way unearths prescient ideas, individuals, and documents that prevailing methods would fail to detect. Applying our model to corpora spanning the disparate worlds of politics, law, and business, we demonstrate that it reliably detects vision in each domain. Moreover, counter to many prevailing intuitions, vision emanates from each domain’s periphery rather than its center. These findings suggest that vision may be as much as property of contexts as of individuals.

scholarly journals Deep Malaria Parasite Detection in Thin Blood Smear Microscopic Images

2021 ◽  
Vol 11 (5) ◽  
pp. 2284
Author(s):  
Asma Maqsood ◽  
Muhammad Shahid Farid ◽  
Muhammad Hassan Khan ◽  
Marcin Grzegorzek
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Red Blood Cells ◽  
Convolutional Neural Networks ◽  
Blood Cells ◽  
Superior Performance ◽  
Learning Models ◽  
Infected Female ◽  
The World ◽  
Augmentation Techniques

Malaria is a disease activated by a type of microscopic parasite transmitted from infected female mosquito bites to humans. Malaria is a fatal disease that is endemic in many regions of the world. Quick diagnosis of this disease will be very valuable for patients, as traditional methods require tedious work for its detection. Recently, some automated methods have been proposed that exploit hand-crafted feature extraction techniques however, their accuracies are not reliable. Deep learning approaches modernize the world with their superior performance. Convolutional Neural Networks (CNN) are vastly scalable for image classification tasks that extract features through hidden layers of the model without any handcrafting. The detection of malaria-infected red blood cells from segmented microscopic blood images using convolutional neural networks can assist in quick diagnosis, and this will be useful for regions with fewer healthcare experts. The contributions of this paper are two-fold. First, we evaluate the performance of different existing deep learning models for efficient malaria detection. Second, we propose a customized CNN model that outperforms all observed deep learning models. It exploits the bilateral filtering and image augmentation techniques for highlighting features of red blood cells before training the model. Due to image augmentation techniques, the customized CNN model is generalized and avoids over-fitting. All experimental evaluations are performed on the benchmark NIH Malaria Dataset, and the results reveal that the proposed algorithm is 96.82% accurate in detecting malaria from the microscopic blood smears.

Deep Learning based Thermal Crack Detection on Structural Concrete Exposed to Elevated Temperature

2021 ◽  
pp. 136943322098663
Author(s):  
Diana Andrushia A ◽  
Anand N ◽  
Eva Lubloy ◽  
Prince Arulraj G
Keyword(s):  
Deep Learning ◽  
Crack Detection ◽  
Detection Method ◽  
Concrete Structures ◽  
Vital Role ◽  
Subjective Knowledge ◽  
Thermal Cracks ◽  
Reinforced Cement ◽  
Novel Method ◽  
Human Inspection

Health monitoring of concrete including, detecting defects such as cracking, spalling on fire affected concrete structures plays a vital role in the maintenance of reinforced cement concrete structures. However, this process mostly uses human inspection and relies on subjective knowledge of the inspectors. To overcome this limitation, a deep learning based automatic crack detection method is proposed. Deep learning is a vibrant strategy under computer vision field. The proposed method consists of U-Net architecture with an encoder and decoder framework. It performs pixel wise classification to detect the thermal cracks accurately. Binary Cross Entropy (BCA) based loss function is selected as the evaluation function. Trained U-Net is capable of detecting major thermal cracks and minor thermal cracks under various heating durations. The proposed, U-Net crack detection is a novel method which can be used to detect the thermal cracks developed on fire exposed concrete structures. The proposed method is compared with the other state-of-the-art methods and found to be accurate with 78.12% Intersection over Union (IoU).

scholarly journals A Novel Method of Billing System Using Deep Learning

2021 ◽  
Vol 1084 (1) ◽  
pp. 012035
Author(s):  
J Haritha ◽  
K Prakash ◽  
B Navina ◽  
S Saveetha
Keyword(s):  
Deep Learning ◽  
Billing System ◽  
Novel Method

Learning About the World by Learning About Images

2021 ◽  
pp. 096372142199033
Author(s):  
Katherine R. Storrs ◽  
Roland W. Fleming
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Visual Processing ◽  
Visual Experience ◽  
Natural Images ◽  
Sensory Coding ◽  
The World ◽  
Unsupervised Deep Learning ◽  
Statistical Regularities ◽  
Sensory Encoding

One of the deepest insights in neuroscience is that sensory encoding should take advantage of statistical regularities. Humans’ visual experience contains many redundancies: Scenes mostly stay the same from moment to moment, and nearby image locations usually have similar colors. A visual system that knows which regularities shape natural images can exploit them to encode scenes compactly or guess what will happen next. Although these principles have been appreciated for more than 60 years, until recently it has been possible to convert them into explicit models only for the earliest stages of visual processing. But recent advances in unsupervised deep learning have changed that. Neural networks can be taught to compress images or make predictions in space or time. In the process, they learn the statistical regularities that structure images, which in turn often reflect physical objects and processes in the outside world. The astonishing accomplishments of unsupervised deep learning reaffirm the importance of learning statistical regularities for sensory coding and provide a coherent framework for how knowledge of the outside world gets into visual cortex.

A novel method based on machine vision system and deep learning to detect fraud in turmeric powder

2021 ◽  
pp. 104728
Author(s):  
Ahmad Jahanbakhshi ◽  
Yousef Abbaspour-Gilandeh ◽  
Kobra Heidarbeigi ◽  
Mohammad Momeny
Keyword(s):  
Deep Learning ◽  
Machine Vision ◽  
Vision System ◽  
Machine Vision System ◽  
Novel Method

Machine Learning as a Silent Observer of Advanced Geoscience Interpretation

2021 ◽  
Author(s):  
Muhammad Sajid
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Transfer Learning ◽  
Learning Performance ◽  
Learning Approach ◽  
Seismic Fault ◽  
Facies Classification ◽  
The World ◽  
Learning Machine ◽  
Fault Interpretation

Abstract Machine learning is proving its successes in all fields of life including medical, automotive, planning, engineering, etc. In the world of geoscience, ML showed impressive results in seismic fault interpretation, advance seismic attributes analysis, facies classification, and geobodies extraction such as channels, carbonates, and salt, etc. One of the challenges faced in geoscience is the availability of label data which is one of the most time-consuming requirements in supervised deep learning. In this paper, an advanced learning approach is proposed for geoscience where the machine observes the seismic interpretation activities and learns simultaneously as the interpretation progresses. Initial testing showed that through the proposed method along with transfer learning, machine learning performance is highly effective, and the machine accurately predicts features requiring minor post prediction filtering to be accepted as the optimal interpretation.

scholarly journals Technical Note: Quantifying music-dance synchrony with the application of a deep learning-based 2D pose estimator

2020 ◽  
Author(s):  
Filip Potempski ◽  
Andrea Sabo ◽  
Kara K Patterson
Keyword(s):  
Deep Learning ◽  
Motor Performance ◽  
Data Extraction ◽  
Effective Means ◽  
Technical Note ◽  
Novel Method ◽  
Synchronous Condition ◽  
The Mean ◽  
Repeated Training

AbstractDance interventions are more effective at improving gait and balance outcomes than other rehabilitation interventions. Repeated training may culminate in superior motor performance compared to other interventions without synchronization. This technical note will describe a novel method using a deep learning-based 2D pose estimator: OpenPose, alongside beat analysis of music to quantify movement-music synchrony during salsa dancing. This method has four components: i) camera setup and recording, ii) tempo/downbeat analysis and waveform cleanup, iii) OpenPose estimation and data extraction, and iv) synchronization analysis. Two trials were recorded: one in which the dancer danced synchronously to the music and one where they did not. The salsa dancer performed a solo basic salsa step continuously for 90 seconds to a salsa track while their movements and the music were recorded with a webcam. This data was then extracted from OpenPose and analyzed. The mean synchronization value for both feet was significantly lower in the synchronous condition than the asynchronous condition, indicating that this is an effective means to track and quantify a dancer’s movement and synchrony while performing a basic salsa step.

scholarly journals The Strasbourg Visual Scale: A Novel Method to Assess Visual Hallucinations

2021 ◽  
Vol 12 ◽  
Author(s):  
Anne Giersch ◽  
Thomas Huard ◽  
Sohee Park ◽  
Cherise Rosen
Keyword(s):  
Visual Information ◽  
Subjective Experience ◽  
Physical Property ◽  
Visual Hallucinations ◽  
Visual Scale ◽  
The World ◽  
Novel Method ◽  
Internal Experience ◽  
The Relationship ◽  
Disorders Of The Self

The experience of oneself in the world is based on sensory afferences, enabling us to reach a first-perspective perception of our environment and to differentiate oneself from the world. Visual hallucinations may arise from a difficulty in differentiating one's own mental imagery from externally-induced perceptions. To specify the relationship between hallucinations and the disorders of the self, we need to understand the mechanisms of hallucinations. However, visual hallucinations are often under reported in individuals with psychosis, who sometimes appear to experience difficulties describing them. We developed the “Strasbourg Visual Scale (SVS),” a novel computerized tool that allows us to explore and capture the subjective experience of visual hallucinations by circumventing the difficulties associated with verbal descriptions. This scale reconstructs the hallucinated image of the participants by presenting distinct physical properties of visual information, step-by-step to help them communicate their internal experience. The strategy that underlies the SVS is to present a sequence of images to the participants whose choice at each step provides a feedback toward re-creating the internal image held by them. The SVS displays simple images on a computer screen that provide choices for the participants. Each step focuses on one physical property of an image, and the successive choices made by the participants help them to progressively build an image close to his/her hallucination, similar to the tools commonly used to generate facial composites. The SVS was constructed based on our knowledge of the visual pathways leading to an integrated perception of our environment. We discuss the rationale for the successive steps of the scale, and to which extent it could complement existing scales.

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