Growing Neural Forest-Based Color Quantization Applied to RGB Images

2017 ◽  
Vol 7 (3) ◽  
pp. 13-25
Author(s):  
Jesús Benito-Picazo ◽  
Ezequiel López-Rubio ◽  
Enrique Domínguez
Keyword(s):  
Data Streams ◽  
Image Data ◽  
Color Quantization ◽  
Perceptual Distortion ◽  
Self Organized ◽  
Novel Approach ◽  
Image Quantization ◽  
Rgb Images ◽  
Storage Technologies ◽  
Future Work

Although last improvements in both physical storage technologies and image handling techniques have eased image managing processes, the large amount of information handled nowadays constantly demands more efficient ways to store and transmit image data streams. Among other alternatives for such purpose, the authors find color quantization, which consists of color indexing for minimal perceptual distortion image compression. In this context, artificial intelligence-based algorithms and more specifically, Artificial Neural Networks, have been consolidated as a powerful tool for unsupervised tasks, and therefore, for color quantization purposes. In this work, a novel approach to color quantization is presented based on the Growing Neural Forest (GNF), which is a Growing Neural Gas (GNG) variation where a set of trees is learnt instead of a general graph. Experimental results support the use of GNF for image quantization tasks where it overcomes other self-organized models including SOM, GHSOM and GNG. Future work will include more datasets and different competitive models to compare to.

A FIRST CRYPTOSYSTEM FOR SECURITY OF TWO-DIMENSIONAL DATA

Fractals ◽  
2017 ◽  
Vol 25 (01) ◽  
pp. 1750011
Author(s):  
D. C. MISHRA ◽  
HIMANI SHARMA ◽  
R. K. SHARMA ◽  
NAVEEN KUMAR
Keyword(s):  
Security Analysis ◽  
Image Data ◽  
Secret Message ◽  
Two Dimensional ◽  
Gray Scale ◽  
Text Data ◽  
Text Document ◽  
Novel Approach ◽  
Encryption And Decryption ◽  
Rgb Images

In this paper, we present a novel technique for security of two-dimensional data with the help of cryptography and steganography. The presented approach provides multilayered security of two-dimensional data. First layer security was developed by cryptography and second layer by steganography. The advantage of steganography is that the intended secret message does not attract attention to itself as an object of scrutiny. This paper proposes a novel approach for encryption and decryption of information in the form of Word Data (.doc file), PDF document (.pdf file), Text document, Gray-scale images, and RGB images, etc. by using Vigenere Cipher (VC) associated with Discrete Fourier Transform (DFT) and then hiding the data behind the RGB image (i.e. steganography). Earlier developed techniques provide security of either PDF data, doc data, text data or image data, but not for all types of two-dimensional data and existing techniques used either cryptography or steganography for security. But proposed approach is suitable for all types of data and designed for security of information by cryptography and steganography. The experimental results for Word Data, PDF document, Text document, Gray-scale images and RGB images support the robustness and appropriateness for secure transmission of these data. The security analysis shows that the presented technique is immune from cryptanalytic. This technique further provides security while decryption as a check on behind which RGB color the information is hidden.

Video Semantic Analysis

2021 ◽  
Vol 11 (2) ◽  
pp. 1-21
Author(s):  
Daniel Danso Essel ◽  
Ben-Bright Benuwa ◽  
Benjamin Ghansah
Keyword(s):  
Dictionary Learning ◽  
Semantic Analysis ◽  
Learning Algorithm ◽  
Recognition Rate ◽  
Image Data ◽  
Video Data ◽  
The Novel ◽  
Video Feature ◽  
Novel Approach ◽  
Video Semantic Analysis

Sparse Representation (SR) and Dictionary Learning (DL) based Classifier have shown promising results in classification tasks, with impressive recognition rate on image data. In Video Semantic Analysis (VSA) however, the local structure of video data contains significant discriminative information required for classification. To the best of our knowledge, this has not been fully explored by recent DL-based approaches. Further, similar coding findings are not being realized from video features with the same video category. Based on the foregoing, a novel learning algorithm, Sparsity based Locality-Sensitive Discriminative Dictionary Learning (SLSDDL) for VSA is proposed in this paper. In the proposed algorithm, a discriminant loss function for the category based on sparse coding of the sparse coefficients is introduced into structure of Locality-Sensitive Dictionary Learning (LSDL) algorithm. Finally, the sparse coefficients for the testing video feature sample are solved by the optimized method of SLSDDL and the classification result for video semantic is obtained by minimizing the error between the original and reconstructed samples. The experimental results show that, the proposed SLSDDL significantly improves the performance of video semantic detection compared with state-of-the-art approaches. The proposed approach also shows robustness to diverse video environments, proving the universality of the novel approach.

scholarly journals Measuring Vascular Recovery Rate After Exercise

Proceedings ◽  
2018 ◽  
Vol 4 (1) ◽  
pp. 12 ◽  
Author(s):  
Halil Dijab ◽  
Jordi Alastruey ◽  
Peter Charlton
Keyword(s):  
Heart Rate ◽  
Recovery Rate ◽  
Wearable Sensors ◽  
Cardiovascular Fitness ◽  
Wave Shape ◽  
Future Studies ◽  
Additional Information ◽  
Novel Approach ◽  
Pulse Waves ◽  
Future Work

The rate at which an individual recovers from exercise is known to be indicative of cardiovascular risk. It has been widely shown that the reduction in heart rate immediately after exercise is predictive of mortality. However, little research has been conducted into whether the time taken for the blood vessels to return to normal is also indicative of risk. In this study, we present a novel approach to assess vascular recovery rate (VRR) using the photoplethysmogram (PPG) signal, which is monitored by smart wearables. The VORTAL dataset (http://peterhcharlton.github.io/RRest/) was used for this study, containing PPG signals from 39 healthy subjects before (baseline) and after exercise. 31 VRR indices were extracted from the PPG pulse wave shape, as well as heart rate for comparison. The rate at which indices returned to baseline after exercise was quantified, and the consistency of changes between subjects was assessed statistically. Many VRR indices exhibited changes after exercise which were consistent between subjects. Indices derived from the timings and second derivative of pulse waves were identified as candidates for future work. The rate at which the indices returned to baseline differed between indices and subjects, indicating that they may provide additional information beyond that of heart rate, and that they may be useful for stratifying subjects. This study demonstrated the feasibility of assessing VRR after exercise from the PPG. Future studies should investigate whether VRR indices are associated with cardiovascular fitness, and the potential utility of incorporating the indices into wearable sensors.

scholarly journals Money: An Integrated Review and Synthesis From a Psychological Perspective

2020 ◽  
Vol 24 (2) ◽  
pp. 172-190 ◽  
Author(s):  
Xijing Wang ◽  
Zhansheng Chen ◽  
Eva G. Krumhuber
Keyword(s):  
Empirical Studies ◽  
Goal Pursuit ◽  
Self Esteem ◽  
Psychological Perspective ◽  
Review Of The Literature ◽  
Social Distinction ◽  
Novel Approach ◽  
Broad Concept ◽  
Underlying Mechanisms ◽  
Future Work

Many empirical studies have demonstrated the psychological effects of various aspects of money, including the aspiration for money, mere thoughts about money, possession of money, and placement of people in economic contexts. Although multiple aspects of money and varied methodologies have been focused on and implemented, the underlying mechanisms of the empirical findings from these seemingly isolated areas significantly overlap. In this article, we operationalize money as a broad concept and take a novel approach by providing an integrated review of the literature and identifying five major streams of mechanisms: (a) self-focused behavior; (b) inhibited other-oriented behavior; (c) favoring of a self–other distinction; (d) money’s relationship with self-esteem and self-efficacy; and (e) goal pursuit, objectification, outcome maximization, and unethicality. Moreover, we propose a unified psychological perspective for the future—money as an embodiment of social distinction—which could potentially account for past findings and generate future work.

scholarly journals Using Data Mining in Educational Administration: A Case Study on Improving School Attendance

2020 ◽  
Vol 10 (9) ◽  
pp. 3116 ◽  
Author(s):  
Raymond Moodley ◽  
Francisco Chiclana ◽  
Jenny Carter ◽  
Fabio Caraffini
Keyword(s):  
School Leadership ◽  
Educational Administration ◽  
School Attendance ◽  
Novel Approach ◽  
Negative Impacts ◽  
Using Data ◽  
The Uk ◽  
Leadership Research ◽  
Future Work

Pupil absenteeism remains a significant problem for schools across the globe with negative impacts on overall pupil performance being well-documented. Whilst all schools continue to emphasize good attendance, some schools still find it difficult to reach the required average attendance, which in the UK is 96%. A novel approach is proposed to help schools improve attendance that leverages the market target model, which is built on association rule mining and probability theory, to target sessions that are most impactful to overall poor attendance. Tests conducted at Willen Primary School, in Milton Keynes, UK, showed that significant improvements can be made to overall attendance, attendance in the target session, and persistent (chronic) absenteeism, through the use of this approach. The paper concludes by discussing school leadership, research implications, and highlights future work which includes the development of a software program that can be rolled-out to other schools.

scholarly journals Real-Time Fruit Recognition and Grasping Estimation for Robotic Apple Harvesting

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5670
Author(s):  
Hanwen Kang ◽  
Hongyu Zhou ◽  
Xing Wang ◽  
Chao Chen
Keyword(s):  
Image Data ◽  
Point Clouds ◽  
Robotic Arm ◽  
Depth Information ◽  
Agricultural Industry ◽  
Weight One ◽  
Rgb Images ◽  
Robotic Harvesting ◽  
Instance Segmentation ◽  
Indoor And Outdoor

Robotic harvesting shows a promising aspect in future development of agricultural industry. However, there are many challenges which are still presented in the development of a fully functional robotic harvesting system. Vision is one of the most important keys among these challenges. Traditional vision methods always suffer from defects in accuracy, robustness, and efficiency in real implementation environments. In this work, a fully deep learning-based vision method for autonomous apple harvesting is developed and evaluated. The developed method includes a light-weight one-stage detection and segmentation network for fruit recognition and a PointNet to process the point clouds and estimate a proper approach pose for each fruit before grasping. Fruit recognition network takes raw inputs from RGB-D camera and performs fruit detection and instance segmentation on RGB images. The PointNet grasping network combines depth information and results from the fruit recognition as input and outputs the approach pose of each fruit for robotic arm execution. The developed vision method is evaluated on RGB-D image data which are collected from both laboratory and orchard environments. Robotic harvesting experiments in both indoor and outdoor conditions are also included to validate the performance of the developed harvesting system. Experimental results show that the developed vision method can perform highly efficient and accurate to guide robotic harvesting. Overall, the developed robotic harvesting system achieves 0.8 on harvesting success rate and cycle time is 6.5 s.

Network Anomaly Analysis using the Microsoft HoloLens

2018 ◽  
Vol 62 (1) ◽  
pp. 2094-2098
Author(s):  
Steve Beitzel ◽  
Josiah Dykstra ◽  
Paul Toliver ◽  
Jason Youzwak
Keyword(s):  
User Study ◽  
Mixed Reality ◽  
Eye Gaze ◽  
Lessons Learned ◽  
Time Dimension ◽  
Desktop Computer ◽  
Novel Approach ◽  
Anomaly Analysis ◽  
Microsoft Hololens ◽  
Future Work

We investigate the feasibility of using Microsoft HoloLens, a mixed reality device, to visually analyze network capture data and locate anomalies. We developed MINER, a prototype application to visualize details from network packet captures as 3D stereogram charts. MINER employs a novel approach to time-series visualization that extends the time dimension across two axes, thereby taking advantage of the immersive 3D space available via the HoloLens. Users navigate the application through eye gaze and hand gestures to view summary and detailed bar graphs. Callouts display additional detail based on the user’s immediate gaze. In a user study, volunteers used MINER to locate network attacks in a dataset from the 2013 VAST Challenge. We compared the time and effort with a similar test using traditional tools on a desktop computer. Our findings suggest that network anomaly analysis with the HoloLens achieved comparable effectiveness, efficiency and satisfaction. We describe user metrics and feedback collected from these experiments; lessons learned and suggested future work.

scholarly journals LPG Representation of the Reification of RDF

2018 ◽  
Vol 7 (3.34) ◽  
pp. 562 ◽  
Author(s):  
Zhanfang Zaho ◽  
Sung Kook Han ◽  
Ju Ri Kim
Keyword(s):  
Data Model ◽  
Graph Data ◽  
Semantic Relationships ◽  
Resource Modeling ◽  
Novel Approach ◽  
Flexible Resource ◽  
Complex Structural ◽  
Complex Knowledge ◽  
Rdf Graphs ◽  
Future Work

Background/Objectives: It is still a challenging issue to represent the reification effectively since the reification representation of RDF standard has been revealed some drawbacks.Methods/Statistical analysis: Currently, there are two main graph data models: RDF and LPG. LPG is a popular graph data model that is usually applied to NoSQL graph databases.This paper derives three types of the reification structures in terms of the structural and semantic relationships of the reification statements. The detailed representation of each type of the reification is presented with the extended LPG model.Findings: This paper proposes a novel approach to represent the reification structure of RDF from the perspective of LPG. The paper explores the formal, conceptual properties of the conventional LPG models and proposes their extension to capture more complex knowledge structures efficiently. These augmentations of LPG can achieve more efficient and flexible resource modeling. This paper derives three types of the reification structures in terms of the structural and semantic relationships of the reification statements: assertion, quantification, and entailment.The proposed approach not only preserves the structure and semantics of the reification but also enables LPG modeling of the complex structural statements to be easy and intuitive.This can contribute to transfer RDF graphs into LPGs.Improvements/Applications: The implementation of the extended LPG and the query processing of the reification remain future work. 

scholarly journals Open Plant Phenotype Database of Common Weeds in Denmark

2020 ◽  
Vol 12 (8) ◽  
pp. 1246 ◽  
Author(s):  
Simon Leminen Madsen ◽  
Solvejg Kopp Mathiassen ◽  
Mads Dyrmann ◽  
Morten Stigaard Laursen ◽  
Laura-Carlota Paz ◽  
...  
Keyword(s):  
Plant Species ◽  
Growth Conditions ◽  
Research Centre ◽  
Species Classification ◽  
Visual Appearance ◽  
Plant Phenotype ◽  
Rgb Images ◽  
The Individual ◽  
High Degree ◽  
Future Work

For decades, significant effort has been put into the development of plant detection and classification algorithms. However, it has been difficult to compare the performance of the different algorithms, due to the lack of a common testbed, such as a public available annotated reference dataset. In this paper, we present the Open Plant Phenotype Database (OPPD), a public dataset for plant detection and plant classification. The dataset contains 7590 RGB images of 47 plant species. Each species is cultivated under three different growth conditions, to provide a high degree of diversity in terms of visual appearance. The images are collected at the semifield area at Aarhus University, Research Centre Flakkebjerg, Denmark, using a customized data acquisition platform that provides well-illuminated images with a ground resolution of ∼6.6 px mm − 1 . All images are annotated with plant species using the EPPO encoding system, bounding box annotations for detection and extraction of individual plants, applied growth conditions and time passed since seeding. Additionally, the individual plants have been tracked temporally and given unique IDs. The dataset is accompanied by two experiments for: (1) plant instance detection and (2) plant species classification. The experiments introduce evaluation metrics and methods for the two tasks and provide baselines for future work on the data.

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