Awarded presentation of my research activity, PhD Day 2011, February 23th 2011, Cagliari, Italy.
This presentation has been awarded as the best one of the track on information engineering.
Want to know more?
see my publications at
http://prag.diee.unica.it/pra/ita/people/satta
FUZZY SET THEORETIC APPROACH TO IMAGE THRESHOLDINGIJCSEA Journal
Thresholding is a fast, popular and computationally inexpensive segmentation technique that is always critical and decisive in some image processing applications. The result of image thresholding is not always satisfactory because of the presence of noise and vagueness and ambiguity among the classes. Since the theory of fuzzy sets is a generalization of the classical set theory, it has greater flexibility to capture faithfully the various aspects of incompleteness or imperfectness in information of situation. To overcome this problem, in this paper we proposed a two-stage fuzzy set theoretic approach to image thresholding utilizing the measure of fuzziness to evaluate the fuzziness of an image and to determine an adequate threshold value. At first, images are preprocessed to reduce noise without any loss of image details by fuzzy rule-based filtering and then in the final stage a suitable threshold is determined with the help of a fuzziness measure as a criterion function. Experimental results on test images have demonstrated the effectiveness of this method.
Semantic Video Segmentation with Using Ensemble of Particular Classifiers and...ITIIIndustries
A new approach based on the use of a deep neural network and an ensemble of particular classifiers is proposed. This approach is based on use of the novel block of fuzzy generalization for combines classes of objects into semantic groups, each of which corresponds to one or more particular classifiers. As result of processing, the sequence of frames is converted into the annotation of the event occurring in the video for a certain time interval
Awarded presentation of my research activity, PhD Day 2011, February 23th 2011, Cagliari, Italy.
This presentation has been awarded as the best one of the track on information engineering.
Want to know more?
see my publications at
http://prag.diee.unica.it/pra/ita/people/satta
FUZZY SET THEORETIC APPROACH TO IMAGE THRESHOLDINGIJCSEA Journal
Thresholding is a fast, popular and computationally inexpensive segmentation technique that is always critical and decisive in some image processing applications. The result of image thresholding is not always satisfactory because of the presence of noise and vagueness and ambiguity among the classes. Since the theory of fuzzy sets is a generalization of the classical set theory, it has greater flexibility to capture faithfully the various aspects of incompleteness or imperfectness in information of situation. To overcome this problem, in this paper we proposed a two-stage fuzzy set theoretic approach to image thresholding utilizing the measure of fuzziness to evaluate the fuzziness of an image and to determine an adequate threshold value. At first, images are preprocessed to reduce noise without any loss of image details by fuzzy rule-based filtering and then in the final stage a suitable threshold is determined with the help of a fuzziness measure as a criterion function. Experimental results on test images have demonstrated the effectiveness of this method.
Semantic Video Segmentation with Using Ensemble of Particular Classifiers and...ITIIIndustries
A new approach based on the use of a deep neural network and an ensemble of particular classifiers is proposed. This approach is based on use of the novel block of fuzzy generalization for combines classes of objects into semantic groups, each of which corresponds to one or more particular classifiers. As result of processing, the sequence of frames is converted into the annotation of the event occurring in the video for a certain time interval
Algorithmic Analysis to Video Object Tracking and Background Segmentation and...Editor IJCATR
Video object tracking and segmentation are the fundamental building blocks for smart surveillance
system. Various algorithms like partial least square analysis, Markov model, Temporal differencing,
background subtraction algorithm, adaptive background updating have been proposed but each were having
drawbacks like object tracking problem, multibackground congestion, illumination changes, occlusion etc.
The background segmentation worked on to principled object tracking by using two models Gaussian mixture
model and level centre model. Wavelet transforms have been one of the important signal processing
developments, especially for the applications such as time-frequency analysis, data compression,
segmentation and vision. The key idea of the wavelet transform approach is to represents any arbitrary
function f (t) as a superposition of a set of such wavelets or basis functions. Results show that algorithm
performs well to remove occlusion and multibackground congestion as well as algorithm worked with
removal of noise in the signals
Synthetic training data for deep cn ns in reidentificationAbdulrahman Kerim
Barbosa IB, Cristani M, Caputo B, Rognhaugen A, Theoharis T. Looking beyond appearances: Synthetic training data for deep cnns in re-identification. Computer Vision and Image Understanding. 2018 Feb 1;167:50-62.
DWT-SVD BASED SECURED IMAGE WATERMARKING FOR COPYRIGHT PROTECTION USING VISUA...cscpconf
In this paper, a new robust watermarking technique for copyright protection based on Discrete
Wavelet Transform and Singular Value Decomposition is proposed. The high frequency subband
of the wavelet decomposed cover image is modified by modifying its singular values. A secret key
is generated from the original watermark with the help of visual cryptography to claim the
ownership of the image. The ownership of the image can be claimed by superimposing this secret
key on the extracted watermark from the watermarked image. The robustness of the technique is
tested by applying different attacks and the visual quality of the extracted watermark after
applying these attacks is good. Also, the visual quality of the watermarked image is undistinguishable from the original image.
In this paper, we present a novel iterative reconstruction algorithm for discrete tomography (DT) named total variation regularized discrete algebraic reconstruction technique (TVR-DART) with automated gray value estimation. This algorithm is more robust and automated than the original DART algorithm, and is aimed at imaging of objects consisting of only a few different material compositions, each corresponding to a different gray value in the reconstruction. By exploiting two types of prior knowledge of the scanned object simultaneously, TVR-DART solves the discrete reconstruction problem within an optimization framework inspired by compressive sensing to steer the current reconstruction toward a solution with the specified number of discrete gray values. The gray values and the thresholds are estimated as the reconstruction improves through iterations. Extensive experiments from simulated data, experimental μCT, and electron tomography data sets show that TVR-DART is capable of providing more accurate reconstruction than existing algorithms under noisy conditions from a small number of projection images and/or from a small angular range. Furthermore, the new algorithm requires less effort on parameter tuning compared with the original DART algorithm. With TVR-DART, we aim to provide the tomography society with an easy-to-use and robust algorithm for DT.
REMOVING OCCLUSION IN IMAGES USING SPARSE PROCESSING AND TEXTURE SYNTHESISIJCSEA Journal
We provide a solution to problem of occlusion in images by removing the occluding region and filling in the gap left behind. Inpainting algorithms fail in filling occlusions when the occluding region is large since there is loss of both structure and texture. We decompose the image into structure and texture images using a decomposition method based on sparseness of the image. The sparse reconstruction of the decomposed images result in an inpainted image with all the structures made intact. A texture synthesis is performed on the texture only image. Finally the structure and texture images are combined to get an image where the occlusion is filled. The performance of our algorithm in terms of visual effectiveness is compared with other algorithms used for inpainting.
Algorithmic Analysis to Video Object Tracking and Background Segmentation and...Editor IJCATR
Video object tracking and segmentation are the fundamental building blocks for smart surveillance
system. Various algorithms like partial least square analysis, Markov model, Temporal differencing,
background subtraction algorithm, adaptive background updating have been proposed but each were having
drawbacks like object tracking problem, multibackground congestion, illumination changes, occlusion etc.
The background segmentation worked on to principled object tracking by using two models Gaussian mixture
model and level centre model. Wavelet transforms have been one of the important signal processing
developments, especially for the applications such as time-frequency analysis, data compression,
segmentation and vision. The key idea of the wavelet transform approach is to represents any arbitrary
function f (t) as a superposition of a set of such wavelets or basis functions. Results show that algorithm
performs well to remove occlusion and multibackground congestion as well as algorithm worked with
removal of noise in the signals
Synthetic training data for deep cn ns in reidentificationAbdulrahman Kerim
Barbosa IB, Cristani M, Caputo B, Rognhaugen A, Theoharis T. Looking beyond appearances: Synthetic training data for deep cnns in re-identification. Computer Vision and Image Understanding. 2018 Feb 1;167:50-62.
DWT-SVD BASED SECURED IMAGE WATERMARKING FOR COPYRIGHT PROTECTION USING VISUA...cscpconf
In this paper, a new robust watermarking technique for copyright protection based on Discrete
Wavelet Transform and Singular Value Decomposition is proposed. The high frequency subband
of the wavelet decomposed cover image is modified by modifying its singular values. A secret key
is generated from the original watermark with the help of visual cryptography to claim the
ownership of the image. The ownership of the image can be claimed by superimposing this secret
key on the extracted watermark from the watermarked image. The robustness of the technique is
tested by applying different attacks and the visual quality of the extracted watermark after
applying these attacks is good. Also, the visual quality of the watermarked image is undistinguishable from the original image.
In this paper, we present a novel iterative reconstruction algorithm for discrete tomography (DT) named total variation regularized discrete algebraic reconstruction technique (TVR-DART) with automated gray value estimation. This algorithm is more robust and automated than the original DART algorithm, and is aimed at imaging of objects consisting of only a few different material compositions, each corresponding to a different gray value in the reconstruction. By exploiting two types of prior knowledge of the scanned object simultaneously, TVR-DART solves the discrete reconstruction problem within an optimization framework inspired by compressive sensing to steer the current reconstruction toward a solution with the specified number of discrete gray values. The gray values and the thresholds are estimated as the reconstruction improves through iterations. Extensive experiments from simulated data, experimental μCT, and electron tomography data sets show that TVR-DART is capable of providing more accurate reconstruction than existing algorithms under noisy conditions from a small number of projection images and/or from a small angular range. Furthermore, the new algorithm requires less effort on parameter tuning compared with the original DART algorithm. With TVR-DART, we aim to provide the tomography society with an easy-to-use and robust algorithm for DT.
REMOVING OCCLUSION IN IMAGES USING SPARSE PROCESSING AND TEXTURE SYNTHESISIJCSEA Journal
We provide a solution to problem of occlusion in images by removing the occluding region and filling in the gap left behind. Inpainting algorithms fail in filling occlusions when the occluding region is large since there is loss of both structure and texture. We decompose the image into structure and texture images using a decomposition method based on sparseness of the image. The sparse reconstruction of the decomposed images result in an inpainted image with all the structures made intact. A texture synthesis is performed on the texture only image. Finally the structure and texture images are combined to get an image where the occlusion is filled. The performance of our algorithm in terms of visual effectiveness is compared with other algorithms used for inpainting.
LA MARQUE AVEC UNE CONSCIENCE SOCIALE
La crise accélère la prise de conscience des impacts sociaux de l'activité de l'entreprise, des externalités positives ou négatives qu'elle génère, et favorise le développement d'écosystèmes gagnant/gagnant plus ou moins applicables à grande échelle. La ré-alliance des parties prenantes de l'entreprise passera en tout cas par l'ouverture de l'entreprise à des communautés d'intérêts proches de son champs d'activité mais plus ou moins éloignées de ses considérations propres. L'entreprise devient concernée par le "bien commun" surtout si elle peut porter une forme de progrès socio culturel et que les projets initiés font vivre les valeurs de la marque.
- Sandrine Plasseraud, European Business Director wearesocial , UK
- Dominique Retoux, responsable développement des agences de conseil et de services en developpement durable, groupe SOS , France
- Stéphan Arino, responsable qualité de services, Danone France
- Caroline Mitanne, présidente Guide Caro , France
- Christopher Lemoine, responsable communication, Michel et Augustin , France
- Pierre Royer, Chef de groupe du developpement durable, Castorama , France
- Pascal Cottereau, Directeur des études, mp 6 , France
- Samia Ghozlane, Responsable du Service Internet & NTIC, AFM-Téléthon, France
- Renaud Attal, Responsable développement, Full Opt'In, Paris
Pour accéder au site de PARIS 2.0 de septembre 2009 : http://www.amiando.com/strategies20aparis.htm
It is a presentation for acamedia talk about cloud computing for intelligent video surveillance, i.e. VSaaS, given in 2010. Some of our research results are also presented in this presentation.
Human Re-identification using Soft Biometrics in Video SurveillanceShengzhe Li
Ph.D. defense on "human re-identification using soft biometrics in video surveillance". The presentation includes two parts:
Part 1: simplified camera calibration
Part 2: human re-identification using soft biometrics
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Analysis and assessment software for multi-user collaborative cognitive radi...IJECEIAES
Computer simulations are without a doubt a useful methodology that allows to explore research queries and develop prototypes at lower costs and timeframes than those required in hardware processes. The simulation tools used in cognitive radio networks (CRN) are undergoing an active process. Currently, there is no stable simulator that enables to characterize every element of the cognitive cycle and the available tools are a framework for discrete-event software. This work presents the spectral mobility simulator in CRN called “App MultiColl-DCRN”, developed with MATLAB’s app designer. In contrast with other frameworks, the simulator uses real spectral occupancy data and simultaneously analyzes features regarding spectral mobility, decision-making, multi-user access, collaborative scenarios and decentralized architectures. Performance metrics include bandwidth, throughput level, number of failed handoffs, number of total handoffs, number of handoffs with interference, number of anticipated handoffs and number of perfect handoffs. The assessment of the simulator involves three scenarios: the first and second scenarios present a collaborative structure using the multi-criteria optimization and compromise solution (VIKOR) decision-making model and the naïve Bayes prediction technique respectively. The third scenario presents a multi-user structure and uses simple additive weighting (SAW) as a decision-making technique. The present development represents a contribution in the cognitive radio network field since there is currently no software with the same features.
Anomaly detection using deep learning based model with feature attentionIAESIJAI
Anomaly detection is a difficult problem with numerous industrial applications, such as analyzing the quality of objects using images. Anomaly detection is the process of identifying outliers in a given dataset. Recently, machine learning approaches to computer vision problems have outperformed classical state-of-the-art approaches. Anomaly detection problems can be solved using supervised approaches. However, labelled datasets are hard to obtain. Thus, many researchers have taken an unsupervised approach to solving the problem of anomaly detection. In this study, we use an adversarial auto encoder model as a base model and create a custom model to detect anomalies in images and videos. The model was trained exclusively on normal data. The modified national institute of standards and technology database (MNIST) dataset achieved an area under curve (AUC) score of 0.872 for anomaly detection, while the University of California San Diego (UCSD) anomaly dataset (Video dataset) achieved an AUC score of 0.74 for Ped1 and 0.87 for Ped2. To calculate the anomaly score, the concept of attention weights is combined with the reconstruction loss, and the proposed method outperformed other similar methods designed for the same problem. However, the usefulness of the proposed model was demonstrated through the detection of anomalies, and the model is still being improved for use in real-world situations.
Real Time Object Detection System with YOLO and CNN Models: A ReviewSpringer
The field of artificial intelligence is built on object detection techniques. YOU ONLY LOOK
ONCE (YOLO) algorithm and it's more evolved versions are briefly described in this research survey. This
survey is all about YOLO and convolution neural networks (CNN) in the direction of real time object detection.
YOLO does generalized object representation more effectively without precision losses than other object
detection models. CNN architecture models have the ability to eliminate highlights and identify objects in any
given image. When implemented appropriately, CNN models can address issues like deformity diagnosis,
creating educational or instructive application, etc. This article reached at number of observations and
perspective findings through the analysis. Also it provides support for the focused visual information and
feature extraction in the financial and other industries, highlights the method of target detection and feature
selection, and briefly describes the development process of yolo algorithm
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 65, NO. 3, M.docxsheronlewthwaite
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 65, NO. 3, MARCH 2018 2727
Electric Locomotive Bearing Fault Diagnosis
Using a Novel Convolutional
Deep Belief Network
Haidong Shao, Hongkai Jiang , Member, IEEE, Haizhou Zhang, and Tianchen Liang
Abstract—Bearing fault diagnosis is of significance to
enhance the reliability and security of electric locomotive.
In this paper, a novel convolutional deep belief network
(CDBN) is proposed for bearing fault diagnosis. First, an
auto-encoder is used to compress data and reduce the di-
mension. Second, a novel CDBN is constructed with Gaus-
sian visible units to learn the representative features. Third,
exponential moving average is employed to improve the
performance of the constructed deep model. The proposed
method is applied to analyze experimental signals collected
from electric locomotive bearings. The results show that
the proposed method is more effective than the traditional
methods and standard deep learning methods.
Index Terms—Convolutional deep belief network (CDBN),
electric locomotive bearing, exponential moving average
(EMA), fault diagnosis, feature learning.
NOMENCLATURE
ANFIS Adaptive neuro fuzzy inference system.
ANN Artificial neural network.
BPNN Back propagation neural network.
CDBN Convolutional deep belief network.
CNN Convolutional neural network.
CRBM Convolutional restricted Boltzmann machine.
DAE Deep auto-encoder.
DBN Deep belief network.
EMA Exponential moving average.
FD Frequency domain.
PCA Principal component analysis.
RBM Restricted Boltzmann machine.
SVM Support vector machine.
TD Time domain.
Manuscript received January 13, 2017; revised April 24, 2017 and
June 26, 2017; accepted August 5, 2017. Date of publication August
25, 2017; date of current version December 15, 2017. This work was
supported in part by the National Natural Science Foundation of China
under Grant 51475368, in part by the Shanghai Engineering Research
Center of Civil Aircraft Health Monitoring Foundation of China under
Grant GCZX-2015-02, and in part by the Innovation Foundation for Doc-
tor Dissertation of Northwestern Polytechnical University under Grant
CX201710. (Corresponding author: Hongkai Jiang.)
The authors are with the School of Aeronautics, Northwestern Poly-
technical University, Xi’an 710072, China (e-mail: [email protected]
edu.cn; [email protected]; [email protected];
[email protected]).
Color versions of one or more of the figures in this paper are available
online at http://ieeexplore.ieee.org.
Digital Object Identifier 10.1109/TIE.2017.2745473
I. INTRODUCTION
E LECTRIC locomotive is playing a more and more impor-tant role in the modern transportation. The key parts of
electric locomotive usually get various faults due to the harsh
operating conditions, which may result in great catastrophes.
Bearing is one of the most widely used components in elec-
tric locomotive [1]; thus, automatic and accurate fault diagnosis
techniques are critically needed to ensure the sa ...
Real Time Object Detection with Audio Feedback using Yolo v3ijtsrd
In this paper, we propose a system that combines real time object detection using the YOLOv3 algorithm with audio feedback to assist visually impaired individuals in locating and identifying objects in their surroundings. The YOLOv3 algorithm is a state of the art object detection algorithm that has been used in numerous studies for various applications. Audio feedback has also been studied in previous research as a useful tool for assisting visually impaired individuals. Our proposed system builds on the effectiveness of both these technologies to provide a valuable tool for improving the independence and quality of life of visually impaired individuals. We present the architecture of our proposed system, which includes a YOLOv3 model for object detection and a text to speech engine for providing audio feedback. We also present the results of our experiments, which demonstrate the effectiveness of our system in detecting and identifying objects in real time. Our proposed system can be used in various settings, such as indoor and outdoor environments, and can assist visually impaired individuals in various activities such as the navigation and object identification. Dr. K. Nagi Reddy | K. Sreeja | M. Sreenivasulu Reddy | K. Sireesha | M. Triveni "Real Time Object Detection with Audio Feedback using Yolo_v3" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-7 | Issue-2 , April 2023, URL: https://www.ijtsrd.com.com/papers/ijtsrd55158.pdf Paper URL: https://www.ijtsrd.com.com/engineering/electronics-and-communication-engineering/55158/real-time-object-detection-with-audio-feedback-using-yolov3/dr-k-nagi-reddy
RECOGNITION OF CDNA MICROARRAY IMAGE USING FEEDFORWARD ARTIFICIAL NEURAL NETWORKijaia
The complementary DNA (cDNA) sequence considered the magic biometric technique for personal identification. Microarray image processing used for the concurrent genes identification. In this paper, we present a new method for cDNA recognition based on the artificial neural network (ANN). We have segmented the location of the spots in a cDNA microarray. Thus, a precise localization and segmenting of a spot are essential to obtain a more exact intensity measurement, leading to a more accurate gene expression measurement. The segmented cDNA microarray image resized and used as an input for the
proposed artificial neural network. For matching and recognition, we have trained the artificial neural
network. Recognition results are given for the galleries of cDNA sequences . The numerical results show
that, the proposed matching technique is an effective in the cDNA sequences process. The experimental
results of our matching approach using different databases shows that, the proposed technique is an effective matching performance.
User Identity Linkage: Data Collection, DataSet Biases, Method, Control and A...IIIT Hyderabad
Online Social Networks (OSNs) are popular platforms for online users. Users typically register and maintain their accounts (user identities) across different OSNs to share a variety of content and remain connected with their friends. Consequently, linking user identities across OSN platforms, referred to as user identity linkage (UIL) becomes a critical problem. Solving this problem enables us to build a more comprehensive view of user’s activities across OSNs, which is highly beneficial for targeted advertisements, recommendations, and many more applications. In the thesis, we propose approaches for analyzing data collection methods, investigating biases in identity linkage datasets, linkage of user identities across social networks, control-ability of user identity linkage, and application of user identity linkage solutions to solve related problems.
Similar to Dissimilarity-based people re-identification and search for intelligent video surveillance (20)
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
2. Outline
University
Of Cagliari
Department of Electrical
and Electronic Engineering
2
• General context
Intelligent Video-Surveillance, and in particular
– Person Re-identification
– Appearance-based People Search
• A framework for constructing descriptors of people
– dissimilarity-based representations and their advantages
– the Multiple Component Dissimilarity (MCD) framework
• MCD and person re-identification
• MCD and people search
• Discussion and conclusions
3. Intelligent Video Surveillance
University
Of Cagliari
Department of Electrical
and Electronic Engineering
3
Machine Learning
Biometrics and pattern
recognition
Novel sensor
technologies
Useful tools for operators and forensic
investigators
• person identification
• on-line tracking of persons and objects
• detection of events of interest
• detection of suspicious actions
• summarisation of long video footages
…
Intelligent
Video Surveillance
4. University
Of Cagliari
Department of Electrical
and Electronic Engineering
Person re-identification
Person Re-Identification is the ability to determine if an
individual has already been observed over a network of video-
surveillance cameras
4
A
B
Scenarios
- on-line (e.g. people
tracking among different
cameras)
- off-line (e.g. retrieve all the
frames showing an individual
of interest)
5. University
Of Cagliari
Department of Electrical
and Electronic Engineering
Person re-identification
Face recognition cannot be used
- bad quality images (low resolution, blur, …)
- unconstrained pose
Other cues must be used
clothing appearance
(easy to extract, good uniqueness in limited time spans)
other ones (e.g. gait) are impractical in real-world
scenarios
5
6. University
Of Cagliari
Department of Electrical
and Electronic Engineering
Clothing appearance descriptors
6
Blob detection
and tracking
BG/FG
segmentation
Descriptor
computation
Descriptor = body part subdivision + appearance
features
Each body part is automatically detected and described
separately by e.g.
- colour (e.g., histograms)
- texture (e.g., DCT, LBP)
- local/global features
7. Appearance-based people search
University
Of Cagliari
Department of Electrical
and Electronic Engineering
7
Clothing appearance descriptors can enable another useful
task, appearance-based people search (a novelty in the literature)
Retrieve images of people via a query expressed as a high-level description of
the
clothes (es. “people with red shirt and blue trousers”), instead of as an image
9. Dissimilarity representations
University
Of Cagliari
Department of Electrical
and Electronic Engineering
9
An alternative way [1] to represent objects in pattern
recognition, useful when
it is unclear how to choose a features
it is difficult to find a good feature set
feature-based representation
dissimilarity-based representation
Object
feature
extraction
[ x1 x2 … xn ]
feature vector
prototypes
[1] Pekalska and Duin. The Dissimilarity Representation for Pattern Recognition: Foundations and
Applications. World Scientific Publishing, 2005
[ d1 d2 … dn ]
dissimilarity vector
Object
dissimilarities
computation
P1 P2 Pn
10. The Multiple Component Dissimilarity framework
University
Of Cagliari
Department of Electrical
and Electronic Engineering
10
Extension of the dissimilarity-based approach to objects represented by
- multiple parts
- multiple local features (components)
Prototypes
for body
part #1
Prototypes
for body
part #2
Dissimilarity vectors
(one for each body
part)
Local
appearance
Global
appearance
11. The Multiple Component Dissimilarity framework
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Of Cagliari
Department of Electrical
and Electronic Engineering
11
Prototype construction
From a design set of images of people
various possible approaches, e.g. clustering
Clustering-based prototype creation example (two body parts)
Design set
Create a set of all the
components of body part 1
Create a set of all the
components of body part 2
Cluster
the set
Take centroids as
prototypes
Cluster
the set
Take centroids as
prototypes
12. The Multiple Component Dissimilarity framework
University
Of Cagliari
Department of Electrical
and Electronic Engineering
12
MCD representations will be exploited for
person re-identification
appearance-based people search
[d1,1 d1,2 d1,3 d1,4 d2,1 d2,2 d2,3 ] [d1,1 d1,2 d1,3 d1,4 d2,1 d2,2 d2,3 ]
[d1,1 d1,2 d1,3 d1,4 d2,1 d2,2 d2,3 ] [d1,1 d1,2 d1,3 d1,4 d2,1 d2,2 d2,3 ]
14. MCD and person re-identification
University
Of Cagliari
Department of Electrical
and Electronic Engineering
14
Person re-identification
MCD salient features for person re-identification:
a very compact representation
descriptors are small real vectors (low storage requirements, fast
matching)
dissimilarity vectors are representation-independent
they can be used to combine different features and modalities
Applications: 1) Speed up person re-identification methods
2) Feature combination for person re-identification
3) Multimodal person re-identification
matching
ranked list of templates
(w.r.t. the degree of similarity)
template gallery
probe
0.03 0.28 0.33 0.34
15. MCD-based matching
University
Of Cagliari
Department of Electrical
and Electronic Engineering
15
A novel weighted Euclidean distance for dissimilarity spaces
RATIONALE: - each dissimilarity is a degree of relevance of the corresponding
prototype;
- lower dissimilarity values carry more information; in fact, they
encode the
most relevant characteristics of the sample.
Weights: where (xi, yi in the range [0,1])
The weighting rule f() is a monotonically increasing
function; its choice governs the difference between
relevant and non-relevant prototypes
x and y: dissimilarity vectors;
W such that
17. MCD to speed up existing methods
University
Of Cagliari
Department of Electrical
and Electronic Engineering
17
MCD has been applied to an existing method, MCMimpl [2]
MCMimpl in short:
part subdivision:
torso – legs exploiting symmetry and
anti-symmetry properties, discarding head
multiple component representation:
for each part, randomly taken and partly
overlapping patches
Four data sets of increasing size:
i-LIDS (119 pedestrians) VIPeR-316 (316 pedestrians)
VIPeR-474 (474 pedestrians) VIPeR-632 (632 pedestrians)
[2] Satta, Fumera, Roli, Cristani, and Murino. A Multiple Component Matching Framework for Person Re-
Identification. In: ICIAP, 2011
19. Experimental evaluation
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Of Cagliari
Department of Electrical
and Electronic Engineering
19
Trade-off between accuracy and computational time
It can be shown that the overall re-identification time* in a practical search
scenario is much lower when using MCD
* sum of processing time plus the average
search time spent by the operator
22. Fusion of different feature sets by MCD
University
Of Cagliari
Department of Electrical
and Electronic Engineering
22
Prototypes in MCD are representation-independent
MCD dissimilarity vectors can be used to combine together different kinds of
features, either global or local
each feature set will be responsible for a different sub-set of prototypes
23. Fusion of different feature sets by MCD
University
Of Cagliari
Department of Electrical
and Electronic Engineering
23
This technique has been used to combine five different feature sets
• RandPatchesHSV
• RandPatchesLBP
• FCTH [3]
• EdgeHistogram [4]
• SCD [4]
exploiting a 4-body-parts subdivision
First two feature sets:
200 prototypes per feature set per body part
Last three feature sets:
100 prototypes per feature set per body part
3200 prototypes in total
[3] Chatzichristofis and Boutalis. FCTH: Fuzzy Color and Texture Histogram – a Low Level Feature for
Accurate Image Retrieval. In: WIAMIS, 2008
[4] Sikora. The MPEG-7 Visual Standard for Content Description – an Overview. IEEE Transactions on
Circuits and Systems for Video Technology, 2001
24. Performance of the single feature sets
University
Of Cagliari
Department of Electrical
and Electronic Engineering
24
I-LIDS: 119 individuals
25. Comparison with the state-of-the-art
University
Of Cagliari
Department of Electrical
and Electronic Engineering
25
Comparison with two state-of-the-art methods
- SDALF [5]
- CPS [6]
[5] Farenzena, Bazzani, Perina, Murino, and Cristani. Person Re-Identification by Symmetry-Driven
Accumulation of Local Features. In: CVPR, 2010
[6] Cheng, Cristani, Stoppa, Bazzani, and Murino. Custom Pictorial Structures for Re-Identification. In:
BMVC, 2011
27. Multi-modal person re-identification
University
Of Cagliari
Department of Electrical
and Electronic Engineering
27
• Appearance is a widely used cue for person re-identification
other cues (e.g., gait) pose constraints that limit their applicability
in real world scenarios
• However, the recent introduction of RGB-D sensors makes it
possible to extract anthropometric measures that can be
combined with appearance
Example MS Kinect™!
By processing RGB-D data, it is possible to estimate a 3D model of a person in real-time [7]
From this model, one can extract various anthropometric measures (e.g., height, arm
length)
[7] Shotton, Fitzgibbon, Cook, Sharp, Finocchio, Moore, Kipman, and Blake. Real-time Pose Recognition in
Parts from Single Depth Images. In: CVPR, 2011
29. Multi-modal person re-identification
University
Of Cagliari
Department of Electrical
and Electronic Engineering
29
A proper fusion strategy must be used to combine different modalities.
Score-level fusion Feature-level fusion
- Performance of score-level fusion is affected by the choice of the fusion
rule (e.g.,
mean, min); a suitable choice for re-id is not trivial
- Feature-level fusion requires homogeneous features
Fusion
Modality 1 Matching score
Modality 2 Matching score
Modality n Matching score
Fusion score
Modality 1
Modality 2
Modality n
Matching
30. Multi-modal person re-identification
University
Of Cagliari
Department of Electrical
and Electronic Engineering
30
MCD provides a way to combine non-homogeneous modalities at feature
level, by exploiting its representation-independency
31. Multi-modal person re-identification
University
Of Cagliari
Department of Electrical
and Electronic Engineering
31
This MCD-based approach has been used to combine appearance with anthropometry
Appearance:
two descriptors, MCMimpl v2 and SDALF
Anthropometry:
three measures from the skeleton:
- normalised height
- normalised average arm length
- normalised average leg length
MCMimpl v2 SDALF
32. Experimental evaluation
University
Of Cagliari
Department of Electrical
and Electronic Engineering
32
Experiments have been carried out on a novel dataset acquired using Kinect
cameras, Kinect4REID
video sequences of 80 individuals taken at different locations
different lighting conditions and view points
2 to 7 different video sequences per person
many persons are carrying bags or accessories
36. MCD for people search
University
Of Cagliari
Department of Electrical
and Electronic Engineering
36
Implementation by MCD: high-level concepts that describe certain clothing
characteristics (e.g., “red shirt”) may be encoded by one or more visual
prototypes, according to the low-level features and part subdivision used
Prototypes (rectangular patches) extracted from a set of
24 people (upper body part)
Correlation with the presence of the concept “red shirt”
37. MCD for people search
University
Of Cagliari
Department of Electrical
and Electronic Engineering
37
How to implement people search
(i) define a set of basic queries
(ii) construct a detector for each basic query, using dissimilarity values as input
Complex queries can be built by connecting basic ones through Boolean
operators,
e.g., “red shirt AND (blue trousers OR black trousers)”
Detector[ d1 d2 … dn ] SCORE
38. Experimental evaluation
University
Of Cagliari
Department of Electrical
and Electronic Engineering
38
Dataset
a subset of 512 images taken from the VIPeR data-set, tagged with respect to 14
different basic queries
Examples:
Three descriptors:
i) MCMimpl
ii) SDALF
iii) MCMimpl-PS, which uses a pictorial structure [8] to subdivide the body into nine
parts
body subdivision,
MCMimpl and SDALF
body subdivision,
MCMimpl-PS
[8] Andriluka, Roth, and Schiele. Pictorial Structures Revisited: People Detection and Articulated Pose
Estimation. In: CVPR 2009
39. Experimental evaluation
University
Of Cagliari
Department of Electrical
and Electronic Engineering
39
For each basic query:
(i) the VIPeR-Tagged is subdivided into a training and a testing sets of equal size
(ii) a linear SVM is trained on training images to implement a detector
(iii) the P-R curve is evaluated on testing images, by varying the SVM decision threshold
This procedure is repeated ten times
Break-even points for all classes:
41. Conclusions
University
Of Cagliari
Department of Electrical
and Electronic Engineering
41
What has been done
(i) MCD, a novel dissimilarity-based framework for describing individuals
(ii) an approach based on MCD to speed up any existing person re-
identification method
(iii) a state-of-the-art re-identification method, that combines different
features obtained through the use of MCD
(iv) a method to perform multi-modal person re-identification based on
MCD and using RGB-D cameras, and a novel data set to assess
performance of multi-modal re-identification systems
(v) a method that uses MCD to perform the novel task of “appearance-
based people search”
42. Conclusions
University
Of Cagliari
Department of Electrical
and Electronic Engineering
42
What to do next (long list…!)
THE FRAMEWORK
(i) explore the commonalities between MCD and Visual Words and Fisher
Vectors
(ii) extend MCD to other domains
MULTIMODAL RE-ID
(i) explore the use of other cues (other anthropometries, skeleton-based
gait…)
(ii) extend the approach to support missing cues
PEOPLE SEARCH
(i) address the problem of ambiguity of concepts
(ii) add semantic interpretation (Natural Language Processing) to support
queries in natural language