The document describes a project that aims to develop a mobile application for real-time object and pose detection. The application will take in a real-time image as input and output bounding boxes identifying the objects in the image along with their class. The methodology involves preprocessing the image, then using the YOLO framework for object classification and localization. The goals are to achieve high accuracy detection that can be used for applications like vehicle counting and human activity recognition.
Image classification with Deep Neural NetworksYogendra Tamang
This document discusses image classification using deep neural networks. It provides background on image classification and convolutional neural networks. The document outlines techniques like activation functions, pooling, dropout and data augmentation to prevent overfitting. It summarizes a paper on ImageNet classification using CNNs with multiple convolutional and fully connected layers. The paper achieved state-of-the-art results on ImageNet in 2010 and 2012 by training CNNs on a large dataset using multiple GPUs.
A presentation on Image Recognition, the basic definition and working of Image Recognition, Edge Detection, Neural Networks, use of Convolutional Neural Network in Image Recognition, Applications, Future Scope and Conclusion
The document summarizes a disease prediction system for rural health services presented by two students. The key points are:
1. The system aims to provide quick medical diagnosis to rural patients using machine learning algorithms like SVM, RF, DT, NB, ANN, KNN, and LR to recognize diseases from symptoms.
2. It seeks to enhance access to medical specialists for rural communities and improve quality of healthcare.
3. The expected outcomes are conducting experiments to evaluate the performance of using 7 machine learning algorithms to predict diseases from symptoms and having doctors select the correct diagnosis from the predictions.
The document describes two feature extraction methods: attention based and statistics based. The attention based method models how human vision finds salient regions using an architecture that decomposes images into channels and creates image pyramids, then combines the information to generate saliency maps. This method was applied to face recognition but had problems with pose and expression changes. The statistics based method aims to select a subset of important features using criteria based on how well the features represent the original data.
Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos. Well-researched domains of object detection include face detection and pedestrian detection. Object detection has applications in many areas of computer vision, including image retrieval and video surveillance.
The document describes a project that aims to develop a mobile application for real-time object and pose detection. The application will take in a real-time image as input and output bounding boxes identifying the objects in the image along with their class. The methodology involves preprocessing the image, then using the YOLO framework for object classification and localization. The goals are to achieve high accuracy detection that can be used for applications like vehicle counting and human activity recognition.
Image classification with Deep Neural NetworksYogendra Tamang
This document discusses image classification using deep neural networks. It provides background on image classification and convolutional neural networks. The document outlines techniques like activation functions, pooling, dropout and data augmentation to prevent overfitting. It summarizes a paper on ImageNet classification using CNNs with multiple convolutional and fully connected layers. The paper achieved state-of-the-art results on ImageNet in 2010 and 2012 by training CNNs on a large dataset using multiple GPUs.
A presentation on Image Recognition, the basic definition and working of Image Recognition, Edge Detection, Neural Networks, use of Convolutional Neural Network in Image Recognition, Applications, Future Scope and Conclusion
The document summarizes a disease prediction system for rural health services presented by two students. The key points are:
1. The system aims to provide quick medical diagnosis to rural patients using machine learning algorithms like SVM, RF, DT, NB, ANN, KNN, and LR to recognize diseases from symptoms.
2. It seeks to enhance access to medical specialists for rural communities and improve quality of healthcare.
3. The expected outcomes are conducting experiments to evaluate the performance of using 7 machine learning algorithms to predict diseases from symptoms and having doctors select the correct diagnosis from the predictions.
The document describes two feature extraction methods: attention based and statistics based. The attention based method models how human vision finds salient regions using an architecture that decomposes images into channels and creates image pyramids, then combines the information to generate saliency maps. This method was applied to face recognition but had problems with pose and expression changes. The statistics based method aims to select a subset of important features using criteria based on how well the features represent the original data.
Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos. Well-researched domains of object detection include face detection and pedestrian detection. Object detection has applications in many areas of computer vision, including image retrieval and video surveillance.
Residual neural networks (ResNets) solve the vanishing gradient problem through shortcut connections that allow gradients to flow directly through the network. The ResNet architecture consists of repeating blocks with convolutional layers and shortcut connections. These connections perform identity mappings and add the outputs of the convolutional layers to the shortcut connection. This helps networks converge earlier and increases accuracy. Variants include basic blocks with two convolutional layers and bottleneck blocks with three layers. Parameters like number of layers affect ResNet performance, with deeper networks showing improved accuracy. YOLO is a variant that replaces the softmax layer with a 1x1 convolutional layer and logistic function for multi-label classification.
This document summarizes techniques for least mean square filtering and geometric transformations. It discusses minimum mean square error (Wiener) filtering, constrained least squares filtering, and geometric mean filtering for noise removal. It also covers spatial transformations, nearest neighbor gray level interpolation, and bilinear interpolation for geometric correction of distorted images. Examples are provided to demonstrate geometric distortion, nearest neighbor interpolation, and bilinear transformation.
This document describes a deep learning approach for detecting diabetic retinopathy using OCT images. It discusses the proposed system which will use OCT images and apply classification algorithms to identify the level of infection. The model will be trained on datasets of infected images to accurately detect regions of infection and the condition level. Image processing techniques like median filtering and edge detection will be used along with statistical data extraction and supervised training to identify clusters and classify images. Results will be compared to evaluate the machine learning models. The system aims to automate diabetic retinopathy detection to improve efficiency over conventional methods.
Adaptive Machine Learning for Credit Card Fraud DetectionAndrea Dal Pozzolo
This document discusses machine learning techniques for credit card fraud detection. It addresses challenges like concept drift, imbalanced data, and limited supervised data. The author proposes contributions in learning from imbalanced and evolving data streams, a prototype fraud detection system using all supervised information, and a software package/dataset. Methods discussed include resampling techniques, concept drift handling, and a "racing" algorithm to efficiently select the best strategy for unbalanced classification on a given dataset. Evaluation measures the ability to accurately rank transactions by fraud risk.
Credit card fraud detection using python machine learningSandeep Garg
COMPANY_NAME provides data-driven business transformation services using advanced analytics and artificial intelligence. It helps businesses contextualize data, generate insights from complex problems, and make data-driven decisions. The document then discusses using machine learning for credit card fraud detection. It explains supervised learning as inferring a function from labeled training and test data to map inputs to outputs with minimal error. Screenshots are provided of exploring and preprocessing a credit card transaction dataset for outlier detection, correlation, and preparing the data for machine learning models.
Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos.
A Small Helping Hand from me to my Engineering collegues and my other friends in need of Object Detection
The document discusses image representation and feature extraction techniques. It describes how representation makes image information more accessible for computer interpretation using either boundaries or pixel regions. Feature extraction quantifies these representations by extracting descriptors like geometric properties, statistical moments, and textures. Desirable properties for descriptors include being invariant to transformations, compact, robust to noise, and having low complexity. Various boundary and regional descriptors are defined, such as chain codes, shape numbers, and moments.
Using prior knowledge to initialize the hypothesis,kbannswapnac12
1) The KBANN algorithm uses a domain theory represented as Horn clauses to initialize an artificial neural network before training it with examples. This helps the network generalize better than random initialization when training data is limited.
2) KBANN constructs a network matching the domain theory's predictions exactly, then refines it with backpropagation to fit examples. This balances theory and data when they disagree.
3) In experiments on promoter recognition, KBANN achieved a 4% error rate compared to 8% for backpropagation alone, showing the benefit of prior knowledge.
After an image has been segmented into regions ; the resulting pixels is usually is represented and described in suitable form for further computer processing.
The document summarizes Jini technology, which provides a simple infrastructure for delivering services like applications, databases, and printing across a network. It discusses Jini's history, goals of enabling universal access to shared resources. The key components are services, lookup services for discovery, leasing to manage access time, and security. Jini architecture uses lookup services for registration and discovery to simplify adding and removing network services and devices.
This document discusses various classification algorithms including k-nearest neighbors, decision trees, naive Bayes classifier, and logistic regression. It provides examples of how each algorithm works. For k-nearest neighbors, it shows how an unknown data point would be classified based on its nearest neighbors. For decision trees, it illustrates how a tree is built by splitting the data into subsets at each node until pure subsets are reached. It also provides an example decision tree to predict whether Amit will play cricket. For naive Bayes, it gives an example of calculating the probability of cancer given a patient is a smoker.
This document discusses image segmentation techniques. It describes how segmentation partitions an image into meaningful regions based on discontinuities or similarities in pixel intensity. The key methods covered are thresholding, edge detection using gradient and Laplacian operators, and the Hough transform for global line detection. Adaptive thresholding is also introduced as a technique to handle uneven illumination.
Computer vision is a field that uses techniques to electronically perceive and understand images. It involves acquiring, processing, analyzing and understanding images and can take forms like video sequences. Computer vision aims to duplicate human vision abilities through artificial systems. It has applications in areas like manufacturing inspection, medical imaging, robotics, traffic monitoring and more. Some techniques used in computer vision include image acquisition, preprocessing, feature extraction, detection, recognition and interpretation.
This document provides an introduction to deep learning. It defines artificial intelligence, machine learning, data science, and deep learning. Machine learning is a subfield of AI that gives machines the ability to improve performance over time without explicit human intervention. Deep learning is a subfield of machine learning that builds artificial neural networks using multiple hidden layers, like the human brain. Popular deep learning techniques include convolutional neural networks, recurrent neural networks, and autoencoders. The document discusses key components and hyperparameters of deep learning models.
The document describes a method for classifying fish species using visual vocabulary. It involves a two stage process of training and testing. In the training stage, visual vocabularies are learned for each species by finding salient points on images and modeling parts, locations, and features. In the testing stage, features are extracted from test images and compared to visual vocabularies to classify the fish by shortest distance. The method achieves 85.2% accuracy but has problems with species appearing different from sides and curved bodies. Future work to improve classification is suggested.
The document discusses iris recognition as a biometric identification method. It provides a brief history of iris recognition from its proposal in 1939 to its implementation in 1990 by Dr. John Daugman who created algorithms for it. The document outlines the iris recognition process including iris localization, normalization, feature extraction using Gabor filters, and matching using techniques like Euclidean distance. It discusses advantages like accuracy and stability of iris patterns, and disadvantages such as cost and inability to capture images from certain positions.
Residual neural networks (ResNets) solve the vanishing gradient problem through shortcut connections that allow gradients to flow directly through the network. The ResNet architecture consists of repeating blocks with convolutional layers and shortcut connections. These connections perform identity mappings and add the outputs of the convolutional layers to the shortcut connection. This helps networks converge earlier and increases accuracy. Variants include basic blocks with two convolutional layers and bottleneck blocks with three layers. Parameters like number of layers affect ResNet performance, with deeper networks showing improved accuracy. YOLO is a variant that replaces the softmax layer with a 1x1 convolutional layer and logistic function for multi-label classification.
This document summarizes techniques for least mean square filtering and geometric transformations. It discusses minimum mean square error (Wiener) filtering, constrained least squares filtering, and geometric mean filtering for noise removal. It also covers spatial transformations, nearest neighbor gray level interpolation, and bilinear interpolation for geometric correction of distorted images. Examples are provided to demonstrate geometric distortion, nearest neighbor interpolation, and bilinear transformation.
This document describes a deep learning approach for detecting diabetic retinopathy using OCT images. It discusses the proposed system which will use OCT images and apply classification algorithms to identify the level of infection. The model will be trained on datasets of infected images to accurately detect regions of infection and the condition level. Image processing techniques like median filtering and edge detection will be used along with statistical data extraction and supervised training to identify clusters and classify images. Results will be compared to evaluate the machine learning models. The system aims to automate diabetic retinopathy detection to improve efficiency over conventional methods.
Adaptive Machine Learning for Credit Card Fraud DetectionAndrea Dal Pozzolo
This document discusses machine learning techniques for credit card fraud detection. It addresses challenges like concept drift, imbalanced data, and limited supervised data. The author proposes contributions in learning from imbalanced and evolving data streams, a prototype fraud detection system using all supervised information, and a software package/dataset. Methods discussed include resampling techniques, concept drift handling, and a "racing" algorithm to efficiently select the best strategy for unbalanced classification on a given dataset. Evaluation measures the ability to accurately rank transactions by fraud risk.
Credit card fraud detection using python machine learningSandeep Garg
COMPANY_NAME provides data-driven business transformation services using advanced analytics and artificial intelligence. It helps businesses contextualize data, generate insights from complex problems, and make data-driven decisions. The document then discusses using machine learning for credit card fraud detection. It explains supervised learning as inferring a function from labeled training and test data to map inputs to outputs with minimal error. Screenshots are provided of exploring and preprocessing a credit card transaction dataset for outlier detection, correlation, and preparing the data for machine learning models.
Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos.
A Small Helping Hand from me to my Engineering collegues and my other friends in need of Object Detection
The document discusses image representation and feature extraction techniques. It describes how representation makes image information more accessible for computer interpretation using either boundaries or pixel regions. Feature extraction quantifies these representations by extracting descriptors like geometric properties, statistical moments, and textures. Desirable properties for descriptors include being invariant to transformations, compact, robust to noise, and having low complexity. Various boundary and regional descriptors are defined, such as chain codes, shape numbers, and moments.
Using prior knowledge to initialize the hypothesis,kbannswapnac12
1) The KBANN algorithm uses a domain theory represented as Horn clauses to initialize an artificial neural network before training it with examples. This helps the network generalize better than random initialization when training data is limited.
2) KBANN constructs a network matching the domain theory's predictions exactly, then refines it with backpropagation to fit examples. This balances theory and data when they disagree.
3) In experiments on promoter recognition, KBANN achieved a 4% error rate compared to 8% for backpropagation alone, showing the benefit of prior knowledge.
After an image has been segmented into regions ; the resulting pixels is usually is represented and described in suitable form for further computer processing.
The document summarizes Jini technology, which provides a simple infrastructure for delivering services like applications, databases, and printing across a network. It discusses Jini's history, goals of enabling universal access to shared resources. The key components are services, lookup services for discovery, leasing to manage access time, and security. Jini architecture uses lookup services for registration and discovery to simplify adding and removing network services and devices.
This document discusses various classification algorithms including k-nearest neighbors, decision trees, naive Bayes classifier, and logistic regression. It provides examples of how each algorithm works. For k-nearest neighbors, it shows how an unknown data point would be classified based on its nearest neighbors. For decision trees, it illustrates how a tree is built by splitting the data into subsets at each node until pure subsets are reached. It also provides an example decision tree to predict whether Amit will play cricket. For naive Bayes, it gives an example of calculating the probability of cancer given a patient is a smoker.
This document discusses image segmentation techniques. It describes how segmentation partitions an image into meaningful regions based on discontinuities or similarities in pixel intensity. The key methods covered are thresholding, edge detection using gradient and Laplacian operators, and the Hough transform for global line detection. Adaptive thresholding is also introduced as a technique to handle uneven illumination.
Computer vision is a field that uses techniques to electronically perceive and understand images. It involves acquiring, processing, analyzing and understanding images and can take forms like video sequences. Computer vision aims to duplicate human vision abilities through artificial systems. It has applications in areas like manufacturing inspection, medical imaging, robotics, traffic monitoring and more. Some techniques used in computer vision include image acquisition, preprocessing, feature extraction, detection, recognition and interpretation.
This document provides an introduction to deep learning. It defines artificial intelligence, machine learning, data science, and deep learning. Machine learning is a subfield of AI that gives machines the ability to improve performance over time without explicit human intervention. Deep learning is a subfield of machine learning that builds artificial neural networks using multiple hidden layers, like the human brain. Popular deep learning techniques include convolutional neural networks, recurrent neural networks, and autoencoders. The document discusses key components and hyperparameters of deep learning models.
The document describes a method for classifying fish species using visual vocabulary. It involves a two stage process of training and testing. In the training stage, visual vocabularies are learned for each species by finding salient points on images and modeling parts, locations, and features. In the testing stage, features are extracted from test images and compared to visual vocabularies to classify the fish by shortest distance. The method achieves 85.2% accuracy but has problems with species appearing different from sides and curved bodies. Future work to improve classification is suggested.
The document discusses iris recognition as a biometric identification method. It provides a brief history of iris recognition from its proposal in 1939 to its implementation in 1990 by Dr. John Daugman who created algorithms for it. The document outlines the iris recognition process including iris localization, normalization, feature extraction using Gabor filters, and matching using techniques like Euclidean distance. It discusses advantages like accuracy and stability of iris patterns, and disadvantages such as cost and inability to capture images from certain positions.
Automatic System for Detection and Classification of Brain TumorsFatma Sayed Ibrahim
Automatic system for brain tumors detection based on DICOM MRI images
Surveying methodologies of from preprocessing to classifications
Implementing comparative study.
Proposed technique with highest accuracy and lest elapsed time.
This document presents a new approach for human identification using sclera recognition. It begins with background on sclera and challenges with sclera recognition. It then describes the proposed methodology which includes sclera segmentation, feature extraction using Gabor filtering, and recognition using Bayesian classification. Experimental results show the false accept and reject rates for the approach. It concludes that sclera recognition is promising for human identification and can achieve accuracy comparable to iris recognition in visible light. The proposed approach uses Bayesian classification for recognition, which is more effective than previous matching score methods.
Machine Learning, Data Mining, Genetic Algorithms, Neural ...butest
The document discusses various machine learning concepts including concept learning, decision trees, genetic algorithms, and neural networks. It provides details on each concept, such as how concept learning uses positive and negative examples to learn concepts, how decision trees use nodes and branches to classify data, and how genetic algorithms and neural networks are modeled after biological processes. It also gives examples of applications for each concept, such as using decision trees for classification and neural networks for tasks like handwriting recognition where explicit rules are difficult to define.
This document discusses automatic detection of blood vessels in digital retinal images using computer vision and image processing (CVIP) tools. It begins with an overview of eye diseases like diabetic retinopathy and glaucoma that can be detected by observing blood vessels in retinal images. It then describes 6 common approaches to blood vessel extraction, including pattern recognition, model-based, tracking-based, and neural network approaches. The document outlines the methods used in the study, including preprocessing retinal images, extracting blood vessels using tools like filters, and postprocessing the results. It provides examples of blood vessel extraction and suggests areas for future work, such as developing techniques to better detect minor blood vessels and separate blood vessels from other structures.
This document summarizes research on developing an automated animal classification system using image processing and support vector machines. The system aims to help animal researchers and wildlife photographers by automatically detecting an animal's presence, capturing images of the animal, and identifying the animal species in the images. The system uses passive infrared sensors to detect an animal and rotate a camera towards it. Captured images are then compared to a photograph database using features like color, texture, shape and machine learning to classify the animal. The document reviews previous research on low-level feature extraction, animal face detection and tracking, visual cues for fast animal detection, and using face identification to distinguish targeted non-native animals.
Evaluation of Iris Recognition System on Multiple Feature Extraction Algorith...Editor IJCATR
Multi-algorithmic approach to enhancing the accuracy of iris recognition system is proposed and
investigated. In this system, features are extracted from the iris using various feature extraction algorithms,
namely LPQ, LBP, Gabor Filter, Haar, Db8 and Db16. Based on the experimental results, it is demonstrated
that Mutli-algorithms Iris Recognition System is performing better than the unimodal system. The accuracy
improvement offered by the proposed approach also showed that using more than two feature extraction
algorithms in extracting the iris system might decrease the system performance. This is due to redundant
features. The paper presents a detailed description of the experiments and provides an analysis of the
performance of the proposed method.
Evaluation of Iris Recognition System on Multiple Feature Extraction Algorith...Editor IJCATR
Multi-algorithmic approach to enhancing the accuracy of iris recognition system is proposed and
investigated. In this system, features are extracted from the iris using various feature extraction algorithms,
namely LPQ, LBP, Gabor Filter, Haar, Db8 and Db16. Based on the experimental results, it is demonstrated
that Mutli-algorithms Iris Recognition System is performing better than the unimodal system. The accuracy
improvement offered by the proposed approach also showed that using more than two feature extraction
algorithms in extracting the iris system might decrease the system performance. This is due to redundant
features. The paper presents a detailed description of the experiments and provides an analysis of the
performance of the proposed method.
Evaluation of Iris Recognition System on Multiple Feature Extraction Algorith...Editor IJCATR
Multi-algorithmic approach to enhancing the accuracy of iris recognition system is proposed and investigated. In this system, features are extracted from the iris using various feature extraction algorithms, namely LPQ, LBP, Gabor Filter, Haar, Db8 and Db16. Based on the experimental results, it is demonstrated that Mutli-algorithms Iris Recognition System is performing better than the unimodal system. The accuracy improvement offered by the proposed approach also showed that using more than two feature extraction algorithms in extracting the iris system might decrease the system performance. This is due to redundant features. The paper presents a detailed description of the experiments and provides an analysis of the performance of the proposed method.
IRJET- Survey of Iris Recognition TechniquesIRJET Journal
This document summarizes several techniques for iris recognition. It begins with an abstract describing iris recognition and its accuracy compared to other biometric traits. It then reviews four iris recognition techniques in the literature:
1. A technique using moment invariants and Euclidean or Mahalanobis distance classifiers that achieved 100% recognition rates.
2. A segmentation algorithm using Daugman's integro differential operator that improved discrimination capabilities over other methods.
3. A pupil localization technique using negative thresholds and neighbors, and iris boundary detection using contrast enhancement and thresholding, achieving accurate segmentation.
4. A technique using Gaussian mixture models, Gabor filter banks, and simulated annealing to generate iris masks and increase recognition rates
PERIOCULAR RECOGNITION USING REDUCED FEATURESijcsit
Biometrics is science of measuring and statistically analyzing biological data. Biometric system establishes identity of a person based on unique physical or behavioural characteristic possessed by an individual.Behavioural biometrics measures characteristics which are acquired naturally over time. Physical biometrics measures inherent physical characteristics on a n individual. Over the last few decades enormous attention is drawn towards ocular biometrics. Cues provided by ocular region have led to exploration of newer traits. Feasibility of periocular region as a useful biometric trait has been explored recently. With the promising results of preliminary examination, research towards periocular region is currently gaining lot of prominence. Researchers have analyzed various techniques of feature extraction and classification in the periocular region. The current paper investigates the effect of using Lower Central Periocular Region (LCPR) for identification. The results obtained are comparable with those acquired for the entire periocular region with an advantage of reduced periocular area
iris recognition system as means of unique identification Being Topper
Project Done and submitted by Students Of final year CBP Government Engineering College
student name : vipin kumar khutail , Krishnanad Mishra , Jaswant kumar, Rahul Vashisht
Project Description :
Iris recognition is an automated method of bio-metric identification that uses mathematical pattern-recognition techniques on video images of one or both of the irises of an individual's eyes, whose complex random patterns are unique, stable, and can be seen from some distance
This document provides an overview of iris recognition technology. It discusses the motivation for using biometric authentication like iris recognition. It then reviews the history of iris recognition, describing how it was first proposed in 1987 and patented by John Daugman in 1994. The document outlines the basic stages of iris recognition including image acquisition, iris localization, normalization, feature extraction, and pattern matching. It notes some advantages like the uniqueness and stability of iris patterns. Finally, it provides an update on the current status of the project, which is currently in the iris localization phase using an eye image dataset from Chinese University of Hong Kong.
This document provides an overview of iris recognition technology. It discusses the motivation for using biometric authentication like iris recognition. It then reviews the history of iris recognition, pioneered by Flom and Safir in 1987 and later patented by John Daugman. The document outlines the structure of the eye and iris, and describes the typical stages of iris recognition including image acquisition, iris localization, normalization, feature extraction, and pattern matching. It notes advantages like the uniqueness and stability of iris patterns. Finally, it states the current status of the project, which is in the iris localization phase using an eye image dataset from Chinese University of Hong Kong.
This document provides an overview of iris recognition technology. It discusses the motivation for using biometric authentication like iris recognition. It then reviews the history of iris recognition, pioneered by Flom and Safir in 1987 and later patented by John Daugman. The document outlines the structure of the eye and iris, and describes the typical stages of iris recognition including image acquisition, iris localization, normalization, feature extraction, and pattern matching. It notes advantages like the uniqueness and stability of iris patterns. Finally, it states the current status of the project, which is in the iris localization phase using an eye image dataset from Chinese University of Hong Kong.
Facial emotion detection on babies' emotional face using Deep Learning.Takrim Ul Islam Laskar
phase- 1
Face Detection.
Facial Landmark detection.
phase- 2
Neural Network Training and Testing.
validation and implementation.
phase - 1 has been completed successfully.
Artificial intelligence has made significant advancements in ophthalmology by analyzing medical images and data. AI algorithms can detect eye diseases like diabetic retinopathy and macular degeneration from retinal images, predict disease risk and progression, and provide treatment recommendations to augment doctors. While AI shows promise in improving diagnosis and access to eye care, limitations include potential data and generalization biases that require addressing through responsible development and validation of these new technologies.
Ccids 2019 cutting edges of ai technology in medicineNamkug Kim
1. The document discusses various applications of artificial intelligence and deep learning in medicine, including image segmentation, classification, and clinical decision support.
2. It outlines several clinical unmet needs such as handling imbalanced datasets and presents solutions like data augmentation using GANs and curriculum learning.
3. Smart labeling techniques are proposed to reduce the time and cost of manual labeling through methods like active learning and semantic segmentation assisted labeling. This allows for cheaper and faster dataset expansion.
This document presents a new iris segmentation method for iris recognition systems. The proposed method uses Canny edge detection and Hough transform to locate the iris boundary after finding the pupil boundary using image gray levels. Experiments on the CASIA iris image database of 756 images show the method can accurately detect the iris boundary in 99.2% of images. This is an improvement over other existing segmentation techniques. The key steps of the proposed method are preprocessing, segmentation using Canny edge detection and Hough transform, normalization using the rubber sheet model, feature encoding with Gabor wavelets, and matching with Hamming distance.
Similar to Animal identification using machine learning techniques (20)
The document discusses intelligent avatars in the metaverse and toward intelligent virtual beings. It provides an overview of the metaverse, its uses cases and applications. Some key points discussed include:
- The metaverse refers to interconnected 3D virtual worlds where physical and digital lives converge.
- Avatars play a central role in the metaverse, pioneered by the video game industry.
- Potential uses of AI in the metaverse include accurate avatar creation, digital humans for interactions, and multilingual accessibility.
- Challenges of AI in the metaverse include issues around ownership of AI-created content, deepfakes, fair use of AI/ML technologies, data use for model training, and accountability for AI bias
هذة المحاضرة تناقش العوالم الافتراضية فى التعليم واهمية الذكاء الاصطناعى والتوأم الرقمى والإستفادة من العلوم المختلفة فى بيئة الميتافيرس وتقنيات عالم الميتافيرس فى التعليم وتم القائها فى المؤتمر الدولى للتعليم الابداعى والتحول الرقمى فى التعليم بجامعة الكويت الدولية يوم 13 نوفمبر 2022
الذكاء الأصطناعى المسؤول ومستقبل الأمن المناخى وانعكاساته الاجتماعية والأمنيةAboul Ella Hassanien
تحت رعاية الاستاذ الدكتور / محمود صقر رئيس اكاديمية البحث العلمي و إشراف الأستاذ الدكتور/ أحمد جبر المشرف علي المجالس النوعية ورئاسة الاستاذ الدكتور / احمد الشربيني مقرر مجلس بحوث الاتصالات وتكنولوجيا المعلومات تم تنظيم ورشة عمل اليوم 7 نوفمبر بمقر اكاديمية البحث العلمي عن " دور الذكاء الاصطناعي وانترنت الاشياء في مكافحة التغيرات المناخية" وذلك بمناسبة انعقاد مؤتمر الاطراف للتغيرات المناخية COP27 والمنعقد بمدينة شرم الشيخ. وقد عرض المتحدثون وهم الاستاذ الدكتو. / ابو العلا حسانين عضو المجلس والاستاذ الدكتور / اشرف درويش عضو المجلس والدكتورة لبني ابو المجد دور وتطبيقات الذكاء الاصطناعي وانترنت الاشياء في مجالات متعددة ومرتبطة بالتغيرات المناخية منها الزراعة ، الطاقة، الصحة , الاقتصاد الاخضر ، النقل والمواصلات والتخطيط العمراني من اجل الحد من التاثيرات المناخية والتي تهدف الي تقليل نسب انبعاث غازات الاحتباس الحراري والتكيف مع التغيرات المناخية. امتدت ورشة العمل لاكثر من ثلاث ساعات. وشارك عدد كبير من الحضور من الجامعات والمراكز البحثية المختلفة ووسائل الاعلام. كما شارك بالحضور معالي الاستاذ الدكتور / عصام شرف رئيس وزراء مصر الاسبق. وفي نهاية ورشة العمل استعرض الاستاذ الدكتور الشربيني النتائج والتوصيات العامة لورشة العمل والتي بدورها تدعو الي تعزيز دور التكنولوجيا البازغة في مكافحة التغيرات المناخية.
الذكاء الأصطناعى المسؤول ومستقبل الأمن المناخى وانعكاساته الاجتماعية والأمنيةAboul Ella Hassanien
تحت رعاية الاستاذ الدكتور محمود صقر رئيس اكاديمية البحث العلمى والتكنولوجيا وإشراف الاستاذ الدكتور احمد جبر المشرف على المجالس النوعية ينظم مجلس تكنولوجيا المعلومات والاتصالات بالاكاديمية ندوة بعنوان "الذكاء الأصطناعى ومستقبل الأمن المناخى" يوم الاثنين الموافق 7 نوفمبر 2022 باكاديمية البحث العلمى بشارع القصر العينى وتناقش الندوة عدد من المحاور اهمها المخاطر الأمنية المتعلقة بالمناخ وتاثيرات التغير المناخى على الأمن العام و التهديدات المتصاعدة للأمن القومي والعلاقة بين التغير المناخى والموارد الطبيعية والامن الانسانى والتاثيرات المجتمعية بالاضافة الى الاثار المتتالية لتأثيرات تغير المناخ على الأمن الغذائي وأمن الطاقة والامن الإجتماعى والانسانى والذكاء الأصطناعى المسؤول ومستقبل الأمن المناخى وانعكاساته الاجتماعية والانسانية والأمنية ومحور الذكاء الاصطناعي وتعزيزإستراتيجية العمل المناخي.
تحت رعاية
الأستاذ الدكتور محمد الخشت رئيس جامعة القاهرة
كلية التجارة-جامعة القاهرة
دور الذكاء الاصطناعي فى دعم الإقتصاد الأخضر لمواجهة التغيرات المناخية
الإستخدام المسؤول للذكاء الإصطناعى فى سياق تغيرالمناخ خارطة طريق فى عال...Aboul Ella Hassanien
تحت رعاية
الأستاذ الدكتور محمد الخشت رئيس جامعة القاهرة
الأستاذ الدكتور محمد سامي - نائب رئيس الجامعة لشئون خدمة المجتمع والبيئة - جامعة القاهرة
الاستاذ الدكتور رضا عبد الوهاب – عميد كلية الحاسبات والذكاء الإصطناعى – جامعة القاهرة
ويبينار بعنوان
الإستخدام المسؤول للذكاء الإصطناعى
فى سياق تغيرالمناخ
خارطة طريق فى عالم شديد التحديات والإضطرابات
الذكاء الإصطناعي والتغيرات المناخية والبيئية:الفرص والتحديات والأدوات السياسيةAboul Ella Hassanien
تحت رعاية الأستاذ الدكتور محمد الخشت رئيس جامعة القاهرة و الأستاذ الدكتور محمد سامي - نائب رئيس الجامعة لشئون خدمة المجتمع والبيئة - جامعة القاهرة ويبينار بعنزان الذكاء الإصطناعي والتغيرات المناخية والبيئية:الفرص والتحديات والأدوات السياسية
تنظم كلية الحاسبات والذكاء الاصطناعى - جامعة دمياط ويبينار بعنون الذكاء الاصطناعى:أسلحة لاتنام وأفاق لاتنتهى يحاضر فيها الاستاذ الدكتور ابوالعلا عطيفى حسنين الاستاذ بكلية الحاسبات والذكاء الاصطناعى - جامعة القاهرة ومؤسس ورئيس المدرسة العلمية البحثية المصرية وذلك يوم الثلاثاء الموافق 26 ابريل الساعة العاشرة مساء على منصة زووم ويناقش فيها مفهوم الطائرات بدون طيار وتطبيقاتها التجارية والمدنية والعسكرية والامن السيبرانى المعزز بالذكاء الاصطناعى ومفهوم الجيوش الالكترونية وعرض بعض النقاط البحثية فى علوم الطيارات بدون طيار المعزز بتقنيات الذكاء الاصطناعى و التؤمة الرقمية ---
ويبينا بالتعاون مع كلية العلوم الادارية - جامعة الكويت بعنوان اقتصاد ميتافيرس - يوم الاربعاء الموافق 20 ابريل 2022 وتناقش العوالم الافتراضية والاقتصاد الافتراضى
How to Setup Warehouse & Location in Odoo 17 InventoryCeline George
In this slide, we'll explore how to set up warehouses and locations in Odoo 17 Inventory. This will help us manage our stock effectively, track inventory levels, and streamline warehouse operations.
Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.
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Walmart Business+ and Spark Good for Nonprofits.pdfTechSoup
"Learn about all the ways Walmart supports nonprofit organizations.
You will hear from Liz Willett, the Head of Nonprofits, and hear about what Walmart is doing to help nonprofits, including Walmart Business and Spark Good. Walmart Business+ is a new offer for nonprofits that offers discounts and also streamlines nonprofits order and expense tracking, saving time and money.
The webinar may also give some examples on how nonprofits can best leverage Walmart Business+.
The event will cover the following::
Walmart Business + (https://business.walmart.com/plus) is a new shopping experience for nonprofits, schools, and local business customers that connects an exclusive online shopping experience to stores. Benefits include free delivery and shipping, a 'Spend Analytics” feature, special discounts, deals and tax-exempt shopping.
Special TechSoup offer for a free 180 days membership, and up to $150 in discounts on eligible orders.
Spark Good (walmart.com/sparkgood) is a charitable platform that enables nonprofits to receive donations directly from customers and associates.
Answers about how you can do more with Walmart!"
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
How to Make a Field Mandatory in Odoo 17Celine George
In Odoo, making a field required can be done through both Python code and XML views. When you set the required attribute to True in Python code, it makes the field required across all views where it's used. Conversely, when you set the required attribute in XML views, it makes the field required only in the context of that particular view.
Chapter wise All Notes of First year Basic Civil Engineering.pptxDenish Jangid
Chapter wise All Notes of First year Basic Civil Engineering
Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps.
Chapter 4
Buildings: Selection of site for Buildings, Layout of Building Plan, Types of buildings, Plinth area, carpet area, floor space index, Introduction to building byelaws, concept of sun light & ventilation. Components of Buildings & their functions, Basic concept of R.C.C., Introduction to types of foundation
Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect
Text Books:
1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
4. BCP, Surveying volume 1
Chapter wise All Notes of First year Basic Civil Engineering.pptx
Animal identification using machine learning techniques
1. 1
Animal Identification using Machine Learning
Techniques
By
Aya Salama Abdelhady
Under the Supervision of:
Professor Aly Fahmy & Professor Abou Elella Hassanein
Ph.D Presentation
Department of Computer Science
2. Agenda
Introduction
Motivation
Problem Statement
Research Objectives
Literature Review and Current Approaches
Proposed Approach (Deep Neural Networks)
Data Sets
Work plan
2
3. 3
Introduction
Animal identification refers to the
recognition process of animals.
Classical animal identification methods
such as ear tags and tattooing are limited
for decision support due to their
vulnerability to loss and manipulation.
Biometric mapped into animal
identification systems are a promising
trend owing to their uniqueness and
immutability.
Ear notching
Branding
4. Animal identification is vital in in large group of animals
Individual identification allow management of
Stockbreeding programs
Disease and treatment
Arabian Horse identification
Arabian horses are precious and expensive
Arabian horses identification is important in international competitions
Classic methods for horses are considered as scars and also vulnerable to
manipulation
Sheep identification
Guarantee users ownership
Avoid manipulation in type and price
4
Motivation
5. Investigation of bio and physical metrics that lead to
best identification results
(Example of these features are eyes, iris, face, weight)
Data Sets Collection
Arabian horses identification has no available data
sets of horses
Sheep identification has no available data sets of
sheep
5
Problem Statement
6. 6
Thesis Objectives
The aim of the thesis are:
To develop real time mobile application for:
• Arabian horse Identification
• Sheep Identification
To build animal weight estimation module
that would help in animal identification
8. 8
Literature Review
Disadvantages of Classical Methods
Vulnerability to losses, duplications and manipulation
Hot branding and freeze branding cause a lot of pain
Difficult to read
Most of these methods are painful and can lead to
infection, and they can also be considered as scars.
9. 9
• Iris Pattern
• Muzzle Prints
• Face Pattern
• Retinal vascular
Iris is considered one of the most reliable and accurate
biometric
Literature Review
Animal Biometric Methods
10. 10
10
Literature Review
Machine Learning Approaches
Machine Learning/
Artificial Intelligence
Supervised
Learning
Unsupervised
Learning
Deep Learning Other Approaches
Regressi
on
Classificati
on
Clusterin
g
Factor
Analysis
Reinforceme
nt Learning
Semi-
supervised
Active
learning
Lasso,
Ridge,
Loess,
KNN,
Spline,
XGBoos
Logistic,
SVM,
Random
Forest,
Hidden
Markov
K-means,
Birch,
Ward
Spectral
Cluster
PCA,
ICA,
NMF
Multilayer Perceptron
(MLP), Convolutional
Neural Nets (CNN),
Long Short-Term
memory (LSTM),
Restricted Boltzman
Machine (RBM)
11. 11
Literature Review
Deep Learning
Ruggedness to shifts and distortion in the
image
Fewer memory requirements
Easier and better training
Reduces the need for feature engineering,
one of the most time-consuming parts of
machine learning practice.
It significantly outperforms other solutions in
multiple domains, this includes speech,
language, vision
Is an architecture that can be adapted to new
problems relatively easily (e.g. Vision, time
series, language)
12. 12
Literature Review
Animal identification systems
A horse identification system using biometrics System
For iris segmentation iris area was extracted by first defining rectangle around the pupil area by largest dark area recognition with
estimation the most common known radius in all collected images (17,000 digital still images)
Then labels were assigned to different part of the eye; Manual labeling was essential part in that procedure.
Then Gabor filter was used to extract iris characteristics.
For horse identification, hamming distance was calculated to measure angle difference and radius difference between different images .
FRR was reported to be 0.201 and FAR 2.55 *10^-7
Face Recognition as a Biometric Identifier of Sheep
Independent component analysis with the cosine distance classifier, has been used for sheep face recognition
Accuracy is 95.3% (has been evaluated for sheep face recognition using a small set of normalized images)
This accuracy varies under scalability
13. 13
The Suggested System
Data Preparation
Data Augmentation
Investigate deep learning architecture in generative
models
Investigate deep learning architecture in
discriminative models
14. 14
Data Sets Collections
14
Data collection was our first step to
achieve our goal very.
( 6 months of preparation)
Since there is no available
benchmark for horses' eyes, we had
to collect our own data.
Data is collected for 145 horses in
different illumination conditions and
from different angles.
1,015 images were collected
16. 161616
Corpora Nigra
During data collection, we were able to capture
the corpora nigra
Corpora nigra which lies in the iris just above
the pupil very unique shape and differs from
one horse to another .
• Pupil with corpora nigra has a color that is
significantly different from all other parts in the
images.
• K-means clustering is proposed to detect the
pupil without any need for human supervision
19. 1919
Preliminary Results and Evaluation
Iris/pupil segmentation is a main step in
the process of horses recognition.
Circular Hough transform with modified
version of Kovesi’s Canny edge detection
used to detect the circular region
containing both iris and pupil.
We could not use the same way to detect
the iris itself, because as we mentioned
before the iris of the horse is different
from the iris of human.
Circular Hough transform
20. 2020
Preliminary Results and Evaluation (2)
• Pupil with retina cogina has a color that is
significantly different from all other parts in
the images.
• K-means clustering is proposed to detect the
pupil without any need for human supervision
• Euclidean distance is calculated is used to
define the distance of the nearest centroid
• Then connected component labeling was
applied to detect the biggest blob
• Then morphological noise was applied to
remove noise
21. 2121
Preliminary Results and Evaluation (3)
• Jaccard similarity is the most common used matrix to
evaluate the performance of segmentation
• After removing all unwanted images we had total of
320 images to work on.
• Ground truth images were built for 320 images.
• For 256 images, Jaccard coefficient ranged between
80% and 95%, while for the rest 64 images ranged
between 40% and 70%
• Low coefficients were due to high brightness around
the iris and very strong reflection of other objects,
which lead to false data sometimes
Noise in eye images
24. 2424
Work Plan
1. Continue survey and identify How to best
recognize animals using deep learning.
2. Investigate generative and descriptive models
3. Continue working on data collection
4. Design and develop a model for animal
identification
5. Conduct a performance analysis of the developed
model with the existing ones. Partners in this work (Suez Canal University (agriculture faculty)
and Cairo University (Faculty Vet. medicine ))
All accepted papers (regular, short, and poster) will be published by ACM – International Conference Proceedings Series (ICPS) and will be available in ACM Digital Library . ISBN: 978-1-4503-5243-7