The document outlines a thesis proposal to develop a method for extracting and tracking a human body skeleton from multiple views without markers. It discusses background work in motion tracking, defines the problem of existing methods requiring markers or expensive equipment, and sets objectives to implement a camera-based system using silhouette segmentation and 3D reconstruction to extract an articulated skeleton that can be tracked across video frames. The proposed methodology includes data acquisition, silhouette segmentation, 3D object reconstruction, and extracting the skeleton representation.
Optical Coherence Tomography: Technology and applications for neuroimagingManish Kumar
Optical coherence tomography (OCT) is an emerging imaging technology with applications in biology, medicine, and materials investigations. Attractive features include high cellular-level resolution, real-time acquisition rates, and spectroscopic feature extraction in a compact noninvasive instrument. OCT can perform ‘‘optical biopsies’’ of tissue, producing images approaching the resolution of histology without having to resect and histologically process tissue specimens for characterization and diagnosis.
The document summarizes the BioEngineering Technical Interest Group at Georgia Tech's School of Electrical and Computer Engineering. The group brings together professionals interested in bioengineering to share information. It offers both undergraduate and graduate courses in areas like biomedical instrumentation and biosensors. The research labs focus on areas such as neural interfaces, bioMEMS, image processing for medical applications, and more. Core faculty and affiliated faculty are listed. One highlighted research project aims to improve navigational accuracy for deep brain stimulation surgery.
Samir Kumar Biswas is an ultrasound and optical physicist/engineer seeking a challenging position in biomedical engineering. He has experience developing photoacoustic and ultrasound imaging systems and has published papers on topics like diffuse optical tomography, angiogenesis monitoring, and rheumatoid arthritis diagnosis. He holds a PhD from the Indian Institute of Science and has held research positions at NUS and the University of Twente.
This document provides information about an upcoming "Multimodal Imaging in Neurosciences" course, including:
1) Dates and topics for upcoming lectures, as well as details about a final test on basic imaging techniques.
2) An overview of various neuroimaging modalities like CT, MRI, PET, and their applications.
3) A brief history of the development of high-intensity focused ultrasound (HIFU) technology from the 1880s to present.
Slides for talk at BMES Conference 2011Kriti Sharma
This document describes several applications of micro-computed tomography (micro-CT) imaging at multiple scales. Micro-CT allows higher resolution imaging of biological samples compared to traditional methods. Applications discussed include imaging of vasculature in mouse limbs, fabrication of osteon-like tissue scaffolds, analysis of mouse lung microstructure, and imaging of early Cambrian animal embryo microfossils. The authors collaborate with researchers from various institutions to develop imaging protocols and gain new biological insights not previously possible.
The IEEE International School of Imaging (I2SI) will take place October 14-16, 2014 on the island of Santorini, Greece. The school will explore principles and advancements in imaging technologies for medical diagnostics, pharmaco-imaging, remote sensing, and more. Engineers, scientists, and medical professionals are invited to attend lectures from worldwide experts and interact with others working to advance imaging sciences. Topics will include medical imaging modalities, nanoscale oncology, space instrumentation, semiconductor inspection, and more. The goal is to foster development of novel imaging technologies and applications across various disciplines.
Diabetic retinopathy is a disease, caused by alternation in the retinal blood vessels. It is a strong sign of early blindness and if it is not treated may tend to complete blindness and the vision lost once cannot be restored once again. In this paper different image processing techniques are used to differentiate between the normal and the diseased image. The attempt is made to see where the problem actually lies so that proper diagnosis of patient can be done. Pre processing of an image, optic disk detection, Blood vessels extraction, Exudates detection are some of the methods that are applied here. Other algorithms are designed to obtain the desired result. A large number of populations are affected by this disease around the world.
The Indian Dental Academy is the Leader in continuing dental education , training dentists in all aspects of dentistry and
offering a wide range of dental certified courses in different formats.for more details please visit
www.indiandentalacademy.com
Optical Coherence Tomography: Technology and applications for neuroimagingManish Kumar
Optical coherence tomography (OCT) is an emerging imaging technology with applications in biology, medicine, and materials investigations. Attractive features include high cellular-level resolution, real-time acquisition rates, and spectroscopic feature extraction in a compact noninvasive instrument. OCT can perform ‘‘optical biopsies’’ of tissue, producing images approaching the resolution of histology without having to resect and histologically process tissue specimens for characterization and diagnosis.
The document summarizes the BioEngineering Technical Interest Group at Georgia Tech's School of Electrical and Computer Engineering. The group brings together professionals interested in bioengineering to share information. It offers both undergraduate and graduate courses in areas like biomedical instrumentation and biosensors. The research labs focus on areas such as neural interfaces, bioMEMS, image processing for medical applications, and more. Core faculty and affiliated faculty are listed. One highlighted research project aims to improve navigational accuracy for deep brain stimulation surgery.
Samir Kumar Biswas is an ultrasound and optical physicist/engineer seeking a challenging position in biomedical engineering. He has experience developing photoacoustic and ultrasound imaging systems and has published papers on topics like diffuse optical tomography, angiogenesis monitoring, and rheumatoid arthritis diagnosis. He holds a PhD from the Indian Institute of Science and has held research positions at NUS and the University of Twente.
This document provides information about an upcoming "Multimodal Imaging in Neurosciences" course, including:
1) Dates and topics for upcoming lectures, as well as details about a final test on basic imaging techniques.
2) An overview of various neuroimaging modalities like CT, MRI, PET, and their applications.
3) A brief history of the development of high-intensity focused ultrasound (HIFU) technology from the 1880s to present.
Slides for talk at BMES Conference 2011Kriti Sharma
This document describes several applications of micro-computed tomography (micro-CT) imaging at multiple scales. Micro-CT allows higher resolution imaging of biological samples compared to traditional methods. Applications discussed include imaging of vasculature in mouse limbs, fabrication of osteon-like tissue scaffolds, analysis of mouse lung microstructure, and imaging of early Cambrian animal embryo microfossils. The authors collaborate with researchers from various institutions to develop imaging protocols and gain new biological insights not previously possible.
The IEEE International School of Imaging (I2SI) will take place October 14-16, 2014 on the island of Santorini, Greece. The school will explore principles and advancements in imaging technologies for medical diagnostics, pharmaco-imaging, remote sensing, and more. Engineers, scientists, and medical professionals are invited to attend lectures from worldwide experts and interact with others working to advance imaging sciences. Topics will include medical imaging modalities, nanoscale oncology, space instrumentation, semiconductor inspection, and more. The goal is to foster development of novel imaging technologies and applications across various disciplines.
Diabetic retinopathy is a disease, caused by alternation in the retinal blood vessels. It is a strong sign of early blindness and if it is not treated may tend to complete blindness and the vision lost once cannot be restored once again. In this paper different image processing techniques are used to differentiate between the normal and the diseased image. The attempt is made to see where the problem actually lies so that proper diagnosis of patient can be done. Pre processing of an image, optic disk detection, Blood vessels extraction, Exudates detection are some of the methods that are applied here. Other algorithms are designed to obtain the desired result. A large number of populations are affected by this disease around the world.
The Indian Dental Academy is the Leader in continuing dental education , training dentists in all aspects of dentistry and
offering a wide range of dental certified courses in different formats.for more details please visit
www.indiandentalacademy.com
This document provides an overview of research topics for Priti Mam, presented by Barkha Tyagi, Aakansha, Dhananjay, Ketan, and Deepak, Ankit. It defines research as a systematic process of searching for answers to unknown problems by formulating hypotheses and collecting and analyzing data. It discusses different types of research approaches, including scientific and non-scientific, as well as descriptive vs analytical and quantitative vs qualitative research. The document also outlines the objectives, roles, and significance of research, and different methods of collecting primary and secondary data.
This document provides an overview of research methodology. It defines research as a systematic process of collecting and analyzing information to generate valid and trustworthy knowledge. The document outlines the objectives, characteristics, types and structure of research. It discusses different types of research including applied, fundamental, descriptive, correlational and explanatory research. It also covers quantitative and qualitative research approaches. The significance and importance of research methodology are emphasized.
The document outlines the research methodology process which consists of 11 steps: 1) formulating the research problem, 2) conducting an extensive literature review, 3) developing a working hypothesis, 4) preparing the research design, 5) determining the sample design, 6) collecting the data, 7) executing the project, 8) analyzing the data, 9) hypothesis testing, 10) drawing generalizations and interpretations, and 11) preparing the report. It also discusses key aspects of each step such as different research objectives, types of research, and components of a good research project.
The document discusses and compares quantitative and qualitative research methods. Quantitative research uses numerical data and statistical analysis, while qualitative research uses narrative and visual data to understand phenomena. Both approaches are described in terms of data collection, research procedures, underlying beliefs, and examples of research questions they can address.
Presentation on the characteristic of scientific research 1Junesh Acharya
The document discusses scientific research. It defines research as the systematic analysis and recording of controlled observations that can lead to generalizations and theories. Scientific research has several key characteristics: it pursues truth through logical consideration, is objective and replicable, reliable and valid, rigorous, and testable/generalizable. The research process involves realizing a problem, formulating a hypothesis, designing a study, collecting and analyzing data, and generalizing findings. Overall, scientific research uses scientific methods and tools to systematically study and explain variables in an objective, replicable manner.
Research has several key characteristics: it is empirical, relying on direct observation or experience; logical, following valid procedures and principles; and cyclical, starting with a problem and ending by identifying a new problem. Research also utilizes proven analytical procedures to gather and analyze data using methods like historical analysis, description, experimentation, or case studies. Valid research designs and procedures allow results to be replicated, leading to conclusions, and research requires critical and precise judgment throughout the process.
The document is a research report on Lakme, a cosmetics brand owned by Hindustan Unilever. It discusses the growth of the cosmetics industry in India and Lakme's position as a market leader. The report aims to understand how Lakme can retain customers and its market position against growing competition. Primary and secondary research was conducted, including questionnaires distributed to Lakme customers. The report examines factors like brand awareness, price, communication, and customer satisfaction to evaluate Lakme's performance and identify ways to strengthen its brand personality and promotional strategies.
Brain Tumor Diagnosis using Image De Noising with Scale Invariant Feature Tra...ijtsrd
It is truly challenging for specialists to distinguish mind growth at a beginning phase. X ray pictures are more helpless to the commotion and other natural aggravations. Subsequently, it becomes challenging for specialists to decide on brain tumor and their causes. Thus, we thought of a framework in which the framework will recognize mind growth from pictures. Here we are switching a picture over completely to a grayscale picture. We apply channels to the picture to eliminate commotion and other natural messes from the picture. The framework will deal with the chosen picture utilizing preprocessing steps. Simultaneously, various calculations are utilized to distinguish the growth from the picture. In any case, the edges of the picture wont be sharp in the beginning phases of cerebrum growth. So here we are applying picture division to the picture to recognize the edges of the pictures. We have proposed a picture division process and an assortment of picture separating procedures to get picture qualities. Through this whole interaction, exactness can be moved along. This framework is carried out in Matlab R2021a. The accuracy, Review, F1 Score, and Precision worth of the proposed model works by 0.16 , 1.99 , 0.47 , and 0.28 for CNN Model. Namit Thakur | Dr. Sunil Phulre "Brain Tumor Diagnosis using Image De-Noising with Scale Invariant Feature Transform" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-7 , December 2022, URL: https://www.ijtsrd.com/papers/ijtsrd52272.pdf Paper URL: https://www.ijtsrd.com/medicine/other/52272/brain-tumor-diagnosis-using-image-denoising-with-scale-invariant-feature-transform/namit-thakur
This document describes the work of the BIOENGINEERING group at the University of Padova on biomedical image processing and analysis. The group analyzes images of the cornea, retina, and chromosomes. Their techniques include cell contour recognition in corneal endothelium images using neural networks, estimating cell density in eye bank images using Fourier analysis, tracking blood vessels in the retina, and designing an adaptive optics fundus camera. The goal is to develop automated analysis tools to aid in medical diagnosis and evaluation.
Introduction to deep learning and recent research topics in medical fieldJimin Lee
This document provides an overview of research topics in artificial intelligence and deep learning for medical image analysis and radiation treatment. It first introduces artificial intelligence and deep learning, including how artificial neural networks use deep learning to perform tasks by learning from large amounts of data. It then discusses specific deep learning techniques like convolutional neural networks. The document concludes by describing several of the author's research topics, including using deep learning for chest X-ray analysis, low-dose CT reconstruction, segmentation of organs from CT and MR images for radiation treatment planning, and isotope identification from gamma-ray spectra.
Survey on “Brain Tumor Detection Using Deep LearningIRJET Journal
This document summarizes a research paper on detecting brain tumors using deep learning techniques. It discusses how convolutional neural networks (CNNs) can be applied to MRI images to detect the presence of brain tumors and classify their types. The paper reviews previous work on brain tumor detection using traditional image processing and machine learning methods. It then describes the methodology used in the proposed research, which involves preprocessing MRI images, extracting features using CNN layers, and classifying tumors. The architecture of the proposed CNN model and the various modules in the brain tumor detection system are outlined. The conclusions discuss the role of image segmentation and data augmentation in medical image analysis for brain tumor detection.
Most of the existing image recognitions systems are based on physical parameters of the images whereas image processing methodologies relies on extraction of color, shape and edge features. Thus Transfer Learning is an efficient approach of solving classification problem with little amount of data. There are many deep learning algorithms but most tested one is AlexNet. It is well known Convolution Neural Network AlexNet CNN for recognition of images using deep learning. So for recognition and detection of the image we have proposed Deep Learning approach in this project which can analyse thousands of images which may take a lot for a human to do. Pretrained convolutional neural network i.e. AlexNet is trained by using the features such as textures, colors and shape. The model is trained on more than 1000 images and can classify images into categories which we have defined. The trained model is tested on various standard and own recorded datasets consist of rotational, translated and shifted images. Thus when a image is passed to the system it will apply AlexNet and return the results with a image category in which the image lies with high accuracy. Thus our project tends to reduce time and cost of image recognition systems using deep learning. Dr. Sachin K. Korde | Manoj J. Munda | Yogesh B. Chintamani | Yasir L. Pirjade | Akshay V. Gurme "Image Classification using Deep Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31653.pdf Paper Url :https://www.ijtsrd.com/computer-science/artificial-intelligence/31653/image-classification-using-deep-learning/dr-sachin-k-korde
Object Discovery using CNN Features in Egocentric VideosMarc Bolaños Solà
This document proposes a method for object discovery in egocentric videos using convolutional neural networks (CNN). The method aims to characterize the environment of the person wearing an egocentric camera. It uses an objectness detector to sample object candidates, extracts CNN features to represent objects, and employs a refill strategy and clustering to discover new concepts in an iterative manner. The method is validated on a dataset of 1,000 images labeled with the most frequent objects, outperforming state-of-the-art approaches. Future work includes discovering objects, scenes and people to further characterize the environment.
This document presents an overview of a thesis project on computer-assisted screening of microcalcifications in digitized mammograms for early detection of breast cancer. The project aims to develop a system that can automatically detect microcalcifications in mammogram images to assist radiologists. The system will use techniques like image segmentation, morphological operations, filtering, and feature extraction to preprocess mammogram images and identify microcalcification clusters. A mini-MIAS database containing 322 mammogram images will be used to test and evaluate the methodology. The document outlines the background, motivation, challenges, plan of action and materials/tools for the project.
The main objective of this work is to facilitate the identification, sharing, and reasoning about cerebral tumors observations via the formalization of their semantic meanings in order to facilitate their exploitation in both the clinical practice and research. We focused our analysis on the VASARI terminology as a proof of concept, but we are convinced that our work can be useful in other biomedical imaging contexts.
The document proposes using nanotechnology to design biocomputers called nanites to treat Parkinson's disease. The nanites would be less than 10 micrometers in size and target the neuromuscular junction to restore muscle function in Parkinson's patients. The goals are to control muscle function, determine how to program and power the nanites, and evaluate them safely through clinical trials to drastically improve the lives of Parkinson's patients without side effects.
The theory and practice of computational cognitive neuroscienceBrian Spiering
This document summarizes the Theory and Practice of Computational Cognitive Neuroscience according to Dr. Brian Spiering. It first provides context by outlining how to evaluate cognitive models. It then discusses the theoretical underpinnings of modeling including modeling individual units, learning, and behavior. Finally, it describes the practical application of modeling through the SPEED model, which proposes that procedural expertise develops through subcortical pathways independently of the striatum where initial procedural learning occurs.
The document discusses image processing techniques for measuring dimensions from images. It proposes using image processing to determine lengths, diameters, splines, and caliper measurements by acquiring an image, smoothing it, segmenting it, and applying the Euclidean algorithm to find exact measurements in pixels. The approach could provide more accurate measurements than physical scales or tapes by marking individual pixel endpoints rather than human-visible lengths.
The document summarizes a presentation on deep learning in radiology. It provides an overview of deep learning concepts like artificial neural networks and convolutional neural networks. It discusses how deep learning can help with tasks in radiology like disease detection, classification of images as benign or malignant, segmentation of organs and lesions. The presentation also reviewed challenges of small medical image datasets and the future potential for deep learning algorithms to assist radiologists.
This document provides an overview of research topics for Priti Mam, presented by Barkha Tyagi, Aakansha, Dhananjay, Ketan, and Deepak, Ankit. It defines research as a systematic process of searching for answers to unknown problems by formulating hypotheses and collecting and analyzing data. It discusses different types of research approaches, including scientific and non-scientific, as well as descriptive vs analytical and quantitative vs qualitative research. The document also outlines the objectives, roles, and significance of research, and different methods of collecting primary and secondary data.
This document provides an overview of research methodology. It defines research as a systematic process of collecting and analyzing information to generate valid and trustworthy knowledge. The document outlines the objectives, characteristics, types and structure of research. It discusses different types of research including applied, fundamental, descriptive, correlational and explanatory research. It also covers quantitative and qualitative research approaches. The significance and importance of research methodology are emphasized.
The document outlines the research methodology process which consists of 11 steps: 1) formulating the research problem, 2) conducting an extensive literature review, 3) developing a working hypothesis, 4) preparing the research design, 5) determining the sample design, 6) collecting the data, 7) executing the project, 8) analyzing the data, 9) hypothesis testing, 10) drawing generalizations and interpretations, and 11) preparing the report. It also discusses key aspects of each step such as different research objectives, types of research, and components of a good research project.
The document discusses and compares quantitative and qualitative research methods. Quantitative research uses numerical data and statistical analysis, while qualitative research uses narrative and visual data to understand phenomena. Both approaches are described in terms of data collection, research procedures, underlying beliefs, and examples of research questions they can address.
Presentation on the characteristic of scientific research 1Junesh Acharya
The document discusses scientific research. It defines research as the systematic analysis and recording of controlled observations that can lead to generalizations and theories. Scientific research has several key characteristics: it pursues truth through logical consideration, is objective and replicable, reliable and valid, rigorous, and testable/generalizable. The research process involves realizing a problem, formulating a hypothesis, designing a study, collecting and analyzing data, and generalizing findings. Overall, scientific research uses scientific methods and tools to systematically study and explain variables in an objective, replicable manner.
Research has several key characteristics: it is empirical, relying on direct observation or experience; logical, following valid procedures and principles; and cyclical, starting with a problem and ending by identifying a new problem. Research also utilizes proven analytical procedures to gather and analyze data using methods like historical analysis, description, experimentation, or case studies. Valid research designs and procedures allow results to be replicated, leading to conclusions, and research requires critical and precise judgment throughout the process.
The document is a research report on Lakme, a cosmetics brand owned by Hindustan Unilever. It discusses the growth of the cosmetics industry in India and Lakme's position as a market leader. The report aims to understand how Lakme can retain customers and its market position against growing competition. Primary and secondary research was conducted, including questionnaires distributed to Lakme customers. The report examines factors like brand awareness, price, communication, and customer satisfaction to evaluate Lakme's performance and identify ways to strengthen its brand personality and promotional strategies.
Brain Tumor Diagnosis using Image De Noising with Scale Invariant Feature Tra...ijtsrd
It is truly challenging for specialists to distinguish mind growth at a beginning phase. X ray pictures are more helpless to the commotion and other natural aggravations. Subsequently, it becomes challenging for specialists to decide on brain tumor and their causes. Thus, we thought of a framework in which the framework will recognize mind growth from pictures. Here we are switching a picture over completely to a grayscale picture. We apply channels to the picture to eliminate commotion and other natural messes from the picture. The framework will deal with the chosen picture utilizing preprocessing steps. Simultaneously, various calculations are utilized to distinguish the growth from the picture. In any case, the edges of the picture wont be sharp in the beginning phases of cerebrum growth. So here we are applying picture division to the picture to recognize the edges of the pictures. We have proposed a picture division process and an assortment of picture separating procedures to get picture qualities. Through this whole interaction, exactness can be moved along. This framework is carried out in Matlab R2021a. The accuracy, Review, F1 Score, and Precision worth of the proposed model works by 0.16 , 1.99 , 0.47 , and 0.28 for CNN Model. Namit Thakur | Dr. Sunil Phulre "Brain Tumor Diagnosis using Image De-Noising with Scale Invariant Feature Transform" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-7 , December 2022, URL: https://www.ijtsrd.com/papers/ijtsrd52272.pdf Paper URL: https://www.ijtsrd.com/medicine/other/52272/brain-tumor-diagnosis-using-image-denoising-with-scale-invariant-feature-transform/namit-thakur
This document describes the work of the BIOENGINEERING group at the University of Padova on biomedical image processing and analysis. The group analyzes images of the cornea, retina, and chromosomes. Their techniques include cell contour recognition in corneal endothelium images using neural networks, estimating cell density in eye bank images using Fourier analysis, tracking blood vessels in the retina, and designing an adaptive optics fundus camera. The goal is to develop automated analysis tools to aid in medical diagnosis and evaluation.
Introduction to deep learning and recent research topics in medical fieldJimin Lee
This document provides an overview of research topics in artificial intelligence and deep learning for medical image analysis and radiation treatment. It first introduces artificial intelligence and deep learning, including how artificial neural networks use deep learning to perform tasks by learning from large amounts of data. It then discusses specific deep learning techniques like convolutional neural networks. The document concludes by describing several of the author's research topics, including using deep learning for chest X-ray analysis, low-dose CT reconstruction, segmentation of organs from CT and MR images for radiation treatment planning, and isotope identification from gamma-ray spectra.
Survey on “Brain Tumor Detection Using Deep LearningIRJET Journal
This document summarizes a research paper on detecting brain tumors using deep learning techniques. It discusses how convolutional neural networks (CNNs) can be applied to MRI images to detect the presence of brain tumors and classify their types. The paper reviews previous work on brain tumor detection using traditional image processing and machine learning methods. It then describes the methodology used in the proposed research, which involves preprocessing MRI images, extracting features using CNN layers, and classifying tumors. The architecture of the proposed CNN model and the various modules in the brain tumor detection system are outlined. The conclusions discuss the role of image segmentation and data augmentation in medical image analysis for brain tumor detection.
Most of the existing image recognitions systems are based on physical parameters of the images whereas image processing methodologies relies on extraction of color, shape and edge features. Thus Transfer Learning is an efficient approach of solving classification problem with little amount of data. There are many deep learning algorithms but most tested one is AlexNet. It is well known Convolution Neural Network AlexNet CNN for recognition of images using deep learning. So for recognition and detection of the image we have proposed Deep Learning approach in this project which can analyse thousands of images which may take a lot for a human to do. Pretrained convolutional neural network i.e. AlexNet is trained by using the features such as textures, colors and shape. The model is trained on more than 1000 images and can classify images into categories which we have defined. The trained model is tested on various standard and own recorded datasets consist of rotational, translated and shifted images. Thus when a image is passed to the system it will apply AlexNet and return the results with a image category in which the image lies with high accuracy. Thus our project tends to reduce time and cost of image recognition systems using deep learning. Dr. Sachin K. Korde | Manoj J. Munda | Yogesh B. Chintamani | Yasir L. Pirjade | Akshay V. Gurme "Image Classification using Deep Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31653.pdf Paper Url :https://www.ijtsrd.com/computer-science/artificial-intelligence/31653/image-classification-using-deep-learning/dr-sachin-k-korde
Object Discovery using CNN Features in Egocentric VideosMarc Bolaños Solà
This document proposes a method for object discovery in egocentric videos using convolutional neural networks (CNN). The method aims to characterize the environment of the person wearing an egocentric camera. It uses an objectness detector to sample object candidates, extracts CNN features to represent objects, and employs a refill strategy and clustering to discover new concepts in an iterative manner. The method is validated on a dataset of 1,000 images labeled with the most frequent objects, outperforming state-of-the-art approaches. Future work includes discovering objects, scenes and people to further characterize the environment.
This document presents an overview of a thesis project on computer-assisted screening of microcalcifications in digitized mammograms for early detection of breast cancer. The project aims to develop a system that can automatically detect microcalcifications in mammogram images to assist radiologists. The system will use techniques like image segmentation, morphological operations, filtering, and feature extraction to preprocess mammogram images and identify microcalcification clusters. A mini-MIAS database containing 322 mammogram images will be used to test and evaluate the methodology. The document outlines the background, motivation, challenges, plan of action and materials/tools for the project.
The main objective of this work is to facilitate the identification, sharing, and reasoning about cerebral tumors observations via the formalization of their semantic meanings in order to facilitate their exploitation in both the clinical practice and research. We focused our analysis on the VASARI terminology as a proof of concept, but we are convinced that our work can be useful in other biomedical imaging contexts.
The document proposes using nanotechnology to design biocomputers called nanites to treat Parkinson's disease. The nanites would be less than 10 micrometers in size and target the neuromuscular junction to restore muscle function in Parkinson's patients. The goals are to control muscle function, determine how to program and power the nanites, and evaluate them safely through clinical trials to drastically improve the lives of Parkinson's patients without side effects.
The theory and practice of computational cognitive neuroscienceBrian Spiering
This document summarizes the Theory and Practice of Computational Cognitive Neuroscience according to Dr. Brian Spiering. It first provides context by outlining how to evaluate cognitive models. It then discusses the theoretical underpinnings of modeling including modeling individual units, learning, and behavior. Finally, it describes the practical application of modeling through the SPEED model, which proposes that procedural expertise develops through subcortical pathways independently of the striatum where initial procedural learning occurs.
The document discusses image processing techniques for measuring dimensions from images. It proposes using image processing to determine lengths, diameters, splines, and caliper measurements by acquiring an image, smoothing it, segmenting it, and applying the Euclidean algorithm to find exact measurements in pixels. The approach could provide more accurate measurements than physical scales or tapes by marking individual pixel endpoints rather than human-visible lengths.
The document summarizes a presentation on deep learning in radiology. It provides an overview of deep learning concepts like artificial neural networks and convolutional neural networks. It discusses how deep learning can help with tasks in radiology like disease detection, classification of images as benign or malignant, segmentation of organs and lesions. The presentation also reviewed challenges of small medical image datasets and the future potential for deep learning algorithms to assist radiologists.
Image annotation - Segmentation & AnnotationTaposh Roy
This document discusses image annotation and segmentation. It begins with an overview of different types of image annotation including whole image classification, object detection, and image segmentation. It then covers supervised and unsupervised machine learning paradigms for image annotation, with a focus on supervised learning. Specific supervised annotation techniques for medical images are discussed like mean shift, normalized cuts, and level sets algorithms. Advanced clustering techniques for image segmentation like DBSCAN, HDBSCAN, and topological data analysis are also mentioned.
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 discusses object detection using deep learning. It provides an introduction to object detection and outlines the history from traditional methods to modern deep learning-based approaches. Several popular deep learning models for object detection are described, including R-CNN, SSD, and YOLO. Three research papers on object detection are reviewed that evaluate methods like YOLOv4, R-CNN, and convolutional neural networks. The results of one proposed approach are presented along with a comparison of test speeds between algorithms. Finally, the conclusion states that deep learning networks can detect objects with more efficiency and accuracy than previous methods.
This document describes research on bio-inspired active vision systems. It discusses how biological vision differs from traditional computer vision in being active rather than passive. The researchers are developing active vision systems using an evolutionary robotics approach, involving neural networks and genetic algorithms. Previous related work is described, including obstacle avoidance by Mars rovers and koala robots. The document outlines plans to design an active vision system to recognize objects using a dataset of images under different conditions, and accelerate it with GPUs. Results showed the system learned to correctly classify objects over generations.
This document describes a study that used convolutional neural networks (CNNs) for animal classification from images. The study proposed a novel method for animal face classification using CNN features. The CNN model was trained on images to classify animals into different classes. The model achieved over 90% accuracy on the test data. The authors concluded that CNNs are well-suited for image classification tasks like animal classification due to their ability to automatically extract relevant features from images. Future work could involve classifying other objects using this deep learning approach.
The complete human body or the various limb postures are involved in human action. These days,
Abnormal Human Activity Recognition (Abnormal HAR) is highly well noticed and surveyed in many
studies. However, because of complicated difficulties such as sensor movement, positioning, and so on,
as well as how individuals carry out their activities, it continues to be a difficult process. Identifying
particular activities benefits human-centric applications such as postoperative trauma recovery, gesture
detection, exercise, fitness, and home care help. The HAR system has the ability to automate or
simplify most of the people’s everyday chores. HAR systems often use supervised or unsupervised
learning as their foundation. Unsupervised systems operate according to a set of rules, whereas
supervised systems need to be trained beforehand using specific datasets. This study conducts detailed
literature reviews on the development of various activity identification techniques currently being used.
The three methods—wearable device-based, pose-based, and smartphone sensor—are examined in this
inquiry for identifying abnormal acts (AAD). The sensors in wearable devices collect data, whereas the
gyroscopes and accelerometers in smartphones provide input to the sensors in wearable devices. To
categorize activities, pose estimation uses a neural network. The Anomalous Action Detection Dataset
(Ano-AAD) is created and improved using several methods. The study examines fresh datasets and
innovative models, including UCF-Crime. A new pattern in anomalous HAR systems has emerged,
linking anomalous HAR tasks to computer vision applications including security, video surveillance,
and home monitoring. In terms of issues and potential solutions, the survey looks at visionbased HAR.
Detection of abnormal human behavior using deep learning
Presentation en seminarioiii
1. Background
Problem Definition
Methodology
Extraction and Tracking of a body Skeleton
from Multiple views
Master’s thesis proposal
Alexander Pinzon Fernandez
August 31, 2009
Alexander Pinzon Fernandez Bioingenium Research Group
2. Background
Problem Definition
Methodology
Outline
1 Background
Background
2 Problem Definition
Problem
Objectives
3 Methodology
Methodology
Alexander Pinzon Fernandez Bioingenium Research Group
3. Background
Problem Definition Background
Methodology
Outline
1 Background
Background
2 Problem Definition
Problem
Objectives
3 Methodology
Methodology
Alexander Pinzon Fernandez Bioingenium Research Group
4. Background
Problem Definition Background
Methodology
Background
The study of the human body has been of interest in several areas.
For example anatomy, engineering and arts.
The movement record has been used to solve different problems.
Diagnosis of gait disorders.
Motion capture for computer character animation [3].
Advanced ergonomics analysis and design.
Alexander Pinzon Fernandez Bioingenium Research Group
5. Background
Problem
Problem Definition
Objectives
Methodology
Outline
1 Background
Background
2 Problem Definition
Problem
Objectives
3 Methodology
Methodology
Alexander Pinzon Fernandez Bioingenium Research Group
6. Background
Problem
Problem Definition
Objectives
Methodology
Problem Definition
Most traditional motion tracking methods are based on optical
systems and present the following disadvantages:
• The use of markers attached to the body ALTER the movement
gesture.
• The need of experts to place the markers because they must be
located in specific anthropometric points.
Alexander Pinzon Fernandez Bioingenium Research Group
7. Background
Problem
Problem Definition
Objectives
Methodology
Problem Definition
The stereo systems that perform a three-dimensional
reconstruction MUST handle large volumes of data of the body
geometry. This process requires high-performance machines
Cost between 60,000 and 130,000 dollars (Data for 2009)
Qualisys AB , Sweden. 8 Oqus cameras: 100.000 USD
Sports Motion, Inc . USA, California. 8 DV Cameras: 60.000
USD
BTS Spa, Italy. 10 infrared cameras Smart-DDigital: 132.000
USD
Alexander Pinzon Fernandez Bioingenium Research Group
8. Background
Problem
Problem Definition
Objectives
Methodology
Description Project
To develop a method to extract the skeleton of an articulated body,
under the following conditions:
• No body model.
• Not having a model is an advantage because it is possible to
extract and track the articulated skeleton of any body.
• The extracted skeleton is a synthesized representation of the
body geometry.
• Once the skeleton is extracted, it is followed for each frame of the
video
Alexander Pinzon Fernandez Bioingenium Research Group
9. Background
Problem
Problem Definition
Objectives
Methodology
Outline
1 Background
Background
2 Problem Definition
Problem
Objectives
3 Methodology
Methodology
Alexander Pinzon Fernandez Bioingenium Research Group
10. Background
Problem
Problem Definition
Objectives
Methodology
Objectives
Overall Objective: To develop a method for motion tracking of the
human body in 3D.
Specific Objectives:
1. To implement a video-camera based system to capture a body
motion, and implement the Σ − ∆ Sigma-Delta method to extract
the silhouette of the body from videos.
2. To propose a method for extracting body markers which
correspond to the fundamental body relations.
3. To propose a method to track the skeleton in each video-frame
and validate the results.
4. To develop a system to visualize the body movement together
with the estimated skeleton.
Alexander Pinzon Fernandez Bioingenium Research Group
11. Background
Problem Definition Methodology
Methodology
Outline
1 Background
Background
2 Problem Definition
Problem
Objectives
3 Methodology
Methodology
Alexander Pinzon Fernandez Bioingenium Research Group
12. Background
Problem Definition Methodology
Methodology
Methodology
Skeleton Extraction and Motion Tracking
• Data acquisition.
• Silhouette Segmentation.
• 3D Reconstruction from the data.
• Skeleton extraction of the reconstructed three-dimensional object.
Alexander Pinzon Fernandez Bioingenium Research Group
13. Background
Problem Definition Methodology
Methodology
Data Acquisition and Segmentation
Objective: To implement a video-cameras based system to capture a body motion
Video Capture system (named optical system) is divided into two
problems, Camera Calibration and Synchronized Multicamera
[1].
The image segmentation of this videos is realized with a set of
methods for dividing an image into regions, given certain
characteristics[2].
Alexander Pinzon Fernandez Bioingenium Research Group
14. Background
Problem Definition Methodology
Methodology
Three Dimensional Reconstruction
Objective: To propose a method for extracting markers of the body fundamental relations
using the videos.
3D Reconstruction steps:
Shape from silhouette
Visuall Hull
Find characteristic points
Extract characteristic points using visual attention model
Stereoscopic reconstruction
Surface reconstruction with the photometric method called
stereo pair, using shape and characteristic points
Alexander Pinzon Fernandez Bioingenium Research Group
15. Background
Problem Definition Methodology
Methodology
Skeleton Extraction
Skeleton extraction is the process of synthesizing and representing a
body in a 1D structure
Based on medial axis concept.
Validation:
• Centered skeleton is its centeredness within the object
• Homotopic skeleton have the same number of connected
components, tunnels, and cavities.
Alexander Pinzon Fernandez Bioingenium Research Group
16. Background
Problem Definition Methodology
Methodology
Bibliography
G.K.M. Cheung, S. Baker, and T. Kanade.
Visual hull alignment and refinement across time: a 3d reconstruction algorithm
combining shape-from-silhouette with stereo.
In Computer Vision and Pattern Recognition, 2003. Proceedings. 2003 IEEE
Computer Society Conference on, volume 2, pages II–375–82 vol.2, June 2003.
Ning Jin and F. Mokhtarian.
Image-based shape model for view-invariant human motion recognition.
In Advanced Video and Signal Based Surveillance, pages 336–341, Sept. 2007.
Fabio Remondino.
3-d reconstruction of static human body shape from image sequence.
Computer Vision and Image Understanding, 93:65–85, 2004.
Alexander Pinzon Fernandez Bioingenium Research Group