image recognition thesis

• Faculty of Arts and Sciences
• FAS Theses and Dissertations
• Communities & Collections
• By Issue Date
• FAS Department
• Quick submit
• Waiver Generator
• DASH Stories
• Accessibility
• COVID-related Research

Terms of Use

• Privacy Policy
• By Collections
• By Departments

Generalizable and Explainable Deep Learning in Medical Imaging with Small Data

Citable link to this page

Collections.

• FAS Theses and Dissertations [6138]

Contact administrator regarding this item (to report mistakes or request changes)

Help | Advanced Search

Computer Science > Computer Vision and Pattern Recognition

Title: master's thesis : deep learning for visual recognition.

Abstract: The goal of our research is to develop methods advancing automatic visual recognition. In order to predict the unique or multiple labels associated to an image, we study different kind of Deep Neural Networks architectures and methods for supervised features learning. We first draw up a state-of-the-art review of the Convolutional Neural Networks aiming to understand the history behind this family of statistical models, the limit of modern architectures and the novel techniques currently used to train deep CNNs. The originality of our work lies in our approach focusing on tasks with a low amount of data. We introduce different models and techniques to achieve the best accuracy on several kind of datasets, such as a medium dataset of food recipes (100k images) for building a web API, or a small dataset of satellite images (6,000) for the DSG online challenge that we've won. We also draw up the state-of-the-art in Weakly Supervised Learning, introducing different kind of CNNs able to localize regions of interest. Our last contribution is a framework, build on top of Torch7, for training and testing deep models on any visual recognition tasks and on datasets of any scale.

Submission history

Access paper:.

• Other Formats

References & Citations

• Google Scholar
• Semantic Scholar

DBLP - CS Bibliography

Bibtex formatted citation.

Bibliographic and Citation Tools

Code, data and media associated with this article, recommenders and search tools.

• Institution

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs .

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to  upgrade your browser .

Enter the email address you signed up with and we'll email you a reset link.

• We're Hiring!
• Help Center

Image Recognition by Deep Learning

Object recognition has become a crucial topic in the field of computer vision. Poor qualities of images unable bring out the desired object as per expectancy. Many models have proposed to recognize object from image. However, most of these approaches hardly achieve high accuracy and precision. It creates a major obstacle to get correctness of the research because of the lighting, illumination, image quality, noise, ethnicity and various angels of similar objects. Therefore, we have proposed a novel approach to detect any object by CNN method including HAAR Cascade classifier where we first detect the most prominent features from scene using Haar Feature Based Cascade Classifier that has been introduced by Paul Viola and Michael Jones. In the second phase, the classification has been used for Convolutional Neural Network to detect the object automatically with better accuracy and more efficiently. It can determine any object after proper training and data set manipulation. Our proposed method for image recognition has achieved very good accuracy than our expectation.

Related Papers

International Journal for Research in Applied Science and Engineering Technology IJRASET

IJRASET Publication

Object Detection is an emerging technology in the field of Computer Vision and Image Processing that deals with detecting objects of a particular class in digital images. It has considered being one of the complicated and challenging tasks in computer vision. Earlier several machine learning-based approaches like SIFT (Scale-invariant feature transform) and HOG (Histogram of oriented gradients) are widely used to classify objects in an image. These approaches use the Support vector machine for classification. The biggest challenges with these approaches are that they are computationally intensive for use in real-time applications, and these methods do not work well with massive datasets. To overcome these challenges, we implemented a Deep Learning based approach Convolutional Neural Network (CNN) in this paper. The Proposed approach provides accurate results in detecting objects in an image by the area of object highlighted in a Bounding Box along with its accuracy.

ijetrm journal

Ijetrm Journal

Image Detection is the branch of the Technology of information and software Systems which can recognize as well as understand images and scenes. Image detection consists of various aspects such as image recognition, image generation, image super-resolution and many more. Object detection is widely used for face detection, vehicle detection, pedestrian counting, web images, security systems and self-driving cars in this project, we are using highly accurate object detection-algorithms and CNN. Using these methods and algorithms, based on deep learning which is also based on machine learning require lots of mathematical and deep learning frameworks understanding by using dependencies such as Tensor Flow, Open CV, Image, Al etc. We can detect each and every object in image by the area object in a highlighted rectangular box and identify each and every object and assign its tag to the object. Image or object detection is a computer technology that processes the image and detects objects in it. We discuss the methods and approaches utilized to detect objects in this study.

Muhammad Afraz Muzammil

Deep learning methods are revolutionizing the image classification. CNN are the most successful method in deep learning because conventional image classification technique were based on the hand coded features which were not robust to different lightning conditions and would fail when exposed to different object orientation. In this paper an architecture is selected after comparing different architectures on the dataset which is also optimized to predict the object belonging to the classes in the dataset. Finally the analysis is given along with conclusions

Science Insights

Insights Publisher

Traditional image classification methods are difficult to process huge image data and cannot meet people's requirements for image classification accuracy and speed. Convolutional neural networks have achieved a series of breakthrough research results in image classification, object detection, and image semantic segmentation. This method broke through the bottleneck of traditional image classification methods and became the mainstream algorithm for image classification. Its powerful feature learning and classification capabilities have attracted widespread attention. How to effectively use convolutional neural networks to classify images have become research hotspots. In this paper, after a systematic study of convolutional neural networks and an in-depth study of the application of convolutional neural networks in image processing, the mainstream structural models, advantages and disadvantages, time / space used in image classification based on convolutional neural networks are given. Complexity, problems that may be encountered during model training, and corresponding solutions. At the same time, the generative adversarial network and capsule network based on the deep learning-based image classification extension model are also introduced; simulation experiments verify the image classification In terms of accuracy, the image classification method based on convolutional neural networks is superior to traditional image classification methods. At the same time, the performance differences between the currently popular convolutional neural network models are comprehensively compared and the advantages and disadvantages of various models are further verified. Experiments and analysis of overfitting problem, data set construction method, generative adversarial network and capsule network performance.■

NWSA Academic Journals

Yıldız Aydın

Computer Science & Engineering: An International Journal

Utkarsh Namdev

Object detection is a computer technique that searches digital images and videos for occurrences of meaningful subjects in particular categories (such as people, buildings, and automobiles). It is related to computer vision and image processing. Two well-studied aspects of identification are facial and pedestrian detection. Object detection is useful in a wide range of visual recognition tasks, including image retrieval and video monitoring. The object detection algorithm has been improved many times to improve the performance in terms of speed and accuracy. “Due to the tireless efforts of many researchers, deep learning algorithms are rapidly improving their object detection performance. Pedestrian detection, medical imaging, robotics, self-driving cars, face recognition and other popular applications have reduced labor in many areas.” It is used in a wide variety of industries, with applications range from individual safeguarding to business productivity. It is a fundamental compo...

International Journal of Engineering Research in Computer Science and Engineering

Chuang-Jan Chang

Deep learning is a scientific field in Machine Learning (ML) that is developing with various applications, one of which is visual image processing technology. With the excellent capabilities of computer vision, image processing from computer visuals is used to duplicate the human ability to understand object information in the image. One of the Machine Learning (ML) methods that can be used for object classification in images is the Convolution Neural Network (CNN) method. The two core stages when processing object classification in the image, the first stage is image classification using feedforward, and the second stage applies the backpropagation method. In this study, before the classification stage, this method was first carried out through preprocessing, which is useful as an image separation to focus on the object to be classified. Furthermore, it is carried out by conducting pre-training using the feedforward method with the bias weights, which are updated after every traini...

WARSE The World Academy of Research in Science and Engineering

Miftahul Hasanah

Based on a survey released by the TomTom Traffic Index in 2018, Indonesia was ranked seventh in the category of the most congested country in the world. One of the factors affecting traffic congestion in Indonesia is an inflexible and conventional traffic management system. In this regard, it is necessary to have a better traffic management system such as a Smart Traffic Light. One way to implement a smart traffic light system is to make a vehicle detection and counting system on the traffic CCTV video automatically. The methods used in this research are Haar Cascade Classifiers and Convolutional Neural Network. Haar Cascade Classifiers have fast computation processes and CNN is applied to validate the detection results of the Haar Cascade method for better accuracy. The average level of accuracy achieved by the system on quiet test data is 82%, normal test data is 69%, and busy test data is 60%. Meanwhile, the average computation time needed by the system for the quiet test data is...

Academic Journal of Nawroz University

Shahad Mohammed

RELATED PAPERS

Anne Mitchell

Acta Clinica Croatica

American Journal of Environmental Sciences

Ilham Elarabi

hamideh asadi

Karen McAllister

Zuzana Havrdova

Designs, Codes and Cryptography

Patrick Sole

Infectious Diseases and Clinical Microbiology

Onder Ergonul

Scripta Metallurgica

Emad Batawi

IEEE Transactions on Services Computing

Mauro Pezze'

Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies

usask毕业证文凭萨省大学本科毕业证书 加拿大学历学位认证如何办理

David Feder

Enjeux et société

Sylvain Rocheleau

IEEE Journal of Oceanic Engineering

Hamza Qayyum

Udit Dhingra

Journal of Atmospheric and Oceanic Technology

Thomas Vonderhaar

The Journal of Physical Chemistry B

Prince Eric Perez

Ferenc Kilár

RELATED TOPICS

•   We're Hiring!
•   Help Center
• Find new research papers in:
• Health Sciences
• Earth Sciences
• Cognitive Science
• Mathematics
• Computer Science
• Academia ©2024

ACCELERATING SPARSE MACHINE LEARNING INFERENCE

Convolutional neural networks (CNNs) have become important workloads due to their impressive accuracy in tasks like image classification and recognition. Convolution operations are compute intensive, and this cost profoundly increases with newer and better CNN models. However, convolutions come with characteristics such as sparsity which can be exploited. In this dissertation, we propose three different works to capture sparsity for faster performance and reduced energy. 

The first work is an accelerator design called SparTen for improving two- sided sparsity (i.e, sparsity in both filters and feature maps) convolutions with fine-grained sparsity. SparTen identifies efficient inner join as the key primitive for hardware acceleration of sparse convolution. In addition, SparTen proposes load balancing schemes for higher compute unit utilization. SparTen performs 4.7x, 1.8x and 3x better than dense architecture, one-sided architecture and SCNN, the previous state of the art accelerator. The second work BARISTA scales up SparTen (and SparTen like proposals) to large-scale implementation with as many compute units as recent dense accelerators (e.g., Googles Tensor processing unit) to achieve full speedups afforded by sparsity. However at such large scales, buffering, on-chip bandwidth, and compute utilization are highly intertwined where optimizing for one factor strains another and may invalidate some optimizations proposed in small-scale implementations. BARISTA proposes novel techniques to balance the three factors in large- scale accelerators. BARISTA performs 5.4x, 2.2x, 1.7x and 2.5x better than dense, one- sided, naively scaled two-sided and an iso-area two-sided architecture, respectively. The last work, EUREKA builds an efficient tensor core to execute dense, structured and unstructured sparsity with losing efficiency. EUREKA achieves this by proposing novel techniques to improve compute utilization by slightly tweaking operand stationarity. EUREKA achieves a speedup of 5x, 2.5x, along with 3.2x and 1.7x energy reductions over Dense and structured sparse execution respectively. EUREKA only incurs area and power overheads of 6% and 11.5%, respectively, over Ampere

1618921-CNS

1405939-cns, degree type.

• Doctor of Philosophy
• Electrical and Computer Engineering

Campus location

• West Lafayette

Advisor/Supervisor/Committee Chair

Advisor/supervisor/committee co-chair, additional committee member 2, additional committee member 3, additional committee member 4, usage metrics.

• Digital processor architectures
• Electronic device and system performance evaluation, testing and simulation
• Electronics, sensors and digital hardware not elsewhere classified
• High performance computing
• Energy-efficient computing

Image Recognition Using Artificial Intelligence

Ieee account.

• Change Username/Password
• Update Address

Purchase Details

• Payment Options
• Order History
• View Purchased Documents

Profile Information

• Communications Preferences
• Profession and Education
• Technical Interests
• US & Canada: +1 800 678 4333
• Worldwide: +1 732 981 0060
• Contact & Support
• About IEEE Xplore
• Accessibility
• Terms of Use
• Nondiscrimination Policy
• Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. © Copyright 2024 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.

Facialemotionrecognition

Object detection, thesis image dataset, 2024-05-14 6:04pm, popular download formats, 1571 total images.

Dataset Split

Preprocessing, augmentations, similar projects.