80,076 research outputs found
A Study of Colour Rendering in the In-Camera Imaging Pipeline
Consumer cameras such as digital single-lens reflex camera (DSLR) and smartphone cameras have onboard hardware that applies a series of processing steps to transform the initial captured raw sensor image to the final output image that is provided to the user. These processing steps collectively make up the in-camera image processing pipeline. This dissertation aims to study the processing steps related to colour rendering which can be categorized into two stages. The first stage is to convert an image's sensor-specific raw colour space to a device-independent perceptual colour space. The second stage is to further process the image into a display-referred colour space and includes photo-finishing routines to make the image appear visually pleasing to a human.
This dissertation makes four contributions towards the study of camera colour rendering. The first contribution is the development of a software-based research platform that closely emulates the in-camera image processing pipeline hardware. This platform allows the examination of the various image states of the captured image as it is processed from the sensor response to the final display output. Our second contribution is to demonstrate the advantage of having access to intermediate image states within the in-camera pipeline that provide more accurate colourimetric consistency among multiple cameras. Our third contribution is to analyze the current colourimetric method used by consumer cameras and to propose a modification that is able to improve its colour accuracy. Our fourth contribution is to describe how to customize a camera imaging pipeline using machine vision cameras to produce high-quality perceptual images for dermatological applications. The dissertation concludes with a summary and future directions
Big Data Framework Using Spark Architecture for Dose Optimization Based on Deep Learning in Medical Imaging
Deep learning and machine learning provide more consistent tools and powerful functions for recognition, classification, reconstruction, noise reduction, quantification and segmentation in biomedical image analysis. Some breakthroughs. Recently, some applications of deep learning and machine learning for low-dose optimization in computed tomography have been developed. Due to reconstruction and processing technology, it has become crucial to develop architectures and/or methods based on deep learning algorithms to minimize radiation during computed tomography scan inspections. This chapter is an extension work done by Alla et al. in 2020 and explain that work very well. This chapter introduces the deep learning for computed tomography scan low-dose optimization, shows examples described in the literature, briefly discusses new methods for computed tomography scan image processing, and provides conclusions. We propose a pipeline for low-dose computed tomography scan image reconstruction based on the literature. Our proposed pipeline relies on deep learning and big data technology using Spark Framework. We will discuss with the pipeline proposed in the literature to finally derive the efficiency and importance of our pipeline. A big data architecture using computed tomography images for low-dose optimization is proposed. The proposed architecture relies on deep learning and allows us to develop effective and appropriate methods to process dose optimization with computed tomography scan images. The real realization of the image denoising pipeline shows us that we can reduce the radiation dose and use the pipeline we recommend to improve the quality of the captured image
A Comprehensive Survey on Rare Event Prediction
Rare event prediction involves identifying and forecasting events with a low
probability using machine learning and data analysis. Due to the imbalanced
data distributions, where the frequency of common events vastly outweighs that
of rare events, it requires using specialized methods within each step of the
machine learning pipeline, i.e., from data processing to algorithms to
evaluation protocols. Predicting the occurrences of rare events is important
for real-world applications, such as Industry 4.0, and is an active research
area in statistical and machine learning. This paper comprehensively reviews
the current approaches for rare event prediction along four dimensions: rare
event data, data processing, algorithmic approaches, and evaluation approaches.
Specifically, we consider 73 datasets from different modalities (i.e.,
numerical, image, text, and audio), four major categories of data processing,
five major algorithmic groupings, and two broader evaluation approaches. This
paper aims to identify gaps in the current literature and highlight the
challenges of predicting rare events. It also suggests potential research
directions, which can help guide practitioners and researchers.Comment: 44 page
Writer Identification Using Inexpensive Signal Processing Techniques
We propose to use novel and classical audio and text signal-processing and
otherwise techniques for "inexpensive" fast writer identification tasks of
scanned hand-written documents "visually". The "inexpensive" refers to the
efficiency of the identification process in terms of CPU cycles while
preserving decent accuracy for preliminary identification. This is a
comparative study of multiple algorithm combinations in a pattern recognition
pipeline implemented in Java around an open-source Modular Audio Recognition
Framework (MARF) that can do a lot more beyond audio. We present our
preliminary experimental findings in such an identification task. We simulate
"visual" identification by "looking" at the hand-written document as a whole
rather than trying to extract fine-grained features out of it prior
classification.Comment: 9 pages; 1 figure; presented at CISSE'09 at
http://conference.cisse2009.org/proceedings.aspx ; includes the the
application source code; based on MARF described in arXiv:0905.123
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