14,748 research outputs found
Multimodal enhancement-fusion technique for natural images.
Masters Degree. University of KwaZulu-Natal, Durban.This dissertation presents a multimodal enhancement-fusion (MEF) technique for natural images. The MEF is expected to contribute value to machine vision applications and personal image collections for the human user. Image enhancement techniques and the metrics that are used to assess their performance are prolific, and each is usually optimised for a specific objective. The MEF proposes a framework that adaptively fuses multiple enhancement objectives into a seamless pipeline. Given a segmented input image and a set of enhancement methods, the MEF applies all the enhancers to the image in parallel. The most appropriate enhancement in each image segment is identified, and finally, the differentially enhanced segments are seamlessly fused. To begin with, this dissertation studies targeted contrast enhancement methods and performance metrics that can be utilised in the proposed MEF. It addresses a selection of objective assessment metrics for contrast-enhanced images and determines their relationship with the subjective assessment of human visual systems. This is to identify which objective metrics best approximate human assessment and may therefore be used as an effective replacement for tedious human assessment surveys. A subsequent human visual assessment survey is conducted on the same dataset to ascertain image quality as perceived by a human observer. The interrelated concepts of naturalness and detail were found to be key motivators of human visual assessment. Findings show that when assessing the quality or accuracy of these methods, no single quantitative metric correlates well with human perception of naturalness and detail, however, a combination of two or more metrics may be used to approximate the complex human visual response.
Thereafter, this dissertation proposes the multimodal enhancer that adaptively selects the optimal enhancer for each image segment. MEF focusses on improving chromatic irregularities such as poor contrast distribution. It deploys a concurrent enhancement pathway that subjects an image to multiple image enhancers in parallel, followed by a fusion algorithm that creates a composite image that combines the strengths of each enhancement path. The study develops a framework for parallel image enhancement, followed by parallel image assessment and selection, leading to final merging of selected regions from the enhanced set. The output combines desirable attributes from each enhancement pathway to produce a result that is superior to each path taken alone. The study showed that the proposed MEF technique performs well for most image types. MEF is subjectively favourable to a human panel and achieves better performance for objective image quality assessment compared to other enhancement methods
Quality metrics for spectral estimation
Tesina realitzada en col.laboració amb Centre de Desenvolupament de Sensors, Instrumentació i SistemesThe quantitative assessment of the spectral estimation quality in multispectral
imaging systems is an active field of research. The design and optimization of
multispectral imaging systems are very dependent on how the cost function is selected.
Several spectral estimation metrics have been used depending on the attribute it is
intended to measure: visual matching, correlation of spectral curves or reduction of
metamerism. The purpose of this project is to analyze various metrics that have been
used for spectral matches and to show the appropriateness and weakness of each metric
On color image quality assessment using natural image statistics
Color distortion can introduce a significant damage in visual quality
perception, however, most of existing reduced-reference quality measures are
designed for grayscale images. In this paper, we consider a basic extension of
well-known image-statistics based quality assessment measures to color images.
In order to evaluate the impact of color information on the measures
efficiency, two color spaces are investigated: RGB and CIELAB. Results of an
extensive evaluation using TID 2013 benchmark demonstrates that significant
improvement can be achieved for a great number of distortion type when the
CIELAB color representation is used
Full Reference Objective Quality Assessment for Reconstructed Background Images
With an increased interest in applications that require a clean background
image, such as video surveillance, object tracking, street view imaging and
location-based services on web-based maps, multiple algorithms have been
developed to reconstruct a background image from cluttered scenes.
Traditionally, statistical measures and existing image quality techniques have
been applied for evaluating the quality of the reconstructed background images.
Though these quality assessment methods have been widely used in the past,
their performance in evaluating the perceived quality of the reconstructed
background image has not been verified. In this work, we discuss the
shortcomings in existing metrics and propose a full reference Reconstructed
Background image Quality Index (RBQI) that combines color and structural
information at multiple scales using a probability summation model to predict
the perceived quality in the reconstructed background image given a reference
image. To compare the performance of the proposed quality index with existing
image quality assessment measures, we construct two different datasets
consisting of reconstructed background images and corresponding subjective
scores. The quality assessment measures are evaluated by correlating their
objective scores with human subjective ratings. The correlation results show
that the proposed RBQI outperforms all the existing approaches. Additionally,
the constructed datasets and the corresponding subjective scores provide a
benchmark to evaluate the performance of future metrics that are developed to
evaluate the perceived quality of reconstructed background images.Comment: Associated source code: https://github.com/ashrotre/RBQI, Associated
Database:
https://drive.google.com/drive/folders/1bg8YRPIBcxpKIF9BIPisULPBPcA5x-Bk?usp=sharing
(Email for permissions at: ashrotreasuedu
Quality metrics for spectral estimation
Tesina realitzada en col.laboració amb Centre de Desenvolupament de Sensors, Instrumentació i SistemesThe quantitative assessment of the spectral estimation quality in multispectral
imaging systems is an active field of research. The design and optimization of
multispectral imaging systems are very dependent on how the cost function is selected.
Several spectral estimation metrics have been used depending on the attribute it is
intended to measure: visual matching, correlation of spectral curves or reduction of
metamerism. The purpose of this project is to analyze various metrics that have been
used for spectral matches and to show the appropriateness and weakness of each metric
Video shot boundary detection: seven years of TRECVid activity
Shot boundary detection (SBD) is the process of automatically detecting the boundaries between shots in video. It is a problem which has attracted much attention since video became available in digital form as it is an essential pre-processing step to almost all video analysis, indexing, summarisation, search, and other content-based operations. Automatic SBD was one of the tracks of activity within the annual TRECVid benchmarking exercise, each year from 2001 to 2007 inclusive. Over those seven years we have seen 57 different research groups from across the world work to determine the best approaches to SBD while using a common dataset and common scoring metrics. In this paper we present an overview of the TRECVid shot boundary detection task, a high-level overview of the most significant of the approaches taken, and a comparison of performances, focussing on one year (2005) as an example
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