1,517 research outputs found
No-Reference Quality Assessment of Contrast-Distorted Images using Contrast Enhancement
No-reference image quality assessment (NR-IQA) aims to measure the image
quality without reference image. However, contrast distortion has been
overlooked in the current research of NR-IQA. In this paper, we propose a very
simple but effective metric for predicting quality of contrast-altered images
based on the fact that a high-contrast image is often more similar to its
contrast enhanced image. Specifically, we first generate an enhanced image
through histogram equalization. We then calculate the similarity of the
original image and the enhanced one by using structural-similarity index (SSIM)
as the first feature. Further, we calculate the histogram based entropy and
cross entropy between the original image and the enhanced one respectively, to
gain a sum of 4 features. Finally, we learn a regression module to fuse the
aforementioned 5 features for inferring the quality score. Experiments on four
publicly available databases validate the superiority and efficiency of the
proposed technique.Comment: Draft versio
Iterative Thresholded Bi-Histogram Equalization for Medical Image Enhancement
Enhancement of human vision to get an insight to information content is of
vital importance. The traditional histogram equalization methods have been
suffering from amplified contrast with the addition of artifacts and a
surprising unnatural visibility of the processed images. In order to overcome
these drawbacks, this paper proposes interative, mean, and multi-threshold
selection criterion with plateau limits, which consist of histogram
segmentation, clipping and transformation modules. The histogram partition
consists of multiple thresholding processes that divide the histogram into two
parts, whereas the clipping process nicely enhances the contrast by having a
check on the rate of enhancement that could be tuned. Histogram equalization to
each segmented sub-histogram provides the output image with preserved
brightness and enhanced contrast. Results of the present study showed that the
proposed method efficiently handles the noise amplification. Further, it also
preserves the brightness by retaining natural look of targeted image.Comment: 8 Pages, 8 Figures, International Journal of Computer Applications
(IJCA
An Interval Type-2 Fuzzy Approach to Automatic PDF Generation for Histogram Specification
Image enhancement plays an important role in several application in the field
of computer vision and image processing. Histogram specification (HS) is one of
the most widely used techniques for contrast enhancement of an image, which
requires an appropriate probability density function for the transformation. In
this paper, we propose a fuzzy method to find a suitable PDF automatically for
histogram specification using interval type - 2 (IT2) fuzzy approach, based on
the fuzzy membership values obtained from the histogram of input image. The
proposed algorithm works in 5 stages which includes - symmetric Gaussian
fitting on the histogram, extraction of IT2 fuzzy membership functions (MFs)
and therefore, footprint of uncertainty (FOU), obtaining membership value (MV),
generating PDF and application of HS. We have proposed 4 different methods to
find membership values - point-wise method, center of weight method, area
method, and karnik-mendel (KM) method. The framework is sensitive to local
variations in the histogram and chooses the best PDF so as to improve contrast
enhancement. Experimental validity of the methods used is illustrated by
qualitative and quantitative analysis on several images using the image quality
index - Average Information Content (AIC) or Entropy, and by comparison with
the commonly used algorithms such as Histogram Equalization (HE), Recursive
Mean-Separate Histogram Equalization (RMSHE) and Brightness Preserving Fuzzy
Histogram Equalization (BPFHE). It has been found out that on an average, our
algorithm improves the AIC index by 11.5% as compared to the index obtained by
histogram equalisation
Over speed detection using Artificial Intelligence
Over speeding is one of the most common traffic violations. Around 41 million people are issued speeding tickets each year in USA i.e one every second. Existing approaches to detect over- speeding are not scalable and require manual efforts. In this project, by the use of computer vision and artificial intelligence, I have tried to detect over speeding and report the violation to the law enforcement officer. It was observed that when predictions are done using YoloV3, we get the best results
Analytical computation of frequency distributions of path-dependent processes by means of a non-multinomial maximum entropy approach
Path-dependent stochastic processes are often non-ergodic and observables can
no longer be computed within the ensemble picture. The resulting mathematical
difficulties pose severe limits to the analytical understanding of
path-dependent processes. Their statistics is typically non-multinomial in the
sense that the multiplicities of the occurrence of states is not a multinomial
factor. The maximum entropy principle is tightly related to multinomial
processes, non-interacting systems, and to the ensemble picture; It loses its
meaning for path-dependent processes. Here we show that an equivalent to the
ensemble picture exists for path-dependent processes, such that the
non-multinomial statistics of the underlying dynamical process, by
construction, is captured correctly in a functional that plays the role of a
relative entropy. We demonstrate this for self-reinforcing P\'olya urn
processes, which explicitly generalise multinomial statistics. We demonstrate
the adequacy of this constructive approach towards non-multinomial pendants of
entropy by computing frequency and rank distributions of P\'olya urn processes.
We show how microscopic update rules of a path-dependent process allow us to
explicitly construct a non-multinomial entropy functional, that, when
maximized, predicts the time-dependent distribution function.Comment: 13 pages, 3 figure
U-Net for MAV-based Penstock Inspection: an Investigation of Focal Loss in Multi-class Segmentation for Corrosion Identification
Periodical inspection and maintenance of critical infrastructure such as
dams, penstocks, and locks are of significant importance to prevent
catastrophic failures. Conventional manual inspection methods require
inspectors to climb along a penstock to spot corrosion, rust and crack
formation which is unsafe, labor-intensive, and requires intensive training.
This work presents an alternative approach using a Micro Aerial Vehicle (MAV)
that autonomously flies to collect imagery which is then fed into a pretrained
deep-learning model to identify corrosion. Our simplified U-Net trained with
less than 40 image samples can do inference at 12 fps on a single GPU. We
analyze different loss functions to solve the class imbalance problem, followed
by a discussion on choosing proper metrics and weights for object classes.
Results obtained with the dataset collected from Center Hill Dam, TN show that
focal loss function, combined with a proper set of class weights yield better
segmentation results than the base loss, Softmax cross entropy. Our method can
be used in combination with planning algorithm to offer a complete, safe and
cost-efficient solution to autonomous infrastructure inspection.Comment: 8 Pages, 4 figure
Enhanced Optic Disk and Cup Segmentation with Glaucoma Screening from Fundus Images using Position encoded CNNs
In this manuscript, we present a robust method for glaucoma screening from
fundus images using an ensemble of convolutional neural networks (CNNs). The
pipeline comprises of first segmenting the optic disk and optic cup from the
fundus image, then extracting a patch centered around the optic disk and
subsequently feeding to the classification network to differentiate the image
as diseased or healthy. In the segmentation network, apart from the image, we
make use of spatial co-ordinate (X \& Y) space so as to learn the structure of
interest better. The classification network is composed of a DenseNet201 and a
ResNet18 which were pre-trained on a large cohort of natural images. On the
REFUGE validation data (n=400), the segmentation network achieved a dice score
of 0.88 and 0.64 for optic disc and optic cup respectively. For the tasking
differentiating images affected with glaucoma from healthy images, the area
under the ROC curve was observed to be 0.85.Comment: 8 page
Auto-Classification of Retinal Diseases in the Limit of Sparse Data Using a Two-Streams Machine Learning Model
Automatic clinical diagnosis of retinal diseases has emerged as a promising
approach to facilitate discovery in areas with limited access to specialists.
Based on the fact that fundus structure and vascular disorders are the main
characteristics of retinal diseases, we propose a novel visual-assisted
diagnosis hybrid model mixing the support vector machine (SVM) and deep neural
networks (DNNs). Furthermore, we present a new clinical retina dataset, called
EyeNet2, for ophthalmology incorporating 52 retina diseases classes. Using
EyeNet2, our model achieves 90.43\% diagnosis accuracy, and the model
performance is comparable to the professional ophthalmologists.Comment: A extension work of a workshop paper arXiv admin note: substantial
text overlap with arXiv:1806.0642
Enabling Pedestrian Safety using Computer Vision Techniques: A Case Study of the 2018 Uber Inc. Self-driving Car Crash
Human lives are important. The decision to allow self-driving vehicles
operate on our roads carries great weight. This has been a hot topic of debate
between policy-makers, technologists and public safety institutions. The recent
Uber Inc. self-driving car crash, resulting in the death of a pedestrian, has
strengthened the argument that autonomous vehicle technology is still not ready
for deployment on public roads. In this work, we analyze the Uber car crash and
shed light on the question, "Could the Uber Car Crash have been avoided?". We
apply state-of-the-art Computer Vision models to this highly practical
scenario. More generally, our experimental results are an evaluation of various
image enhancement and object recognition techniques for enabling pedestrian
safety in low-lighting conditions using the Uber crash as a case study.Comment: 10 pages, 8 figures, 3 table
Hierarchical Convolutional-Deconvolutional Neural Networks for Automatic Liver and Tumor Segmentation
Automatic segmentation of liver and its tumors is an essential step for
extracting quantitative imaging biomarkers for accurate tumor detection,
diagnosis, prognosis and assessment of tumor response to treatment. MICCAI 2017
Liver Tumor Segmentation Challenge (LiTS) provides a common platform for
comparing different automatic algorithms on contrast-enhanced abdominal CT
images in tasks including 1) liver segmentation, 2) liver tumor segmentation,
and 3) tumor burden estimation. We participate this challenge by developing a
hierarchical framework based on deep fully convolutional-deconvolutional neural
networks (CDNN). A simple CDNN model is firstly trained to provide a quick but
coarse segmentation of the liver on the entire CT volume, then another CDNN is
applied to the liver region for fine liver segmentation. At last, the segmented
liver region, which is enhanced by histogram equalization, is employed as an
additional input to the third CDNN for tumor segmentation. Jaccard distance is
used as loss function when training CDNN models to eliminate the need of sample
re-weighting. Our framework is trained using the 130 challenge training cases
provided by LiTS. The evaluation on the 70 challenge testing cases resulted in
a mean Dice Similarity Coefficient (DSC) of 0.963 for liver segmentation, a
mean DSC of 0.657 for tumor segmentation, and a root mean square error (RMSE)
of 0.017 for tumor burden estimation, which ranked our method in the first,
fifth and third place, respectivelyComment: 2017 MICCAI-LiTS challeng
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