76,631 research outputs found
Quantitative Analysis of High-Resolution Microendoscopic Images for Diagnosis of Esophageal Squamous Cell Carcinomaďľ
Background & Aims: High-resolution microendoscopy is an optical imaging technique with the potential to improve the accuracy of endoscopic screening for esophageal squamous neoplasia. Although these microscopic images can be interpreted readily by trained personnel, quantitative image analysis software could facilitate the use of this technology in low-resource settings. In this study, we developed and evaluated quantitative image analysis criteria for the evaluation of neoplastic and non-neoplastic squamous esophageal mucosa. Methods: We performed an image analysis of 177 patients undergoing standard upper endoscopy for screening or surveillance of esophageal squamous neoplasia, using high-resolution microendoscopy, at 2 hospitals in China and at 1 hospital in the United States from May 2010 to October 2012. Biopsy specimens were collected from imaged sites (n = 375), and a consensus diagnosis was provided by 2 expert gastrointestinal pathologists and used as the standard. Results: Quantitative information from the high-resolution images was used to develop an algorithm to identify high-grade squamous dysplasia or invasive squamous cell cancer, based on histopathology findings. Optimal performance was obtained using the mean nuclear area as the basis for classification, resulting in sensitivities and specificities of 93% and 92% in the training set, 87% and 97% in the test set, and 84% and 95% in an independent validation set, respectively. Conclusions: High-resolution microendoscopy with quantitative image analysis can aid in the identification of esophageal squamous neoplasia. Use of software-based image guides may overcome issues of training and expertise in low-resource settings, allowing for widespread use of these optical biopsy technologies
Real Time Surveillance for Low Resolution and Limited-Data Scenarios: An Image Set Classification Approach
This paper proposes a novel image set classification technique based on the concept of linear regression. Unlike most other approaches, the proposed technique does not involve any training or feature extraction. The gallery image sets are represented as subspaces in a high dimensional space. Class specific gallery subspaces are used to estimate regression models for each image of the test image set. Images of the test set are then projected on the gallery subspaces. Residuals, calculated using the Euclidean distance between the original and the projected test images, are used as the distance metric. Three different strategies are devised to decide on the final class of the test image set. We performed extensive evaluations of the proposed technique under the challenges of low resolution, noise and less gallery data for the tasks of surveillance, video-based face recognition and object recognition. Experiments show that the proposed technique achieves a better classification accuracy and a faster execution time compared to existing techniques especially under the challenging conditions of low resolution and small gallery and test data
UG^2: a Video Benchmark for Assessing the Impact of Image Restoration and Enhancement on Automatic Visual Recognition
Advances in image restoration and enhancement techniques have led to
discussion about how such algorithmscan be applied as a pre-processing step to
improve automatic visual recognition. In principle, techniques like deblurring
and super-resolution should yield improvements by de-emphasizing noise and
increasing signal in an input image. But the historically divergent goals of
the computational photography and visual recognition communities have created a
significant need for more work in this direction. To facilitate new research,
we introduce a new benchmark dataset called UG^2, which contains three
difficult real-world scenarios: uncontrolled videos taken by UAVs and manned
gliders, as well as controlled videos taken on the ground. Over 160,000
annotated frames forhundreds of ImageNet classes are available, which are used
for baseline experiments that assess the impact of known and unknown image
artifacts and other conditions on common deep learning-based object
classification approaches. Further, current image restoration and enhancement
techniques are evaluated by determining whether or not theyimprove baseline
classification performance. Results showthat there is plenty of room for
algorithmic innovation, making this dataset a useful tool going forward.Comment: Supplemental material: https://goo.gl/vVM1xe, Dataset:
https://goo.gl/AjA6En, CVPR 2018 Prize Challenge: ug2challenge.or
Computationally Efficient Target Classification in Multispectral Image Data with Deep Neural Networks
Detecting and classifying targets in video streams from surveillance cameras
is a cumbersome, error-prone and expensive task. Often, the incurred costs are
prohibitive for real-time monitoring. This leads to data being stored locally
or transmitted to a central storage site for post-incident examination. The
required communication links and archiving of the video data are still
expensive and this setup excludes preemptive actions to respond to imminent
threats. An effective way to overcome these limitations is to build a smart
camera that transmits alerts when relevant video sequences are detected. Deep
neural networks (DNNs) have come to outperform humans in visual classifications
tasks. The concept of DNNs and Convolutional Networks (ConvNets) can easily be
extended to make use of higher-dimensional input data such as multispectral
data. We explore this opportunity in terms of achievable accuracy and required
computational effort. To analyze the precision of DNNs for scene labeling in an
urban surveillance scenario we have created a dataset with 8 classes obtained
in a field experiment. We combine an RGB camera with a 25-channel VIS-NIR
snapshot sensor to assess the potential of multispectral image data for target
classification. We evaluate several new DNNs, showing that the spectral
information fused together with the RGB frames can be used to improve the
accuracy of the system or to achieve similar accuracy with a 3x smaller
computation effort. We achieve a very high per-pixel accuracy of 99.1%. Even
for scarcely occurring, but particularly interesting classes, such as cars, 75%
of the pixels are labeled correctly with errors occurring only around the
border of the objects. This high accuracy was obtained with a training set of
only 30 labeled images, paving the way for fast adaptation to various
application scenarios.Comment: Presented at SPIE Security + Defence 2016 Proc. SPIE 9997, Target and
Background Signatures I
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