535 research outputs found
No-reference Point Cloud Geometry Quality Assessment Based on Pairwise Rank Learning
Objective geometry quality assessment of point clouds is essential to
evaluate the performance of a wide range of point cloud-based solutions, such
as denoising, simplification, reconstruction, and watermarking. Existing point
cloud quality assessment (PCQA) methods dedicate to assigning absolute quality
scores to distorted point clouds. Their performance is strongly reliant on the
quality and quantity of subjective ground-truth scores for training, which are
challenging to gather and have been shown to be imprecise, biased, and
inconsistent. Furthermore, the majority of existing objective geometry quality
assessment approaches are carried out by full-reference traditional metrics. So
far, point-based no-reference geometry-only quality assessment techniques have
not yet been investigated. This paper presents PRL-GQA, the first pairwise
learning framework for no-reference geometry-only quality assessment of point
clouds, to the best of our knowledge. The proposed PRL-GQA framework employs a
siamese deep architecture, which takes as input a pair of point clouds and
outputs their rank order. Each siamese architecture branch is a geometry
quality assessment network (GQANet), which is designed to extract multi-scale
quality-aware geometric features and output a quality index for the input point
cloud. Then, based on the predicted quality indexes, a pairwise rank learning
module is introduced to rank the relative quality of a pair of degraded point
clouds.Extensive experiments demonstrate the effectiveness of the proposed
PRL-GQA framework. Furthermore, the results also show that the fine-tuned
no-reference GQANet performs competitively when compared to existing
full-reference geometry quality assessment metrics
Ultrasound volume projection image quality selection by ranking from convolutional RankNet.
Periodic inspection and assessment are important for scoliosis patients. 3D ultrasound imaging has become an important means of scoliosis assessment as it is a real-time, cost-effective and radiation-free imaging technique. With the generation of a 3D ultrasound volume projection spine image using our Scolioscan system, a series of 2D coronal ultrasound images are produced at different depths with different qualities. Selecting a high quality image from these 2D images is the crucial task for further scoliosis measurement. However, adjacent images are similar and difficult to distinguish. To learn the nuances between these images, we propose selecting the best image automatically, based on their quality rankings. Here, the ranking algorithm we use is a pairwise learning-to-ranking network, RankNet. Then, to extract more efficient features of input images and to improve the discriminative ability of the model, we adopt the convolutional neural network as the backbone due to its high power of image exploration. Finally, by inputting the images in pairs into the proposed convolutional RankNet, we can select the best images from each case based on the output ranking orders. The experimental result shows that convolutional RankNet achieves better than 95.5% top-3 accuracy, and we prove that this performance is beyond the experience of a human expert
Binocular Rivalry Oriented Predictive Auto-Encoding Network for Blind Stereoscopic Image Quality Measurement
Stereoscopic image quality measurement (SIQM) has become increasingly
important for guiding stereo image processing and commutation systems due to
the widespread usage of 3D contents. Compared with conventional methods which
are relied on hand-crafted features, deep learning oriented measurements have
achieved remarkable performance in recent years. However, most existing deep
SIQM evaluators are not specifically built for stereoscopic contents and
consider little prior domain knowledge of the 3D human visual system (HVS) in
network design. In this paper, we develop a Predictive Auto-encoDing Network
(PAD-Net) for blind/No-Reference stereoscopic image quality measurement. In the
first stage, inspired by the predictive coding theory that the cognition system
tries to match bottom-up visual signal with top-down predictions, we adopt the
encoder-decoder architecture to reconstruct the distorted inputs. Besides,
motivated by the binocular rivalry phenomenon, we leverage the likelihood and
prior maps generated from the predictive coding process in the Siamese
framework for assisting SIQM. In the second stage, quality regression network
is applied to the fusion image for acquiring the perceptual quality prediction.
The performance of PAD-Net has been extensively evaluated on three benchmark
databases and the superiority has been well validated on both symmetrically and
asymmetrically distorted stereoscopic images under various distortion types
Who's Better? Who's Best? Pairwise Deep Ranking for Skill Determination
We present a method for assessing skill from video, applicable to a variety
of tasks, ranging from surgery to drawing and rolling pizza dough. We formulate
the problem as pairwise (who's better?) and overall (who's best?) ranking of
video collections, using supervised deep ranking. We propose a novel loss
function that learns discriminative features when a pair of videos exhibit
variance in skill, and learns shared features when a pair of videos exhibit
comparable skill levels. Results demonstrate our method is applicable across
tasks, with the percentage of correctly ordered pairs of videos ranging from
70% to 83% for four datasets. We demonstrate the robustness of our approach via
sensitivity analysis of its parameters. We see this work as effort toward the
automated organization of how-to video collections and overall, generic skill
determination in video.Comment: CVPR 201
Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment
We present a deep neural network-based approach to image quality assessment
(IQA). The network is trained end-to-end and comprises ten convolutional layers
and five pooling layers for feature extraction, and two fully connected layers
for regression, which makes it significantly deeper than related IQA models.
Unique features of the proposed architecture are that: 1) with slight
adaptations it can be used in a no-reference (NR) as well as in a
full-reference (FR) IQA setting and 2) it allows for joint learning of local
quality and local weights, i.e., relative importance of local quality to the
global quality estimate, in an unified framework. Our approach is purely
data-driven and does not rely on hand-crafted features or other types of prior
domain knowledge about the human visual system or image statistics. We evaluate
the proposed approach on the LIVE, CISQ, and TID2013 databases as well as the
LIVE In the wild image quality challenge database and show superior performance
to state-of-the-art NR and FR IQA methods. Finally, cross-database evaluation
shows a high ability to generalize between different databases, indicating a
high robustness of the learned features
Face Anti-Spoofing and Deep Learning Based Unsupervised Image Recognition Systems
One of the main problems of a supervised deep learning approach is that it requires large amounts of labeled training data, which are not always easily available. This PhD dissertation addresses the above-mentioned problem by using a novel unsupervised deep learning face verification system called UFace, that does not require labeled training data as it automatically, in an unsupervised way, generates training data from even a relatively small size of data. The method starts by selecting, in unsupervised way, k-most similar and k-most dissimilar images for a given face image. Moreover, this PhD dissertation proposes a new loss function to make it work with the proposed method. Specifically, the method computes loss function k times for both similar and dissimilar images for each input image in order to increase the discriminative power of feature vectors to learn the inter-class and intra-class face variability. The training is carried out based on the similar and dissimilar input face image vector rather than the same training input face image vector in order to extract face embeddings.
The UFace is evaluated on four benchmark face verification datasets: Labeled Faces in the Wild dataset (LFW), YouTube Faces dataset (YTF), Cross-age LFW (CALFW) and Celebrities in Frontal Profile in the Wild (CFP-FP) datasets. The results show that we gain an accuracy of 99.40\%, 96.04\%, 95.12\% and 97.89\% respectively. The achieved results, despite being unsupervised, is on par to a similar but fully supervised methods.
Another, related to face verification, area of research is on face anti-spoofing systems. State-of-the-art face anti-spoofing systems use either deep learning, or manually extracted image quality features. However, many of the existing image quality features used in face anti-spoofing systems are not well discriminating spoofed and genuine faces. Additionally, State-of-the-art face anti-spoofing systems that use deep learning approaches do not generalize well.
Thus, to address the above problem, this PhD dissertation proposes hybrid face anti-spoofing system that considers the best from image quality feature and deep learning approaches. This work selects and proposes a set of seven novel no-reference image quality features measurement, that discriminate well between spoofed and genuine faces, to complement the deep learning approach. It then, proposes two approaches: In the first approach, the scores from the image quality features are fused with the deep learning classifier scores in a weighted fashion. The combined scores are used to determine whether a given input face image is genuine or spoofed. In the second approach, the image quality features are concatenated with the deep learning features. Then, the concatenated features vector is fed to the classifier to improve the performance and generalization of anti-spoofing system.
Extensive evaluations are conducted to evaluate their performance on five benchmark face anti-spoofing datasets: Replay-Attack, CASIA-MFSD, MSU-MFSD, OULU-NPU and SiW. Experiments on these datasets show that it gives better results than several of the state-of-the-art anti-spoofing systems in many scenarios
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