370 research outputs found
A contrast-sensitive reversible visible image watermarking technique
A reversible (also called lossless, distortion-free, or
invertible) visible watermarking scheme is proposed to satisfy the applications, in which the visible watermark is expected to combat copyright piracy but can be removed to losslessly recover the original image. We transparently reveal the watermark image by overlapping it on a user-specified region of the host image through adaptively adjusting the pixel values beneath the watermark, depending on the human visual system-based scaling factors. In order to achieve reversibility, a reconstruction/ recovery packet, which is utilized to restore the watermarked area, is reversibly inserted into non-visibly-watermarked region. The packet is established according to the difference image between the original image and its approximate version instead of its visibly watermarked version so as to alleviate its overhead. For the generation of the approximation, we develop a simple prediction technique that makes use of the unaltered neighboring pixels as auxiliary information. The recovery packet is uniquely encoded before hiding so that the original watermark pattern can be reconstructed based on the encoded packet. In this way, the image recovery process is carried out without needing the availability of the watermark. In addition, our method adopts data compression for further reduction in the recovery packet size and improvement in embedding capacity. The experimental results demonstrate the superiority of the proposed scheme compared to the existing methods
Graph-based Facial Affect Analysis: A Review of Methods, Applications and Challenges
Facial affect analysis (FAA) using visual signals is important in
human-computer interaction. Early methods focus on extracting appearance and
geometry features associated with human affects, while ignoring the latent
semantic information among individual facial changes, leading to limited
performance and generalization. Recent work attempts to establish a graph-based
representation to model these semantic relationships and develop frameworks to
leverage them for various FAA tasks. In this paper, we provide a comprehensive
review of graph-based FAA, including the evolution of algorithms and their
applications. First, the FAA background knowledge is introduced, especially on
the role of the graph. We then discuss approaches that are widely used for
graph-based affective representation in literature and show a trend towards
graph construction. For the relational reasoning in graph-based FAA, existing
studies are categorized according to their usage of traditional methods or deep
models, with a special emphasis on the latest graph neural networks.
Performance comparisons of the state-of-the-art graph-based FAA methods are
also summarized. Finally, we discuss the challenges and potential directions.
As far as we know, this is the first survey of graph-based FAA methods. Our
findings can serve as a reference for future research in this field.Comment: 20 pages, 12 figures, 5 table
The DKU-OPPO System for the 2022 Spoofing-Aware Speaker Verification Challenge
This paper describes our DKU-OPPO system for the 2022 Spoofing-Aware Speaker
Verification (SASV) Challenge. First, we split the joint task into speaker
verification (SV) and spoofing countermeasure (CM), these two tasks which are
optimized separately. For ASV systems, four state-of-the-art methods are
employed. For CM systems, we propose two methods on top of the challenge
baseline to further improve the performance, namely Embedding Random Sampling
Augmentation (ERSA) and One-Class Confusion Loss(OCCL). Second, we also explore
whether SV embedding could help improve CM system performance. We observe a
dramatic performance degradation of existing CM systems on the
domain-mismatched Voxceleb2 dataset. Third, we compare different fusion
strategies, including parallel score fusion and sequential cascaded systems.
Compared to the 1.71% SASV-EER baseline, our submitted cascaded system obtains
a 0.21% SASV-EER on the challenge official evaluation set.Comment: Accepted by Interspeech202
Implicit Motion-Compensated Network for Unsupervised Video Object Segmentation
Unsupervised video object segmentation (UVOS) aims at automatically
separating the primary foreground object(s) from the background in a video
sequence. Existing UVOS methods either lack robustness when there are visually
similar surroundings (appearance-based) or suffer from deterioration in the
quality of their predictions because of dynamic background and inaccurate flow
(flow-based). To overcome the limitations, we propose an implicit
motion-compensated network (IMCNet) combining complementary cues
(, appearance and motion) with aligned motion information from
the adjacent frames to the current frame at the feature level without
estimating optical flows. The proposed IMCNet consists of an affinity computing
module (ACM), an attention propagation module (APM), and a motion compensation
module (MCM). The light-weight ACM extracts commonality between neighboring
input frames based on appearance features. The APM then transmits global
correlation in a top-down manner. Through coarse-to-fine iterative inspiring,
the APM will refine object regions from multiple resolutions so as to
efficiently avoid losing details. Finally, the MCM aligns motion information
from temporally adjacent frames to the current frame which achieves implicit
motion compensation at the feature level. We perform extensive experiments on
and . Our network
achieves favorable performance while running at a faster speed compared to the
state-of-the-art methods.Comment: Accepted by IEEE Transactions on Circuits and Systems for Video
Technology (TCSVT
NDDepth: Normal-Distance Assisted Monocular Depth Estimation
Monocular depth estimation has drawn widespread attention from the vision
community due to its broad applications. In this paper, we propose a novel
physics (geometry)-driven deep learning framework for monocular depth
estimation by assuming that 3D scenes are constituted by piece-wise planes.
Particularly, we introduce a new normal-distance head that outputs pixel-level
surface normal and plane-to-origin distance for deriving depth at each
position. Meanwhile, the normal and distance are regularized by a developed
plane-aware consistency constraint. We further integrate an additional depth
head to improve the robustness of the proposed framework. To fully exploit the
strengths of these two heads, we develop an effective contrastive iterative
refinement module that refines depth in a complementary manner according to the
depth uncertainty. Extensive experiments indicate that the proposed method
exceeds previous state-of-the-art competitors on the NYU-Depth-v2, KITTI and
SUN RGB-D datasets. Notably, it ranks 1st among all submissions on the KITTI
depth prediction online benchmark at the submission time.Comment: Accepted by ICCV 2023 (Oral
Expression of androgen and estrogen receptors in the testicular tissue of chickens, quails and chicken-quail hybrids
36 New Roman cocks, 30 Korean male quails and 30 chicken-quail hybrids of different day-age were selected and their body weight and testes weights were measured and as well, their testes were collected. Real-time polymerase chain reaction (RT-PCR) was performed to evaluate the messenger ribonucleic acid (mRNA) expression patterns of androgen receptors (AR) and estrogen receptors (ER) genes in testicular tissue of chickens, quails and chicken-quail hybrids at different growth stages. The results show that the testes of chickens and quails grew and developed normally with body weight gain, but the testes of chicken-quail hybrids had a slower growth rate and stunted growth. Real-time PCR showed AR and ER mRNA expression patterns in testes of chickens and quails at different growth stages were similar. AR mRNA expression in chickens and quails reached a significant peak at 80 and 30 days of age, respectively and their ER gene expression showed fluctuation slightly. The AR and ER expression of chicken-quail hybrids were different from the above expression patterns; the hybrids AR gene expression showed a gradual decline and ER gene expression gradually increased. The chickenquail hybrids AR and ER gene expression was abnormal and we speculate this is an important molecular factor for the testicular dysplasia of chicken-quail hybrids. Our results show that AR gene expression was upregulated by ER gene and we suggest that the synergetic effect of AR and ER gene regulated the normal testis growth and development of chicken and quail.Keywords: Chicken, quail, chicken-quail hybrid, testis, androgen receptors (AR), estrogen receptors (ER) expressionAfrican Journal of Biotechnology Vol. 11(29), pp. 7344-7353, 10 April, 201
IEBins: Iterative Elastic Bins for Monocular Depth Estimation
Monocular depth estimation (MDE) is a fundamental topic of geometric computer
vision and a core technique for many downstream applications. Recently, several
methods reframe the MDE as a classification-regression problem where a linear
combination of probabilistic distribution and bin centers is used to predict
depth. In this paper, we propose a novel concept of iterative elastic bins
(IEBins) for the classification-regression-based MDE. The proposed IEBins aims
to search for high-quality depth by progressively optimizing the search range,
which involves multiple stages and each stage performs a finer-grained depth
search in the target bin on top of its previous stage. To alleviate the
possible error accumulation during the iterative process, we utilize a novel
elastic target bin to replace the original target bin, the width of which is
adjusted elastically based on the depth uncertainty. Furthermore, we develop a
dedicated framework composed of a feature extractor and an iterative optimizer
that has powerful temporal context modeling capabilities benefiting from the
GRU-based architecture. Extensive experiments on the KITTI, NYU-Depth-v2 and
SUN RGB-D datasets demonstrate that the proposed method surpasses prior
state-of-the-art competitors. The source code is publicly available at
https://github.com/ShuweiShao/IEBins.Comment: Accepted by NeurIPS 202
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