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

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

Forepaw Sensorimotor Deprivation in Early Life Leads to the Impairments on Spatial Memory and Synaptic Plasticity in Rats

To investigate the influence of forepaw sensorimotor deprivation on memory and synaptic plasticity, Sprague-Dawley rats were divided into two groups: a sham-operated group and a group deprived of forepaw sensorimotor function by microsurgical operation at postnatal day 13 (PN13). Behavioral and electrophysiological studies were performed at PN25, PN35, PN45, and PN60. Open field test was used to assess the spontaneous locomotor activity. Morris water maze was used to evaluate spatial reference learning and memory. The long-term potentiation (LTP) in the medial perforant path—dentate gyrus (MPP-DG) pathway was examined with hippocampal slices. We found that forepaw sensorimotor deprivation did not affect spontaneous activity of the rats. However, spatial reference learning and memory were significantly impaired in their early life (PN25, PN35, and PN45). In accordance with the behavior results, LTP in MPP-DG pathway was significantly suppressed in their early life. These data demonstrated that forepaw sensorimotor deprivation led to the impairments on spatial memory via inducing pronounced deficits in the MPP-DG pathway to exhibit LTP, one of the major cellular mechanisms underlying learning and memory