Mesh representation matters:investigating the influence of different mesh features on perceptual and spatial fidelity of deep 3D morphable models

Background Deep 3D morphable models(deep 3DMMs)play an essential role in computer vision.They are used in facial synthesis,compression,reconstruction and animation,avatar creation,virtual try-on,facial recognition systems and medical imaging.These applications require high spatial and perceptual quality of synthesised meshes.Despite their significance,these models have not been compared with different mesh representations and evaluated jointly with point-wise distance and perceptual metrics.Methods We compare the influence of different mesh representation features to various deep 3DMMs on spatial and perceptual fidelity of the reconstructed meshes.This paper proves the hypothesis that building deep 3DMMs from meshes represented with global representations leads to lower spatial reconstruction error measured with L_(1) and L_(2) norm metrics and underperforms on perceptual metrics.In contrast,using differential mesh representations which describe differential surface properties yields lower perceptual FMPD and DAME and higher spatial fidelity error.The influence of mesh feature normalisation and standardisation is also compared and analysed from perceptual and spatial fidelity perspectives.Results The results presented in this paper provide guidance in selecting mesh representations to build deep 3DMMs accordingly to spatial and perceptual quality objectives and propose combinations of mesh representations and deep 3DMMs which improve either perceptual or spatial fidelity of existing methods.

Effectiveness of anti-psychiatric treatment on visual and haptic perceptual disorder for a patient with Alzheimer’s disease: A case report

BACKGROUND Perception is frequently impaired in patients with Alzheimer’s disease(AD).Several patients exhibit visual or haptic hallucinations.CASE SUMMARY A 71-year-old Chinese man presented with visual and haptic hallucinations he had been experiencing for 2 weeks.The clinical manifestations were the feeling of insects crawling and biting the limbs and geison.He looked for the insects while itching and scratching,which led to skin breakage on the limbs.He was treated with topical and anti-allergic drugs in several dermatology departments without any significant improvement.After admission,the patient was administered risperidone(0.5 mg)and duloxetine(2 mg/day).One week later,the dose of risperidone was increased to 2 mg/day,and that of duloxetine was increased to 60 mg/day.After 2 weeks of treatment,the patient’s sensation of insects crawling and biting disappeared,and his mood stabilized.CONCLUSION This patient manifested psychiatric behavioral symptoms caused by AD brain atrophy.It was important to re-evaluate the patient’s cognitive-psychological status when the patient repeatedly went to the hospital for treatment.Follow-up attention to cognitive function and the consideration of perceptual deficits as early manifestations of AD should be considered.

Perceptual Optimization for Point-Based Point Cloud Rendering

Point-based rendering is a common method widely used in point cloud rendering.It realizes rendering by turning the points into the base geometry.The critical step in point-based rendering is to set an appropriate rendering radius for the base geometry,usually calculated using the average Euclidean distance of the N nearest neighboring points to the rendered point.This method effectively reduces the appearance of empty spaces between points in rendering.However,it also causes the problem that the rendering radius of outlier points far away from the central region of the point cloud sequence could be large,which impacts the perceptual quality.To solve the above problem,we propose an algorithm for point-based point cloud rendering through outlier detection to optimize the perceptual quality of rendering.The algorithm determines whether the detected points are outliers using a combination of local and global geometric features.For the detected outliers,the minimum radius is used for rendering.We examine the performance of the proposed method in terms of both objective quality and perceptual quality.The experimental results show that the peak signal-to-noise ratio(PSNR)of the point cloud sequences is improved under all geometric quantization,and the PSNR improvement ratio is more evident in dense point clouds.Specifically,the PSNR of the point cloud sequences is improved by 3.6%on average compared with the original algorithm.The proposed method significantly improves the perceptual quality of the rendered point clouds and the results of ablation studies prove the feasibility and effectiveness of the proposed method.

Perceptual Learning of Grate Orientation Discrimination in Cats

Perceptual learning of orientation discrimination was investigated using cats. Two adult cats （Cat 1 and 2） were trained to monocularly discriminate between two static striped sinusoidal grates with 30° orientation difference. After greater than 80% correct performance was reached, cats were then required to monocularly perform a discrimination between two grates with consecutively shifting orientation difference（2°, 4°, 6°, 8°, 10°, 12°, 16°, 20°, 24°, 30°） . The staircase method （two correct-down and one error-up） was applied throughout the training to track the threshold of orientation difference that cats could detect. The performance of detecting grates with varied orientation difference was measured respectively for beth trained and untrained eyes before and after training. Our results showed that the learning effect of discrimination for grates with a fixed orientation difference transferred completely from the trained eye to the untrained eye, whereas the inter-eye transfer for detecting °ates with gradually reducing orientation difference was almost nonegrates. The two opposite learning effects in the same subject strongly suggest that different information processing mechanisms might mediate the learning processes.

Umami taste components in chicken-spices blends and potential effect of aroma on umami taste intensity

In this study,umami taste intensity(UTI)and umami taste components in chicken breast(CB)and chickenspices blends were characterized using sensory and instrumental analysis.Our main objective was to assess the aroma-umami taste interactions in different food matrices and reconcile the aroma-taste perception to assist future product development.The impact of key aroma,including vegetable-note"2-pentylfuran",meaty"methional",green"hexanal",and spicy-note-estragole and caryophyllene"on UTI was evaluated in monosodium glutamate and chicken extract.We found that spices significantly decreased UTI and umami taste components in CB.Interestingly,the perceptually similar odorants and tastants exhibited the potential to enhance UTI in food matrices.Methional was able to increase the UTI,whereas spicy and green-note components could reduce the UTI significantly.This information would be valuable to food engineers and formulators in aroma selection to control the UTI perceived by consumers,thus,improving the quality and acceptability of the chicken products.

Image quality assessment based on perceptual grouping

To further explore the human visual system（ HVS）,the perceptual grouping（ PG）, which has been proven to play an important role in the HVS, is adopted to design an effective image quality assessment（ IQA） model. Compared with the existing fixed-window-based models, the proposed one is an adaptive window-like model that introduces the perceptual grouping strategy into the IQA model. It works as follows： first,it preprocesses the images by clustering similar pixels into a group to the greatest extent; then the structural similarity is used to compute the similarity of the superpixels between reference and distorted images; finally, it integrates all the similarity of superpixels of an image to yield a quality score. Experimental results on three databases（ LIVE, IVC and MICT） showthat the proposed method yields good performance in terms of correlation with human judgments of visual quality.

FMHADP:Design of an Efficient Pre-Forensic Layer for Mitigating Hybrid Attacks via Deep Learning Pattern Analysis

Network attack detection and mitigation require packet collection,pre-processing,feature analysis,classification,and post-processing.Models for these tasks sometimes become complex or inefficient when applied to real-time data samples.To mitigate hybrid assaults,this study designs an efficient forensic layer employing deep learning pattern analysis and multidomain feature extraction.In this paper,we provide a novel multidomain feature extraction method using Fourier,Z,Laplace,Discrete Cosine Transform(DCT),1D Haar Wavelet,Gabor,and Convolutional Operations.Evolutionary method dragon fly optimisation reduces feature dimensionality and improves feature selection accuracy.The selected features are fed into VGGNet and GoogLeNet models using binary cascaded neural networks to analyse network traffic patterns,detect anomalies,and warn network administrators.The suggested model tackles the inadequacies of existing approaches to hybrid threats,which are growing more common and challenge conventional security measures.Our model integrates multidomain feature extraction,deep learning pattern analysis,and the forensic layer to improve intrusion detection and prevention systems.In diverse attack scenarios,our technique has 3.5% higher accuracy,4.3% higher precision,8.5% higher recall,and 2.9% lower delay than previous models.

Perceptual video coding method based on JND and AR model

In order to achieve better perceptual coding quality while using fewer bits, a novel perceptual video coding method based on the just-noticeable-distortion （JND） model and the auto-regressive （AR） model is explored. First, a new texture segmentation method exploiting the JND profile is devised to detect and classify texture regions in video scenes. In this step, a spatial-temporal JND model is proposed and the JND energy of every micro-block unit is computed and compared with the threshold. Secondly, in order to effectively remove temporal redundancies while preserving high visual quality, an AR model is applied to synthesize the texture regions. All the parameters of the AR model are obtained by the least-squares method and each pixel in the texture region is generated as a linear combination of pixels taken from the closest forward and backward reference frames. Finally, the proposed method is compared with the H.264/AVC video coding system to demonstrate the performance. Various sequences with different types of texture regions are used in the experiment and the results show that the proposed method can reduce the bit-rate by 15% to 58% while maintaining good perceptual quality.

Perceptual learning for treating amblyopia in children based on activation of visual signal pathway Relationship of curative effects and time

BACKGROUND： Conventional methods （such as occlusion therapy, fine manipulation, complementary, and alternative medicine） take effects slowly, are time and labor consuming, and have uncertain curative effects in the treatment of amblyopia. Perceptual learning, a new method for treating amblyopia, improves the ability to process signals from the cerebral optic nerve system by specific visual stimulation and visual learning, as well as activation of the visual signal pathway utilizing brain nervous system plasticity. OBJECTIVE： This study investigated and evaluated the curative effects of perceptual learning, which can directionally increase brain plasticity, on the treatment of amblyopia in children. The relationship between curative effect and time was also analyzed. DESIGN： A self-control experiment. SETTING： Visual Science and Optometry Center, People＇s Hospital of Guangxi Zhuang Autonomous Region. PARTICIPANTS： A total of 125 amblyopic children （250 amblyopic eyes）, 73 males, 52 females, averaging （6±2） years of age, received treatment at the Visual Science and Optometry Center, People＇s Hospital of Guangxi Zhuang Autonomous Region between September 2006 and February 2007 and were recruited for this study. All children presented with no structural disease of the eyeballs. Written informed consent for therapeutic regiments was obtained from each child＇s parent. The protocol received approval from the Hospital＇s Ethics Committee. METHODS： Visual function was tested with a perceptual learning system （Research Center for Human Health and Development of Sun Yat-sen University, National Engineering Technique Research Center for Medical Care Implement） for visual noise, position noise, contour discrimination, contrast sensitivity, grating stereogram, and random-dot fusion. These tests helped to evaluate the efficiency of visual information processing of these children, and to determine the degree of defects of the optic nerve cells and the connections of visual cortical neurons. According to results of visual function tests, individualized treatment was adopted for each amblyopia patient using perceptual learning system. One course of treatment lasted one month, and treatment was performed twice every day with two training procedures （each training procedure lasted for ten minutes）. There was a ten-minute time interval between the two training procedures. The training treatment was performed in a quiet and dark environment. Visual acuity and recovery of visual function were tested every month. Original training procedure was continued or adjusted according to the results of visual function. MAIN OUTCOME MEASURES： Visual function change; relationship of curative effects and curative time. RESULTS： A total of 125 amblyopia children were included in the final analysis. The total efficiency of perceptual learning for treating amblyopia in children was 75.2%. Visual acuity began to greatly increase 3 months after treatment （P 〈 0.05）. Visual acuity was best corrected from 0.60 ± 0.23 before treatment to 0.86 ± 0.26 after treatment （P 〈 0.05）. The mean time to reach improved levels with curative effects was （2.82 ± 1.30） months, and to reach a basically cured level was （2.87 ±1.40） months. Percentage of improved visual acuity was the highest [98% （39/40）] in children that received 3 months of treatment and the lowest [55% （31/56）] in children that received 1 month of treatment （P 〈 0.05）. The percentage of basically cured levels with curative effects increased with length of learning time and was the greatest in children that received 4 months of treatment [67% （31/46）, P 〈 0.05]. CONCLUSION： Perceptual learning rapidly and remarkably improves visual function of amblyopia children; however, the curative effects are first apparent two and three months after intervention.

Recent development of perceptual image hashing

The easy generation, storage, transmission and reproduction of digital images have caused serious abuse and security problems. Assurance of the rightful ownership, integrity, and authenticity is a major concern to the academia as well as the industry. On the other hand, efficient search of the huge amount of images has become a great challenge. Image hashing is a technique suitable for use in image authentication and content based image retrieval （CBIR）. In this article, we review some representative image hashing techniques proposed in the recent years, with emphases on how to meet the conflicting requirements of perceptual robustness and security. Following a brief introduction to some earlier methods, we focus on a typical two-stage structure and some geometric-distortion resilient techniques. We then introduce two image hashing approaches developed in our own research, and reveal security problems in some existing methods due to the absence of secret keys in certain stage of the image feature extraction, or availability of a large quantity of images, keys, or the hash function to the adversary. More research efforts are needed in developing truly robust and secure image hashing techniques.

Perceptual Gradient Similarity Deviation for Full Reference Image Quality Assessment

Perceptual image quality assessment(IQA)is one of the most indispensable yet challenging problems in image processing and computer vision.It is quite necessary to develop automatic and efficient approaches that can accurately predict perceptual image quality consistently with human subjective evaluation.To further improve the prediction accuracy for the distortion of color images,in this paper,we propose a novel effective and efficient IQA model,called perceptual gradient similarity deviation(PGSD).Based on the gradient magnitude similarity,we proposed a gradient direction selection method to automatically determine the pixel-wise perceptual gradient.The luminance and chrominance channels are both took into account to characterize the quality degradation caused by intensity and color distortions.Finally,a multi-scale strategy is utilized and pooled with different weights to incorporate image details at different resolutions.Experimental results on LIVE,CSIQ and TID2013 databases demonstrate the superior performances of the proposed algorithm.

NEW VISUAL PERCEPTUAL POOLING STRATEGY FOR IMAGE QUALITY ASSESSMENT

Most of Image Quality Assessment (IQA) metrics consist of two processes. In the first process, quality map of image is measured locally. In the second process, the last quality score is converted from the quality map by using the pooling strategy. The first process had been made effective and significant progresses, while the second process was always done in simple ways. In the second process of the pooling strategy, the optimal perceptual pooling weights should be determined and computed according to Human Visual System (HVS). Thus, a reliable spatial pooling mathematical model based on HVS is an important issue worthy of study. In this paper, a new Visual Perceptual Pooling Strategy (VPPS) for IQA is presented based on contrast sensitivity and luminance sensitivity of HVS. Experimental results with the LIVE database show that the visual perceptual weights, obtained by the proposed pooling strategy, can effectively and significantly improve the performances of the IQA metrics with Mean Structural SIMilarity (MSSIM) or Phase Quantization Code (PQC). It is confirmed that the proposed VPPS demonstrates promising results for improving the performances of existing IQA metrics.

Defend Against Adversarial Samples by Using Perceptual Hash

Image classifiers that based on Deep Neural Networks(DNNs)have been proved to be easily fooled by well-designed perturbations.Previous defense methods have the limitations of requiring expensive computation or reducing the accuracy of the image classifiers.In this paper,we propose a novel defense method which based on perceptual hash.Our main goal is to destroy the process of perturbations generation by comparing the similarities of images thus achieve the purpose of defense.To verify our idea,we defended against two main attack methods(a white-box attack and a black-box attack)in different DNN-based image classifiers and show that,after using our defense method,the attack-success-rate for all DNN-based image classifiers decreases significantly.More specifically,for the white-box attack,the attack-success-rate is reduced by an average of 36.3%.For the black-box attack,the average attack-success-rate of targeted attack and non-targeted attack has been reduced by 72.8%and 76.7%respectively.The proposed method is a simple and effective defense method and provides a new way to defend against adversarial samples.

Perceptual Quality Assessment of Omnidirectional Images:Subjective Experiment and Objective Model Evaluation

Virtual reality(VR) environment can provide immersive experience to viewers.Under the VR environment, providing a good quality of experience is extremely important.Therefore, in this paper, we present an image quality assessment(IQA) study on omnidirectional images. We first build an omnidirectional IQA(OIQA) database, including 16 source images with their corresponding 320 distorted images. We add four commonly encountered distortions. These distortions are JPEG compression, JPEG2000 compression, Gaussian blur, and Gaussian noise. Then we conduct a subjective quality evaluation study in the VR environment based on the OIQA database. Considering that visual attention is more important in VR environment, head and eye movement data are also tracked and collected during the quality rating experiments. The 16 raw and their corresponding distorted images,subjective quality assessment scores, and the head-orientation data and eye-gaze data together constitute the OIQA database. Based on the OIQA database, we test some state-of-the-art full-reference IQA(FR-IQA) measures on equirectangular format or cubic formatomnidirectional images. The results show that applying FR-IQA metrics on cubic format omnidirectional images could improve their performance. The performance of some FR-IQA metrics combining the saliency weight of three different types are also tested based on our database. Some new phenomena different from traditional IQA are observed.

Polarization visualization for low-irradiance regions by perceptually uniform color space

Currently,polarization visualization strategies are accomplished by mapping polarization information into a perceptually uniform color appearance model CAM02-UCS.However,the deviation of the CAM02-UCS space from the lightness prediction results in an inaccurate match between the polarization information and the perceptual information.In this paper,we propose a novel polarization visualization strategy based on the perceptual uniform space Jzazbz.The polarization visualization be completed by placing the polarization information into the lightness Jz,colorfulness Cz and hue angle hz channels of the Jzazbz space.The experimental results show that the proposed method can significantly improve the lightness of the low irradiance and high polarization region,hence more polarization information can be sensed by human visual system.

Network-aware perceptual error concealment method for H.264 video with side information

In order to improve the video quality of transmission with data loss,a spatial and temporal error concealment method was proposed,which considered both the state information of the network and the perceptual weight of the video content.The proposed method dynamically changed the reliability weight of the neighboring macroblock,which was used to conceal the lost macroblocks according to the packet loss rate of the current channel state.The perceptual weight map was utilized as side information to do weighted pixel interpolation and side-match based motion compensation for spatial and temporal error concealment,respectively.And the perceptual weight of the neighboring macroblocks was adaptively modified according to the perceptual weight of the lost macroblocks.Compared with the method used in H.264 joint model,experiment results show that the proposed method performs well both in subjective video quality and objective video quality,and increases the average peak signal-to-noise ratio(PSNR) of the whole frame by about 0.4 dB when the video bitstreams are transmitted with packets loss.

A NEW ACOUSTIC PERCEPTUALl MODEL

On the basis of psychological acoustic theories and experiments, this paper proposes an acoustic model which is based on acoustic perceptual feature. Compared with the physiological acoustics based acoustic model, this model is more suitable to represent human’s perceptual features of continuous speech, so it is suitable for recognition of continuous speech.

An Improved Scalar Costa Scheme Based on Watson Perceptual Model

An improved scalar Costa scheme (SCS) was proposed by using improved Watson perceptual model to adaptively decide quantization step size and scaling factor. The improved scheme equals to embed hiding data based on an actual image. In order to withstand amplitude scaling attack, the Watson perceptual model was redefined, and the improved scheme using the new definition can insure quantization step size in decoder that is proportional to amplitude scaling attack factor. The performance of the improved scheme outperforms that of SCS with fixed quantization step size. The improved scheme combines information theory and visual model.

Task analysis approach based on perceptual control theory

Based on perceptual control theory,a task analysis approach is proposed to describe more accurately user tasks in dynamic environments,which is of more powerful and flexible descriptive ability. Theoretically,a task meta model is established to describe the interactive process in an individual,dynamic,and flexible way.Methodologically,an implementation framework is illustrated to map the user-oriented description into implementation-oriented models,which will be as a technical tool to transform from a task model to a user interface prototype.

Metrics analysis of tactile perceptual space based on improved NMDS for leather textures

To solve the fuzzy and unstable tactile similarity relationship between some sample points in the perception experiment,an improved non-metric multidimensional scaling(INMDS)is proposed in this paper.In view of the inconsistency of each sample s contribution,the maximum marginal decision when constructing the perception space to describe the tactile perception characteristics is also proposed.The corresponding constraints are set according to the degree of similarity,and controlling the relaxation variable factor is proposed to optimize the perception dimension and coordinate measurement.The effectiveness of the INMDS algorithm is verified by two perception experiments.The results show that compared with the metric multidimensional scaling(MDS)and non-metric multidimensional scaling(NMDS)algorithms,the perceptual space constructed by INMDS can more accurately reflect the difference relationship between different leather sample points perceived by people.Moreover,the relative position of sample points in the perceptual space is more consistent with subjective perception results.