Texture Cues Research Articles

Depth Perception in Optical See-Through Augmented Reality: Investigating the Impact of Texture Density, Luminance Contrast, and Color Contrast.

The immersive augmented reality (AR) system necessitates precise depth registration between virtual objects and the real scene. Prior studies have emphasized the efficacy of surface texture in providing depth cues to enhance depth perception across various media, including the real scene, virtual reality, and AR. However, these studies predominantly focus on black-and-white textures, leaving a gap in understanding the effectiveness of colored textures. To address this gap and further explore texture-related factors in AR, a series of experiments were conducted to investigate the effects of different texture cues on depth perception using the perceptual matching method. Findings indicate that the absolute depth error increases with decreasing contrast under black-and-white texture. Moreover, textures with higher color contrast also contribute to enhanced accuracy of depth judgments in AR. However, no significant effect of texture density on depth perception was observed. The findings serve as a theoretical reference for texture design in AR, aiding in the optimization of virtual-real registration processes.

Comparable Theta Phase Coding Dynamics Along the Transverse Axis of CA1.

Topographical projection patterns from the entorhinal cortex to area CA1 of the hippocampus have led to a hypothesis that proximal CA1 (pCA1, closer to CA2) is spatially more selective than distal CA1 (dCA1, closer to the subiculum). While earlier studies have shown evidence supporting this hypothesis, we recently showed that this difference does not hold true under all experimental conditions. In a complex environment with distinct local texture cues on a circular track and global visual cues, pCA1 and dCA1 display comparable spatial selectivity. Correlated with the spatial selectivity differences, the earlier studies also showed differences in theta phase coding dynamics between pCA1 and dCA1 neurons. Here we show that there are no differences in theta phase coding dynamics between neurons in these two regions under the experimental conditions where pCA1 and dCA1 neurons are equally spatially selective. These findings challenge the established notion of dCA1 being inherently less spatially selective and theta modulated than pCA1 and suggest further experiments to understand theta-mediated activation of the CA1 sub-networks to represent space.

Transfer-learning enabled micro-expression recognition using dense connections and mixed attention

Micro-expression recognition (MER) is a challenging computer vision problem, where the limited amount of available training data and insufficient intensity of the facial expressions are among the main issues adversely affecting the performance of existing recognition models. To address these challenges, this paper explores a transfer–learning enabled MER model using a densely connected feature extraction module with mixed attention. Unlike previous works that utilize transfer learning to facilitate MER and extract local facial-expression information, our model relies on pretraining with three diverse macro-expression datasets and, as a result, can: (i) overcome the problem of insufficient sample size and limited training data availability, (ii) leverage (related) domain-specific information from multiple datasets with diverse characteristics, and (iii) improve the model adaptability to complex scenes. Furthermore, to enhance the intensity of the micro-expressions and improve the discriminability of the extracted features, the Euler video magnification (EVM) method is adopted in the preprocessing stage and then used jointly with a densely connected feature extraction module and a mixed attention mechanism to derive expressive feature representations for the classification procedure. The proposed feature extraction mechanism not only guarantees the integrity of the extracted features but also efficiently captures local texture cues by aggregating the most salient information from the generated feature maps, which is key for the MER task. The experimental results on multiple datasets demonstrate the robustness and effectiveness of our model compared to the state-of-the-art.

Food Texture Perception and Its Influence on Consumer Preferences

Abstract Purpose: The general purpose of this study was to evaluate food texture perception and its influence on consumer preferences. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to food texture perception and its influence on consumer preferences. Preliminary empirical review revealed that that food texture plays a pivotal role in shaping consumer preferences and purchase decisions across various product categories. Through a review of empirical studies, it was found that texture preferences are influenced by sensory experiences, cultural norms, and individual differences. Cross-cultural variability in texture preferences was noted among Western, Asian, and Middle Eastern populations, highlighting the importance of considering cultural factors in product development. Furthermore, texture was found to elicit emotional responses during eating, with certain textures evoking positive emotions while others elicited negative ones. The practical implications of texture research were underscored, emphasizing its relevance for food manufacturers, marketers, and policymakers in developing products that align with consumer expectations and promoting healthier food choices. Unique Contribution to Theory, Practice and Policy: The Sensory-Specific Satiety theory, Elaboration Likelihood Model (ELM) and Schema theory may be used in anchoring future studies on food texture perception and its influence on consumer preferences. The study provided recommendations that contributed to theory, practice, and policy in the field of food science and consumer behavior. The study suggested further exploration of texture perception mechanisms, tailored product development strategies based on sensory evaluations, and sensory marketing approaches emphasizing texture cues. Additionally, it recommended integrating sensory considerations into food regulations, promoting consumer education on texture-related labeling, and investing in sensory research and innovation. These recommendations aimed to enhance understanding of texture perception, improve product development and marketing strategies, and inform policy initiatives to meet consumer preferences effectively.

Investigating visual determinants of visuomotor performance in virtual reality

We report the relative efficiency of visually guided movement in virtual reality (VR) compared to that in the real world using a standardised visuomotor task based on Fitts’ tapping. Haptic cues were veridical across both displays to ensure that any differences in performance could be attributed to characteristics of the visual display. The presence of binocular cues, and of monocular surface texture and motion parallax cues was manipulated across four ordinal visual conditions: binocular + texture + motion, monocular + texture + motion, monocular + motion, monocular. Binocular cues were found to have a similar benefit for VR and real-world performance. Surface texture cues were found to have no benefit for either display. Motion parallax cues were found to have a similar benefit for VR and real-world performance. Overall, task performance in VR was lower than in the real world due to both a slowing of movements and a decrease in their precision. Taken together, these results did not provide evidence for deficiencies in the presentation of binocular, texture or motion parallax cues in VR and suggests a non-visual locus for the deficit in visuomotor performance in VR. Limitations in the accuracy and precision of hand tracking are sufficient to explain the observed decrement in performance in VR.

Characterization, Experimental Validation and Pilot User Study of the Vibro-Inertial Bionic Enhancement System (VIBES).

This study presents the characterization and validation of the VIBES, a wearable vibrotactile device that provides high-frequency tactile information embedded in a prosthetic socket. A psychophysical characterization involving ten able-bodied participants is performed to compute the Just Noticeable Difference (JND) related to the discrimination of vibrotactile cues delivered on the skin in two forearm positions, with the goal of optimising vibrotactile actuator position to maximise perceptual response. Furthermore, system performance is validated and tested both with ten able-bodied participants and one prosthesis user considering three tasks. More specifically, in the Active Texture Identification, Slippage and Fragile Object Experiments, we investigate if the VIBES could enhance users' roughness discrimination and manual usability and dexterity. Finally, we test the effect of the vibrotactile system on prosthetic embodiment in a Rubber Hand Illusion (RHI) task. Results show the system's effectiveness in conveying contact and texture cues, making it a potential tool to restore sensory feedback and enhance the embodiment in prosthetic users.

VIBES: Vibro-Inertial Bionic Enhancement System in a Prosthetic Socket.

The use of vibrotactile feedback is of growing interest in the field of prosthetics, but few devices fully integrate this technology in the prosthesis to transmit high-frequency contact information (such as surface roughness and first contact) arising from the interaction of the prosthetic device with external items. This study describes a wearable vibrotactile system for high-frequency tactile information embedded in the prosthetic socket. The device consists of two compact planar vibrotactile actuators in direct contact with the user's skin to transmit tactile cues. These stimuli are directly related to the acceleration profiles recorded with two IMUS placed on the distal phalanx of a soft under-actuated robotic prosthesis (Soft-Hand Pro). We characterized the system from a psychophysical point of view with fifteen able-bodied participants by computing participants' Just Noticeable Difference (JND) related to the discrimination of vibrotactile cues delivered on the index finger, which are associated with the exploration of different sandpapers. Moreover, we performed a pilot experiment with one SoftHand Pro prosthesis user by designing a task, i.e. Active Texture Identification, to investigate if our feedback could enhance users' roughness discrimination. Results indicate that the device can effectively convey contact and texture cues, which users can readily detect and distinguish.

The developmental trajectory of object recognition robustness: Children are like small adults but unlike big deep neural networks.

In laboratory object recognition tasks based on undistorted photographs, both adult humans and deep neural networks (DNNs) perform close to ceiling. Unlike adults', whose object recognition performance is robust against a wide range of image distortions, DNNs trained on standard ImageNet (1.3M images) perform poorly on distorted images. However, the last 2 years have seen impressive gains in DNN distortion robustness, predominantly achieved through ever-increasing large-scale datasets-orders of magnitude larger than ImageNet. Although this simple brute-force approach is very effective in achieving human-level robustness in DNNs, it raises the question of whether human robustness, too, is simply due to extensive experience with (distorted) visual input during childhood and beyond. Here we investigate this question by comparing the core object recognition performance of 146 children (aged 4-15 years) against adults and against DNNs. We find, first, that already 4- to 6-year-olds show remarkable robustness to image distortions and outperform DNNs trained on ImageNet. Second, we estimated the number of images children had been exposed to during their lifetime. Compared with various DNNs, children's high robustness requires relatively little data. Third, when recognizing objects, children-like adults but unlike DNNs-rely heavily on shape but not on texture cues. Together our results suggest that the remarkable robustness to distortions emerges early in the developmental trajectory of human object recognition and is unlikely the result of a mere accumulation of experience with distorted visual input. Even though current DNNs match human performance regarding robustness, they seem to rely on different and more data-hungry strategies to do so.

Identity-Preserved Human Posture Detection in Infrared Thermal Images: A Benchmark

Human pose estimation has a variety of real-life applications, including human action recognition, AI-powered personal trainers, robotics, motion capture and augmented reality, gaming, and video surveillance. However, most current human pose estimation systems are based on RGB images, which do not seriously take into account personal privacy. Although identity-preserved algorithms are very desirable when human pose estimation is applied to scenarios where personal privacy does matter, developing human pose estimation algorithms based on identity-preserved modalities, such as thermal images concerned here, is very challenging due to the limited amount of training data currently available and the fact that infrared thermal images, unlike RGB images, lack rich texture cues which makes annotating training data itself impractical. In this paper, we formulate a new task with privacy protection that lies between human detection and human pose estimation by introducing a benchmark for IPHPDT (i.e., Identity-Preserved Human Posture Detection in Thermal images). This task has a threefold novel purpose: the first is to establish an identity-preserved task with thermal images; the second is to achieve more information other than the location of persons as provided by human detection for more advanced computer vision applications; the third is to avoid difficulties in collecting well-annotated data for human pose estimation in thermal images. The presented IPHPDT dataset contains four types of human postures, consisting of 75,000 images well-annotated with axis-aligned bounding boxes and postures of the persons. Based on this well-annotated IPHPDT dataset and three state-of-the-art algorithms, i.e., YOLOF (short for You Only Look One-level Feature), YOLOX (short for Exceeding YOLO Series in 2021) and TOOD (short for Task-aligned One-stage Object Detection), we establish three baseline detectors, called IPH-YOLOF, IPH-YOLOX, and IPH-TOOD. In the experiments, three baseline detectors are used to recognize four infrared human postures, and the mean average precision can reach 70.4%. The results show that the three baseline detectors can effectively perform accurate posture detection on the IPHPDT dataset. By releasing IPHPDT, we expect to encourage more future studies into human posture detection in infrared thermal images and draw more attention to this challenging task.

Individual differences in behavioral flexibility predict future volitional ethanol consumption in mice.

Cognitive control is key to regulating alcohol intake and preventing relapse. Behavioral inflexibility can prevent adaptive strategies such as mindfulness or other relapse-prevention behaviors. In a mouse model we investigated whether individual variability in behavioral flexibility (using attentional set-shifting task; ASST) predicts future alcohol intake. Adult male and female C57BL/6J mice were subjected to ASST using a bowl-digging paradigm where mice identify a baited bowl based on compound odor and textural cues. This was completed prior to any alcohol exposure. Individual performance across mice varied within the group. We integrated several metrics, specifically ASST stage completed, trials to completion, and errors performed to produce an individual performance index measure of behavioral flexibility. Afterward, ASST mice were trained to drink ethanol (15%, v/v, 1h/day) for 3-4 weeks until intake stabilized. Using this prospective approach, we identified an inverse relationship between behavioral flexibility and drinking-less-flexible mice had a propensity to consume more alcohol. Similar relationships have been identified previously in non-human primates and rats. Our results show that the relationship between alcohol and behavioral flexibility is a robust trait that is conserved across species and can be used in mice to study neural substrates underlying these behaviors.

Face presentation attack detection based on optical flow and texture analysis

Towards the security threats brought by presented fake faces to face recognition systems, many countermeasures have been presented to resist fake faces and achieved promising performance, where facial movement is one of the commonly used cues. However, the more detailed and distinguishable information in the motion cue is not well explored in these methods. In addition, the texture cue used for face presentation attack detection (PAD) is also not well integrated into motion. Therefore, we propose a detection method by analyzing the cues of facial movement and texture. More specifically, the optical flows of a continuous video sequence are first extracted, which can describe the detailed movement direction and movement amplitude. Then, the extracted optical flows are concatenated with the video frames as the input of the network. After that, region and channel attention mechanisms are jointly introduced to adaptively allocate the classification weights. Finally, the fused motion and texture cues are fed into a convolutional network to extract features and identify whether the input video sequence is from live face or not. The proposed detection method is tested on the databases of Replay-Attack, OULU-NPU and HKBU-MARs V1. The experiments show that our proposed face PAD method can well separate various types of fake faces compared to state-of-the-art methods.

An Edge Filter Based Approach of Neural Style Transfer to the Image Stylization

Transferring artistic styles onto any image or photograph has become popular in industry and academia in recent years. The use of neural style transfer (NST) for image style transfer is getting more popular. Convolution Neural Networks (CNN) based style transfer provides a new edge and life to the images, videos, and games. The re-rendering procedure of the content of one image with the style of another using various models and approaches is widely used for image style transfer. However, there are many drawbacks, including image quality, enormous loss, unrealistic artefacts, and the style of localized regions being less compared to the desired artistic style. For the reason that transfer technique fails to capture detailed, miniature textures and keep the true artwork’s texture scales. We propose a multimodal CNN that stylizes hierarchically with several losses of increasing sizes while considering faithful representations of both colour and luminance channels. We may transfer not only large-scale, evident style cues but also subtle, exquisite ones by effectively handling style and texture cues at different sizes using various modalities. Our approach providing aesthetically pleasing results and is more comparable to multiple desirable creative styles using colour and texture cues at different scales.

The role of viscosity in flavor preference: plasticity and interactions with taste.

The brain combines gustatory, olfactory, and somatosensory information to create our perception of flavor. Within the somatosensory modality, texture attributes such as viscosity appear to play an important role in flavor preference. However, research into the role of texture in flavor perception is relatively sparse, and the contribution of texture cues to hedonic evaluation of flavor remains largely unknown. Here, we used a rat model to investigate whether viscosity preferences can be manipulated through association with nutrient value, and how viscosity interacts with taste to inform preferences for taste + viscosity mixtures. To address these questions, we measured preferences for moderately viscous solutions prepared with xanthan gum using 2-bottle consumption tests. By experimentally exposing animals to viscous solutions with and without nutrient value, we demonstrate that viscosity preferences are susceptible to appetitive conditioning. By independently varying viscosity and taste content of solutions, we further show that taste and viscosity cues both contribute to preferences for taste + viscosity mixtures. How these 2 modalities are combined depended on relative palatability, with mixture preferences falling in between component preferences, suggesting that hedonic aspects of taste and texture inputs are centrally integrated. Together, these findings provide new insight into how texture aspects of flavor inform hedonic perception and impact food choice behavior.

Boundary segmentation from luminance and texture cues: Underlying mechanisms

Boundary segmentation from luminance and texture cues: Underlying mechanisms

Similar use of shape and texture cues for own- and other-race faces during face learning and recognition

Although the other-race effect (ORE; superior recognition of own- relative to other-race faces) is well established, the mechanisms underlying it are not well understood. We examined whether the ORE is attributable to differential use of shape and texture cues for own- vs. other-race faces. Shape cues are particularly important for detecting that an own-race face is unfamiliar, whereas texture cues are more important for recognizing familiar and newly learned own-race faces. We compared the influence of shape and texture cues on Caucasian participants’ recognition of Caucasian and East Asian faces using two complementary approaches. In Experiment 1, participants studied veridical, shape-caricatured, or texture-caricatured faces and then were asked to recognize them in an old/new recognition task. In Experiment 2, all study faces were veridical and we independently removed the diagnosticity of shape (or texture) cues in the test phase by replacing original shape (or texture) with average shape (or texture). Despite an overall own-race advantage, participants’ use of shape and texture cues was comparable for own- and other-race faces. These results suggest that the other-race effect is not attributable to qualitative differences in the use of shape and texture cues.

Inferring Camouflaged Objects by Texture-Aware Interactive Guidance Network

Camouflaged objects, similar to the background, show indefinable boundaries and deceptive textures, which increases the difficulty of detection task and makes the model rely on features with more information. Herein, we design a texture label to facilitate our network for accurate camouflaged object segmentation. Motivated by the complementary relationship between texture labels and camouflaged object labels, we propose an interactive guidance framework named TINet, which focuses on finding the indefinable boundary and the texture difference by progressive interactive guidance. It maximizes the guidance effect of refined multi-level texture cues on segmentation. Specifically, texture perception decoder (TPD) makes a comprehensive analysis of texture information in multiple scales. Feature interaction guidance decoder (FGD) interactively refines multi-level features of camouflaged object detection and texture detection level by level. Holistic perception decoder (HPD) enhances FGD results by multi-level holistic perception. In addition, we propose a boundary weight map to help the loss function pay more attention to the object boundary. Sufficient experiments conducted on COD and SOD datasets demonstrate that the proposed method performs favorably against 23 state-of-the-art methods.

On visual masking estimation for adaptive quantization using steerable filters

A fast and accurate assessment of visual masking effects is desirable while encoding in order to utilize such effects to improve the quality of compressed videos through an adaptive quantization (AQ) scheme. Here, we propose a method of estimating the contrast masking threshold on natural scene patches, using texture cues imparted by steerable filter responses. We then employ the estimated thresholds to perform AQ for AV1 encoding. Our experimental results establish that the proposed method is able to outperform existing visual masking models in terms of estimation performance while being relatively computationally inexpensive than these models, and is also able to improve the variance based AQ algorithm that is currently deployed in the SVT-AV1 codec. Using the multi-scale structural similarity index measure (MS-SSIM) as the quality model, our approach achieves an average BD-rate of -1.82% using the uniform quantization scheme as anchor as compared to 5.83% obtained with the variance based method. We note that the proposed approach produces less visible compression artifacts than the variance based AQ approach at lower bitrates, while maintaining similar encoding complexity.

Persistent grasping errors produce depth cue reweighting in perception

When a grasped object is larger or smaller than expected, haptic feedback automatically recalibrates motor planning. Intriguingly, haptic feedback can also affect 3D shape perception through a process called depth cue reweighting. Although signatures of cue reweighting also appear in motor behavior, it is unclear whether this motor reweighting is the result of upstream perceptual reweighting, or a separate process. We propose that perceptual reweighting is directly related to motor control; in particular, that it is caused by persistent, systematic movement errors that cannot be resolved by motor recalibration alone. In Experiment 1, we inversely varied texture and stereo cues to create a set of depth-metamer objects: when texture specified a deep object, stereo specified a shallow object, and vice versa, such that all objects appeared equally deep. The stereo-texture pairings that produced this perceptual metamerism were determined for each participant in a matching task (Pre-test). Next, participants repeatedly grasped these depth metamers, receiving haptic feedback that was positively correlated with one cue and negatively correlated with the other, resulting in persistent movement errors. Finally, participants repeated the perceptual matching task (Post-test). In the condition where haptic feedback reinforced the texture cue, perceptual changes were correlated with changes in grasping performance across individuals, demonstrating a link between perceptual reweighting and improved motor control. Experiment 2 showed that cue reweighting does not occur when movement errors are rapidly corrected by standard motor adaptation. These findings suggest a mutual dependency between perception and action, with perception directly guiding action, and actions producing error signals that drive motor and perceptual learning.

Multiple texture cues are integrated for perception of 3D slant from texture.

The projected image of a textured surface contains multiple texture cues to three-dimensional (3D) surface orientation. Previous studies have reported conflicting findings about the roles of various texture cues. We tested the influence of texture compression relative to other texture cues using a cue conflict paradigm. Observers viewed images of textured planar surfaces with varied slants (0°–70°) and estimated 3D slant by aligning their hand with the virtual surface. Conflicts between texture cues were created by stretching or compression the texture along the surface, which selectively changes the slant specified by texture compression. The texture distortions were relatively small (±10% or ±20%) to limit the size of the cue conflicts. Across three experiments, we varied the field of view (10° vs. 20°), texture regularity (circles vs. Voronoi), and availability of binocular cues. In monocular conditions, slant estimates were strongly affected by texture distortions. Analyses of cue weighting found that texture compression had more influence on slant settings than other texture cues and the relative influence of texture compression decreased with larger field of view and less regular textures. In binocular conditions, we also observed effects of texture distortion, and the influence of texture compression relative to information from stereo and other texture cues increased with slant. Our results provide evidence that texture compression contributes to perceived slant, in addition to other texture cues such as texture scaling. The observed effects of simulated slant, field of view, and texture regularity on cue weighting were all consistent with a model that integrates multiple sources of information according to their reliability.

ZoomNet: Part-Aware Adaptive Zooming Neural Network for 3D Object Detection

3D object detection is an essential task in autonomous driving and robotics. Though great progress has been made, challenges remain in estimating 3D pose for distant and occluded objects. In this paper, we present a novel framework named ZoomNet for stereo imagery-based 3D detection. The pipeline of ZoomNet begins with an ordinary 2D object detection model which is used to obtain pairs of left-right bounding boxes. To further exploit the abundant texture cues in rgb images for more accurate disparity estimation, we introduce a conceptually straight-forward module – adaptive zooming, which simultaneously resizes 2D instance bounding boxes to a unified resolution and adjusts the camera intrinsic parameters accordingly. In this way, we are able to estimate higher-quality disparity maps from the resized box images then construct dense point clouds for both nearby and distant objects. Moreover, we introduce to learn part locations as complementary features to improve the resistance against occlusion and put forward the 3D fitting score to better estimate the 3D detection quality. Extensive experiments on the popular KITTI 3D detection dataset indicate ZoomNet surpasses all previous state-of-the-art methods by large margins (improved by 9.4% on APbv (IoU=0.7) over pseudo-LiDAR). Ablation study also demonstrates that our adaptive zooming strategy brings an improvement of over 10% on AP3d (IoU=0.7). In addition, since the official KITTI benchmark lacks fine-grained annotations like pixel-wise part locations, we also present our KFG dataset by augmenting KITTI with detailed instance-wise annotations including pixel-wise part location, pixel-wise disparity, etc.. Both the KFG dataset and our codes will be publicly available at https://github.com/detectRecog/ZoomNet.