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Developmental myelination is a protracted process in the mammalian brain1. One theory for why oligodendrocytes mature so slowly posits that myelination may stabilize neuronal circuits and temper neuronal plasticity as animals age2-4. We tested this theory in the visual cortex, which has a well-defined critical period for experience-dependent neuronal plasticity5. During adolescence, visual experience modulated the rate of oligodendrocyte maturation in visual cortex. To determine whether oligodendrocyte maturation in turn regulates neuronal plasticity, we genetically blocked oligodendrocyte differentiation and myelination in adolescent mice. In adult mice lacking adolescent oligodendrogenesis, a brief period of monocular deprivation led to a significant decrease in visual cortex responses to the deprived eye, reminiscent of the plasticity normally restricted to adolescence. This enhanced functional plasticity was accompanied by a greater turnover of dendritic spines and coordinated reductions in spine size following deprivation. Furthermore, inhibitory synaptic transmission, which gates experience-dependent plasticity at the circuit level, was diminished in the absence of adolescent oligodendrogenesis. These results establish a critical role for oligodendrocytes in shaping the maturation and stabilization of cortical circuits and support the concept of developmental myelination acting as a functional brake on neuronal plasticity.

A monocular ranging method for forward vehicles in intelligent driving is proposed. This method measures vehicle distance more accurately under the condition of a single camera and can estimate camera attitude in real-time. For the estimation of camera pitch and yaw angles, it is achieved using road vanishing points. The images collected by the camera are sequentially processed through the Roberts operator amplitude calculation, feature point extraction, feature line segment generation, road vanishing point voting, and estimation of camera attitude to obtain pitch and yaw angles. A distance estimation network was designed, which is divided into multiple levels based on image size and incorporates image feature, integrating vehicle grounding point, and vehicle width information, effectively improving ranging accuracy. Finally, validation was conducted on KITTI data, with a relative error (AbsRel) of 8.3%. Additionally, the TuSimple dataset and continuous driving scenarios were also validated, resulting in improved performance compared to previous algorithms. [ABSTRACT FROM AUTHOR]

Remotely Operated Vehicles (ROVs) are essential instruments in most industrial applications of subsea Inspection, Maintenance, and Repair (IMR). In IMR applications, especially in short-distance inspection and intervention operations, ROVs should be able to find its position and orientation with respect to underwater platforms or specific objects on the platform. Current work class ROVs are normally operated by two pilots. Improving the situational awareness and sensing capabilities is an essential necessity from industrial actors in such a challenging environment. Automated visual based interpretation shows promising results and essential tools toward higher level of autonomy within IMR and ROV operations. Motivated by the large amount of available information in ROV cameras, this article presents a pipeline for object detection, 6D pose estimation and tracking. The main contribution of the paper is its new detection procedure where the current tracker state is evaluated for acceptance or rejection of the new detection. The decomposed tasks of detection, 6D pose estimation and tracking in the proposed pipeline is tested on an open underwater dataset featuring pose annotated objects used for ROV intervention. The results show that the method is highly suitable for supervised autonomy and a step forward to autonomous operations in underwater IMR applications.

Deep learning models and algorithms facilitate relatively easier ways of hand pose estimation from monocular RGB images compared to traditional approaches. Despite this, a majority of available algorithms use multiple-stage models to perform hand pose estimation. Moreover, the single-stage methods are mainly limited to a single hand and it is difficult for them to scale to multiple hands. To this end, we propose an approach that takes the features of the saliency map extracted for hand region of interest (ROI) localization. An integrated network uses these features for pose estimation. This arrangement of layers forms an end-to-end pipeline that allows simultaneous pose estimation for multiple hands. The model is designed to run on multiple cores of CPU/GPU to independently perform inference for each detected hands’pose making possible faster inference and hence suitable for real-time applications. In addition, a new approach using grid-based design to estimate hand-keypoints position with high precision is also proposed. Both the proposed designs are validated on multiple datasets to prove their feasibility and effectiveness. The probability of the correct keypoint (PCK) value at threshold value of 0.2 is above 95% on the test sets from Interhand dataset and Rendered HandPose Dataset (RHD).

This survey provides a comprehensive overview of traditional techniques and deep learning-based methodologies for monocular visual odometry (VO), with a focus on displacement measurement applications. This paper outlines the fundamental concepts and general procedures for VO implementation, including feature detection, tracking, motion estimation, triangulation, and trajectory estimation. This paper also explores the research challenges inherent in VO implementation, including scale estimation and ground plane considerations. The scientific literature is rife with diverse methodologies aiming to overcome these challenges, particularly focusing on the problem of accurate scale estimation. This issue has been typically addressed through the reliance on knowledge regarding the height of the camera from the ground plane and the evaluation of feature movements on that plane. Alternatively, some approaches have utilized additional tools, such as LiDAR or depth sensors. This survey of approaches concludes with a discussion of future research challenges and opportunities in the field of monocular visual odometry. [ABSTRACT FROM AUTHOR]

A monocular ranging method for forward vehicles in intelligent driving is proposed. This method measures vehicle distance more accurately under the condition of a single camera and can estimate camera attitude in real-time. For the estimation of camera pitch and yaw angles, it is achieved using road vanishing points. The images collected by the camera are sequentially processed through the Roberts operator amplitude calculation, feature point extraction, feature line segment generation, road vanishing point voting, and estimation of camera attitude to obtain pitch and yaw angles. A distance estimation network was designed, which is divided into multiple levels based on image size and incorporates image feature, integrating vehicle grounding point, and vehicle width information, effectively improving ranging accuracy. Finally, validation was conducted on KITTI data, with a relative error (AbsRel) of 8.3%. Additionally, the TuSimple dataset and continuous driving scenarios were also validated, resulting in improved performance compared to previous algorithms.

We present a synthetic augmentation approach towards improving monocular face presentation–attack–detection (PAD) robustness to real-world noise additions. Face PAD algorithms secure authentication systems against spoofing attacks, such as pictures, videos, and 2D-inspired masks. Best-in-class PAD methods typically use 3D imagery, but these can be expensive. To reduce application cost, there is a growing field investigating monocular algorithms that detect facial artifacts. These approaches work well in laboratory conditions, but can be sensitive to the imaging environment (e.g., sensor noise, dynamic lighting, etc.). The ideal solution for noise robustness is training under all expected conditions; however, this is time consuming and expensive. Instead, we propose that physics-informed noise-augmentations can pragmatically achieve robustness. Our toolbox contains twelve sensor and lighting effect generators. We demonstrate that our toolbox generates more robust PAD features than popular augmentation methods in noisy test-evaluations. We also observe that the toolbox improves accuracy on clean test data, suggesting that it inherently helps discern spoof artifacts from imaging artifacts. We validate this hypothesis through an ablation study, where we remove liveliness pairs (e.g., live or spoof imagery only for participants) to identify how much real data can be replaced with synthetic augmentations. We demonstrate that using these noise augmentations allows us to achieve better test accuracy while only requiring 30% of participants to be fully imaged under all conditions. These findings indicate that synthetic noise augmentations are a great way to improve PAD, addressing noise robustness while simplifying data collection. [ABSTRACT FROM AUTHOR]

The mammalian visual cortex contains multiple retinotopically defined areas that process distinct features of the visual scene. Little is known about what guides the functional differentiation of visual cortical areas during development. Recent studies in mice have revealed that visual input from the two eyes provides spatiotemporally distinct signals to primary visual cortex (V1), such that contralateral eye-dominated V1 neurons respond to higher spatial frequencies than ipsilateral eye-dominated neurons. To test whether binocular visual input drives the differentiation of visual cortical areas, we used two-photon calcium imaging to characterize the effects of juvenile monocular deprivation (MD) on the responses of neurons in V1 and two higher visual areas, LM (lateromedial) and PM (posteromedial). In adult mice of either sex, we find that MD prevents the emergence of distinct spatiotemporal tuning in V1, LM, and PM. We also find that, within each of these areas, MD reorganizes the distinct spatiotemporal tuning properties driven by the two eyes. Moreover, we find a relationship between speed tuning and ocular dominance in all three areas that MD preferentially disrupts in V1, but not in LM or PM. Together, these results reveal that balanced binocular vision during development is essential for driving the functional differentiation of visual cortical areas. The higher visual areas of mouse visual cortex may provide a useful platform for investigating the experience-dependent mechanisms that set up the specialized processing within neocortical areas during postnatal development.SIGNIFICANCE STATEMENT Little is known about the factors guiding the emergence of functionally distinct areas in the brain. Using in vivo Ca2+ imaging, we recorded visually evoked activity from cells in V1 and higher visual areas LM (lateromedial) and PM (posteromedial) of mice. Neurons in these areas normally display distinct spatiotemporal tuning properties. We found that depriving one eye of normal input during development prevents the functional differentiation of visual areas. Deprivation did not disrupt the degree of speed tuning, a property thought to emerge in higher visual areas. Thus, some properties of visual cortical neurons are shaped by binocular experience, while others are resistant. Our study uncovers the fundamental role of binocular experience in the formation of distinct areas in visual cortex.

Myopia is induced when a growing eye wears a diffuser that deprives it of detailed spatial vision (form deprivation, FD). In chickens with optic nerve section (ONS), FD myopia still occurs, suggesting that the signals underlying myopia reside within the eye. As avian eyes differ from mammals, we asked whether local mechanisms also underlie FD myopia in a mammalian model. Young guinea pigs underwent either sham surgery followed by FD (SHAM + FD, n = 7); or ONS followed by FD (ONS + FD, n = 7); or ONS without FD (ONS, n = 9). FD was initiated 3 days after surgery with a diffuser that was worn on the surgically treated eye for 14 days. Animals with ONS + FD developed -8.9 D of relative myopia and elongated by 135 μm more than in their untreated eyes after 2 weeks of FD. These changes were significantly greater than those in SHAM + FD animals (-5.5 D and 40 μm of elongation after 14 days of FD), and reflected exaggerated elongation of the posterior vitreous chamber. The myopia reversed when FD was discontinued, despite ONS, but eyes did not recover back to normal (30 days after surgery, ONS + FD eyes still retained -3 D of relative myopia when SHAM+FD animals had returned to normal). No long-term residual myopia was present after ONS alone, ruling out a surgical artifact. Although the gross mechanism signaling myopic ocular growth and its recovery in the young mammalian eye does not require an intact optic nerve, its fine-tuning is disrupted by ONS.

Purpose: To evaluate the determinants affecting the quality of life in monocular glaucoma patients using the Indian vision function questionnaire. Methods: In this prospective cross-sectional study, total of 196 patients were divided into two groups: cases and controls. Indian Vision Function Questionnaire (IND-VFQ) was administered and analyzed. One hundred twenty-nine (58.6%) patients who had lost their vision in one eye due to glaucoma were included as cases and 67 (30.4%) patients who had lost their vision due to other causes were taken as controls. Results: Median composite score of subscales was 54.62 (29.7-74.7) in group 1 and 45.38 (23.7-76.7) in group 2. The psychosocial impact scale was the most affected scale, the median scores were 33.02 (0 to 60.0) and 19.07 (0 to 53.0) in groups 1 and 2, respectively. Among all dimensions of IND-VFQ, the highest score was for color vision 100.0 (0-100.0) and 100.0 (0-100.0), and the lowest median score was found in mental health and dependency in both the groups. Multiple linear regression analysis demonstrated that visual acuity was associated with a low score (P < 0.001). Female gender was significantly associated with the overall score in the univariate model (P = 0.006). Conclusion: Monocular glaucoma patients have a poor general and vision-related quality of life. Depression associated with monocularity and the perception of dependency and being a burden on their family members greatly impacted the mental health of the participants. [ABSTRACT FROM AUTHOR]

High acuity stereopsis emerges during an early postnatal critical period when binocular neurons in the primary visual cortex sharpen their receptive field tuning properties. We find that this sharpening is achieved by dismantling the binocular circuit present at critical period onset and building it anew. Longitudinal imaging of receptive field tuning (e.g., orientation selectivity) of thousands of neurons reveals that most binocular neurons present in layer 2/3 at critical period onset are poorly tuned and are rendered monocular. In parallel, new binocular neurons are established by conversion of well-tuned monocular neurons as they gain matched input from the other eye. These improvements in binocular tuning in layer 2/3 are not inherited from layer 4 but are driven by the experience-dependent sharpening of ipsilateral eye responses. Thus, vision builds a new and more sharply tuned binocular circuit in layer 2/3 by cellular exchange and not by refining the original circuit.

Study of the neural deficits caused by mismatched binocular vision in early childhood has predominantly focused on circuits in the primary visual cortex (V1). Recent evidence has revealed that neurons in mouse dorsolateral geniculate nucleus (dLGN) can undergo rapid ocular dominance plasticity following monocular deprivation (MD). It remains unclear, however, whether the long-lasting deficits attributed to MD during the critical period originate in the thalamus. Using in vivo two-photon Ca2+ imaging of dLGN afferents in superficial layers of V1 in female and male mice, we demonstrate that 14 d MD during the critical period leads to a chronic loss of binocular dLGN inputs while sparing response strength and spatial acuity. Importantly, MD leads to profoundly mismatched visual tuning properties in remaining binocular dLGN afferents. Furthermore, MD impairs binocular modulation, reducing facilitation of responses of both binocular and monocular dLGN inputs during binocular viewing. As predicted by our findings in thalamic inputs, Ca2+ imaging from V1 neurons revealed spared spatial acuity but impaired binocularity in L4 neurons. V1 L2/3 neurons in contrast displayed deficits in both binocularity and spatial acuity. Our data demonstrate that critical-period MD produces long-lasting disruptions in binocular integration beginning in early binocular circuits in dLGN, whereas spatial acuity deficits first arise from circuits further downstream in V1. Our findings indicate that the development of normal binocular vision and spatial acuity depend upon experience-dependent refinement of distinct stages in the mammalian visual system.SIGNIFICANCE STATEMENT Abnormal binocular vision and reduced acuity are hallmarks of amblyopia, a disorder that affects 2%-5% of the population. It is widely thought that the neural deficits underlying amblyopia begin in the circuits of primary visual cortex. Using in vivo two-photon calcium imaging of thalamocortical axons in mice, we show that depriving one eye of input during a critical period in development chronically impairs binocular integration in thalamic inputs to primary visual cortex. In contrast, visual acuity is spared in thalamic inputs. These findings shed new light on the role for developmental mechanisms in the thalamus in establishing binocular vision and may have critical implications for amblyopia.

Purpose: To evaluate the determinants affecting the quality of life in monocular glaucoma patients using the Indian vision function questionnaire. Methods: In this prospective cross-sectional study, total of 196 patients were divided into two groups: cases and controls. Indian Vision Function Questionnaire (IND-VFQ) was administered and analyzed. One hundred twenty-nine (58.6%) patients who had lost their vision in one eye due to glaucoma were included as cases and 67 (30.4%) patients who had lost their vision due to other causes were taken as controls. Results: Median composite score of subscales was 54.62 (29.7–74.7) in group 1 and 45.38 (23.7–76.7) in group 2. The psychosocial impact scale was the most affected scale, the median scores were 33.02 (0 to 60.0) and 19.07 (0 to 53.0) in groups 1 and 2, respectively. Among all dimensions of IND-VFQ, the highest score was for color vision 100.0 (0–100.0) and 100.0 (0–100.0), and the lowest median score was found in mental health and dependency in both the groups. Multiple linear regression analysis demonstrated that visual acuity was associated with a low score (P < 0.001). Female gender was significantly associated with the overall score in the univariate model (P = 0.006). Conclusion: Monocular glaucoma patients have a poor general and vision-related quality of life. Depression associated with monocularity and the perception of dependency and being a burden on their family members greatly impacted the mental health of the participants.

AIM:To evaluate the underlying aetiology and clinical characteristics of retinal detachment(RD)in school-age pediatric monocular RD.METHODS:Patients with RD and contralateral blind(monocular RD)aged 7-14 years, from November 2015 to May 2021 in our hospital were retrospectively reviewed. Demographic characteristics and etiology of RD, clinical type, surgical modality, type of intraocular tamponade, pre-and postoperative visual and anatomical outcomes were recorded and evaluated.RESULTS: There were 27 children(27 eyes)with monocular RD at least 6mo follow-up. The average age at presentation was 10.63±2.30 years. Familial exudative vitreoretinopathy(FEVR)(11/27, 41%), postoperative congenital glaucoma(6/27, 22%)and Stickler syndrome(3/27, 11%)were main underlying etiologies. Among them, rhegmatogenous retinal detachment(RRD)comprised 78%(21/27)of the patients, of which 81% patients(17/21)had proliferative vitreoretinopathy(PVR)C3 or worse. Pars plana vitrectomy(PPV)was done in 85%(23/27)of the patients, of which 83%(19/23)received silicone oil tamponade. Best corrected visual acuity(BCVA, LogMAR)worse than 1.7 was seen in 78%(21/27)of the patients at final visit, and 82%(22/27)had reattached retina, but 41%(11/27)of the patients remained status of silicone oil tamponade at last visit.CONCLUSION:School-age pediatric monocular RD is often associated with underlying congenital or hereditary conditions, and often presented with severe RD and severe PVR reaction which needed vitrectomy combined with silicon oil tamponade, and with poor visual and anatomical short-term prognosis.

Object pose detection and tracking has recently attracted increasing attention due to its wide applications in many areas, such as autonomous driving, robotics, and augmented reality. Among methods for object pose detection and tracking, deep learning is the most promising one that has shown better performance than others. However, survey study about the latest development of deep learning-based methods is lacking. Therefore, this study presents a comprehensive review of recent progress in object pose detection and tracking that belongs to the deep learning technical route. To achieve a more thorough introduction, the scope of this study is limited to methods taking monocular RGB/RGBD data as input and covering three kinds of major tasks: instance-level monocular object pose detection, category-level monocular object pose detection, and monocular object pose tracking. In our work, metrics, datasets, and methods of both detection and tracking are presented in detail. Comparative results of current state-of-the-art methods on several publicly available datasets are also presented, together with insightful observations and inspiring future research directions. [ABSTRACT FROM AUTHOR]

We introduce Vision UFormer (ViUT), a novel deep neural long-range monocular depth estimator. The input is an RGB image, and the output is an image that stores the absolute distance of the object in the scene as its per-pixel values. ViUT consists of a Transformer encoder and a ResNet decoder combined with the UNet style of skip connections. It is trained on 1M images across ten datasets in a staged regime that starts with easier-to-predict data such as indoor photographs and continues to more complex long-range outdoor scenes. We show that ViUT provides comparable results for normalized relative distances and short-range classical datasets such as NYUv2 and KITTI. We further show that it successfully estimates absolute long-range depth in meters. We validate ViUT on a wide variety of long-range scenes showing its high estimation capabilities with a relative improvement of up to 23%. Absolute depth estimation finds application in many areas, and we show its usability in image composition, range annotation, defocus, and scene reconstruction. Our models are available at cphoto.fit.vutbr.cz/viut. [Display omitted] • Vision UFormer: Dense depth prediction model combining Vision Transformer with a UNet. • Staged Training: Moving from easier to difficult data allows successful training. • Predictor reaches SOTA results, surpassing others in long-range natural environments • Depths are used for further applications, e.g., scene reconstruction or manipulation. [ABSTRACT FROM AUTHOR]

This survey provides a comprehensive overview of traditional techniques and deep learning-based methodologies for monocular visual odometry (VO), with a focus on displacement measurement applications. This paper outlines the fundamental concepts and general procedures for VO implementation, including feature detection, tracking, motion estimation, triangulation, and trajectory estimation. This paper also explores the research challenges inherent in VO implementation, including scale estimation and ground plane considerations. The scientific literature is rife with diverse methodologies aiming to overcome these challenges, particularly focusing on the problem of accurate scale estimation. This issue has been typically addressed through the reliance on knowledge regarding the height of the camera from the ground plane and the evaluation of feature movements on that plane. Alternatively, some approaches have utilized additional tools, such as LiDAR or depth sensors. This survey of approaches concludes with a discussion of future research challenges and opportunities in the field of monocular visual odometry.

The developing brain can respond quickly to altered sensory experience by circuit reorganization. During a critical period in early life, neurons in the primary visual cortex rapidly lose responsiveness to an occluded eye and come to respond better to the open eye. While physiological and some of the molecular mechanisms of this process have been characterized, its structural basis, except for the well-known changes in the thalamocortical projection, remains obscure. To elucidate the relationship between synaptic remodeling and functional changes during this experience-dependent process, we used 2-photon microscopy to image synaptic structures of sparsely labeled layer 2/3 neurons in the binocular zone of mouse primary visual cortex. Anatomical changes at presynaptic and postsynaptic sites in mice undergoing monocular visual deprivation (MD) were compared to those in control mice with normal visual experience. We found that postsynaptic spines remodeled quickly in response to MD, with neurons more strongly dominated by the deprived eye losing more spines. These postsynaptic changes parallel changes in visual responses during MD and their recovery after restoration of binocular vision. In control animals with normal visual experience, the formation of presynaptic boutons increased during the critical period and then declined. MD affected bouton formation, but with a delay, blocking it after 3 d. These findings reveal intracortical anatomical changes in cellular layers of the cortex that can account for rapid activity-dependent plasticity.

Featured Application: Face-recognition for monocular systems (e.g., phones and buildings). This paper presents a novel material spectroscopy approach to facial presentation–attack–defense (PAD). Best-in-class PAD methods typically detect artifacts in the 3D space. This paper proposes similar features can be achieved in a monocular, single-frame approach by using controlled light. A mathematical model is produced to show how live faces and their spoof counterparts have unique reflectance patterns due to geometry and albedo. A rigorous dataset is collected to evaluate this proposal: 30 diverse adults and their spoofs (paper-mask, display-replay, spandex-mask and COVID mask) under varied pose, position, and lighting for 80,000 unique frames. A panel of 13 texture classifiers are then benchmarked to verify the hypothesis. The experimental results are excellent. The material spectroscopy process enables a conventional MobileNetV3 network to achieve 0.8% average-classification-error rate, outperforming the selected state-of-the-art algorithms. This demonstrates the proposed imaging methodology generates extremely robust features. [ABSTRACT FROM AUTHOR]

We present a synthetic augmentation approach towards improving monocular face presentation–attack–detection (PAD) robustness to real-world noise additions. Face PAD algorithms secure authentication systems against spoofing attacks, such as pictures, videos, and 2D-inspired masks. Best-in-class PAD methods typically use 3D imagery, but these can be expensive. To reduce application cost, there is a growing field investigating monocular algorithms that detect facial artifacts. These approaches work well in laboratory conditions, but can be sensitive to the imaging environment (e.g., sensor noise, dynamic lighting, etc.). The ideal solution for noise robustness is training under all expected conditions; however, this is time consuming and expensive. Instead, we propose that physics-informed noise-augmentations can pragmatically achieve robustness. Our toolbox contains twelve sensor and lighting effect generators. We demonstrate that our toolbox generates more robust PAD features than popular augmentation methods in noisy test-evaluations. We also observe that the toolbox improves accuracy on clean test data, suggesting that it inherently helps discern spoof artifacts from imaging artifacts. We validate this hypothesis through an ablation study, where we remove liveliness pairs (e.g., live or spoof imagery only for participants) to identify how much real data can be replaced with synthetic augmentations. We demonstrate that using these noise augmentations allows us to achieve better test accuracy while only requiring 30% of participants to be fully imaged under all conditions. These findings indicate that synthetic noise augmentations are a great way to improve PAD, addressing noise robustness while simplifying data collection.

The monocular simultaneous localization and mapping (SLAM) system is one of the most important among all visual or visual-inertial SLAM (VSLAM or VISLAM) systems due to its low cost, easy calibration and identification. Initialization is always crucial to bootstrap the monocular SLAM system. With the rapid growth of some quick-start required SLAM applications, e.g., augmented reality (AR) and unmanned aerial vehicles (UAVs), devising faster and more accurate initialization has become a central problem. Traditional initialization uses the first two frames to create landmarks and then computes camera poses for the subsequential frames, using a 3D-to-2D perspective-n-points (PnP) mechanism. In this paper, we propose a novel three-frame-only (3FO) initialization approach for the monocular SLAM system, which consists of two steps. In the first step, we use the first two frames to preinitialize poses and landmarks, and in the second step, we use the second and third frames to improve the preinitialization by using the scale consistency of the landmarks generated in the first step to filter out outliers and using inliers to generate more robust landmarks. In both steps, we use the pretrained multilayer perceptron (MLP) combined with homotopy continuation to solve the essential matrices. Finally, we perform a global bundle adjustment (BA) to refine the three camera poses and all the created landmarks. The proposed 3FO initialization approach is evaluated experimentally on the EuRoC benchmark data set with the initialization time and trajectory metrics. The results show that, compared to the traditional ORB-SLAM2 initialization, the 3FO approach reduces the initialization time by 5 times and improves the accuracy by 36.7% on average.

Peters and Lau (eLife, 4, e09651, 2015) found that when criterion bias is controlled for, there is no evidence for unconscious visual perception in normal observers, in the sense that they cannot directly discriminate a target above chance without knowing it. One criticism of that study is that the visual suppression method used, forward and backward masking (FBM), may be too blunt in the way it interferes with visual processing to allow for unconscious forced-choice discrimination. To investigate this question, we compared FBM directly to continuous flash suppression (CFS) in a two-interval forced-choice task. Although CFS is popular, and may be thought of as a more powerful visual suppression technique, we found no difference in the degree of perceptual impairment between the two suppression types. To the extent that CFS impairs perception, both objective discrimination and subjective awareness are impaired to similar degrees under FBM. This pattern was consistently observed across three experiments in which various experimental parameters were varied. These findings provide evidence for an ongoing debate about unconscious perception: normal observers cannot perform forced-choice discrimination tasks unconsciously.

When occlusion and binocular disparity cues conflict, what visual features determine how they combine? Sensory cues, such as T-junctions, have been suggested to be necessary for occlusion to influence stereoscopic depth perception. Here we show that illusory occlusion, with no retinal sensory cues, interacts with binocular disparity when perceiving depth. We generated illusory occlusion using stimuli filled in across the retinal blind spot. Observers viewed two bars forming a cross with the intersection positioned within the blind spot. One of the bars was presented binocularly with a disparity signal; the other was presented monocularly, extending through the blind spot, with no defined disparity. When the monocular bar was perceived as filled in through the blind spot, it was perceived as occluding the binocular bar, generating illusory occlusion. We found that this illusory occlusion influenced perceived stereoscopic depth: depth estimates were biased to be closer or farther, depending on whether a bar was perceived as in front of or behind the other bar, respectively. Therefore, the perceived relative depth position, based on filling-in cues, set boundaries for interpreting metric stereoscopic depth cues. This suggests that filling-in can produce opaque surface representations that can trump other depth cues such as disparity.

In order to improve the initialization robustness of visual inertial SLAM, the complementarity of the optical flow method and the feature-based method can be used in vision data processing. The parallel initialization method is proposed, where the optical flow inertial initialization and the monocular feature-based initialization are carried out at the same time. After the initializations, the state estimation results are jointly optimized by bundle adjustment. The proposed method retains more mapping information, and correspondingly is more adaptable to the initialization scene. It is found that the initialization map constructed by the proposed method features a comparable accuracy to the one constructed by ORB-SLAM3 in monocular inertial mode. Since the online extrinsic parameter estimation can be realized by the proposed method, it is considered better than ORB-SLAM3 in the aspect of portability. By the experiments performed on the benchmark dataset EuRoC, the effectiveness and robustness of the proposed method are validated. [ABSTRACT FROM AUTHOR]

Detecting and localizing objects in the real 3D space, which plays a crucial role in scene understanding, is particularly challenging given only a monocular image due to the geometric information loss during imagery projection. We propose MonoGRNet for the amodal 3D object detection from a monocular image via geometric reasoning in both the observed 2D projection and the unobserved depth dimension. MonoGRNet decomposes the monocular 3D object detection task into four sub-tasks including 2D object detection, instance-level depth estimation, projected 3D center estimation and local corner regression. The task decomposition significantly facilitates the monocular 3D object detection, allowing the target 3D bounding boxes to be efficiently predicted in a single forward pass, without using object proposals, post-processing or the computationally expensive pixel-level depth estimation utilized by previous methods. In addition, MonoGRNet flexibly adapts to both fully and weakly supervised learning, which improves the feasibility of our framework in diverse settings. Experiments are conducted on KITTI, Cityscapes and MS COCO datasets. Results demonstrate the promising performance of our framework in various scenarios. [ABSTRACT FROM AUTHOR]

During a Research Career Development Award from the National Eye Institute, I spent a year at the University of Cambridge doing research with John Robson. The goal was to use a visual stimulation approach that had not been previously attempted, with the intention of exploring fundamental organization principles of the neural basis of binocular vision. The idea was to use sinusoidal gratings that drifted before both eyes such that the relative phase for one eye was fixed while that of the other was varied. This provided binocular stimuli of variable relative phase, i.e. retinal disparity, to enable testing of binocular response characteristics. We were able to obtain different types of disparity tuning functions for neurons in the primary visual cortex. This work, followed by extended investigations in Berkeley, provided basic information regarding response characteristics of simple and complex cells. We have also shown for monocular deprivation, an approximate model for human amblyopia, that many neurons remain connected to the deprived eye, as demonstrated with dichoptic activation. A selected portion of this work is described here.

Vision-based detection of fingertips is useful for freehand Human-Computer Interaction (HCI)—especially in virtual, augmented, and mixed reality—to have a seamless experience. The estimation of fingertips position in an RGB image involves overcoming various challenges like occlusion, appearance ambiguities, etc. The general approach relies on a two-stage pipeline involving hand location and detection of fingertips for a single hand. This paper presents an effective single-stage Convolutional Neural Network (CNN) for the detection of fingertips of both hands. We use a set of reference points, referred to as pose particles, and train a CNN model end-to-end to find the N-nearest particles in the proximity of each fingertip. Moreover, the same CNN model is used to compute the position vector’s components with reference to these N-nearest neighbors. Finally, a fingertip position is estimated by computing the centroid of all the points given by these position vectors. With the proposed approach, it is possible to estimate the fingertips position for single or double hands. Moreover, there is no requirement for prior hand localization. We demonstrated the feasibility and effectiveness of the proposed methodology by performing experiments on three different datasets. [ABSTRACT FROM AUTHOR]

Background: Chronic myelogenous leukemia (CML) is a malignant myeloproliferative neoplasm of pluripotent stem cell origin. Ophthalmic manifestation as an initial presentation in cases of CML is extremely rare. Frequently, ocular lesions in CML are asymptomatic. However, vitreous or foveal involvement can result in a symptomatic visual loss and earlier presentation. Here, we report a rare case of monocular vision loss due to subhyaloid hemorrhage in a case of CML. Case Report: A 19-year-old healthy woman presented to the Emergency Department with sudden painless decrease in vision in her left eye for 1 day. Fundus examination revealed multiple intraretinal hemorrhages with some whitecentered hemorrhages in 4 quadrants in both eyes, and subhyaloid hemorrhage involving the fovea in the left eye. Complete blood count and peripheral blood smear were consistent with the diagnosis of chronic myeloid leukemia. After referral to the hematology service, the diagnosis was confirmed based on bone marrow aspiration and chromosomal analysis. The patient then received the appropriate management and continued to follow up with the hematology service. Conclusions: This case report highlights the rarity of ocular involvement as an initial manifestation of chronic myeloid leukemia, and the importance of systemic work-up for the diagnosis of this entity. A multidisciplinary team approach involving ophthalmologists, hematologists, and oncologists is paramount for the diagnosis and management of CML. [ABSTRACT FROM AUTHOR]

Shape-from-Template (SfT) solves the registration and 3D reconstruction of a deformable 3D object, represented by the template, from a single image. Recently, methods based on deep learning have been able to solve SfT for the wide-baseline case in real-time, clearly surpassing classical methods. However, the main limitation of current methods is the need for fine tuning of the neural models to a specific geometry and appearance represented by the template texture map. We propose the first texture-generic deep learning SfT method which adapts to new texture maps at run-time, without the need for texture specific fine tuning. We achieve this by dividing the problem into a segmentation step and a registration and reconstruction step, both solved with deep learning. We include the template texture map as one of the neural inputs in both steps, training our models to adapt to different ones. We show that our method obtains comparable or better results to previous deep learning models, which are texture specific. It works in challenging imaging conditions, including complex deformations, occlusions, motion blur and poor textures. Our implementation runs in real-time, with a low-cost GPU and CPU.

UnlabelledMyelin controls the time required for an action potential to travel from the neuronal soma to the axon terminal, defining the temporal manner in which information is processed within the CNS. The presence of myelin, the internodal length, and the thickness of the myelin sheath are powerful structural factors that control the velocity and fidelity of action potential transmission. Emerging evidence indicates that myelination is sensitive to environmental experience and neuronal activity. Activity-dependent modulation of myelination can dynamically alter action potential conduction properties but direct functional in vivo evidence and characterization of the underlying myelin changes is lacking. We demonstrate that in mice long-term monocular deprivation increases oligodendrogenesis in the retinogeniculate pathway but shortens myelin internode lengths without affecting other structural properties of myelinated fibers. We also demonstrate that genetically attenuating synaptic glutamate neurotransmission from retinal ganglion cells phenocopies the changes observed after monocular deprivation, suggesting that glutamate may constitute a signal for myelin length regulation. Importantly, we demonstrate that visual deprivation and shortened internodes are associated with a significant reduction in nerve conduction velocity in the optic nerve. Our results reveal the importance of sensory input in the building of myelinated fibers and suggest that this activity-dependent alteration of myelination is important for modifying the conductive properties of brain circuits in response to environmental experience.Significance statementOligodendrocyte precursor cells differentiate into mature oligodendrocytes and are capable of ensheathing axons with myelin without molecular cues from neurons. However, this default myelination process can be modulated by changes in neuronal activity. Here, we show, for the first time, that experience-dependent activity modifies the length of myelin internodes along axons altering action potential conduction velocity. Such a mechanism would allow for variations in conduction velocities that provide a degree of plasticity in accordance to environmental needs. It will be important in future work to investigate how these changes in myelination and conduction velocity contribute to signal integration in postsynaptic neurons and circuit function.

Maturation of cortical inhibition just after eye opening is a necessary precedent for the emergence of competitive, experience-dependent ocular dominance plasticity in the visual cortex. What inhibition is doing in this context, though, is not clear. Here I outline new hypotheses on the roles of somatic and dendritic inhibition in the opening and closure of critical periods, and their roles in the competitive processes therein.

AIM: To observe the changes of binocular eye accommodation function in correcting anisometropia by monocular orthokeratology in anisometropic children, and to explore the mechanism of regulation in the progression of myopia control by orthokeratology.METHODS: It was a prospective self-control study. A total of 22 anisometropic children(44 eyes), aged 8-13 years, who wore an orthokeratology lens in only one eye were enrolled in the outpatient clinic of Hainan Provincial Eye Hospital from September 2016 to September 2018. The eyes with an orthokeratology lens had a mean spherical equivalent refraction of -2.751±1.16 diopter(D), and fellow eyes without a lens had a refraction of -0.10±0.32D. The eyes wearing orthokeratology lens were used as the lens wearing group, and the fellow eyes without a lens were used as non-wearing lens group. During the follow-up period,we measured changes of diopter difference, monocular accommodative amplitude(MAA), monocular accommodative facility(MAF)and accommodative response(AR)before wearing the lens and wearing the lens for 12mo.RESULTS: The monocular accommodative amplitude and monocular accommodative facility of the wearing group before wearing the lens were lower than those of the non-wear group(PPt=3.63, Pt=0.75, P=0.46)compared with the non-wearing group. The monocular accommodative facility 14.63±1.58 cyc/min after wearing the lens for 12mo was higher than that before wearing the lens 9.25±3.38cyc/min(t=2.83, P=0.01), and the difference was not statistically significant(t=0.38, P=0.71)compared with the non-wearing group. The accommodative lag 0.62±0.29D of the wearing group after 12mo was reduced compared with that before wearing the lens 1.35±0.26D(t=2.57, P=0.02),and compared with the non-wearing group, the difference was not statistical significance(t=0.61, P=0.55). The mean equivalent diopter of the non-lens group was -0.75±0.35D after 12mo, and the number of diopter group increased by -0.65±0.39D(t=4.24, Pt=2.90, Pt=5.30, PP>0.05).CONCLUSION: The accommodation function of anisometropic children in correcting anisometropia by monocular orthokeratology was significantly improved compared with that before wearing the lens and was consistent with the contralateral eyes without lens group. The refractive state of the group wearing orthokeratology lens was more stable, and the contralateral eyes without lens gradually showed mild myopia refractive state, but the accommodation did not change significantly.

AIM: To study the curative efficacy of different surgical procedures in the treatment of congenital unilateral monocular oblique paralysis and its effects on binocular vision function.METHODS: Totally 82 children of congenital unilateral monocular oblique paralysis who received therapy from May 2016 to 2019 in our hospital were selected as research objects, operative plan was determined by the degree of hyperfunction of the inferior oblique muscle, the movement of the eyeball and the vertical obliquity of the eye position, the inferior oblique muscle tendon amputation(3 cases), partial inferior oblique muscle resection(63 cases), inferior oblique muscle recession(6 cases), posterior rectus muscle repair(4 cases), inferior oblique muscle weakening+contralateral/ipsilateral rectus muscle operation(5 cases), upper oblique folding(1 cases). The effects of different surgical procedures and the changes of binocular visual function, corrected visual acuity and compensatory head position before and after surgery were compared. RESULTS: Compared with before surgery, the fusion function, hyperstereopsis, myopia, corrected visual acuity and compensatory head position were significantly improved after operation(PP>0.05). CONCLUSION: Choosing different surgical methods according to the severity of congenital superior oblique paralysis has positive significance, which can improve the vision and compensating head position of children, and it's helpful to rebuild binocular vision function.

This paper presents a novel material spectroscopy approach to facial presentation–attack–defense (PAD). Best-in-class PAD methods typically detect artifacts in the 3D space. This paper proposes similar features can be achieved in a monocular, single-frame approach by using controlled light. A mathematical model is produced to show how live faces and their spoof counterparts have unique reflectance patterns due to geometry and albedo. A rigorous dataset is collected to evaluate this proposal: 30 diverse adults and their spoofs (paper-mask, display-replay, spandex-mask and COVID mask) under varied pose, position, and lighting for 80,000 unique frames. A panel of 13 texture classifiers are then benchmarked to verify the hypothesis. The experimental results are excellent. The material spectroscopy process enables a conventional MobileNetV3 network to achieve 0.8% average-classification-error rate, outperforming the selected state-of-the-art algorithms. This demonstrates the proposed imaging methodology generates extremely robust features.

Current models of retinogeniculate development have proposed that connectivity between the retina and the dorsal lateral geniculate nucleus (dLGN) is established by gradients of axon guidance molecules, to allow initial coarse connections, and by competitive Hebbian-like processes, to drive eye-specific segregation and refine retinotopy. Here we show that when intereye competition is eliminated by monocular enucleation, blocking cholinergic stage II retinal waves disrupts the intraeye competition-mediated expansion of the retinogeniculate projection and results in the permanent disorganization of its laminae. This disruption of stage II retinal waves also causes long-term impacts on receptive field size and fine-scale retinotopy in the dLGN. Our results reveal a novel role for stage II retinal waves in regulating retinogeniculate afferent terminal targeting by way of intraeye competition, allowing for correct laminar patterning and the even allocation of synaptic territory. These findings should contribute to answering questions regarding the role of neural activity in guiding the establishment of neural circuits.

In 2000, monocular vision was restored to M. M., who had been blind between the ages of 3 and 46 years. Tests carried out over 2 years following the surgery revealed impairments of 3-D form, object, and face processing and an absence of object- and face-selective blood-oxygen-level-dependent responses in ventral visual cortex. In the present research, we reexamined M. M. to test for experience-dependent recovery of visual function. Behaviorally, M. M. remains impaired in 3-D form, object, and face processing. Accordingly, we found little to no evidence of the category-selective organization within ventral visual cortex typically associated with face, body, scene, or object processing. We did observe remarkably normal object selectivity within lateral occipital cortex, consistent with M. M.'s previously reported shape-discrimination performance. Together, these findings provide little evidence for recovery of high-level visual function after more than a decade of visual experience in adulthood.

Monocular 3D object detection has recently become prevalent in autonomous driving and navigation applications due to its cost-efficiency and easy-to-embed to existent vehicles. The most challenging task in monocular vision is to estimate a reliable object’s location cause of the lack of depth information in RGB images. Many methods tackle this ill-posed problem by directly regressing the object’s depth or take the depth map as a supplement input to enhance the model’s results. However, the performance relies heavily on the estimated depth map quality, which is bias to the training data. In this work, we propose depth-adaptive convolution to replace the traditional 2D convolution to deal with the divergent context of the image’s features. This lead to significant improvement in both training convergence and testing accuracy. Second, we propose a ground plane model that utilizes geometric constraints in the pose estimation process. With the new method, named GAC3D, we achieve better detection results. We demonstrate our approach on the KITTI 3D Object Detection benchmark, which outperforms existing monocular methods.

Objective: This study aims to investigate the monocular and binocular accommodative amplitude (AMP) and accommodative function (AF) in children with different types of intermittent exotropia (IXT).Methods: A total of 40 children with IXT were enrolled in the study. Monocular and binocular AMP and AF were measured using the modified approach method and the ±2D flip method, and the differences between the fixing and non-fixing eyes of non-strabismic children and children with different types of IXT were compared.Results: The AMP of the fixing eyes of children with IXT was lower than that of their non-fixing eyes (p = 0.007). Conversely, the AF was higher in the fixing eyes than in the non-fixing eyes (p < 0.001). The AMPs of each group of children with IXT were lower than those of the control group, while the AMP of the group with convergence insufficiency was lower than that of the other two groups with IXT. In addition, the AF of the group with convergence insufficiency was lower than that of the group with basic exotropia and the control group (p < 0.05).Conclusion: There is a difference in accommodation between the fixing and non-fixing eyes of children with IXT, and the degree of variation depends on the type of IXT. Moreover, the binocular accommodative function of children with IXT is lower than that of non-strabismic children.

In order to improve the initialization robustness of visual inertial SLAM, the complementarity of the optical flow method and the feature-based method can be used in vision data processing. The parallel initialization method is proposed, where the optical flow inertial initialization and the monocular feature-based initialization are carried out at the same time. After the initializations, the state estimation results are jointly optimized by bundle adjustment. The proposed method retains more mapping information, and correspondingly is more adaptable to the initialization scene. It is found that the initialization map constructed by the proposed method features a comparable accuracy to the one constructed by ORB-SLAM3 in monocular inertial mode. Since the online extrinsic parameter estimation can be realized by the proposed method, it is considered better than ORB-SLAM3 in the aspect of portability. By the experiments performed on the benchmark dataset EuRoC, the effectiveness and robustness of the proposed method are validated.

Aim: To investigate the incidence and clinical features of primary iris and ciliary body cysts in Chinese primary angle closure disease (PACD). Patients were evaluated by measuring and analyzing the cysts with an ultrasound biomicroscope (UBM).Methods: The data of patients diagnosed with PACD were reviewed. Demographic data were collected, and the cyst number, size, location, and trabecular-iris angle (TIA) were measured, with the size including the longest diameter (LD) and its corresponding vertical diameter (CVD).Results: A total of 1,334 cases (2,317 eyes) were reviewed, and 409 cysts were found in 131 cases (168 eyes), with an average of 2.43 ± 3.14 cysts per eye. The ages of the patients with cysts ranged from 25 to 80 years, with an average age of 55.24 ± 12.22 years. The detection rate was 7.3%, and the majority of cysts were located in the iridociliary sulcus. Among the 131 patients, 94 had monocular cysts, while binocular cysts occurred in 37 patients. The locations of the cysts in both eyes were mainly in the inferior and temporal quadrants (42.5 and 34.0%, respectively). The cysts were mainly of medium size (49.9%), followed by small cysts (33.3%), large cysts (14.7%) and giant cysts (2.2%). The average LD was 0.68 ± 0.33 mm, and the average CVD was 0.45 ± 0.23 mm. There were no statistically significant differences in the TIA between the cyst area and unaffected area.Conclusions: The incidence of cysts is 7.3% in the PACD population. The cysts are mainly monocular, medium in size, and located in the iridociliary sulcus. Additionally, the cysts were located mainly in the inferior and temporal quadrants. These cysts have little effect on the anterior chamber angle.

Existing unsupervised monocular depth estimation methods resort to stereo image pairs instead of ground-truth depth maps as supervision to predict scene depth. Constrained by the type of monocular input in testing phase, they fail to fully exploit the stereo information through the network during training, leading to the unsatisfactory performance of depth estimation. Therefore, we propose a novel architecture which consists of a monocular network (Mono-Net) that infers depth maps from monocular inputs, and a stereo network (Stereo-Net) that further excavates the stereo information by taking stereo pairs as input. During training, the sophisticated Stereo-Net guides the learning of Mono-Net and devotes to enhance the performance of Mono-Net without changing its network structure and increasing its computational burden. Thus, monocular depth estimation with superior performance and fast runtime can be achieved in testing phase by only using the lightweight Mono-Net. For the proposed framework, our core idea lies in: 1) how to design the Stereo-Net so that it can accurately estimate depth maps by fully exploiting the stereo information; 2) how to use the sophisticated Stereo-Net to improve the performance of Mono-Net. To this end, we propose a recursive estimation and refinement strategy for Stereo-Net to boost its performance of depth estimation. Meanwhile, a multi-space knowledge distillation scheme is designed to help Mono-Net amalgamate the knowledge and master the expertise from Stereo-Net in a multi-scale fashion. Experiments demonstrate that our method achieves the superior performance of monocular depth estimation in comparison with other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In this article, we present a very lightweight neural network architecture, trained on stereo data pairs, which performs view synthesis from one single image. With the growing success of multi-view formats, this problem is indeed increasingly relevant. The network returns a prediction built from disparity estimation, which fills in wrongly predicted regions using a occlusion handling technique. To do so, during training, the network learns to estimate the left-right consistency structural constraint on the pair of stereo input images, to be able to replicate it at test time from one single image. The method is built upon the idea of blending two predictions: a prediction based on disparity estimation and a prediction based on direct minimization in occluded regions. The network is also able to identify these occluded areas at training and at test time by checking the pixelwise left-right consistency of the produced disparity maps. At test time, the approach can thus generate a left-side and a right-side view from one input image, as well as a depth map and a pixelwise confidence measure in the prediction. The work outperforms visually and metric-wise state-of-the-art approaches on the challenging KITTI dataset, all while reducing by a very significant order of magnitude (5 or 10 times) the required number of parameters (6.5 M). [ABSTRACT FROM AUTHOR]