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Automatic measurements via image processing can accelerate measurements and provide comprehensive evaluations of mechanical parts. This paper presents a comprehensive approach to automating evaluations of planar dimensions in mechanical parts, providing significant advancements in terms of cost-effectiveness, accuracy, and repeatability. The methodology employed in this study utilizes a configuration comprising commonly available products in the industrial computer vision market, therefore enabling precise determinations of external contour specifications for mechanical components. Furthermore, it presents a functional prototype for making planar measurements by incorporating an improved subpixel edge-detection method, thus ensuring precise image-based measurements. The article highlights key concepts, describes the measurement procedures, and provides comparisons and traceability tests as a proof of concept for the system. The results show that this vision system did achieve suitable precision, with a mean error of 0.008 mm and a standard deviation of 0.0063 mm, when measuring gauge blocks of varying lengths at different heights. Moreover, when evaluating a circular sample, the system resulted in a maximum deviation of 0.013 mm, compared to an alternative calibrated measurement machine. In conclusion, the prototype validates the methods for planar dimension evaluations, highlighting the potential for enhancing manual measurements, while also maintaining accessibility. The presented system expands the possibilities of machine vision in manufacturing, especially in cases where the cost or agility of current systems is limited.
Altitude estimation is one of the fundamental tasks of unmanned aerial vehicle (UAV) automatic navigation, where it aims to accurately and robustly estimate the relative altitude between the UAV and specific areas. However, most methods rely on auxiliary signal reception or expensive equipment, which are not always available, or applicable owing to signal interference, cost or power-consuming limitations in real application scenarios. In addition, fixed-wing UAVs have more complex kinematic models than vertical take-off and landing UAVs. Therefore, an altitude estimation method which can be robustly applied in a GPS denied environment for fixed-wing UAVs must be considered. In this paper, we present a method for high-precision altitude estimation that combines the vision information from a monocular camera and poses information from the inertial measurement unit (IMU) through a novel end-to-end deep neural network architecture. Our method has numerous advantages over existing approaches. First, we utilize the visual-inertial information and physics-based reasoning to build an ideal altitude model that provides general applicability and data efficiency for neural network learning. A further advantage is that we have designed a novel feature fusion module to simplify the tedious manual calibration and synchronization of the camera and IMU, which are required for the standard visual or visual-inertial methods to obtain the data association for altitude estimation modeling. Finally, the proposed method was evaluated, and validated using real flight data obtained during a fixed-wing UAV landing phase. The results show the average estimation error of our method is less than 3% of the actual altitude, which vastly improves the altitude estimation accuracy compared to other visual and visual-inertial based methods.
Masking, adaptation, and summation paradigms have been used to investigate the characteristics of early spatio-temporal vision. Each has been taken to provide evidence for (i) oriented and (ii) nonoriented spatial-filtering mechanisms. However, subsequent findings suggest that the evidence for nonoriented mechanisms has been misinterpreted: those experiments might have revealed the characteristics of suppression (eg, gain control), not excitation, or merely the isotropic subunits of the oriented detecting mechanisms. To shed light on this, we used all three paradigms to focus on the 'high-speed' corner of spatio-temporal vision (low spatial frequency, high temporal frequency), where cross-oriented achromatic effects are greatest. We used flickering Gabor patches as targets and a 2IFC procedure for monocular, binocular, and dichoptic stimulus presentations. To account for our results, we devised a simple model involving an isotropic monocular filter-stage feeding orientation-tuned binocular filters. Both filter stages are adaptable, and their outputs are available to the decision stage following nonlinear contrast transduction. However, the monocular isotropic filters (i) adapt only to high-speed stimuli-consistent with a magnocellular subcortical substrate-and (ii) benefit decision making only for high-speed stimuli (ie, isotropic monocular outputs are available only for high-speed stimuli). According to this model, the visual processes revealed by masking, adaptation, and summation are related but not identical.
In humans and other primates, sensory signals from each eye remain separated until they arrive in the primary visual cortex (V1), but their exact meeting point is unknown. In V1, some neurons respond to stimulation of only one eye (monocular neurons), while most neurons respond to stimulation of either eye (binocular neurons). The main input layers of V1 contain most of the monocular neurons while binocular neurons dominate the layers above and below. This observation has given rise to the idea that the two eyes' signals remain separate until they converge outside V1's input layers. Here, we show that, despite responding to only one eye, monocular neurons in all layers, including the input layers, of V1 discriminate between stimulation of their driving eye alone and stimulation of both eyes. Some monocular V1 neurons' responses were significantly enhanced, or facilitated, when both eyes were stimulated. Binocular facilitation within V1's input layers tended to occur at the onset of the visual response, which could be explained by converging thalamocortical inputs. However, most V1 monocular neurons were significantly reduced, or suppressed, to binocular stimulation. In contrast to facilitation, binocular suppression occurred several milliseconds following the onset of the visual response, suggesting that the bulk of binocular modulation involves cortical inhibition. These findings, combined, suggest that binocular signals arise at an earlier processing stage than previously appreciated, as even so-called monocular neurons in V1's input layers encode what is shown to both eyes.
Motion discrimination of large stimuli is impaired at high contrast and short durations. This psychophysical result has been linked with the center-surround suppression found in neurons of area MT. Recent physiology results have shown that most frontoparallel MT cells respond more strongly to binocular than to monocular stimulation. Here we measured the surround suppression strength under binocular and monocular viewing. Thirty-nine participants took part in two experiments: (a) where the nonstimulated eye viewed a blank field of the same luminance (n = 8) and (b) where it was occluded with a patch (n = 31). In both experiments, we measured duration thresholds for small (1 deg diameter) and large (7 deg) drifting gratings of 1 cpd with 85% contrast. For each subject, a Motion Suppression Index (MSI) was computed by subtracting the duration thresholds in logarithmic units of the large minus the small stimulus. Results were similar in both experiments. Combining the MSI of both experiments, we found that the strength of suppression for binocular condition (MSIbinocular = 0.249 ± 0.126 log10 (ms)) is 1.79 times higher than under monocular viewing (MSImonocular = 0.139 ± 0.137 log10 (ms)). This increase is too high to be explained by the higher perceived contrast of binocular stimuli and offers a new way of testing whether MT neurons account for surround suppression. Potentially, differences in surround suppression reported in clinical populations may reflect altered binocular processing.
In this paper, we present a novel monocular simultaneous localization and mapping (SLAM) initialization algorithm that relies on structural features by tracking structural lines. This approach addresses the limitations of the traditional method, which can fail to account for a lack of features or their uneven distribution. Our proposed method utilizes a sliding window approach to guarantee the quality and stability of the initial pose estimation. We incorporate multiple geometric constraints, orthogonal dominant directions, and coplanar structural lines to construct an efficient pose optimization strategy. Experimental evaluations conducted on both the collected chessboard datasets and real scene datasets show that our approach provides superior results in terms of accuracy and real-time performance compared to the well-tuned baseline methods. Notably, our algorithm achieves these improvements while being computationally lightweight, without the need for matrix decomposition.
Retinoblastoma is a rare eye cancer that generally occurs before 5 years of age and often results in enucleation (surgical removal) of the cancerous eye. In the present study, we sought to determine the consequences of early monocular enucleation on the morphological development of the anterior visual pathway including the optic chiasm and lateral geniculate nucleus.
Contrast sensitivity is mostly used as a tool for testing aspects of visual functions. Infantile nystagmus is a pathological phenomenon that affects the spatial-temporal visual functions due to spontaneous oscillating movements of the eyes. We examined the spatial-temporal aspects of nystagmus perception, aiming to investigate the mechanisms underlying the deterioration of their visual performance. We tested the monocular and binocular contrast sensitivity of nystagmus and normally sighted subjects by measuring contrast detection of a Gabor target with spatial frequencies slightly above the cutoff threshold of each subject (nystagmus ~3; controls = 9cpd; presentation times 60-480 ms). The dominant eye of nystagmus revealed large differences over the non-dominant eye, highlighting the superiority of the dominant over the non-dominant eye in nystagmus. In addition, binocular summation mechanism was impaired in majority of the nystagmus subjects. Furthermore, these differences are not attributed to differences in visual acuity. Moreover, the visual performance in nystagmus continue to improve for longer presentation time compared with controls and was longer in the poor eye. Since the results are not due to differences in eye movements and strabismus, we suggest that the differences are due to developmental impairment in the visual system during the critical period.
Human pose estimation refers to the estimation of the location of body parts and how they are connected in an image. Human pose estimation from monocular images has wide applications (e.g., image indexing). Several surveys on human pose estimation can be found in the literature, but they focus on a certain category; for example, model-based approaches or human motion analysis, etc. As far as we know, an overall review of this problem domain has yet to be provided. Furthermore, recent advancements based on deep learning have brought novel algorithms for this problem. In this paper, a comprehensive survey of human pose estimation from monocular images is carried out including milestone works and recent advancements. Based on one standard pipeline for the solution of computer vision problems, this survey splits the problem into several modules: feature extraction and description, human body models, and modeling methods. Problem modeling methods are approached based on two means of categorization in this survey. One way to categorize includes top-down and bottom-up methods, and another way includes generative and discriminative methods. Considering the fact that one direct application of human pose estimation is to provide initialization for automatic video surveillance, there are additional sections for motion-related methods in all modules: motion features, motion models, and motion-based methods. Finally, the paper also collects 26 publicly available data sets for validation and provides error measurement methods that are frequently used.
In natural contexts, sensory processing and motor output are closely coupled, which is reflected in the fact that many brain areas contain both sensory and movement signals. However, standard reductionist paradigms decouple sensory decisions from their natural motor consequences, and head-fixation prevents the natural sensory consequences of self-motion. In particular, movement through the environment provides a number of depth cues beyond stereo vision that are poorly understood. To study the integration of visual processing and motor output in a naturalistic task, we investigated distance estimation in freely moving mice. We found that mice use vision to accurately jump across a variable gap, thus directly coupling a visual computation to its corresponding ethological motor output. Monocular eyelid suture did not affect gap jumping success, thus mice can use cues that do not depend on binocular disparity and stereo vision. Under monocular conditions, mice altered their head positioning and performed more vertical head movements, consistent with a shift from using stereopsis to other monocular cues, such as motion or position parallax. Finally, optogenetic suppression of primary visual cortex impaired task performance under both binocular and monocular conditions when optical fiber placement was localized to binocular or monocular zone V1, respectively. Together, these results show that mice can use monocular cues, relying on visual cortex, to accurately judge distance. Furthermore, this behavioral paradigm provides a foundation for studying how neural circuits convert sensory information into ethological motor output.
Similar to early blindness, monocular enucleation (the removal of one eye) early in life results in crossmodal behavioral and morphological adaptations. Previously it has been shown that partial visual deprivation from early monocular enucleation results in structural white matter changes throughout the visual system (Wong et al., 2018). The current study investigated structural white matter of the auditory system in adults who have undergone early monocular enucleation compared to binocular control participants.
Image-based localization is one of the most widely researched localization techniques in the robotics and computer vision communities. As enormous image data sets are provided through the Internet, many studies on estimating a location with a pre-built image-based 3D map have been conducted. Most research groups use numerous image data sets that contain sufficient features. In contrast, this paper focuses on image-based localization in the case of insufficient images and features. A more accurate localization method is proposed based on a probabilistic map using 3D-to-2D matching correspondences between a map and a query image. The probabilistic feature map is generated in advance by probabilistic modeling of the sensor system as well as the uncertainties of camera poses. Using the conventional PnP algorithm, an initial camera pose is estimated on the probabilistic feature map. The proposed algorithm is optimized from the initial pose by minimizing Mahalanobis distance errors between features from the query image and the map to improve accuracy. To verify that the localization accuracy is improved, the proposed algorithm is compared with the conventional algorithm in a simulation and realenvironments.
Sensory cortical circuits are shaped by experience during sensitive periods in development. In the primary visual cortex (V1) altered visual experience results in changes in visual responsiveness of cortical neurons. The experience-dependent refinement of the circuit in V1 is thought to rely on competitive interactions between feedforward circuits driven by the two eyes. However, recent data have provided evidence for an additional role of cortico-cortical circuits in this process. Indeed, experience-dependent changes in intracortical circuits can be induced rapidly and may result in rapid-onset functional changes. Unilateral occlusion of vision rapidly alters visual responsiveness, synaptic strength and connectivity of local circuits in the binocular region of V1 (V1b), where the inputs from the two eyes converge. In the monocular region of rodent V1 (V1m), where feedforward inputs from the ipsilateral eye are virtually absent, visual deprivation induces rapid plasticity in local circuits; however, functional changes seem to occur only after long periods of deprivation. In V1m there is currently no evidence for functional changes occurring within a time window compatible with that of local circuit plasticity. Here, we probed the visual responsiveness of neurons in rat V1m and assessed the effect of one day unilateral eye lid suture on single neuron visual responses. We report a novel form of plasticity within V1m that occurs on a timescale consistent with the earliest known changes in synaptic strength. Our data provide new insights into how sensory experience can rapidly modulate neuronal responses, even in the absence of direct competition between feedforward thalamocortical inputs.
Activity in neurons drives afferent competition that is critical for the refinement of nascent neural circuits. In ferrets, when an eye is lost in early development, surviving retinogeniculate afferents from the spared eye spread across the thalamus in a manner that is dependent on spontaneous retinal activity. However, how this spontaneous activity, also known as retinal waves, might dynamically regulate afferent terminal targeting remains unknown.
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 human brain retains a striking degree of plasticity into adulthood. Recent studies have demonstrated that a short period of altered visual experience (via monocular deprivation) can change the dynamics of binocular rivalry in favor of the deprived eye, a compensatory action thought to be mediated by an upregulation of cortical gain control mechanisms. Here, we sought to better understand the impact of monocular deprivation on multisensory abilities, specifically examining audiovisual temporal perception. Using an audiovisual simultaneity judgment task, we discovered that 90 minutes of monocular deprivation produced opposing effects on the temporal binding window depending on the eye used in the task. Thus, in those who performed the task with their deprived eye there was a narrowing of the temporal binding window, whereas in those performing the task with their nondeprived eye there was a widening of the temporal binding window. The effect was short lived, being observed only in the first 10 minutes of postdeprivation testing. These findings indicate that changes in visual experience in the adult can rapidly impact multisensory perceptual processes, a finding that has important clinical implications for those patients with adult-onset visual deprivation and for therapies founded on monocular deprivation.
Dynamic random dot stereograms (DRDSs) and correlograms (DRDCs) are cyclopean stimuli containing binocular depth cues that are ideally, invisible by one eye alone. Thus, they are important tools in assessing stereoscopic function in experimental or ophthalmological diagnostic settings. However, widely used filter-based three-dimensional display technologies often cannot guarantee complete separation of the images intended for the two eyes. Without proper calibration, this may result in unwanted monocular cues in DRDSs and DRDCs, which may bias scientific or diagnostic results. Here, we use a simple mathematical model describing the relationship of digital video values and average luminance and dot contrast in the two eyes. We present an optimization algorithm that provides the set of digital video values that achieve minimal crosstalk at user-defined average luminance and dot contrast for both eyes based on photometric characteristics of a given display. We demonstrated in a psychophysical experiment with color normal participants that this solution is optimal because monocular cues were not detectable at either the calculated or the experimentally measured optima. We also explored the error by which a range of luminance and contrast combinations can be implemented. Although we used a specific monitor and red-green glasses as an example, our method can be easily applied for other filter based three-dimensional systems. This approach is useful for designing psychophysical experiments using cyclopean stimuli for a specific display.
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