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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]

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]

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]

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]

• Birth to surgery interval of 4–6 months crucial for stereopsis in congenital cataract. • Better stereopsis in bilateral congenital cataract than unilateral counterpart. • Stereopsis drops by 9%–16% in congenital cataracts with pre-existing strabismus. • No association found between strabismus and stereopsis in developmental cataracts. We estimated the proportion of children with stereopsis following surgery in congenital and developmental cataracts by systematic review and meta-analysis and also considered the factors influencing stereopsis, such as intervention age and presence of strabismus. Stereopsis is directly related to quality of life, and investigating its levels following cataract surgery in children may help decide the right time to intervene, particularly in the context of brain plasticity. We conducted a systematic literature search using Scopus, PubMed, and Web of Science and found 25 case series, 3 cohorts, and 3 clinical trial studies from 1/1/1995 to 31/12/2020. Study-specific proportions of stereopsis from 923 children were pooled using a random-effects model, and stratified analyses were conducted based on intervention age and pre-existing strabismus as a confounder. We appraised the risk of bias using tools published by National Institutes of Health and evaluated publication bias with funnel plots and the Egger test. The pooled proportions of stereopsis based on 8 unilateral and 6 bilateral congenital cataract studies were 0.37 (95% CIs: [0.24, 0.53]) and 0.45 (95% CIs: [0.24,0.68]) when patients with preexisting strabismus were excluded as a confounder. When the intervention age was ≤6 months, proportions in unilateral congenital cataract group significantly increased to 0.52 (95% CIs: [0.37, 0.66]; P = 0.49) compared to 0.26 (95% CIs: [0.14, 0.44]; P = 0.16) otherwise. A similar increase in proportions was found when intervention age ≤4 months. In both unilateral and bilateral congenital cataract groups, proportions increased significantly when the confounder was excluded. Overall, proportions in bilateral congenital cataracts were significantly greater than unilateral cases (irrespective of confounder). Eight unilateral and 5 bilateral developmental cataract studies resulted in pooled proportions of 0.62 (95% CIs: [0.27, 0.88] and 0.82 (95% CIs: [0.4, 0.97]), respectively. Although proportions for bilateral developmental cataracts were greater than unilateral cataracts (irrespective of confounder), results were not statistically significant. Finally, proportions in unilateral developmental cataracts were significantly greater than unilateral congenital cataracts (Z = 7.413, P = 6.173694e−14). We conclude that surgical intervention within first 4–6 months can significantly affect postoperative outcomes in unilateral congenital cataracts. Analysis of existing data does not show a significant effect of intervention age on stereopsis outcomes for developmental cataracts. [ABSTRACT FROM AUTHOR]

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]

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]

The pupil size artefact (PSA) is the gaze deviation reported by an eye tracker during pupil size changes if the eye does not rotate. In the present study, we ask three questions: 1) how stable is the PSA over time, 2) does the PSA depend on properties of the eye tracker set up, and 3) does the PSA depend on the participants' viewing direction? We found that the PSA is very stable over time for periods as long as 1 year, but may differ between participants. When comparing the magnitude of the PSA between eye trackers, we found the magnitude of the obtained PSA to be related to the direction of the eye-tracker-camera axis, suggesting that the angle between the participants' viewing direction and the camera axis affects the PSA. We then investigated the PSA as a function of the participants' viewing direction. The PSA was non-zero for viewing direction 0∘ and depended on the viewing direction. These findings corroborate the suggestion by Choe et al. (Vision Research 118(6755):48–59, 2016), that the PSA can be described by an idiosyncratic and a viewing direction-dependent component. Based on a simulation, we cannot claim that the viewing direction-dependent component of the PSA is caused by the optics of the cornea. [ABSTRACT FROM AUTHOR]

In this paper, we propose an unsupervised learning method for jointly estimating monocular depth and ego-motion, which is capable to recover the absolute scale of global camera trajectory. In order to solve the general problems of scale drift and scale ambiguity of monocular camera, we fuse geometric movement data from inertial measurement unit (IMU), and use Bi-directional Long Short-Term Memory (BiLSTM) to extract temporal features. Besides, we add a lightweight and efficient attention mechanism, Convolutional Block Attention Module (CBAM), to Convolutional Neural Networks (CNNs) to complete the extraction of image features. Considering the scenes with severe illumination changes, ambiguous structures, moving objects and occlusions, especially scenes with progressively-variant textures, the geometric features can provide adaptive estimation results in the case of the degeneration of visual features. Experiments on the KITTI driving dataset reveal that our scheme achieves promising results in the estimation of camera pose and depth. Moreover, the absolute scale recovery for the global camera trajectory is effective. [ABSTRACT FROM AUTHOR]

Many species use eye movements to direct their overt attention toward specific targets within their environments. Some species can move each eye independently but we have a limited understanding of whether they can simultaneously monitor different targets with each eye. This study, therefore, tested whether a songbird can independently move its eyes towards two different targets. Captive great-tailed grackles (Quiscalus mexicanus) were simultaneously presented with one target in their left visual field and another target in their right visual field; the targets were both in the upper visual fields, both in the lower visual fields, or one target was in the upper visual field of one eye, while the other target was in the lower visual field of the other eye. The grackles correctly directed their left and right eyes toward the targets regardless of where the targets appeared at levels greater than chance. These results demonstrate that an avian species can perform simultaneous eye movements towards two different targets. [ABSTRACT FROM AUTHOR]

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]

Unmanned Aerial Vehicles (UAVs) have become an essential photogrammetric measurement as they are affordable, easily accessible and versatile. Aerial images captured from UAVs have applications in small and large scale texture mapping, 3D modelling, object detection tasks, Digital Terrain Model (DTM) and Digital Surface Model (DSM) generation etc. Photogrammetric techniques are routinely used for 3D reconstruction from UAV images where multiple images of the same scene are acquired. Developments in computer vision and deep learning techniques have made Single Image Depth Estimation (SIDE) a field of intense research. Using SIDE techniques on UAV images can overcome the need for multiple images for 3D reconstruction. This paper aims to estimate depth from a single UAV aerial image using deep learning. We follow a self-supervised learning approach, Self-Supervised Monocular Depth Estimation (SMDE), which does not need ground truth depth or any extra information other than images for learning to estimate depth. Monocular video frames are used for training the deep learning model which learns depth and pose information jointly through two different networks, one each for depth and pose. The predicted depth and pose are used to reconstruct one image from the viewpoint of another image utilising the temporal information from videos. We propose a novel architecture with two 2D Convolutional Neural Network (CNN) encoders and a 3D CNN decoder for extracting information from consecutive temporal frames. A contrastive loss term is introduced for improving the quality of image generation. Our experiments are carried out on the public UAVid video dataset. The experimental results demonstrate that our model outperforms the state-of-the-art methods in estimating the depths. [ABSTRACT FROM AUTHOR]

In this paper, we present a localization system for the use of drone in a remote laboratory. The objective is to allow a drone to inspect remote electronic instruments autonomously, as well as to return to its base and land on a platform for the recharge of its batteries. In addition, the drone should be able to detect the presence of a teacher in the laboratory and to center the human face in the image in order to enable remote student–teacher communication. To achieve the first objective, the localization approach is composed of a monocular simultaneous localization and mapping algorithm, parallel tracking and mapping and an estimation based on the homography transform. For the face-drone servoing, the approach is based on the 3D Candide model. Both approaches work in real time. Quantitative and qualitative experiments are presented that show the robustness of both methods. [ABSTRACT FROM AUTHOR]

We present a method to recover the 3D flying shape of a sail using passive markers. In the navigation and naval architecture domain, retrieving the sail shape may be of immense value to confirm or contest simulation results, and to aid the design of new optimal sails. Our acquisition setup is very simple and low-cost, as it is only necessary to fix a series of printable markers on the sail and register the flying shape in real sailing conditions from a side vessel with a single camera. We reconstruct the average sail shape during an interval where the sailor maintains the sail as stable as possible. The average is further improved by a Bundle Adjustment algorithm. We tested our method in a real sailing scenario and present promising results. Quantitatively, we show the precision in regards to the reconstructed markers area and the reprojected points. Qualitatively, we present feedback from domain experts who evaluated our results and confirmed the usefulness and quality of the reconstructed shape. [ABSTRACT FROM AUTHOR]

In this paper, we propose an unsupervised learning framework to address the problems of the inaccurate inference of depth details and the loss of spatial information for monocular depth estimation. First, as an unsupervised technique, the proposed framework takes easily collected stereo image pairs instead of ground truth depth data as inputs for training. Second, we design a rectangle convolution to capture global dependencies between neighboring pixels across entire rows or columns in an image, which can bring significant promotion on depth details inference. Third, we propose a learned depth refinement module including a color-guided refinement layer and a learned composite proximal operator to preserve depth discontinuities and obtain high quality depth map. The proposed network is fully differentiable and end-to-end trainable. Extensive experiments evaluated on KITTI, Cityscapes and Make3D dataset demonstrate our state-of-the-art performance and good cross-dataset generalization ability. [ABSTRACT FROM AUTHOR]

Monocular visual SLAM methods can accurately track the camera pose and infer the scene structure by building sparse correspondence between two/multiple views of the scene. However, the reconstructed 3D maps of these methods are extremely sparse. On the other hand, deep learning is widely used to predict dense depth maps from single-view color images, but the results are subject to blurry depth boundaries, which severely deform the structure of 3D scene. Therefore, this paper proposes a dense reconstruction method under the monocular SLAM framework (DRM-SLAM), in which a novel scene depth fusion scheme is designed to fully utilize both the sparse depth samples from monocular SLAM and predicted dense depth maps from convolutional neural network (CNN). In the scheme, a CNN architecture is carefully designed for robust depth estimation. Besides, our approach also accounts for the problem of scale ambiguity existing in the monocular SLAM. Extensive experiments on benchmark datasets and our captured dataset demonstrate the accuracy and robustness of the proposed DRM-SLAM. The evaluation of runtime and adaptability under challenging environments also verify the practicability of our method. [ABSTRACT FROM AUTHOR]

Unlike many other sports, Rugby Union has not permitted players to wear spectacles or eye protection. With an industrial partner, World Rugby developed goggles suitable for use while playing rugby for the purposes of growing participation amongst those that need to wear corrective lenses. This study reports on the profile and experiences of goggle wearers. 387 players received the goggles. Data were obtained from 188 (49%) using an online, 75-item questionnaire. 87% "strongly agreed/agreed" that goggles are beneficial and 75% are happy with goggle performance. Common problems reported by 49.7 and 32.6% of respondents were issues with fogging-up and getting dirty. 15 (8%) players stopped wearing the goggles because of fogging-up, limits to peripheral vision and poor comfort/fit. Injuries were reported in 3% of respondents. In none of these cases did the player stop wearing the goggles. From the positive experience of players in the trial, the goggles were adopted into the Laws of the game on July 1, 2019. As the need to correct vision with spectacles is common, and contact lenses are not worn by 80%+of spectacle wearers, the new Rugby goggles will widen participation for those that need to wear refractive correction, or have an existing/increased risk of uniocular visual impairment. [ABSTRACT FROM AUTHOR]

• A large-scale multi-view dataset that provides accurate annotations for hand poses. • A pipeline that improves the state-of-the-art results for 3D hand pose estimation. • We successfully applied our approach to robot hand teleoperation. In this paper, we present a novel deep learning-based architecture, which is under the scope of expert and intelligent systems, to perform accurate real-time tridimensional hand pose estimation using a single RGB frame as an input, so there is no need to use multiple cameras or points of view, or RGB-D devices. The proposed pipeline is composed of two convolutional neural network architectures. The first one is in charge of detecting the hand in the image. The second one is able to accurately infer the tridimensional position of the joints retrieving, thus, the full hand pose. To do this, we captured our own large-scale dataset composed of images of hands and the corresponding 3D joints annotations. The proposal achieved a 3D hand pose mean error of below 5 mm on both the proposed dataset and Stereo Hand Pose Tracking Benchmark, which is a public dataset. Our method also outperforms the state-of-the-art methods. We also demonstrate in this paper the application of the proposal to perform a robotic hand teleoperation with high success. [ABSTRACT FROM AUTHOR]

Most modern video eye trackers deliver binocular data. Many researchers take the average of the left and right eye signals (the version signal) to decrease the variable error (precision) up to a factor of 2 . What happens to the systematic error (accuracy) if the left and right eye signals are averaged? To determine the systematic error, we conducted a calibration validation in two experiments (n= 79 and n = 64). The systematic error was computed for the left eye, right eye, and version signals separately. In respectively 29.5 and 25.8% of the participants, the systematic error of a single eye signal was lower than that of the version signal at the cost of a higher variable error. If a small variable error is desirable, and the difference between the left and the right eye is not the topic of study, one should average position data from the left and the right eye (in other words, use the version signal). If a small systematic error is desirable, one should use the signal (from left eye, right eye or version) that delivers the best accuracy. In the latter case, this may cause worse precision than that of the version signal. [ABSTRACT FROM AUTHOR]

• Vision from the ipsilateral eye yields more robust corrections to target jumps. • Such a pattern only emerged when the limb originated from right, ipsilateral space. • The frequent use of the ipsilateral eye and limb may account for this advantage. A limb's initial position is often biased to the right of the midline during activities of daily living. Given this specific initial limb position, visual cues of the limb become first available to the ipsilateral eye relative to the contralateral eye. The current study investigated online control of the dominant limb as a function of having visual cues available to the ipsilateral or contralateral eye, in relation to the initial start position of the limb. Participants began each trial with their right limb on a home position to the left or right of the midline. After movement onset, a brief visual sample was provided to the ipsilateral or contralateral eye. On one third of the trials, an imperceptible 3 cm target jump was introduced. If visual information from the eye ipsilateral to the limb is preferentially used to control ongoing movements of the dominant limb, corrections for the target jump should be observed when movements began from the right of the body's midline and vision was available to the ipsilateral eye. As expected, limb trajectory corrections for the target jump were only observed when participants started from the right home position and visual information was provided to the ipsilateral eye. We purport that such visuomotor asymmetry specialization emerges via neurophysiological developments, which may arise from naturalistic and probabilistic limb trajectory asymmetries. [ABSTRACT FROM AUTHOR]

Aim: To evaluate the results of phacoemulsification (PE) surgery in completely monocular cases and to emphasize the important points in the pre-, intra- and post-operative periods. Material and Methods: A total of 42 patients who had no light perception in one eye and had underwent PE surgery in the other eye at our clinic between January 2014 and March 2016 were included in the study. The charts of the patients were evaluated retrospectively. The age and gender of the cases, whether there was an additional pathology in the eye undergoing surgery, the reason of visual loss in the other eye, the type of anesthesia used, pre- and post-operative best corrected visual acuity (BCVA) with the Snellen chart, intraocular pressure and intra- and post-operative complications were investigated. Results: There were17 (40.5%) female and 25 (59.5%) male patients with a mean age of 74.2±10.5 (45-93) years. The most common causes of the monocular state were glaucoma, cataract and trauma, the most common accompanying pathologies in the eyes undergoing cataract surgery were glaucoma, zonular weakness and age-related macular degeneration. Mean BCVA was 0.07±0.1 (0.01-0.4) pre-operatively and 0.5±0.3 (0.01-1) post-operatively (p=0.001). The mean follow-up duration was 3.9±5.6 months (1 week to 24 months). The most common post-operative complication was corneal edema. Conclusion: The stress created by cataract surgery on the physician and patient is quite high in monocular cases. However, satisfactory results with PE are obtained in these cases by carefully using pre-, intra- and post-operative methods. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • A novel exemplar-based algorithm is proposed to implicitly augment the exemplar set. • The augmentation of exemplars relies on synthesizing virtual candidate poses. • The validity of best-matched candidate is guaranteed by two-step resorting. Abstract 3D human pose estimation from a single image is a challenging problem due to occlusion, viewpoint variance, and the ill-posed nature of back projection. We follow a standard two-step pipeline which first detects 2D joint locations and uses them to infer 3D pose. For the first step, we use a recent deep learning-based detector. For the second step, we propose a novel exemplar-based algorithm to implicitly augment the exemplar set for 3D human pose estimation. The motivation of this algorithm is to well represent various poses in the real world with finite real exemplars. We achieve it by a strategy of synthesizing virtual candidate poses which ensures that the augmented exemplar set has much more variety. Moreover, we also present an effective approach to select the best exemplar from candidate set to well match the detected 2D pose. Experimental results show that our method achieves competitive performance on Human3.6M dataset. [ABSTRACT FROM AUTHOR]

This paper presents a structured approach for efficiently exploiting the perspective information of a scene to enhance the detection of objects in monocular systems. It defines a finite grid of 3D positions on the dominant ground plane and computes occupancy maps from which object location estimates are extracted. This method works on the top of any detection method, either pixel-wise (e.g. background subtraction) or region-wise (e.g. detection-by-classification) technique, which can be linked to the proposed scheme with minimal fine tuning. Its flexibility thus allows for applying this approach in a wide variety of applications and sectors, such as surveillance applications (e.g. person detection) or driver assistance systems (e.g. vehicle or pedestrian detection). Extensive results provide evidence of its excellent performance and its ease of use in combination with different image processing techniques. [ABSTRACT FROM AUTHOR]

In the context of scene understanding, a variety of methods exists to estimate different information channels from mono or stereo images, including disparity, depth, and normals. Although several advances have been reported in the recent years for these tasks, the estimated information is often imprecise particularly near depth discontinuities or creases. Studies have however shown that precisely such depth edges carry critical cues for the perception of shape, and play important roles in tasks like depth-based segmentation or foreground selection. Unfortunately, the currently extracted channels often carry conflicting signals, making it difficult for subsequent applications to effectively use them. In this paper, we focus on the problem of obtaining high-precision depth edges (i.e., depth contours and creases) by jointly analyzing such unreliable information channels. We propose DEPTHCUT, a data-driven fusion of the channels using a convolutional neural network trained on a large dataset with known depth. The resulting depth edges can be used for segmentation, decomposing a scene into depth layers with relatively flat depth, or improving the accuracy of the depth estimate near depth edges by constraining its gradients to agree with these edges. Quantitatively, we compare against 18 variants of baselines and demonstrate that our depth edges result in an improved segmentation performance and an improved depth estimate near depth edges compared to data-agnostic channel fusion. Qualitatively, we demonstrate that the depth edges result in superior segmentation and depth orderings. (Code and datasets will be made available.) [ABSTRACT FROM AUTHOR]

This paper presents a novel hierarchical approach to solving structure-from-motion (SFM) problems. The algorithm begins with small local reconstructions based on nonlinear bundle adjustment (BA). These are then joined in a hierarchical manner using a strategy that requires solving a linear least squares optimization problem followed by a nonlinear transform. The algorithm can handle ordered monocular and stereo image sequences. Two stereo images or three monocular images are adequate for building each initial reconstruction. The bulk of the computation involves solving a linear least squares problem and, therefore, the proposed algorithm avoids three major issues associated with most of the nonlinear optimization algorithms currently used for SFM: the need for a reasonably accurate initial estimate, the need for iterations, and the possibility of being trapped in a local minimum. Also, by summarizing all the original observations into the small local reconstructions with associated information matrices, the proposed Linear SFM manages to preserve all the information contained in the observations. The paper also demonstrates that the proposed problem formulation results in a sparse structure that leads to an efficient numerical implementation. The experimental results using publicly available datasets show that the proposed algorithm yields solutions that are very close to those obtained using a global BA starting with an accurate initial estimate. The C/C++ source code of the proposed algorithm is publicly available at https://github.com/LiangZhaoPKUImperial/LinearSFM . [ABSTRACT FROM AUTHOR]

We describe a case of unusual ocular movement with features of both the Heimann–Bielshowsky Phenomenon and Ocular Neuromyotonia that might suggest that they share some common pathophysiology or be on a spectrum of abnormal firing of ocular motor cranial nerves. We are the first to propose such a relationship. [ABSTRACT FROM AUTHOR]

We present a dataset collected from a canoe along the Sangamon River in Illinois. The canoe was equipped with a stereo camera, an inertial measurement unit (IMU), and a global positioning system (GPS) device, which provide visual data suitable for stereo or monocular applications, inertial measurements, and position data for ground truth. We recorded a canoe trip up and down the river for 44 minutes covering a 2.7 km round trip. The dataset adds to those previously recorded in unstructured environments and is unique in that it is recorded on a river, which provides its own set of challenges and constraints that are described in this paper. The dataset is stored on the Illinois Data Bank and can be accessed at: https://doi.org/10.13012/B2IDB-9342111_V1. [ABSTRACT FROM AUTHOR]

Extensive research in the field of monocular SLAM for the past fifteen years has yielded workable systems that found their way into various applications in robotics and augmented reality. Although filter-based monocular SLAM systems were common at some time, the more efficient keyframe-based solutions are becoming the de facto methodology for building a monocular SLAM system. The objective of this paper is threefold: first, the paper serves as a guideline for people seeking to design their own monocular SLAM according to specific environmental constraints. Second, it presents a survey that covers the various keyframe-based monocular SLAM systems in the literature, detailing the components of their implementation, and critically assessing the specific strategies made in each proposed solution. Third, the paper provides insight into the direction of future research in this field, to address the major limitations still facing monocular SLAM; namely, in the issues of illumination changes, initialization, highly dynamic motion, poorly textured scenes, repetitive textures, map maintenance, and failure recovery. [ABSTRACT FROM AUTHOR]

Despite early reports and the contemporary consensus on microsaccades as purely binocular phenomena, recent work has proposed not only the existence of monocular microsaccades, but also that they serve functional purposes. We take a critical look at the detection of monocular microsaccades from a signal perspective, using raw data and a state-of-the-art, video-based eye tracker. In agreement with previous work, monocular detections were present in all participants using a standard microsaccade detection algorithm. However, a closer look at the raw data invalidates the vast majority of monocular detections. These results again raise the question of the existence of monocular microsaccades, as well as the need for improved methods to study small eye movements recorded with video-based eye trackers. [ABSTRACT FROM AUTHOR]

Industrial robots are expected to undertake ever more advanced tasks in the modern manufacturing industry, such as intelligent grasping, in which robots should be capable of recognizing the position and orientation of a part before grasping it. In this paper, a monocular-based 6-degree of freedom (DOF) pose estimation technology to enable robots to grasp large-size parts at informal poses is proposed. A camera was mounted on the robot end-flange and oriented to measure several featured points on the part before the robot moved to grasp it. In order to estimate the part pose, a nonlinear optimization model based on the camera object space collinearity error in different poses is established, and the initial iteration value is estimated with the differential transformation. Measuring poses of the camera are optimized based on uncertainty analysis. Also, the principle of the robotic intelligent grasping system was developed, with which the robot could adjust its pose to grasp the part. In experimental tests, the part poses estimated with the method described in this paper were compared with those produced by a laser tracker, and results show the RMS angle and position error are about 0.0228° and 0.4603 mm. Robotic intelligent grasping tests were also successfully performed in the experiments. [ABSTRACT FROM AUTHOR]

In recent years, agencies around the world have invested huge amounts of effort toward digitizing many aspects of the world's cultural heritage. Of particular importance is the digitization of outdoor archaeological sites. In the spirit of valorization of this digital information, many groups have developed virtual or augmented reality (AR) computer applications themed around a particular archaeological object. The problem of pose tracking in outdoor AR applications is addressed. Different positional systems are analyzed, resulting in the selection of a monocular camera-based user tracker. The limitations that challenge this technique from map generation, scale, anchoring, to lighting conditions are analyzed and systematically addressed. Finally, as a case study, our pose tracking system is implemented within an AR experience in the Byblos Roman theater in Lebanon. [ABSTRACT FROM AUTHOR]

We proposed and developed a novel monocular hyper-realistic head dome projector for an amusement application and a novel monocular windshield augmented reality projector for augmented reality and head-up display applications. They use monocular vision that eliminates the depth cues caused by binocular parallax information. Our developed monocular hyper-realistic head dome projector and windshield augmented reality projector system achieved a high hyper-reality performance with free depth perception and high visibility. [ABSTRACT FROM AUTHOR]

Shape-from-Template (SfT) solves 3D vision from a single image and a deformable 3D object model, called a template. Concretely, SfT computes registration (the correspondence between the template and the image) and reconstruction (the depth in camera frame). It constrains the object deformation to quasi-isometry. Real-time and automatic SfT represents an open problem for complex objects and imaging conditions. We present four contributions to address core unmet challenges to realise SfT with a Deep Neural Network (DNN). First, we propose a novel DNN called DeepSfT, which encodes the template in its weights and hence copes with highly complex templates. Second, we propose a semi-supervised training procedure to exploit real data. This is a practical solution to overcome the render gap that occurs when training only with simulated data. Third, we propose a geometry adaptation module to deal with different cameras at training and inference. Fourth, we combine statistical learning with physics-based reasoning. DeepSfT runs automatically and in real-time and we show with numerous experiments and an ablation study that it consistently achieves a lower 3D error than previous work. It outperforms in generalisation and achieves great performance in terms of reconstruction and registration error with wide-baseline, occlusions, illumination changes, weak texture and blur. [Display omitted] • DeepSfT is a DNN fully-convolutional based on residual encoder-decoder for SfT. • We propose a semi-supervised end-to-end training procedure with synthetic and real data. • DeepSfT is a solution to cope with multiple imaging geometries, at training and inference. • We propose to combine DeepSfT with a physics-based estimation procedure. [ABSTRACT FROM AUTHOR]

In our study, the increase in visual acuity of monocular patients in the early period following cataract surgery with phacoemulsification (PHACO), the reasons for being monocular and the comorbid pathologies in the operated eye were examined. The files of 24 patients who had PHACO and intraocular lens (IOL) implantation were analyzed retrospectively. The patients who have 0.1 and worse best-corrected visual acuity (BCVA) in the other eye with Snellen chart and whose vision could not be increased by a medical or surgical treatment were accepted as monocular. All operations were performed with clear corneal incision and standard PHACO methods. IOLs were implanted into capsular bag for all patients. The postoperative visits were made on the 1st, 7th and 30th days. The mean age of the 24 patients included in the study was 65.0±14.1. Seven (29.1%) patients were female, while 17 (70.9%) were male. Right eyes of 4 (16.6%) patients and left eyes of 20 (83.4%) patients were operated. Comorbid disease was found in 16 (66.6%) of the eyes operated. When non-operated eyes of the cases were analyzed, the first three reasons for being monocular were age related macula degeneration (AMD), phthisis bulbi following penetrating injury and glaucomatous optic atrophy. The mean BCVA was 1.31±0.71 logMAR before the surgery, 0.32±0.57 logMAR on the 7th day control and 0.14±0.16 logMAR on the first month control. 22 (91.1%) patients had better visual acuity after the surgery and on first month control 18 (75%) patients’ BCVA was equal to or better than 0.3 logMAR. No complication was seen in the 30 day followup of our cases. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new approach to the decentralized Simultaneous Localization And Mapping (SLAM) problem. The goal is to demonstrate the feasibility of decentralized localization using low-density maps built with low-cost sensors. This problem is challenging at different levels. Indeed, each vehicle localization tends to drift over time independently of one another making the global localization of a fleet hard to achieve. To counter this effect, called SLAM inconsistency and which has been stated numerous times in the literature, we introduce a model to represent the natural drift of SLAM algorithms. Its integration inside an Extended Kalman Filter is explained along with simulations validating its use. The second part of this paper presents the fusion architecture designed to solve the different problems arising in a decentralized scheme. It avoids data incest, which is an important source of inconsistency, and integrates the previously mentioned SLAM drift in the estimates produced. This architecture also separates the SLAM classically used for mono-vehicle applications from the high-level decentralized part offering flexibility regarding sensors and algorithms at a low-level. Other aspects, involved by the multi-vehicle settings, are also taken into account (communication losses, latencies, desynchronizations, unknown initial positions of the fleet members and data association). The whole algorithm has been tested in various scenarios with vehicles equipped with a single camera and an odometer. The results, from both simulated and real scenarios, show that our approach can work in real time with very small bandwidth requirements. [ABSTRACT FROM AUTHOR]

Flicker adaptation reduces subsequent temporal contrast sensitivity. Recent studies show that this adaptation likely results from neural changes in the magnocellular visual pathway, but whether this adaptation occurs at a monocular or a binocular level, or both, is unclear. Here, two experiments address this question. The first experiment exploits the observation that flicker adaptation is stronger at higher than lower temporal frequencies. Observers' two eyes adapted to 3Hz flicker with an incremental pulse at 1/4 duty cycle, either in-phase or out-of-phase in the two eyes. At the binocular level, the flicker rate was 6Hz in the out-of-phase condition if the two eyes' pulse trains sum. Similar sensitivity reduction was found in both phase conditions, as expected for independent monocular adapting mechanisms. The second experiment tested for interocular transfer of adaptation between eyes. Results showed that (1) flicker adaptation was strongest with adapting and test fields in only the same eye, (2) adaptation can be partially transferred interocularly with adaptation in only the opposite eye, and (3) adaptation was weakened when both eyes were adapted simultaneously at different contrasts, compared to test-eye adaptation alone. Taken together, the findings are consistent with mechanisms of flicker adaptation at both the monocular and binocular level. [ABSTRACT FROM AUTHOR]

BACKGROUND: Perception of upright is often assessed by aligning a luminous line to the subjective visual vertical (SVV). OBJECTIVE: Here we investigated the effects of visual line rotation and viewing eye on SVV responses and whether there was any change with head tilt. METHODS: SVV was measured using a forced-choice paradigm and by combining the following conditions in 22 healthy subjects: head position (20? left tilt, upright and 20? right tilt), viewing eye (left eye, both eyes and right eye) and direction of visual line rotation (clockwise [CW] and counter clockwise [CCW]). RESULTS: The accuracy and precision of SVV responses were not different between the viewing eye conditions in all head positions (P >0.05, Kruskal-Wallis test). The accuracy of SVV responses was different between the CWand CCW line rotations (p ? 0.0001; Kruskal-Wallis test) and SVV was tilted in the same direction as the line rotation. This effect of line rotation was however not consistent across head tilts and was only present in the upright and right tilt head positions. The accuracy of SVV responses showed a higher variability among subjects in the left head tilt position with no significant difference between the CW and CCW line rotations (P >0.05; post-hoc Dunn's test). CONCLUSIONS: In spite of the challenges to the estimate of upright with head tilt, normal subjects did remarkably well irrespective of the viewing eye. The physiological significance of the asymmetry in the effect of line rotation between the head tilt positions is unclear but it suggests a lateralizing effect of head tilt on the visual perception of upright. [ABSTRACT FROM AUTHOR]

In this paper, we propose a method to directly estimate a camera's motion parameters by using normal flow vectors. In contrast to traditional methods, which tackle the problem by calculating optical flows or establishing motion correspondences, our proposed approach does not require conventional assumptions about the captured scene, such as consistent smoothness or distinct feature availability. In the proposed algorithm, the normal flows are classified into different groups, and each group will provide a possible solution regarding the camera's motion parameters. Then, the strategy of hypothesis and confirmation is adopted to eliminate the incorrect solutions. Finally, the optimal solution is obtained via the clustering algorithm. We have tested the proposed method on both synthetic image data and real image sequences. The experimental results illustrate the feasibility and reliability of the algorithm. [ABSTRACT FROM AUTHOR]

We propose a solution towards the problem of autonomous flight in man-made indoor environments with a micro aerial vehicle (MAV), using a frontal camera, a downward-facing sonar, and odometry inputs. While steering an MAV towards distant features that we call vistas , we build a map of the environment in a parallel tracking and mapping fashion to infer the wall structure and avoid lateral collisions in real-time. Our framework overcomes the limitations of traditional monocular SLAM approaches that are prone to failure when operating in feature-poor environments and when the camera purely rotates. First, we overcome the common dependency on feature-rich environments by detecting Wall–Floor Features (WFFs) , a novel type of low-dimensional landmarks that are specifically designed for man-made environments to capture the geometric structure of the scene. We show that WFFs not only reveal the structure of the scene, but can also be tracked reliably. Second, we cope with difficult robot motions and environments by fusing the visual data with odometry measurements in a principled manner. This allows the robot to continue tracking when it purely rotates and when it temporarily navigates across a completely featureless environment. We demonstrate our results on a small commercially available quad-rotor platform flying in a typical feature-poor indoor environment. [ABSTRACT FROM AUTHOR]

A monocular method to measure the pose of a translation-only one-dimensional (1D) object containing at least two known characteristic points is presented. The position of any other point on the 1D object can be located after the pose measurement. Scene information is not needed to apply the method. If the distance between the characteristic points is unknown, there is a scale factor between the object's real positions and the measurement results. The method can also be used to measure the pose and shape parameters of a parallelogram, prism or cylinder object. [ABSTRACT FROM AUTHOR]

Background It is well known that problems with binocular vision can cause issues for reading; less known is to what extent binocular vision improves reading performance. The purpose of this study was to explore the role of binocularity by directly comparing monocular and binocular reading in subjects with typical reading skills and normal binocular vision. A secondary purpose was to assess any asymmetry in monocular performance and its association with the sighting dominant eye. Methods In a balanced repeated measures experiment, 18 subjects read paragraphs of text under monocular and binocular conditions. All subjects went through an optometric examination before inclusion. Reading speed and eye movements were recorded with an eye tracker. Results The mean difference in reading speed (2.1 per cent) between monocular (dominant and non-dominant eye averaged) and binocular reading speed was not significant. A significant difference in reading speed was found between binocular and the non-dominant eye, as determined by the far sighting test (p = 0.03). Monocular reading showed significantly increased (8.9 per cent) fixation duration (p < 0.01) and longer regressive saccades by 0.43 character spaces (p < 0.01). Reading with the non-dominant eye, as determined by the near sighting test, showed increased progressive saccade length by 0.2 characters compared to the dominant eye (p = 0.03). No other significant differences between dominant and non-dominant eyes were found. The agreement between the faster reading eye and ocular dominance was 44 to 56 per cent depending on whether dominance was determined at near or far. Conclusion The outcomes suggest that in subjects with normal binocular vision, there is no marked enhancement in reading performance by binocular vision when reading paragraphs of text. Furthermore, the monocular reading performance appears to be close to equal and any small differences in performance appear not to be strongly associated with ocular dominance. [ABSTRACT FROM AUTHOR]

Jumping spiders (Salticidae) use exceptional vision, largely mediated by their forward-facing anterior lateral (AL) and anterior medial (AM) eyes, to pounce on prey from a distance. We evaluated depth perception through the use of 'texture density' (depth estimation through surface texture comparisons, with greater distances having higher textural density) in the salticid Trite planiceps. In visual cliff experiments, spiders tended to choose an area with a false 'low drop' over a false 'high drop' with the same texture densities, but showed no preference for either area when presented with substrates with different texture densities at a constant height. This was corroborated when T. planiceps did not avoid jumping over an illusion resembling a trench compared to a no-illusion control pattern. We then selectively occluded both AL and 1 AM eye (monocular treatment), both AL eyes (ALE-occluded binocular treatment) or no eyes (control), and induced spiders to jump across a gap at different heights. Neither control spiders, spiders with binocular cues from the AM eyes or monocular treatment spiders exhibited a height preference. These results suggest that while T. planiceps accurately perceives depth, it does not appear to rely on texture gradients as a depth cue. • We investigated depth perception in jumping spiders (salticids) using texture density. • Salticids lept between islands or walked on floating glass with textured substrates. • Spiders were averse to heights, and gauged depth using monocular cues. • Visual illusions, however, were not perceived as illusory. • Salticids did not appear to use texture density to gauge distance. [ABSTRACT FROM AUTHOR]

Event-based cameras have increasingly become more commonplace in the commercial space as the performance of these cameras has also continued to increase to the degree where they can exponentially outperform their frame-based counterparts in many applications. However, instantiations of event-based cameras for depth estimation are sparse. After a short introduction detailing the salient differences and features of an event-based camera compared to that of a traditional, frame-based one, this work summarizes the published event-based methods and systems known to date. An analytical review of these methods and systems is performed, justifying the conclusions drawn. This work is concluded with insights and recommendations for further development in the field of event-based camera depth estimation. [ABSTRACT FROM AUTHOR]

Understanding the mechanisms that link sensory stimuli to animal behavior is a central challenge in neuroscience. The quantitative description of behavioral responses to defined stimuli has led to a rich understanding of different behavioral strategies in many species. One important navigational cue perceived by many vertebrates and insects is the e-vector orientation of linearly polarized light. Drosophila manifests an innate orientation response to this cue ('polarotaxis'), aligning its body axis with the e-vector field. We have established a population-based behavioral paradigm for the genetic dissection of neural circuits guiding polarotaxis to both celestial as well as reflected polarized stimuli. However, the behavioral mechanisms by which flies align with a linearly polarized stimulus remain unknown. Here, we present a detailed quantitative description of Drosophila polarotaxis, systematically measuring behavioral parameters that are modulated by the stimulus. We show that angular acceleration is modulated during alignment, and this single parameter may be sufficient for alignment. Furthermore, using monocular deprivation, we show that each eye is necessary for modulating turns in the ipsilateral direction. This analysis lays the foundation for understanding how neural circuits guide these important visual behaviors. [ABSTRACT FROM AUTHOR]

People with one eye show altered sensory processing. Such changes might reflect a central reweighting of sensory information that might impact on how multisensory cues are integrated. We assessed whether people who lost an eye early in life differ from controls with respect to audiovisual integration. In order to quantify the relative weightings assigned to each sensory system, participants were asked to spatially localize audiovisual events that have been previously shown to be optimally combined and perceptually fused from the point of view of location in a normal population, where the auditory and visual components were spatially disparate. There was no difference in the variability of localizing unimodal visual and auditory targets by people with one eye compared to controls. People with one eye did however, demonstrate slower reaction times to localize visual stimuli compared to auditory stimuli and were slower than binocular and eye-patched control groups. When localizing bimodal targets, the weightings assigned to each sensory modality in both people with one eye and controls were predictable from their unimodal performance, in accordance with Maximum Likelihood Estimation and the time it took all three groups to localize the bimodal targets was faster than for vision alone. Regardless of demonstrating a longer response time to visual stimuli, people with one eye appear to integrate the auditory and visual components of multisensory events optimally when determining spatial location. [ABSTRACT FROM AUTHOR]

This paper presents a three-dimensional (3-D) reconstruction method of outdoor scenes from an image sequence captured by a moving handheld video camera. The proposed method works in a coarse-to-fine manner and can be divided into three steps: First, an initial 3-D shape is reconstructed according to the image correspondences obtained by optic flow tracking. Second, an initial depth map is generated according to the initial 3-D shape. Finally, a global energy function for refining the initial depth is used to acquire better results. Experimental results are demonstrated for a variety of complex scenes. [ABSTRACT FROM AUTHOR]