Dissertations / Theses on the topic 'Binocular vision. Depth perception. Computer vision'

Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2003.<br>Includes bibliographical references (leaves 64-66). Also available in electronic version. Access restricted to campus users.

Thesis (Ph. D.)--University of Alabama at Birmingham, 2008.<br>Title from PDF title page (viewed Mar. 30, 2010). Additional advisors: Franklin R. Amthor, James E. Cox, Timothy J. Gawne, Rosalyn E. Weller. Includes bibliographical references (p. 104-114).

A series of experiments is reported that examined the perception of the depth structure of a visual scene on the basis of binocular disparity and motion parallax information. Initial experiments (2.1-2.4) revealed that there are considerable differences in the perception of depth in computer simulated surfaces specified by each cue individually. These differences were interpreted as indicating a variation in the relative sensitivity of the visual system to different components of the geometric transformations generated between retinal images within the two domains. Subsequent experiments assessed observers' perception of depth, on the basis of disparity and/or parallax information, in configurations of point light sources in a dark limited cue environment viewed (i) directly (experiments 3.1, 3.2, 3.3 and 5) and (ii) through a head mounted CRT display (experiments 4.1-4.3). They showed that in these viewing conditions the visual system cannot derive a metric (Euclidean) representation of scene structure but it has access to a range of strategies (and consequently representations) that may enable it to complete tasks accurately. The strategy used depended upon the range of available information sources, an assessment of their reliability and the nature of the experimental task. Two final experiments examined the effect of increasing the available depth cues by (i) performing a task in an illuminated structured viewing environment and (ii) introducing surface texture cues. They showed that biases in depth perception persist in such environments and that they cannot be entirely explained by conflicting depth information signalled by accommodation (Frisby et al, 1996). It is argued that strong fusion models of depth cue interaction best describe the range of interactions found across of the all experiments. However, there are limitations on the types of strong interaction used by the visual system, i.e. no evidence was found for Richards' (1985) proposal that the simultaneous presence of disparity and motion information allow the recovery depth structure.

This thesis investigates the use of binocular information for motion-in-depth (MID) perception. There are at least two different types of binocular information available to the visual system from which to derive a perception of MID: changing disparity (CD) and inter-ocular velocity differences (IOVD). In the following experiments, we manipulate the availability of CD and IOVD information in order to assess the relative influence of each on MID judgements. In the first experiment, we assessed the relative effectiveness of CD and IOVD information for MID detection, and whether the two types of binocular information are processed by separate mechanisms with differing characteristics. Our results suggest that, both CD and IOVD information can be utilised for MID detection, yet, the relative dependence on either of these types of MID information varies between observers. We then went on to explore the contribution of CD and IOVD information to time-to-contact (TTC) perception, whereby an observer judges the time at which an approaching stimulus will contact them. We confirmed that the addition of congruent binocular information to looming stimuli can influence TTC judgements, but that there is no influence from binocular information indicating no motion. Further to this, we found that observers could utilise both CD and IOVD for TTC judgements, although once again, individual receptiveness to CD and/or IOVD information varied. Thus, we demonstrate that the human visual system is able to process both CD and IOVD information, but the influence of either (or both) of these cues on an individual’s perception has been shown to be mutually independent.

In very few mobile robotic applications stereo vision based navigation and mapping is used because dealing with stereo images is very hard and very time consuming. Despite all the problems, stereo vision still becomes one of the most important resources of knowing the world for a mobile robot because imaging provides much more information than most other sensors. Real robotic applications are very complicated because besides the problems of finding how the robot should behave to complete the task at hand, the problems faced while controlling the robot&rsquo<br>s internal parameters bring high computational load. Thus, finding the strategy to be followed in a simulated world and then applying this on real robot for real applications is preferable. In this study, we describe an algorithm for object recognition and cognitive map formation using stereo image data in a 3D virtual world where 3D objects and a robot with active stereo imaging system are simulated. Stereo imaging system is simulated so that the actual human visual system properties are parameterized. Only the stereo images obtained from this world are supplied to the virtual robot. By applying our disparity algorithm, depth map for the current stereo view is extracted. Using the depth information for the current view, a cognitive map of the environment is updated gradually while the virtual agent is exploring the environment. The agent explores its environment in an intelligent way using the current view and environmental map information obtained up to date. Also, during exploration if a new object is observed, the robot turns around it, obtains stereo images from different directions and extracts the model of the object in 3D. Using the available set of possible objects, it recognizes the object.

PURPOSE. Gait during obstacle negotiation is adapted in visually normal subjects whose vision is temporarily and unilaterally blurred or occluded. This study was conducted to examine whether gait parameters in individuals with long-standing deficient stereopsis are similarly adapted. METHODS. Twelve visually normal subjects and 16 individuals with deficient stereopsis due to amblyopia and/or its associated conditions negotiated floor-based obstacles of different heights (7-22 cm). Trials were conducted during binocular viewing and monocular occlusion. Analyses focused on foot placement before the obstacle and toe clearance over it. RESULTS. Across all viewing conditions, there were significant group-by-obstacle height interactions for toe clearance (P < 0.001), walking velocity (P = 0.003), and penultimate step length (P = 0.022). Toe clearance decreased (similar to 0.7 cm) with increasing obstacle height in visually normal subjects, but it increased (similar to 1.5 cm) with increasing obstacle height in the stereo-deficient group. Walking velocity and penultimate step length decreased with increasing obstacle height in both groups, but the reduction was more pronounced in stereo-deficient individuals. Post hoc analyses indicated group differences in toe clearance and penultimate step length when negotiating the highest obstacle (P < 0.05). CONCLUSIONS. Occlusion of either eye caused significant and similar gait changes in both groups, suggesting that in stereo-deficient individuals, as in visually normal subjects, both eyes contribute usefully to the execution of adaptive gait. Under monocular and binocular viewing, obstacle-crossing performance in stereo-deficient individuals was more cautious when compared with that of visually normal subjects, but this difference became evident only when the subjects were negotiating higher obstacles; suggesting that such individuals may be at greater risk of tripping or falling during everyday locomotion.<br>RCUK (Research Councils, UK)

Le système visuel du primate s'appuie sur les légères différences entre les deux projections rétiniennes pour percevoir la profondeur. Cependant, on ne sait pas exactement comment ces disparités binoculaires sont traitées et intégrées par le système nerveux. D'un côté, des enregistrements unitaires chez le macaque permettent d'avoir accès au codage neuronal de la disparité à un niveau local. De l'autre côté, la neuroimagerie fonctionnelle (IRMf) chez l'humain met en lumière les réseaux corticaux impliqués dans le traitement de la disparité à un niveau macroscopique mais chez une espèce différente. Dans le cadre de cette thèse, nous proposons d'utiliser la technique de l'IRMf chez le macaque pour permettre de faire le lien entre les enregistrements unitaires chez le macaque et les enregistrements IRMf chez l'humain. Cela, afin de pouvoir faire des comparaisons directes entre les deux espèces. Plus spécifiquement, nous nous sommes intéressés au traitement spatial et temporal des disparités binoculaires au niveau cortical mais aussi au niveau perceptif. En étudiant l'activité corticale en réponse au mouvement tridimensionnel (3D), nous avons pu montrer pour la première fois 1) qu'il existe un réseau dédié chez le macaque qui contient des aires allant au-delà du cluster MT et des aires environnantes et 2) qu'il y a des homologies avec le réseau trouvé chez l'humain en réponse à des stimuli similaires. Dans une deuxième étude, nous avons tenté d'établir un lien entre les biais perceptifs qui reflètent les régularités statistiques 3D ans l'environnement visuel et l'activité corticale. Nous nous sommes demandés si de tels biais existent et peuvent être reliés à des réponses spécifiques au niveau macroscopique. Nous avons trouvé de plus fortes activations pour le stimulus reflétant les statistiques naturelles chez un sujet, démontrant ainsi une possible influence des régularités spatiales sur l'activité corticale. Des analyses supplémentaires sont cependant nécessaires pour conclure de façon définitive. Néanmoins, nous avons pu confirmer de façon robuste l'existence d'un vaste réseau cortical répondant aux disparités corrélées chez le macaque. Pour finir, nous avons pu mesurer pour la première fois les points rétiniens correspondants au niveau du méridien vertical chez un sujet macaque qui réalisait une tâche comportementale (procédure à choix forcé). Nous avons pu comparer les résultats obtenus avec des données également collectées chez des participants humains avec le même protocole. Dans les différentes sections de discussion, nous montrons comment nos différents résultats ouvrent la voie à de nouvelles perspectives<br>The primate visual system strongly relies on the small differences between the two retinal projections to perceive depth. However, it is not fully understood how those binocular disparities are computed and integrated by the nervous system. On the one hand, single-unit recordings in macaque give access to neuronal encoding of disparity at a very local level. On the other hand, functional neuroimaging (fMRI) studies in human shed light on the cortical networks involved in disparity processing at a macroscopic level but with a different species. In this thesis, we propose to use an fMRI approach in macaque to bridge the gap between single-unit and fMRI recordings conducted in the non-human and human primate brain, respectively, by allowing direct comparisons between the two species. More specifically, we focused on the temporal and spatial processing of binocular disparities at the cortical but also at the perceptual level. Investigating cortical activity in response to motion-in-depth, we could show for the first time that 1) there is a dedicated network in macaque that comprises areas beyond the MT cluster and its surroundings and that 2) there are homologies with the human network involved in processing very similar stimuli. In a second study, we tried to establish a link between perceptual biases that reflect statistical regularities in the three-dimensional visual environment and cortical activity, by investigating whether such biases exist and can be related to specific responses at a macroscopic level. We found stronger activity for the stimulus reflecting natural statistics in one subject, demonstrating a potential influence of spatial regularities on the cortical activity. Further work is needed to firmly conclude about such a link. Nonetheless, we robustly confirmed the existence of a vast cortical network responding to correlated disparities in the macaque brain. Finally, we could measure for the first time retinal corresponding points on the vertical meridian of a macaque subject performing a behavioural task (forced-choice procedure) and compare it to the data we also collected in several human observers with the very same protocol. In the discussion sections, we showed how these findings open the door to varied perspectives

The human visual ability to perceive depth looks like a puzzle. We perceive three-dimensional spatial information quickly and efficiently by using the binocular stereopsis of our eyes and, what is mote important the learning of the most common objects which we achieved through living. Nowadays, modelling the behaviour of our brain is a fiction, that is why the huge problem of 3D perception and further, interpretation is split into a sequence of easier problems. A lot of research is involved in robot vision in order to obtain 3D information of the surrounded scene. Most of this research is based on modelling the stereopsis of humans by using two cameras as if they were two eyes. This method is known as stereo vision and has been widely studied in the past and is being studied at present, and a lot of work will be surely done in the future. This fact allows us to affirm that this topic is one of the most interesting ones in computer vision.<br/><br/>The stereo vision principle is based on obtaining the three dimensional position of an object point from the position of its projective points in both camera image planes. However, before inferring 3D information, the mathematical models of both cameras have to be known. This step is known as camera calibration and is broadly describes in the thesis. Perhaps the most important problem in stereo vision is the determination of the pair of homologue points in the two images, known as the correspondence problem, and it is also one of the most difficult problems to be solved which is currently investigated by a lot of researchers. The epipolar geometry allows us to reduce the correspondence problem. An approach to the epipolar geometry is describes in the thesis. Nevertheless, it does not solve it at all as a lot of considerations have to be taken into account. As an example we have to consider points without correspondence due to a surface occlusion or simply due to a projection out of the camera scope.<br/>The interest of the thesis is focused on structured light which has been considered as one of the most frequently used techniques in order to reduce the problems related lo stereo vision. Structured light is based on the relationship between a projected light pattern its projection and an image sensor. The deformations between the pattern projected into the scene and the one captured by the camera, permits to obtain three dimensional information of the illuminated scene. This technique has been widely used in such applications as: 3D object reconstruction, robot navigation, quality control, and so on. Although the projection of regular patterns solve the problem of points without match, it does not solve the problem of multiple matching, which leads us to use hard computing algorithms in order to search the correct matches.<br/>In recent years, another structured light technique has increased in importance. This technique is based on the codification of the light projected on the scene in order to be used as a tool to obtain an unique match. Each token of light is imaged by the camera, we have to read the label (decode the pattern) in order to solve the correspondence problem. The advantages and disadvantages of stereo vision against structured light and a survey on coded structured light are related and discussed. The work carried out in the frame of this thesis has permitted to present a new coded structured light pattern which solves the correspondence problem uniquely and robust. Unique, as each token of light is coded by a different word which removes the problem of multiple matching. Robust, since the pattern has been coded using the position of each token of light with respect to both co-ordinate axis. Algorithms and experimental results are included in the thesis. The reader can see examples 3D measurement of static objects, and the more complicated measurement of moving objects. The technique can be used in both cases as the pattern is coded by a single projection shot. Then it can be used in several applications of robot vision.<br/>Our interest is focused on the mathematical study of the camera and pattern projector models. We are also interested in how these models can be obtained by calibration, and how they can be used to obtained three dimensional information from two correspondence points. Furthermore, we have studied structured light and coded structured light, and we have presented a new coded structured light pattern. However, in this thesis we started from the assumption that the correspondence points could be well-segmented from the captured image. Computer vision constitutes a huge problem and a lot of work is being done at all levels of human vision modelling, starting from a)image acquisition; b) further image enhancement, filtering and processing, c) image segmentation which involves thresholding, thinning, contour detection, texture and colour analysis, and so on. The interest of this thesis starts in the next step, usually known as depth perception or 3D measurement.

Deep learning for safe autonomous transport is rapidly emerging. Fast and robust perception for autonomous vehicles will be crucial for future navigation in urban areas with high traffic and human interplay. Previous work focuses on extracting full image depth maps, or finding specific road features such as lanes. However, in urban environments lanes are not always present, and sensors such as LiDAR with 3D point clouds provide a quite sparse depth perception of road with demanding algorithmic approaches. In this thesis we derive a novel convolutional neural network that we call AutoNet. It is designed as an encoder-decoder network for pixel-wise depth estimation of an urban drivable free-space road, using only a monocular camera, and handled as a supervised regression problem. AutoNet is also constructed as a classification network to solely classify and segment the drivable free-space in real- time with monocular vision, handled as a supervised classification problem, which shows to be a simpler and more robust solution than the regression approach. We also implement the state of the art neural network ENet for comparison, which is designed for fast real-time semantic segmentation and fast inference speed. The evaluation shows that AutoNet outperforms ENet for every performance metrics, but shows to be slower in terms of frame rate. However, optimization techniques are proposed for future work, on how to advance the frame rate of the network while still maintaining the robustness and performance. All the training and evaluation is done on the Cityscapes dataset. New ground truth labels for road depth perception are created for training with a novel approach of fusing pre-computed depth maps with semantic labels. Data collection with a Scania vehicle is conducted, mounted with a monocular camera to test the final derived models. The proposed AutoNet shows promising state of the art performance in regards to road depth estimation as well as road classification.<br>Deep learning för säkra autonoma transportsystem framträder mer och mer inom forskning och utveckling. Snabb och robust uppfattning om miljön för autonoma fordon kommer att vara avgörande för framtida navigering inom stadsområden med stor trafiksampel. I denna avhandling härleder vi en ny form av ett neuralt nätverk som vi kallar AutoNet. Där nätverket är designat som en autoencoder för pixelvis djupskattning av den fria körbara vägytan för stadsområden, där nätverket endast använder sig av en monokulär kamera och dess bilder. Det föreslagna nätverket för djupskattning hanteras som ett regressions problem. AutoNet är även konstruerad som ett klassificeringsnätverk som endast ska klassificera och segmentera den körbara vägytan i realtid med monokulärt seende. Där detta är hanterat som ett övervakande klassificerings problem, som även visar sig vara en mer simpel och mer robust lösning för att hitta vägyta i stadsområden. Vi implementerar även ett av de främsta neurala nätverken ENet för jämförelse. ENet är utformat för snabb semantisk segmentering i realtid, med hög prediktions- hastighet. Evalueringen av nätverken visar att AutoNet utklassar ENet i varje prestandamätning för noggrannhet, men visar sig vara långsammare med avseende på antal bilder per sekund. Olika optimeringslösningar föreslås för framtida arbete, för hur man ökar nätverk-modelens bildhastighet samtidigt som man behåller robustheten.All träning och utvärdering görs på Cityscapes dataset. Ny data för träning samt evaluering för djupskattningen för väg skapas med ett nytt tillvägagångssätt, genom att kombinera förberäknade djupkartor med semantiska etiketter för väg. Datainsamling med ett Scania-fordon utförs även, monterad med en monoculär kamera för att testa den slutgiltiga härleda modellen. Det föreslagna nätverket AutoNet visar sig vara en lovande topp-presterande modell i fråga om djupuppskattning för väg samt vägklassificering för stadsområden.

The recent emergence of time-of-flight cameras has opened up new possibilities in the world of computer vision. These compact sensors, capable of recording the depth of a scene in real-time, are very advantageous in many applications, such as scene or object reconstruction. This thesis first addresses the problem of fusing depth data with color images. A complete process to combine a time-of-flight camera with a color camera is described and its accuracy is evaluated. The results show that a satisfying precision is reached and that the step of calibration is very important. The second part of the work consists of applying super-resolution techniques to the time-of-flight camera in order to improve its low resolution. Different types of super-resolution algorithms exist but this thesis focuses on the combination of multiple shifted depth maps. The proposed framework is made of two steps: registration and reconstruction. Different methods for each step are tested and compared according to the improvements reached in term of level of details, sharpness and noise reduction. The results obtained show that Lucas-Kanade performs the best for the registration and that a non-uniform interpolation gives the best results in term of reconstruction. Finally, a few suggestions are made about future work and extensions for our solutions.

La percepció per visió es millorada quan es pot gaudir d'un camp de visió ampli. Aquesta tesi es concentra en la percepció visual de la profunditat amb l'ajuda de càmeres omnidireccionals. La percepció 3D s'obté generalment en la visió per computadora utilitzant configuracions estèreo amb el desavantatge del cost computacional elevat a l'hora de buscar els elements visuals comuns entre les imatges. La solució que ofereix aquesta tesi és l'ús de la llum estructurada per resoldre el problema de relacionar les correspondències.<br/>S'ha realitzat un estudi sobre els sistemes de visió omnidireccional. S'han avaluat vàries configuracions estèreo i s'ha escollit la millor. Els paràmetres del model són difícils de mesurar directament i, en conseqüència, s'ha desenvolupat una sèrie de mètodes de calibració.<br/>Els resultats obtinguts són prometedors i demostren que el sensor pot ésser utilitzat en aplicacions per a la percepció de la profunditat com serien el modelatge de l'escena, la inspecció de canonades, navegació de robots, etc.<br>Vision perception is enhanced when a large field of view is available. This thesis is focused on the visual perception of depth by means of omnidirectional cameras. The 3D sensing is obtained in computer vision by means of stereo configurations with the drawback of feature matching between images. The solution offered in this dissertation uses structured light projection for solving the matching problem. <br/>First, a survey on omnidirectional vision systems was realized. Then, the sensor design was addressed and the particular stereo configuration of the proposed sensor was decided. An accurate model is obtained by a careful study of both components of the sensor. The model parameters are measured by a set of calibration methods.<br/>The results obtained are encouraging and prove that the sensor can be used in depth perception applications such as scene modeling, pipe inspections, robot navigation, etc.

We explore the relative utility of shape from shading and binocular disparity for depth perception. Ray-traced images either featured a smooth surface illuminated from above (shading-only) or were defined by small dots (disparity-only). Observers judged which of a pair of smoothly curved convex objects had most depth. The shading cue was around half as reliable as the rich disparity information for depth discrimination. Shading- and disparity-defined cues where combined by placing dots in the stimulus image, superimposed upon the shaded surface, resulting in veridical shading and binocular disparity. Independently varying the depth delivered by each channel allowed creation of conflicting disparity-defined and shading-defined depth. We manipulated the reliability of the disparity information by adding disparity noise. As noise levels in the disparity channel were increased, perceived depths and variances shifted toward those of the now more reliable shading cue. Several different models of cue combination were applied to the data. Perceived depths and variances were well predicted by a classic maximum likelihood estimator (MLE) model of cue integration, for all but one observer. We discuss the extent to which MLE is the most parsimonious model to account for observer performance.

M. Ing.<br>The process of extracting depth information from multiple two-dimensional images taken of the same scene is known as stereo vision. It is of central importance to the field of machine vision as it is a low level task required for many higher level applications. The past few decades has witnessed the development of hundreds of different stereo vision algorithms. This has made it difficult to classify and compare the various approaches to the problem. In this research we provide an overview of the types of approaches that exist to solve the problem of stereo vision. We focus on a specific subset of algorithms, known as local stereo algorithms. Our goal is to critically analyse and compare a representative sample of local stereo algorithm in terms of both speed and accuracy. We also divide the algorithms into discrete interchangeable components and experiment to determine the effect that each of the alternative components has on an algorithm’s speed and accuracy. We investigate even further to quantify and analyse the effect of various design choices within specific algorithm components. Finally we assemble all of the knowledge gained through the experimentation to compose and optimise a novel algorithm. The experimentation highlighted the fact that by far the most important component of a local stereo algorithm is the manner in which it aggregates matching costs. All of the top performing local stereo algorithms dynamically define the shape of the windows over which the matching costs are aggregated. This is done in a manner that aims to only include pixels in a window that is likely to be at the same depth as the depth of the centre pixel of the window. Since the depth is unknown, the cost aggregation techniques use colour and proximity information to best guess whether pixels are at the same depth when defining the shape of the aggregation windows. Local stereo algorithms are usually less accurate than global methods but they are supposed to be faster and more parallelisable. These cost aggregation techniques result in very accurate depth estimates but unfortunately they are also very expensive computationally. We believe the focus of local stereo algorithm development should be speed. Using the experimental results we developed an algorithm that achieves accuracies in the same order of magnitude as the state-of-the-art algorithms while reducing the computation time by over 50%.

Yes<br>Purpose: Two binocular sources of information serve motion-in-depth (MID) perception: changes in disparity over time (CD), and interocular velocity differences (IOVD). While CD requires the computation of small spatial disparities, IOVD could be computed from a much lower-resolution signal. IOVD signals therefore might still be available under conditions of binocular vision impairment (BVI) with limited or no stereopsis, e.g. amblyopia. Methods: Sensitivity to CD and IOVD was measured in adults who had undergone therapy to correct optical misalignment or amblyopia in childhood (n=16), as well as normal vision controls with good stereoacuity (n=8). Observers discriminated the interval containing a smoothly-oscillating MID “test” stimulus from a “control” stimulus in a two-interval forced choice (2IFC) paradigm. Results: Of the BVI observers with no static stereoacuity (n=9), one displayed evidence for sensitivity to IOVD only, while there was otherwise no sensitivity for either CD or IOVD in the group. Generally, BVI observers with measurable stereoacuity (n=7) displayed a pattern resembling the control group: showing a similar sensitivity for both cues. A neutral-density (ND) filter placed in front of the fixing eye in a subset of BVI observers did not improve performance. Conclusions: In one BVI observer there was preserved sensitivity to IOVD but not CD, though overall only those BVI observers with at least gross stereopsis were able to detect disparity-based or velocity-based cues to MID. The results imply that these logically distinct information sources are somehow coupled, and in some cases BVI observers with no stereopsis may still retain sensitivity to IOVD.<br>UK Biotechnology and Biological 498 Sciences Research Council (BBSRC): BB/M002543/1 (Alex R. Wade) BB/M001660/1 (Julie 499 M. Harris) and BB/M001210/1 (Marina Bloj)

AIM: To determine the test-retest reliability of the Randot stereoacuity test when used as part of vision screening in schools. METHODS: Randot stereoacuity (graded-circles) and logMAR visual acuity measures were gathered in an unselected sample of 139 children (aged 4-12, mean 8.1+/-2.1 years) in two schools. Randot testing was repeated on two occasions (average interval between successive tests 8 days, range: 1-21 days). Three Randot scores were obtained in 97.8% of children. RESULTS: Randot stereoacuity improved by an average of one plate (ie, one test level) on repeat testing but was little changed when tested on the third occasion. Within-subject variability was up to three test levels on repeat testing. When stereoacuity was categorised as 'fine', 'intermediate' or 'coarse', the greatest variability was found among younger children who exhibited 'intermediate' or 'coarse'/nil stereopsis on initial testing. Whereas 90.8% of children with 'fine' stereopsis (</=50 arc-seconds) on the first test exhibited 'fine' stereopsis on both subsequent tests, only approximately 16% of children with 'intermediate' (>50 but </=140 arc-seconds) or 'coarse'/nil (>/=200 arc-seconds) stereoacuity on initial testing exhibited stable test results on repeat testing. CONCLUSIONS: Children exhibiting abnormal stereoacuity on initial testing are very likely to exhibit a normal result when retested. The value of a single, abnormal Randot graded-circles stereoacuity measure from school screening is therefore questionable.

Indiana University-Purdue University Indianapolis (IUPUI)<br>Obstacle detection, avoidance and path finding for autonomous vehicles requires precise information of the vehicle’s system environment for faultless navigation and decision making. As such vision and depth perception sensors have become an integral part of autonomous vehicles in the current research and development of the autonomous industry. The advancements made in vision sensors such as radars, Light Detection And Ranging (LIDAR) sensors and compact high resolution cameras is encouraging, however individual sensors can be prone to error and misinformation due to environmental factors such as scene illumination, object reflectivity and object transparency. The application of sensor fusion in a system, by the utilization of multiple sensors perceiving similar or relatable information over a network, is implemented to provide a more robust and complete system information and minimize the overall perceived error of the system. 3D LIDAR and monocular camera are the most commonly utilized vision sensors for the implementation of sensor fusion. 3D LIDARs boast a high accuracy and resolution for depth capturing for any given environment and have a broad range of applications such as terrain mapping and 3D reconstruction. Despite 3D LIDAR being the superior sensor for depth, the high cost and sensitivity to its environment make it a poor choice for mid-range application such as autonomous rovers, RC cars and robots. 2D LIDARs are more affordable, easily available and have a wider range of applications than 3D LIDARs, making them the more obvious choice for budget projects. The primary objective of this thesis is to implement a smart and robust sensor fusion system using 2D LIDAR and a stereo depth camera to capture depth and color information of an environment. The depth points generated by the LIDAR are fused with the depth map generated by the stereo camera by a Fuzzy system that implements smart fusion and corrects any gaps in the depth information of the stereo camera. The use of Fuzzy system for sensor fusion of 2D LIDAR and stereo camera is a novel approach to the sensor fusion problem and the output of the fuzzy fusion provides higher depth confidence than the individual sensors provide. In this thesis, we will explore the multiple layers of sensor and data fusion that have been applied to the vision system, both on the camera and lidar data individually and in relation to each other. We will go into detail regarding the development and implementation of fuzzy logic based fusion approach, the fuzzification of input data and the method of selection of the fuzzy system for depth specific fusion for the given vision system and how fuzzy logic can be utilized to provide information which is vastly more reliable than the information provided by the camera and LIDAR separately

捕捉攝像機運動和重建攝像機成像場景深度圖的測定是在計算機視覺和機器任務包括可視化控制和自主導航是非常重要。在執行上述任務時，一個攝像機（或攝像機群組）通常安裝在機器的執行端部。攝像機和執行端部之間的手眼校準在視覺控制的正常操作中是不可缺少的。同樣，在對於需要使用多個攝像機的情况下，它們的相對幾何關係也是對各種計算機視覺應用來說也是非常重要。<br>攝像機和場景的相對運動通常產生出optical flow。問題的困難主要在於，在直接觀察視頻中的optical flow通常不是完全由運動誘導出的optical flow，而只是它的一部分。這個部分就是空間圖像等光線輪廓的正交。這部分的流場被稱為normal flow。本論文提出直接利用normal flow，而不是由normal flow引申出的optical flow，去解決以下的問題：尋找攝像機運動，場景深度圖和手眼校準。這種方法有許多顯著的貢獻，它不需引申流場，進而不要求平滑的成像場景。跟optical flow相反，normal flow不需要複雜的優化處理程序去解決流場不連續性的問題，這種技術一般是需要用大量的計算量。這也打破了傳統攝像機運動與場景深度之間的問題，在沒有預先知道不連續位置的情況下也可找出攝像機的運動。這篇論提出了幾個直接方法運用在三種不同類型的視覺系統，分別是單個攝像機，雙攝像機和多個攝像機，去找出攝像機的運動。<br>本論文首先提通過Apparent Flow 正深度 (AFPD) 約束去利用所有觀察到的normal flow去找出單個攝像機的運動參數。AFPD約束是利用一個優化問題來估計運動參數。一個反复由粗到細雙重約束的投票框架能使AFPD約束尋找出運動參數。<br>由於有限的視頻採樣率，normal flow在提取方向比其幅度部分更準確。本論文提出了兩個約束條件：一個是Apparent Flow方向（AFD）的約束，另外一個是Apparent Flow 幅度（AFM）的約束去尋找運動參數。第一個約束本身是作為一個線性不等式系統去約束運動方向的參數，第二個是利用所有圖像位置的旋轉幅度的統一性去進一步限制運動參數。一個兩階段從粗到細的約束框架能使AFD及AFM約束尋找出運動參數。<br>然而，如果沒有optical flow，normal flow是唯一的原始資料，它通常遭受到有限影像分辨率和有限視頻採樣率的問題而產生出錯誤。本文探討了這個問題的補救措施，方法是把一些攝像機併在一起，形成一個近似球形的攝像機，以增加成像系統的視野。有了一個加寬視野，normal flow的數量可更大，這可以用來抵銷normal flow在每個成像點的提取錯誤。更重要的是，攝像頭的平移和旋轉運動方向可以透過Apparent Flow分離 (AFS) 約束 及 延伸Apparent Flow分離 (EAFS) 約束來獨立估算。<br>除了使用單攝像機或球面成像系統之外，立體視覺成像系統提供了其它的視覺線索去尋找攝像機在沒有被任意縮放大小的平移運動和深度圖。傳統的立體視覺方法是確定在兩個輸入圖像特徵的對應。然而，對應的建立是非常困難。本文探討了兩個直接方法來恢復完整的攝像機運動，而沒有需要利用一對影像明確的點至點對應。第一種方法是利用AFD和AFM約束伸延到立體視覺系統，並提供了一個穩定的幾何方法來確定平移運動的幅度。第二個方法需要利用有一個較大的重疊視場，以提供一個不需反覆計算的closed-form算法。一旦確定了運動參數，深度圖可以沒有任何困難地重建。從normal flow產生的深度圖一般是以稀疏的形式存在。我們可以通過擴張深度圖，然後利用它作為在常見的TV-L₁框架的初始估計。其結果不僅有一個更好的重建性能，也產生出更快的運算時間。<br>手眼校準通常是基於像圖特徵對應。本文提出一個替代方法，是從動態攝像系統產生的normal flow來做自我校準。為了使這個方法有更強防備噪音的能力，策略是使用normal flow的流場方向去尋找手眼幾何的方向部份。偏離點及部分的手眼幾何可利用normal flow固有的流場屬性去尋找。最後完整的手眼幾何可使用穩定法來變得更可靠。手眼校準還可以被用來確定多個攝像機的相對幾何關係，而不需要求它們有重疊的視場。<br>Determination of general camera motion and reconstructing depth map from a captured video of the imaged scene relative to a camera is important for computer vision and various robotics tasks including visual control and autonomous navigation. A camera (or a cluster of cameras) is usually mounted on the end-effector of a robot arm when performing the above tasks. The determination of the relative geometry between the camera frame and the end-effector frame which is commonly referred as hand-eye calibration is essential to proper operation in visual control. Similarly, determining the relative geometry of multiple cameras is also important to various applications requiring the use of multi-camera rig.<br>The relative motion between an observer and the imaged scene generally induces apparent flow in the video. The difficulty of the problem lies mainly in that the flow pattern directly observable in the video is generally not the full flow field induced by the motion, but only partial information of it, which is orthogonal to the iso-brightness contour of the spatial image intensity profile. The partial flow field is known as the normal flow field. This thesis addresses several important problems in computer vision: determination of camera motion, recovery of depth map, and performing hand-eye calibration from the apparent flow (normal flow) pattern itself in the video data directly but not from the full flow interpolated from the apparent flow. This approach has a number of significant contributions. It does not require interpolating the flow field and in turn does not demand the imaged scene to be smooth. In contrast to optical flow, no sophisticated optimization procedures that account for handling flow discontinuities are required, and such techniques are generally computational expensive. It also breaks the classical chicken-and-egg problem between scene depth and camera motion. No prior knowledge about the locations of the discontinuities is required for motion determination. In this thesis, several direct methods are proposed to determine camera motion using three different types of imaging systems, namely monocular camera, stereo camera, and multi-camera rig.<br>This thesis begins with the Apparent Flow Positive Depth (AFPD) constraint to determine the motion parameters using all observable normal flows from a monocular camera. The constraint presents itself as an optimization problem to estimate the motion parameters. An iterative process in a constrained dual coarse-to-fine voting framework on the motion parameter space is used to exploit the constraint.<br>Due to the finite video sampling rate, the extracted normal flow field is generally more accurate in direction component than its magnitude part. This thesis proposes two constraints: one related to the direction component of the normal flow field - the Apparent Flow Direction (AFD) constraint, and the other to the magnitude component of the field - the Apparent Flow Magnitude (AFM) constraint, to determine motion. The first constraint presents itself as a system of linear inequalities to bind the direction of motion parameters; the second one uses the globality of rotational magnitude to all image positions to constrain the motion parameters further. A two-stage iterative process in a coarse-to-fine framework on the motion parameter space is used to exploit the two constraints.<br>Yet without the need of the interpolation step, normal flow is only raw information extracted locally that generally suffers from flow extraction error arisen from finiteness of the image resolution and video sampling rate. This thesis explores a remedy to the problem, which is to increase the visual field of the imaging system by fixating a number of cameras together to form an approximate spherical eye. With a substantially widened visual field, the normal flow data points would be in a much greater number, which can be used to combat the local flow extraction error at each image point. More importantly, the directions of translation and rotation components in general motion can be separately estimated with the use of the novel Apparent Flow Separation (AFS) and Extended Apparent Flow Separation (EAFS) constraints.<br>Instead of using a monocular camera or a spherical imaging system, stereo vision contributes another visual clue to determine magnitude of translation and depth map without the problem of arbitrarily scaling of the magnitude. The conventional approach in stereo vision is to determine feature correspondences across the two input images. However, the correspondence establishment is often difficult. This thesis explores two direct methods to recover the complete camera motion from the stereo system without the explicit point-to-point correspondences matching. The first method extends the AFD and AFM constraints to stereo camera, and provides a robust geometrical method to determine translation magnitude. The second method which requires the stereo image pair to have a large overlapped field of view provides a closed-form solution, requiring no iterative computation. Once the motion parameters are here, depth map can be reconstructed without any difficulty. The depth map resulted from normal flows is generally sparse in nature. We can interpolate the depth map and then utilizing it as an initial estimate in a conventional TV-L₁ framework. The result is not only a better reconstruction performance, but also a faster computation time.<br>Calibration of hand-eye geometry is usually based on feature correspondences. This thesis presents an alternative method that uses normal flows generated from an active camera system to perform self-calibration. In order to make the method more robust to noise, the strategy is to use the direction component of the flow field which is more noise-immune to recover the direction part of the hand-eye geometry first. Outliers are then detected using some intrinsic properties of the flow field together with the partially recovered hand-eye geometry. The final solution is refined using a robust method. The method can also be used to determine the relative geometry of multiple cameras without demanding overlap in their visual fields.<br>Detailed summary in vernacular field only.<br>Detailed summary in vernacular field only.<br>Detailed summary in vernacular field only.<br>Detailed summary in vernacular field only.<br>Detailed summary in vernacular field only.<br>Detailed summary in vernacular field only.<br>Detailed summary in vernacular field only.<br>Hui, Tak Wai.<br>Thesis (Ph.D.)--Chinese University of Hong Kong, 2013.<br>Includes bibliographical references (leaves 159-165).<br>Abstracts in English and Chinese.<br>Acknowledgements --- p.i<br>Abstract --- p.ii<br>Lists of Figures --- p.xiii<br>Lists of Tables --- p.xix<br>Chapter Chapter 1 --- Introduction --- p.1<br>Chapter 1.1 --- Background --- p.1<br>Chapter 1.2 --- Motivation --- p.4<br>Chapter 1.3 --- Research Objectives --- p.6<br>Chapter 1.4 --- Thesis Outline --- p.7<br>Chapter Chapter 2 --- Literature Review --- p.10<br>Chapter 2.1 --- Introduction --- p.10<br>Chapter 2.2 --- Recovery of Optical Flows --- p.10<br>Chapter 2.3 --- Egomotion Estimation Based on Optical Flow Field --- p.14<br>Chapter 2.3.1 --- Bilinear Constraint --- p.14<br>Chapter 2.3.2 --- Subspace Method --- p.15<br>Chapter 2.3.3 --- Partial Search Method --- p.16<br>Chapter 2.3.4 --- Fixation --- p.17<br>Chapter 2.3.5 --- Region Alignment --- p.17<br>Chapter 2.3.6 --- Linearity and Divergence Properties of Optical Flows --- p.18<br>Chapter 2.3.7 --- Constraint Lines and Collinear Points --- p.18<br>Chapter 2.3.8 --- Multi-Camera Rig --- p.19<br>Chapter 2.3.9 --- Discussion --- p.21<br>Chapter 2.4 --- Determining Egomotion Using Direct Methods --- p.22<br>Chapter 2.4.1 --- Introduction --- p.22<br>Chapter 2.4.2 --- Classical Methods --- p.23<br>Chapter 2.4.3 --- Pattern Matching --- p.24<br>Chapter 2.4.4 --- Search Subspace Method --- p.25<br>Chapter 2.4.5 --- Histogram-Based Method --- p.26<br>Chapter 2.4.6 --- Multi-Camera Rig --- p.26<br>Chapter 2.4.7 --- Discussion --- p.27<br>Chapter 2.5 --- Determining Egomotion Using Feature Correspondences --- p.28<br>Chapter 2.6 --- Hand-Eye Calibration --- p.30<br>Chapter 2.7 --- Summary --- p.31<br>Chapter Chapter 3 --- Determining Motion from Monocular Camera Using Merely the Positive Depth Constraint --- p.32<br>Chapter 3.1 --- Introduction --- p.32<br>Chapter 3.2 --- Related Works --- p.33<br>Chapter 3.3 --- Background --- p.34<br>Chapter 3.3 --- Apparent Flow Positive Depth (AFPD) Constraint --- p.39<br>Chapter 3.4 --- Numerical Solution to AFPD Constraint --- p.40<br>Chapter 3.5 --- Constrained Coarse-to-Fine Searching --- p.40<br>Chapter 3.6 --- Experimental Results --- p.43<br>Chapter 3.7 --- Conclusion --- p.47<br>Chapter Chapter 4 --- Determining Motion from Monocular Camera Using Direction and Magnitude of Normal Flows Separately --- p.48<br>Chapter 4.1 --- Introduction --- p.48<br>Chapter 4.2 --- Related Works --- p.50<br>Chapter 4.3 --- Apparent Flow Direction (AFD) Constraint --- p.51<br>Chapter 4.3.1 --- The Special Case: Pure Translation --- p.51<br>Chapter 4.3.1.1 --- Locus of Translation Using Full Flow as a Constraint --- p.51<br>Chapter 4.3.1.2 --- Locus of Translation Using Normal Flow as a Constraint --- p.53<br>Chapter 4.3.2 --- The Special Case: Pure Rotation --- p.54<br>Chapter 4.3.2.1 --- Locus of Rotation Using Full Flow as a Constraint --- p.54<br>Chapter 4.3.2.2 --- Locus of Rotation Using Normal Flow as a Constraint --- p.54<br>Chapter 4.3.3 --- Solving the System of Linear Inequalities for the Two Special Cases --- p.55<br>Chapter 4.3.5 --- Ambiguities of AFD Constraint --- p.59<br>Chapter 4.4 --- Apparent Flow Magnitude (AFM) Constraint --- p.60<br>Chapter 4.5 --- Putting the Two Constraints Together --- p.63<br>Chapter 4.6 --- Experimental Results --- p.65<br>Chapter 4.6.1 --- Simulation --- p.65<br>Chapter 4.6.2 --- Video Data --- p.67<br>Chapter 4.6.2.1 --- Pure Translation --- p.67<br>Chapter 4.6.2.2 --- General Motion --- p.68<br>Chapter 4.7 --- Conclusion --- p.72<br>Chapter Chapter 5 --- Determining Motion from Multi-Cameras with Non-Overlapping Visual Fields --- p.73<br>Chapter 5.1 --- Introduction --- p.73<br>Chapter 5.2 --- Related Works --- p.75<br>Chapter 5.3 --- Background --- p.76<br>Chapter 5.3.1 --- Image Sphere --- p.77<br>Chapter 5.3.2 --- Planar Case --- p.78<br>Chapter 5.3.3 --- Projective Transformation --- p.79<br>Chapter 5.4 --- Constraint from Normal Flows --- p.80<br>Chapter 5.5 --- Approximation of Spherical Eye by Multiple Cameras --- p.81<br>Chapter 5.6 --- Recovery of Motion Parameters --- p.83<br>Chapter 5.6.1 --- Classification of a Pair of Normal Flows --- p.84<br>Chapter 5.6.2 --- Classification of a Triplet of Normal Flows --- p.86<br>Chapter 5.6.3 --- Apparent Flow Separation (AFS) Constraint --- p.87<br>Chapter 5.6.3.1 --- Constraint to Direction of Translation --- p.87<br>Chapter 5.6.3.2 --- Constraint to Direction of Rotation --- p.88<br>Chapter 5.6.3.3 --- Remarks about the AFS Constraint --- p.88<br>Chapter 5.6.4 --- Extension of Apparent Flow Separation Constraint (EAFS) --- p.89<br>Chapter 5.6.4.1 --- Constraint to Direction of Translation --- p.90<br>Chapter 5.6.4.2 --- Constraint to Direction of Rotation --- p.92<br>Chapter 5.6.5 --- Solution to the AFS and EAFS Constraints --- p.94<br>Chapter 5.6.6 --- Apparent Flow Magnitude (AFM) Constraint --- p.96<br>Chapter 5.7 --- Experimental Results --- p.98<br>Chapter 5.7.1 --- Simulation --- p.98<br>Chapter 5.7.2 --- Real Video --- p.103<br>Chapter 5.7.2.1 --- Using Feature Correspondences --- p.108<br>Chapter 5.7.2.2 --- Using Optical Flows --- p.108<br>Chapter 5.7.2.3 --- Using Direct Methods --- p.109<br>Chapter 5.8 --- Conclusion --- p.111<br>Chapter Chapter 6 --- Motion and Shape from Binocular Camera System: An Extension of AFD and AFM Constraints --- p.112<br>Chapter 6.1 --- Introduction --- p.112<br>Chapter 6.2 --- Related Works --- p.112<br>Chapter 6.3 --- Recovery of Camera Motion Using Search Subspaces --- p.113<br>Chapter 6.4 --- Correspondence-Free Stereo Vision --- p.114<br>Chapter 6.4.1 --- Determination of Full Translation Using Two 3D Lines --- p.114<br>Chapter 6.4.2 --- Determination of Full Translation Using All Normal Flows --- p.115<br>Chapter 6.4.3 --- Determination of Full Translation Using a Geometrical Method --- p.117<br>Chapter 6.5 --- Experimental Results --- p.119<br>Chapter 6.5.1 --- Synthetic Image Data --- p.119<br>Chapter 6.5.2 --- Real Scene --- p.120<br>Chapter 6.6 --- Conclusion --- p.122<br>Chapter Chapter 7 --- Motion and Shape from Binocular Camera System: A Closed-Form Solution for Motion Determination --- p.123<br>Chapter 7.1 --- Introduction --- p.123<br>Chapter 7.2 --- Related Works --- p.124<br>Chapter 7.3 --- Background --- p.125<br>Chapter 7.4 --- Recovery of Camera Motion Using a Linear Method --- p.126<br>Chapter 7.4.1 --- Region-Correspondence Stereo Vision --- p.126<br>Chapter 7.3.2 --- Combined with Epipolar Constraints --- p.127<br>Chapter 7.4 --- Refinement of Scene Depth --- p.131<br>Chapter 7.4.1 --- Using Spatial and Temporal Constraints --- p.131<br>Chapter 7.4.2 --- Using Stereo Image Pairs --- p.134<br>Chapter 7.5 --- Experiments --- p.136<br>Chapter 7.5.1 --- Synthetic Data --- p.136<br>Chapter 7.5.2 --- Real Image Sequences --- p.137<br>Chapter 7.6 --- Conclusion --- p.143<br>Chapter Chapter 8 --- Hand-Eye Calibration Using Normal Flows --- p.144<br>Chapter 8.1 --- Introduction --- p.144<br>Chapter 8.2 --- Related Works --- p.144<br>Chapter 8.3 --- Problem Formulation --- p.145<br>Chapter 8.3 --- Model-Based Brightness Constraint --- p.146<br>Chapter 8.4 --- Hand-Eye Calibration --- p.147<br>Chapter 8.4.1 --- Determining the Rotation Matrix R --- p.148<br>Chapter 8.4.2 --- Determining the Direction of Position Vector T --- p.149<br>Chapter 8.4.3 --- Determining the Complete Position Vector T --- p.150<br>Chapter 8.4.4 --- Extrinsic Calibration of a Multi-Camera Rig --- p.151<br>Chapter 8.5 --- Experimental Results --- p.151<br>Chapter 8.5.1 --- Synthetic Data --- p.151<br>Chapter 8.5.2 --- Real Image Data --- p.152<br>Chapter 8.6 --- Conclusion --- p.153<br>Chapter Chapter 9 --- Conclusion and Future Work --- p.154<br>Related Publications --- p.158<br>Bibliography --- p.159<br>Appendix --- p.166<br>Chapter A --- Apparent Flow Direction Constraint --- p.166<br>Chapter B --- Ambiguity of AFD Constraint --- p.168<br>Chapter C --- Relationship between the Angle Subtended by any two Flow Vectors in Image Plane and the Associated Flow Vectors in Image Sphere --- p.169

PURPOSE: Gait during obstacle negotiation is adapted in visually normal subjects whose vision is temporarily and unilaterally blurred or occluded. This study was conducted to examine whether gait parameters in individuals with long-standing deficient stereopsis are similarly adapted. METHODS: Twelve visually normal subjects and 16 individuals with deficient stereopsis due to amblyopia and/or its associated conditions negotiated floor-based obstacles of different heights (7-22 cm). Trials were conducted during binocular viewing and monocular occlusion. Analyses focused on foot placement before the obstacle and toe clearance over it. RESULTS: Across all viewing conditions, there were significant group-by-obstacle height interactions for toe clearance (P < 0.001), walking velocity (P = 0.003), and penultimate step length (P = 0.022). Toe clearance decreased (approximately 0.7 cm) with increasing obstacle height in visually normal subjects, but it increased (approximately 1.5 cm) with increasing obstacle height in the stereo-deficient group. Walking velocity and penultimate step length decreased with increasing obstacle height in both groups, but the reduction was more pronounced in stereo-deficient individuals. Post hoc analyses indicated group differences in toe clearance and penultimate step length when negotiating the highest obstacle (P < 0.05). CONCLUSIONS: Occlusion of either eye caused significant and similar gait changes in both groups, suggesting that in stereo-deficient individuals, as in visually normal subjects, both eyes contribute usefully to the execution of adaptive gait. Under monocular and binocular viewing, obstacle-crossing performance in stereo-deficient individuals was more cautious when compared with that of visually normal subjects, but this difference became evident only when the subjects were negotiating higher obstacles; suggesting that such individuals may be at greater risk of tripping or falling during everyday locomotion.

Estimation of depth within an imaged scene can be formulated as a stereo correspondence problem. Software solutions tend to be too slow for high frame rate (i.e. > 30 fps) performance. Hardware solutions can result in marked improvements. This thesis explores one such hardware implementation that generates dense binocular disparity estimates at frame rates of over 200 fps using a dynamic programming formulation (DPML) developed by Cox et. al. A highly parameterizable field programmable gate array implementation of this architecture demonstrates equivalent accuracy while executing at significantly higher frame rates to those of current approaches. Existing hardware implementations for dense disparity estimation often use sum of squared difference, sum of absolute difference or other similar algorithms that typically perform poorly in comparison to DPML. The presented system runs at 248 fps for a resolution of 320 x 240 pixels and disparity range of 128 pixels, a performance of 2.477 billion DPS.

D.Ed.<br>Stereopsis -- binocular depth perception is a visual function which falls within the ambit of the hyperacuities. The term, Hyperacuity, is one coined by Westheimer (1976) to describe thresholds of discrimination which cannot be explained on the basis of the optical components or sensory elements of the eyes alone. By implication such levels of discrimination are effected by higher levels of brain function. It is reasoned that an individual's stereoscopic hyperacuity should in some way relate to other measures of higher sensory and motor brain functions. In a school situation hyperacuity should relate to measures of intelligence, as well as scholastic and sporting achievement. The design and implementation of an experiment to test this premise forms the basis of this thesis. A literature review is reported of current knowledge relevant to this study together with a description of the stereoscopic testing instruments commonly available in clinical practice. A rationale for modifying these instruments and testing methods to suit the needs of this study is also included. This study exposes new knowledge about the process of static nearpoint stereopsis. This stereopsis proves to be a complex of diverse skills, which are significantly age-related and developmental in nature. These skills are seen to influence and be influenced by educational interventions. It may be concluded from this study that there is value in measuring stereopsis in more depth than has been done previously and that it is crucial to measure the speed of stereo performance in its own right in addition to the measures of stereoacuity. The study reveals significant differences of performance which relate to stereopsis in front as opposed to behind the plane of regard and also related to figure/ground contrast differences. The two non-stereoscopic tests and the six different stereoscopic tests described in this thesis prove to be highly discriminative and diagnostic with respect to age, grade level, I.Q., scholastic achievement and sporting ability.

PURPOSE: A recent study suggested that updated spectacles could increase fall rate in frail older people. The authors hypothesized that the increased risk may be due to changes in spectacle magnification. The present study was conducted to assess the effects of spectacle magnification on step negotiation. METHODS: Adaptive gait and visual function were measured in 10 older adults (mean age, 77.1 +/- 4.3 years) with the participants' optimal refractive correction and when blurred with +1.00, +2.00, -1.00, and -2.00 DS lenses. Adaptive gait measurements for the leading and trailing foot included foot position before the step, toe clearance of the step edge, and foot position on the step. Vision measurements included visual acuity, contrast sensitivity, and stereoacuity. RESULTS: The blur lenses led to equal decrements in visual acuity and stereoacuity for the +1.00 and -1.00 DS and the +2.00 and -2.00 DS lenses. However, they had very different effects on step negotiation compared with the optimal correction. Positive-blur lenses led to an increased distance of the feet from the step, increased vertical toe clearance and reduced distance of the leading heel position on the step. Negative lenses led to the opposite of these changes. CONCLUSIONS: The step negotiation changes did not mirror the effects of blur on vision, but were driven by the magnification changes of the lenses. Steps appear closer and larger with positive lenses and farther away and smaller with negative ones. Magnification is a likely explanation of the mobility problems some older adults have with updated spectacles and after cataract surgery.