View Selection with Geometric Uncertainty Modeling

Estimating positions of world points from features observed in images is a key problem in 3D reconstruction, image mosaicking,simultaneous localization and mapping and structure from motion. We consider a special instance in which there is a dominant ground plane $\mathcal{G}$ viewed from a parallel viewing plane $\mathcal{S}$ above it. Such instances commonly arise, for example, in aerial photography. Consider a world point $g \in \mathcal{G}$ and its worst case reconstruction uncertainty $\varepsilon(g,\mathcal{S})$ obtained by merging \emph{all} possible views of $g$ chosen from $\mathcal{S}$. We first show that one can pick two views $s_p$ and $s_q$ such that the uncertainty $\varepsilon(g,\{s_p,s_q\})$ obtained using only these two views is almost as good as (i.e. within a small constant factor of) $\varepsilon(g,\mathcal{S})$. Next, we extend the result to the entire ground plane $\mathcal{G}$ and show that one can pick a small subset of $\mathcal{S'} \subseteq \mathcal{S}$ (which grows only linearly with the area of $\mathcal{G}$) and still obtain a constant factor approximation, for every point $g \in \mathcal{G}$, to the minimum worst case estimate obtained by merging all views in $\mathcal{S}$. Finally, we present a multi-resolution view selection method which extends our techniques to non-planar scenes. We show that the method can produce rich and accurate dense reconstructions with a small number of views. Our results provide a view selection mechanism with provable performance guarantees which can drastically increase the speed of scene reconstruction algorithms. In addition to theoretical results, we demonstrate their effectiveness in an application where aerial imagery is used for monitoring farms and orchards

Alignment using an uncalibrated camera system

We describe a method for the visual control of a robotic system which does not require the formulation of an explicit calibration between image coordinates and the world coordinates. By extracting control information directly from the image, we free our technique from the errors normally associated with a fixed calibration. We attach a camera system to a robot such that the camera system and the robot's gripper rotate simultaneously. As the camera system rotates about the gripper's rotational axis, the circular path traced out by a point-like feature projects to an elliptical path in image space. We gather the projected feature points over part of a rotation and fit the gathered data to an ellipse. The distance from the rotational axis to the feature point in world space is proportional to the size of the generated ellipse. As the rotational axis gets closer to the feature, the feature's projected path will form smaller and smaller ellipses. When the rotational axis is directly above the object, the trajectory degenerates from an ellipse to a single point. We demonstrate the efficacy of the algorithm on the peg-in-hole problem

Task-Driven Video Collection

Vision systems are increasingly being deployed to perform complex surveillance tasks. While improved algorithms are being developed to perform these tasks, it is also important that data suitable for these algorithms be acquired - a non-trivial task in a dynamic and crowded scene viewed by multiple PTZ cameras. In this paper, we describe a multi-camera system that collects images and videos of moving objects in such scenes, subject to task constraints. The system constructs "task visibility intervals" that contain information about what can be sensed in future time intervals. Constructing these intervals requires prediction of future object motion and consideration of several factors such as object occlusion and camera control parameters. Using a plane-sweep algorithm, these atomic intervals can be combined to form multi-task intervals, during which a single camera can collect videos suitable for multiple tasks simultaneously. Although cameras can then be scheduled based on the constructed intervals, finding an optimal schedule is a typical NP-hard problem. Due to this, and the lack of exact future information in a dynamic environment, we propose several methods for fast camera scheduling that yield solutions within a small constant factor of optimal. Experimental results illustrate system capabilities for both real and more complicated simulated scenarios

Ordinal depth from SFM and its application in robust scene recognition

Ph.DDOCTOR OF PHILOSOPH

Setting "survivorship" in context : the role of everyday resources in adjusting to life after cancer treatment with curative intent

Background: The number of people living beyond cancer in the UK is rapidly increasing, and their supportive care needs are a pressing issue. Patients treated with curative intent move on to a self management pathway, which uses checklists to measure care requirements. Patients are expected to consider ways of addressing their own psychosocial needs. The focus of care on the quantification of needs and the cognitive strategies used to manage them fails to take the subtleties of patients’ social and material context into account. Research suggests that the self management agenda does not adequately acknowledge the challenges of day-to-day experiences of illness, and how people engage with their resources to adapt to life after treatment.Aim of the study: To explore how everyday social and material resources can be used to adapt to life in the year beyond cancer treatment with curative intent.Methods: In 2014–15, in-depth interviews were conducted with twenty-six people recently treated for breast, colorectal or prostate cancer (twenty using photo elicitation), followed by seventeen longitudinal interviews approximately six months later. Participants from a range of social backgrounds were recruited through clinics in Yorkshire and the Humber. The method of analysis was constructivist grounded theory.Findings: Treatment with curative intent is interpreted as turning a curve in life’s pathway, requiring gradual reorientation. This is shaped by three processes. In Responding to diagnosis and treatment, participants drew on past identities to reinforce their sense of self, and personalised care was crucial. In Using social resources for meaning-making, perspectives from the worlds of the family, clinic and workplace contributed to participants’ understanding of their situation, and the “survivor” label was rejected. Developing assets for recovery involved consolidating the meaning of their illness, negotiating personal change, and using material and environmental resources to regain control, create comfort and chase continuity.Conclusion: People with good prognoses have a unique outlook on adaptation after treatment. Finding ways of assessing the assets that people do have, rather than what they do not have, is a good starting point for follow-up care. Everyday resources can be used to address three key objectives in adaptation: control, comfort and continuity

Image-based visual servoing on planar objects of unknown shape

[Notes_IRSTEA]bibl. [Departement_IRSTEA]GEAPA [TR1_IRSTEA]42 - ALITECH / CAPORALInternational audienceThis paper proposes a way to achieve positioning tasks by visual servoing, for any orientation of the camera, when the desired image of the observed object cannot be precisely described. The object is assumed to be planar and motionless but no knowledge about its shape or pose is required. To simplify the problem, first, we treat the case of a threadlike object and then we show how our approach can be generalized to an object with three particular points. The control law is based on the use of 2d visual servoing and on an estimation of a 3d parameter. Experimental results relative to objects of unknown shape are given to validate the approach. In addition, an algorithm to estimate the depth between the object and the camera is provided which leads to the dimensions of the object

Depth Enhancement and Surface Reconstruction with RGB/D Sequence

Surface reconstruction and 3D modeling is a challenging task, which has been explored for decades by the computer vision, computer graphics, and machine learning communities. It is fundamental to many applications such as robot navigation, animation and scene understanding, industrial control and medical diagnosis. In this dissertation, I take advantage of the consumer depth sensors for surface reconstruction. Considering its limited performance on capturing detailed surface geometry, a depth enhancement approach is proposed in the first place to recovery small and rich geometric details with captured depth and color sequence. In addition to enhancing its spatial resolution, I present a hybrid camera to improve the temporal resolution of consumer depth sensor and propose an optimization framework to capture high speed motion and generate high speed depth streams. Given the partial scans from the depth sensor, we also develop a novel fusion approach to build up complete and watertight human models with a template guided registration method. Finally, the problem of surface reconstruction for non-Lambertian objects, on which the current depth sensor fails, is addressed by exploiting multi-view images captured with a hand-held color camera and we propose a visual hull based approach to recovery the 3D model