Abstract

In this paper we present a novel real time algorithm to sequentially solve a class of multi-view geometry problems. The triangulation problem is considered as study case. The problem concerns with the estimation of 3D point coordinates given its images as well as the matrices of the concern cameras used in the imaging process. The algorithm has direct application to real time systems like virtual reality, visual SLAM, and visual servoing. Application to visual servoing is considered in detail. Experiments have been carried out for the general triangulation problem as well as the application to visual servoing.

Abstract

In this paper, we present a novel boosted robot vision control algorithm. This method utilizes on-line boosting to produce a strong vision-based robot control starting from two weak algorithms. These weak methods are image-based and position-based visual servoing algorithms. The notion of weak and strong algorithms have been presented in the context of robot vision control. Appropriate error functions are defined for the weak algorithms to evaluate their suitability in the task. The integrated algorithm has superior performance both in image and Cartesian spaces. Experiments validate this claim.

Abstract

Biometrics traits such as fingerprints, hand geometry, face and voice verification provide a reliable alternative for identity verification and are gaining commercial and high user acceptibilty rate. Hand geometry based biometric verification has proven to be the most suitable and acceptable biometric trait for medium and low security application. Geometric measurements of the human hand have been used for identity authentication in a number of commercial systems. However not much research has been done in the area of selection of the optimal discriminating features for hand-gemetry based authentication system. In this paper, We argue that the biometric verification problem can be best posed as the single-class problem. We propose to apply Biased Discriminant Analysis and Nonparametric Discriminant Analysis in order to transform the features into a new space where the samples are well separated.

Abstract

Web search personalization has been well studied in the recent few years. Relevance feedback has been used in various ways to improve relevance of search results. In this paper, we propose a novel usage of relevance feedback to effectively model the process of query formulation and better characterize how a user relates his query to the document that he intends to retrieve using a noisy channel model. We model a user profile as the probabilities of translation of query to document in this noisy channel using the relevance feedback obtained from the user. The user profile thus learnt is applied in a re-ranking phase to rescore the search results retrieved using an underlying search engine. We evaluate our approach by conducting experiments using relevance feedback data collected from users using a popular search engine. The results have shown improvement over baseline, proving that our approach can be applied to personalization of web search. The experiments have also resulted in some valuable observations that learning these user profiles using snippets surrounding the results for a query gives better performance than learning from entire document collection

Abstract

The Oxford/IIIT team participated in the high-level feature extraction and interactive search tasks. A vision only approach was used for both tasks, with no use of the text or audio information. For the high-level feature extraction task, we used two different approaches, both based on a combination of visual features. One used a SVM classifier using a linear combination of kernels, the other used a random forest classifier. For both methods, we trained all high-level features using publicly available annotations [3]. The advantage of the random forest classifier is the speed of training and testing. In addition, for the people feature, we took a more targeted approach. We used a real-time face detector and an upper body detector, in both cases running on every frame. Our best performing submission, C OXVGG 1 1, which used a rank fusion of our random forest and SVM approach, achieved an mAP of 0.101 and was above the median for all but one feature. In the interactive search task, our team came third overall with an mAP of 0.158. The system used was identical to last year with the only change being a source of accurate upper body detections.

Abstract

Augmenting virtual objects between real objectsof a video poses a challenging problem, primarily because itnecessitates accurate knowledge of the scene’s depth. In thispaper, we propose an approach that generates an approximatedepth for every pixel in the vicinity of the virtual object, which weshow is enough to decide the ordering of objects in every image.We also draw a similarity between layered segmentation andaugmentation. We argue that augmentation only needs to knowwhich layers are in front of the object and which are behind it.Our algorithm makes effective use of object boundaries detectedin an image using image segmentation. The assumption thatthese boundaries correspond to depth discontinuities provides auseful simplification of the problem, sufficient to produce realisticresults. We use a combination of segmentation and featuredetection for sparse depth assignment. Results on challengingdata sets show the effectiveness of our approach

Abstract

This paper studies the problem of traversing a rough terrain by wheeled vehicles. Here rough terrain implies terrain which is geometrically not ideal. The criterion for mobility of a wheeled vehicle in any terrain is formally developed, providing insights into the mechanical structure requirements of the vehicle. A vehicle structure with an actively articulated suspension is found as a solution to improved rough terrain mobility. The contact forces of the vehicle with the surface being traversed are identified as the critical factor in determining the traversability of the surface. Hence a control strategy involving the control of the contact forces (normal and traction) is proposed. The key feature of the locomotion strategy, thus developed, is that it provides a solution involving dynamics of the main body for improving mobility in rough terrain.

Abstract

The algorithm presented in this paper is designed to be used in automated multi-sensor surveillance systems which require observation of targets in a bounded area to optimize the performance of the system. There have been many approaches which deal with multi-sensor tracking and observation, but there haven't been many which deal purely with targets detections i.e. each target needs to only be detected once. The metric used to gauge the performance of the system is percentage of targets detected among those that enter the area. Targets enter the area through source points on the side of the area according to Poisson distribution, the rate of entry is constant for all sources. The algorithm presented here uses target arrival information, sensor positions to generate an optimal motion strategy for the multi-sensor system every T time-steps i.e. every T time-steps, the probability of finding undetected targets is estimated, the optimal sensor paths for the next T time-steps are calculated. The algorithm performs robustly and optimally detecting around 80% of the targets that enter the area

Abstract

A methodology for real-time navigation of an all terrain vehicle capable of navigation between floors of a building mediated by a wireless sensor network is detailed in this paper. The map of the building is unknown to the robot. Sensor motes scattered in the building guide the robot to its desired destination. The guidance is in the form of specifying the next waypoint to the goal by the network. Local navigation between waypoints is achieved by a fuzzy logic based navigation system

Abstract

A cooperative methodology for collision avoidance of multiple wheeled robots, having respective goals to reach, is detailed in this paper. The paths executed by the robots are continuous not only in terms of positions reached by the robots but also in terms of their velocities. A navigation function is designed that, apart from taking into account goal reaching and avoidance behaviors, also implicitly captures cooperative behavior amongst robots by identifying spaces where collisions between robots tend to be minimized. A search in the joint space of linear and angular velocities of the robots results in selection of a linear and angular velocity tuple for each robot that minimizes the navigation function. Simulated results portray the efficacy of the methodology