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

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

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

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

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

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 approach to robust visual servoing. This method removes the feature tracking step from a typical visual servoing algorithm. We do not need correspondences of the features for deriving the control signal. This is achieved by modeling the image features as a Mixture of Gaussians in the current as well as desired images. Using Lyapunov theory, a control signal is derived to minimize a distance function between the two Gaussian mixtures. The distance function is given in a closed form, and its gradient can be efficiently computed and used to control the system. For simplicity, we first consider the 2D motion case. Then, the general case is presented by introducing the depth distribution of the features to control the six degrees of freedom. Experiments are conducted within a simulation framework to validate our proposed method.

Abstract

Video completion algorithms have concentrated on obtaining visually consistent solutions to fill-in the missing portions, without any emphasis on the physical correctness of the video. Resulting solutions thus use texture or image structure based cues and are limited in the situations they can handle. In this paper we take a model based signal processing approach to video completion [1]. Completion of the video is then defined as satisfying the given model by detecting and removing the error (selected parts of the video to be replaced). Given a probabilistic model, video completion then becomes an unsupervised learning algorithm with the input video giving a “noisy” version. Dense completion is the automatic inferencing of the “noise-less” or “true” video from the input. This approach finds a solution that satisfies visual coherence and is applicable to a wide variety of scenarios. We demonstrate the efficacy of our approach and its wide applicability using two scenarios.

Abstract

Fourier domain methods have had a long association with geometric vision. In this paper, we introduce Fourier domain methods into the field of visual servoing for the first time. We show how different properties of Fourier transforms may be used to address specific issues in traditional visual servoing methods, giving rise to algorithms that are more flexible. Specifically, we demonstrate how Fourier analysis may be used to obtain straight camera paths in the Cartesian space, do path following and correspondenceless visual servoing. Most importantly, by introducing Fourier techniques, we set a framework into which robust Fourier based geometry processing algorithms may be incorporated to address the various issues in servoing.

Abstract

Text classification techniques such as Bayesian classifiers have been proved to be giving as good or better results than much more sophisticated approaches to induction. These algorithms depend on the conditional independence of the attributes given the class and term based statistical representations of documents. However, one of the main shortcomings of such methods is that they largely disregard lexical semantics and, as a consequence, are not sufficiently robust with respect to variations in word usage. In this paper we investigate the use of word sense disambiguation and other lexical semantic based techniques to achieve consistent improvement with Bayesian classifiers. Finally, we discuss the test cases experimented with Rainbow-a standard Bayesian classifier.