Performance characterization of fundamental matrix estimation under image degradation

The fundamental matrix represents the epipolar geometry between two images. We describe an algorithm for simultaneously estimating the fundamental matrix and corresponding points automatically from the two images. The performance of this algorithm is then assessed as the images are degraded by JPEG lossy compression. A number of performance measures are proposed and evaluated over image pairs corresponding to different camera motions and scene types

Learning layered motion segmentations of video

We present an unsupervised approach for learning a layered representation of a scene from a video for motion segmentation. Our method is applicable to any video containing piecewise parametric motion. The learnt model is a composition of layers, which consist of one or more segments. The shape of each segment is represented using a binary matte and its appearance is given by the rgb value for each point belonging to the matte. Included in the model are the effects of image projection, lighting, and motion blur. Furthermore, spatial continuity is explicitly modeled resulting in contiguous segments. Unlike previous approaches, our method does not use reference frame(s) for initialization. The two main contributions of our method are: (i) A novel algorithm for obtaining the initial estimate of the model by dividing the scene into rigidly moving components using efficient loopy belief propagation; and (ii) Refining the initial estimate using α β-swap and α-expansion algorithms, which guarantee a strong local minima. Results are presented on several classes of objects with different types of camera motion, e.g. videos of a human walking shot with static or translating cameras. We compare our method with the state of the art and demonstrate significant improvements. © 2007 Springer Science+Business Media, LLC

Modelling and interpretation of architecture from several images

This paper describes the automatic acquisition of three dimensional architectural models from short image sequences. The approach is Bayesian and model based. Bayesian methods necessitate the formulation of a prior distribution; however designing a generative model for buildings is a difficult task. In order to overcome this a building is described as a set of walls together with a ‘Lego’ kit of parameterised primitives, such as doors or windows. A prior on wall layout, and a prior on the parameters of each primitive can then be defined. Part of this prior is learnt from training data and part comes from expert architects. The validity of the prior is tested by generating example buildings using MCMC and verifying that plausible buildings are generated under varying conditions. The same MCMC machinery can also be used for optimising the structure recovery, this time generating a range of possible solutions from the posterior. The fact that a range of solutions can be presented allows the user to select the best when the structure recovery is ambiguous

Robust detection of degenerate configurations while estimating the fundamental matrix

We present a new method for the detection of multiple solutions or degeneracy when estimating thefundamental matrix, with specific emphasis on robustness to data contamination (mismatches). The fundamental matrix encapsulates all the information on camera motion and internal parameters available from image feature correspondences between two views. It is often used as a first step in structure from motion algorithms. If the set of correspondences is degenerate, then this structure cannot be accurately recovered and many solutions explain the data equally well. It is essential that we are alerted to such eventualities. As current feature matchers are very prone to mismatching the degeneracy detection method must also be robust to outliers. In this paper a definition of degeneracy is given and all two-view nondegenerate and degenerate cases are catalogued in a logical way by introducing the language of varieties from algebraic geometry. It is then shown how each of the cases can be robustly determined from image correspondences via a scoring function we develop. These ideas define a methodology which allows the simultaneous detection of degeneracy and outliers. The method is called PLUNDER-DL and is a generalization of the robust estimator RANSAC. The method is evaluated on many differing pairs of real images. In particular it is demonstrated that proper modeling of degeneracy in the presence of outliers enables the detection of mismatches which would otherwise be missed. All processing including point matching, degeneracy detection, and outlier detection is automatic

Twenty Years of Student Scholarship: Celebrating the Dalhousie Journal of Legal Studies

In this paper, we propose a novel controllable text-to-image generative adversarial network (ControlGAN), which can effectively synthesise high-quality images and also control parts of the image generation according to natural language descriptions. To achieve this, we introduce a word-level spatial and channel-wise attention-driven generator that can disentangle different visual attributes, and allow the model to focus on generating and manipulating subregions corresponding to the most relevant words. Also, a word-level discriminator is proposed to provide fine-grained supervisory feedback by correlating words with image regions, facilitating training an effective generator which is able to manipulate specific visual attributes without affecting the generation of other content. Furthermore, perceptual loss is adopted to reduce the randomness involved in the image generation, and to encourage the generator to manipulate specific attributes required in the modified text. Extensive experiments on benchmark datasets demonstrate that our method outperforms existing state of the art, and is able to effectively manipulate synthetic images using natural language descriptions. Code is available at https://github.com/mrlibw/ControlGAN.Comment: NeurIPS 201

Estimating 3D hand pose using hierarchical multi-label classification

This paper presents an analysis of the design of classifiers for use in a hierarchical object recognition approach. In this approach, a cascade of classifiers is arranged in a tree in order to recognize multiple object classes. We are interested in the problem of recognizing multiple patterns as it is closely related to the problem of locating an articulated object. Each different pattern class corresponds to the hand in a different pose, or set of poses. For this problem obtaining labelled training data of the hand in a given pose can be problematic. Given a parametric 3D model, generating training data in the form of example images is cheap, and we demonstrate that it can be used to design classifiers almost as good as those trained using non-synthetic data. We compare a variety of different template-based classifiers and discuss their merits

The use of a MED calendar to increase medication compliance

This study describes the successful design and implementation of a medications calendar to increase medication compliance among Navajo patients who have difficulty complying with prescription instructions. This paper is presented as an example of a successful method for trying to ensure that medications are taken according to instructions. The MED calendar is designed to help non-English speaking and elderly patients in particular.Initially the calendars were hand made by the drivers from the Public Health Nursing Department. Their primary duty was to serve as interpreters for the Public Health Nurse. Poster board (20 x26 ) was used to simulate a monthly calendar. The days of the week were marked on each grid on each board. The boards were then laminated and the laminated surface was used to mark the name and days of the month for which the calendar was being used. The patients medications were then placed in single unit dose packages. The dose packages were then taped to the calendar according to the prescribed schedule. The patient then received a detailed verbal explanation on when and how to take his or her medicine. The calendar was attached to the wall of the patient\\u27s residence with stick pins and medications were placed for 2-4 weeks at a time. The material cost of the original calendars was $1.75 without labor. Now a professional printer produces them at a total cost of$3.00 per unit. There were two primary safety considerations explored with the implementation of the MED calendars. The first was concern for the stability of the medication in a clear package as opposed to opaque bottle. The Chief of Pharmacy indicated that medicine can be kept in unit dose packages up to six months. The benefits of patient compliance were much greater than any small risk of medication instability. The second concern was safety around small children. In most cases the calendar can be placed high enough on the wall to be out of reach of the children. If this is not possible then the use of the MED calendar is not considered.MED calendars were well accepted by the patients. Navajo patients relate well to ordinary monthly calendars, and this does not require knowledge of the English language. Also, the calendars are highly visible making them difficult to ignore. Medication doses are more easily understood with a pictorial association. The calendars are durable and last at least two or three years. From 1985 to 1987, the MED calendars were used with non-compliant patients. Seventy-three percent of the patients showed some improvement. Improvement was measured by 1) improvement in clinical symptoms including decreased hospitalization, 2) accurate or improved pill count, and 3) patient\\u27s and/or doctor\\u27s affirmation of compliance. There are several difficulties noted in the use of the MED calendar. Safety in the presence of small children is a major concern. Some patients become very dependent on the MED calendar, and this becomes time consuming for the Public Health Nurse who must visit every 2-4 weeks to refill the unit dose packages. Sometimes the unit dose packages do not remain secured to the calendar. Finally, the large size of the calendar can create difficulties in transporting them and are therefore objectionable to some of the patients.The study concludes that the benefits of the MED calendar far outweigh the difficulties encountered in using this system of promoting and facilitating patient compliance

EC Agricultural Prices. Price Indices and absolute prices-Quarterly Statistics 1-1993

We propose MAD-GAN, an intuitive generalization to the Generative Adversarial Networks (GANs) and its conditional variants to address the well known problem of mode collapse. First, MAD-GAN is a multi-agent GAN architecture incorporating multiple generators and one discriminator. Second, to enforce that different generators capture diverse high probability modes, the discriminator of MAD-GAN is designed such that along with finding the real and fake samples, it is also required to identify the generator that generated the given fake sample. Intuitively, to succeed in this task, the discriminator must learn to push different generators towards different identifiable modes. We perform extensive experiments on synthetic and real datasets and compare MAD-GAN with different variants of GAN. We show high quality diverse sample generations for challenging tasks such as image-to-image translation and face generation. In addition, we also show that MAD-GAN is able to disentangle different modalities when trained using highly challenging diverse-class dataset (e.g. dataset with images of forests, icebergs, and bedrooms). In the end, we show its efficacy on the unsupervised feature representation task

Phytoplankton Community Composition in Gowrikere Tank Anandapura, Sagara, Shivamogga, Karnataka, India

No Abstract