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The Diabetes Care in Haemodialysis (DiH) working group aims to improve the care of people with diabetes mellitus on the haemodialysis unit by supporting the implementation of the 2016 JBDS guidelines. In order to support the implementation of the guideline recommendations the DiH working group have established a set of standards, developed an audit tool to demonstrate adherence to standards, and have developed educational support for haemodialysis staff both online and delivered face-to-face. We publish audit findings from five distinct haemodialysis units, highlighting the impact of the educational programme on the achievement of guideline standards. We also report the impact of the COVID-19 pandemic on diabetes care and quality improvement.

Osteochondrosis is a developmental condition affecting the endochondral ossification. It is commonly idiopathic however can be due to vascular anomalies, dietary conditions, hormonal irregularity, or overuse trauma. Osteochondrosis occurring in the inferior pole of the scapula is an extremely rare condition and is referred to as roca disease. A higher degree of suspicion is required especially in a young patient with atraumatic shoulder pain and additional unconventional MRI sequences focusing on the inferior pole of scapula can be taken to rule out such conditions. We report a case of Roca disease in a 16-year male who presented with right shoulder pain. This is the third case report of roca disease in the English literature according to our knowledge and the first case report to demonstrate extensive MRI imaging features with a normal radiograph. Imaging, particularly MRI, plays a pivotal role in the diagnosis of this extremely rare entity. Also, the inferior pole of the scapula is usually not included in routine shoulder MRI imaging thus close scrutiny with additional MRI sequences should be done to diagnose such a rare entity.

The outbreak of acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in mid-December 2019, from Wuhan, Hubei Province, China, and its spread across China and beyond has taken the world by surprise. On March 11, 2020 WHO (World Health Organization) declared it a global pandemic. Herein, we discuss the epidemiological trends, clinical and diagnostic findings, management, and investigative therapies of this disease and also reflect upon how it might be different in India from the rest of the world. How to cite this article:Sharma P, Bali RK, Sawhney H, Gandhi D, Bali NK. Coronavirus Disease (COVID-19) - Epidemiology, Detection and Management with Respect to the Indian Subcontinent - Current Updates and Theories. J Commun Dis 2020; 52(2): 52-62. DOI: https://doi.org/10.24321/0019.5138.202019

Seizures are not uncommon in renal transplant patients. The common aetiologies are metabolic disturbance associated with renal failure, immunosuppression and associated complications and infections. Their management can be challenging because of altered pharmacokinetics of antiepileptic drugs (AEDs) and their removal by dialysis. A practical approach to the management of seizure in renal transplant patients is discussed. This review highlights the guidelines for use of various AEDs in renal transplants.

Cerebral venous thrombosis (CVT) is an uncommon but fatal disease in the pediatric age group, with a neonatal preponderance. The clinical presentation is nonspecific. Within the first 48 hours, the neonate usually presents with nonspecific signs such as respiratory distress, poor tone, and asphyxia. Neurological signs such as seizures, lethargy, apnea, and poor feeding set in later. We present the case of a neonatal CVT, presenting as asphyxia and respiratory depression on day 1 of life. CVT was diagnosed on neonatal cranial ultrasound. The patient died 3 days later.

Gastrointestinal tract duplication cysts are rare congenital malformations which can be diagnosed as early as the prenatal period but are frequently found in infancy or incidentally in adulthood. They can be seen throughout the alimentary tract with the most common involving the distal ileum and second most common the esophagus. Many duplication cysts are asymptomatic and thus discovered as an incidental imaging finding, though they can also be symptomatic with an array of clinical presentations dependent largely on their location. The vast majority of duplication cysts are benign; however, there are rare instances of malignant transformation reported. The aim of this review is to show how multimodality imaging can help in the diagnosis of duplication cysts at various anatomical locations. Duplication cyst can become symptomatic and in rare cases undergo malignant transformation; therefore, they are typically managed with surgical excision, particularly if found prenatally or during infancy. Given the diversity of anatomic locations, multiple differential diagnoses, and the need for surgical intervention, it is valuable to comprehend the role of multimodality imaging role in diagnosing duplication cysts.

The lack of large-scale real datasets with annotations makes transfer learning a necessity for video activity understanding. We aim to develop an effective method for few-shot transfer learning for first-person action classification. We leverage independently trained local visual cues to learn representations that can be transferred from a source domain, which provides primitive action labels, to a different target domain using only a handful of examples. Visual cues we employ include object-object interactions, hand grasps and motion within regions that are a function of hand locations. We employ a framework based on meta-learning to extract the distinctive and domain invariant components of the deployed visual cues. This enables transfer of action classification models across public datasets captured with diverse scene and action configurations. We present comparative results of our transfer learning methodology and report superior results over state-of-the-art action classification approaches for both inter-class and inter-dataset transfer., Paper has been accepted in Transactions on Pattern Analysis and Machine Intelligence

A 25-year-old otherwise healthy woman presented to the hospital with sore throat and dysphagia for 5 days. On her computed tomography images, thickening and edema of the right aryepiglottic fold was noted, associated with an impacted foreign body.

This paper presents an approach to extract semantic layers from aerial surveillance videos for scene understanding and object tracking. The input videos are captured by low flying aerial platforms and typically consist of strong parallax from non-ground-plane structures as well as moving objects. Our approach leverages the geo-registration between video frames and reference images (such as those available from Terraserver and Google satellite imagery) to establish a unique geo-spatial coordinate system for pixels in the video. The geo-registration process enables Euclidean 3D reconstruction with absolute scale unlike traditional monocular structure from motion where continuous scale estimation over long periods of time is an issue. Geo-registration also enables correlation of video data to other stored information sources such as GIS (Geo-spatial Information System) databases. In addition to the geo-registration and 3D reconstruction aspects, the other key contributions of this paper also include: (1) providing a reliable geo-based solution to estimate camera pose for 3D reconstruction, (2) exploiting appearance and 3D shape constraints derived from geo-registered videos for labeling of structures such as buildings, foliage, and roads for scene understanding, and (3) elimination of moving object detection and tracking errors using 3D parallax constraints and semantic labels derived from geo-registered videos. Experimental results on extended time aerial video data demonstrates the qualitative and quantitative aspects of our work.

In this paper, we presented a fully integratedreal-time computer vision system that can detect and track multiple humans in a wide-area using a network of stereo cameras. Continuous human identities are achieved by fusing video tracking with different kinds of biometric devices. The system also provides immersive visualization which enables the users to conveniently navigate through space and time and query useful events. The key innovations include stereo-based multi-object detection and tracking, a unified approach for fusing multiple sensors of different modalities, visualization and user interface design.

This paper addresses the problem of matching vehicles across multiple sightings under variations in illumination and camera poses. Since multiple observations of a vehicle are separated in large temporal and/or spatial gaps, thus prohibiting the use of standard frame-to-frame data association, we employ features extracted over a sequence during one time interval as a vehicle fingerprint that is used to compute the likelihood that two or more sequence observations are from the same or different vehicles. Furthermore, since our domain is aerial video tracking, in order to deal with poor image quality and large resolution and quality variations, our approach employs robust alignment and match measures for different stages of vehicle matching. Most notably, we employ a heterogeneous collection of features such as lines, points, and regions in an integrated matching framework. Heterogeneous features are shown to be important. Line and point features provide accurate localization and are employed for robust alignment across disparate views. The challenges of change in pose, aspect, and appearances across two disparate observations are handled by combining a novel feature-based quasi-rigid alignment with flexible matching between two or more sequences. However, since lines and points are relatively sparse, they are not adequate to delineate the object and provide a comprehensive matching set that covers the complete object. Region features provide a high degree of coverage and are employed for continuous frames to provide a delineation of the vehicle region for subsequent generation of a match measure. Our approach reliably delineates objects by representing regions as robust blob features and matching multiple regions to multiple regions using Earth Mover's Distance (EMD). Extensive experimentation under a variety of real-world scenarios and over hundreds of thousands of Confirmatory Identification (CID) trails has demonstrated about 95 percent accuracy in vehicle reacquisition with both visible and Infrared (IR) imaging cameras.

Histograms of shape signature or prototypical shapes, called shapemes, have been used effectively in previous work for 2D/3D shape matching and recognition. We extend the idea of shapeme histogram to recognize partially observed query objects from a database of complete model objects. We propose representing each model object as a collection of shapeme histograms and match the query histogram to this representation in two steps: 1) compute a constrained projection of the query histogram onto the subspace spanned by all the shapeme histograms of the model and 2) compute a match measure between the query histogram and the projection. The first step is formulated as a constrained optimization problem that is solved by a sampling algorithm. The second step is formulated under a Bayesian framework, where an implicit feature selection process is conducted to improve the discrimination capability of shapeme histograms. Results of matching partially viewed range objects with a 243 model database demonstrate better performance than the original shapeme histogram matching algorithm and other approaches.

The problem of training a classifier from a handful of positive examples, without having to supply class specific negatives is of great practical importance. The proposed approach to solving this problem builds on the idea of training LDA classifiers using only class specific foreground images and a large collection of unlabelled images, as described in [11]. While we adopt the LDA training methodology of [11], we depart from HOG features and work with those extracted from a Convolutional Neural Network (CNN) pre-trained on Image Net (Over feat). We combine Over feat features with the LDA training methodology to derive generative classifiers. When evaluated on a K-way classification problem, these classifiers are almost as good as those trained discriminatively using the same features. Unlike the HOG based approach of [11], our classifiers do not need any post-processing step of calibration, a step that requires positives and negatives. Finally, we show that in an instance retrieval setup, we can employ these generative classifiers to derive a novel query-expansion framework that achieves a significant performance boost by utilizing only the top ranked positive examples from an initial nearest-neighbor list.

Decomposing video frames into coherent 2D motion layers is a powerful method for representing videos. Such a representation provides an intermediate description that enables applications such as object tracking, video summarization and visualization, video insertion, and sprite-based video compression. Previous work on motion layer analysis has largely concentrated on two-frame or multi-frame batch formulations. The temporal coherency of motion layers and the domain constraints on shapes have not been exploited. This paper introduces a complete dynamic motion layer representation in which spatial and temporal constraints on shape, motion and layer appearance are modeled and estimated in a maximum a-posteriori (MAP) framework using the generalized expectation-maximization (EM) algorithm. In order to limit the computational complexity of tracking arbitrarily shaped layer ownership, we propose a shape prior that parameterizes the representation of shape and prevents motion layers from evolving into arbitrary shapes. In this work, a Gaussian shape prior is chosen to specifically develop a near-real-time tracker for vehicle tracking in aerial videos. However, the general idea of using a parametric shape representation as part of the state of a tracker is a powerful one that can be extended to other domains as well. Based on the dynamic layer representation, an iterative algorithm is developed for continuous object tracking over time. The proposed method has been successfully applied in an airborne vehicle tracking system. Its performance is compared with that of a correlation-based tracker and a motion change-based tracker to demonstrate the advantages of the new method. Examples of tracking when the backgrounds are cluttered and the vehicles undergo various rigid motions and complex interactions such as passing, turning, and stop-and-go demonstrate the strength of the complete dynamic layer representation.

Detecting pedestrians at a distance from large-format wide-area imagery is a challenging problem because of low ground sampling distance (GSD) and low frame rate of the imagery. In such a scenario, the approaches based on appearance cues alone mostly fail because pedestrians are only a few pixels in size. Frame-differencing and optical flow based approaches also give poor detection results due to noise, camera jitter and parallax in aerial videos. To overcome these challenges, we propose a novel approach to extract Multi-scale Intrinsic Motion Structure features from pedestrian's motion patterns for pedestrian detection. The MIMS feature encodes the intrinsic motion properties of an object, which are location, velocity and trajectory-shape invariant. The extracted MIMS representation is robust to noisy flow estimates. In this paper, we give a comparative evaluation of the proposed method and demonstrate that MIMS outperforms the state of the art approaches in identifying pedestrians from low resolution airborne videos.

Despite recent advances, automatic blood vessel extraction from low quality retina images remains difficult. We propose an interactive approach that enables a user to efficiently obtain near perfect vessel segmentation with a few mouse clicks. Given two seed points, the approach seeks an optimal path between them by minimizing a cost function. In contrast to the Live-Vessel approach, the graph in our approach is based on the curve fragments generated with vessel tracing instead of individual pixels. This enables our approach to overcome the shortcut problem in extracting tortuous vessels and the problem of vessel interference in extracting neighboring vessels in minimal-cost path techniques, resulting in less user interaction for extracting thin and tortuous vessels from low contrast images. It also makes the approach much faster.

Important diagnostic criteria for glaucoma are changes in the 3D structure of the optic disc due to optic nerve damage. We propose an automatic approach for detecting these changes in 3D models reconstructed from fundus images of the same patient taken at different times. For each time session, only two uncalibated fundus images are required. The approach applies a 6-point algorithm to estimate relative camera pose assuming a constant camera focal length. To deal with the instability of 3D reconstruction associated with fundus images, our approach keeps multiple candidate reconstruction solutions for each image pair. The best 3D reconstruction is found by optimizing the 3D registration of all images after an iterative bundle adjustment that tolerates possible structure changes. The 3D structure changes are detected by evaluating the reprojection errors of feature points in image space. We validate the approach by comparing the diagnosis results with manual grading by human experts on a fundus image dataset.

We propose a novel hybrid model that exploits the strength of discriminative classifiers along with the representational power of generative models. Our focus is on detecting multimodal events in time varying sequences. Discriminative classifiers have been shown to achieve higher performances than the corresponding generative likelihood-based classifiers. On the other hand, generative models learn a rich informative space which allows for data generation and joint feature representation that discriminative models lack. We employ a deep temporal generative model for unsupervised learning of a shared representation across multiple modalities with time varying data. The temporal generative model takes into account short term temporal phenomena and allows for filling in missing data by generating data within or across modalities. The hybrid model involves augmenting the temporal generative model with a temporal discriminative model for event detection, and classification, which enables modeling long range temporal dynamics. We evaluate our approach on audio-visual datasets (AVEC, AVLetters, and CUAVE) and demonstrate its superiority compared to the state-of-the-art.

This paper presents an algorithmic approach to the problem of detecting independently moving objects in 3D scenes that are viewed under camera motion. There are two fundamental constraints that can be exploited for the problem: 1) two/multiview camera motion constraint (for instance, the epipolar/trilinear constraint) and 2) shape constancy constraint. Previous approaches to the problem either use only partial constraints, or rely on dense correspondences or flow. We employ both the fundamental constraints in an algorithm that does not demand a priori availability of correspondences or flow. Our approach uses the plane-plus-parallax decomposition to enforce the two constraints. It is also demonstrated that for a class of scenes, called sparse 3D scenes in which genuine parallax and independent motions may be confounded, how the plane-plus-parallax decomposition allows progressive introduction, and verification of the fundamental constraints. Results of the algorithm on some difficult sparse 3D scenes are promising.

Multiple images of a scene are related through 2D/3D view transformations and linear and non-linear camera transformations. In all the traditional techniques to compute these transformations, especially the ones relying on direct intensity gradients, one image and its coordinate system have been assumed to be ideal and distortion free. In this paper, we present a formulation and an algorithm for true multi-image alignment that does not rely on the measurements of a reference image being distortion free. For instance, in the presence of lens distortion, none of the images can be assumed to be ideal. In our formulation, all the images are modeled as intensity measurements represented in their respective coordinate systems, each of which is related to an ideal coordinate system through an interior camera transformation and an exterior view transformation. The goal of the accompanying algorithm is to compute an image in the ideal coordinate system while solving for the transformations that relate the ideal system with each of the data images. Key advantages of the technique presented in this paper are: (i) no reliance on one distortion free image, (ii) ability to register images and compute coordinate transformations even when the multiple images are of an extended scene with no overlap between the first and last frame of the sequence, and (iii) ability to handle linear and non-linear transformations within the same framework. The new algorithm is evaluated in the context of two applications: (i) correction of lens distortion, and (ii) creation of video mosaics.

We present an algorithm to estimate depth in dynamic video scenes. We propose to learn and infer depth in videos from appearance, motion, occlusion boundaries, and geometric context of the scene. Using our method, depth can be estimated from unconstrained videos with no requirement of camera pose estimation, and with significant background/foreground motions. We start by decomposing a video into spatio-temporal regions. For each spatio-temporal region, we learn the relationship of depth to visual appearance, motion, and geometric classes. Then we infer the depth information of new scenes using piecewise planar parametrization estimated within a Markov random field (MRF) framework by combining appearance to depth learned mappings and occlusion boundary guided smoothness constraints. Subsequently, we perform temporal smoothing to obtain temporally consistent depth maps. To evaluate our depth estimation algorithm, we provide a novel dataset with ground truth depth for outdoor video scenes. We present a thorough evaluation of our algorithm on our new dataset and the publicly available Make3d static image dataset., Comment: British Machine Vision Conference (BMVC) 2014

Complex event detection is very challenging in open source such as You-Tube videos, which usually comprise very diverse visual contents involving various object, scene and action concepts. Not all of them, however, are relevant to the event. In other words, a video may contain a lot of "junk" information which is harmful for recognition. Hence, we propose a semantic pooling approach to tackle this issue. Unlike the conventional pooling over the entire video or specific spatial regions of a video, we employ a discriminative approach to acquire abstract semantic "regions" for pooling. For this purpose, we first associate low-level visual words with semantic concepts via their co-occurrence relationship. We then pool the low-level features separately according to their semantic information. The proposed semantic pooling strategy also provides a new mechanism for incorporating semantic concepts for low-level feature based event recognition. We evaluate our approach on TRECVID MED [1] dataset and the results show that semantic pooling consistently improves the performance compared with conventional pooling strategies.

We present a novel approach for multi-modal affect analysis in human interactions that is capable of integrating data from multiple modalities while also taking into account temporal dynamics. Our fusion approach, Joint Hidden Conditional Random Fields (JHRCFs), combines the advantages of purely feature level (early fusion) fusion approaches with late fusion (CRFs on individual modalities) to simultaneously learn the correlations between features from multiple modalities as well as their temporal dynamics. Our approach addresses major shortcomings of other fusion approaches such as the domination of other modalities by a single modality with early fusion and the loss of cross-modal information with late fusion. Extensive results on the AVEC 2011 dataset show that we outperform the state-of-the-art on the Audio Sub-Challenge, while achieving competitive performance on the Video Sub-Challenge and the Audiovisual Sub-Challenge.

In this work, we propose a novel video representation for activity recognition that models video dynamics with attributes of activities. A video sequence is decomposed into short-term segments, which are characterized by the dynamics of their attributes. These segments are modeled by a dictionary of attribute dynamics templates, which are implemented by a recently introduced generative model, the binary dynamic system~(BDS). We propose methods for learning a dictionary of BDSs from a training corpus, and for quantizing attribute sequences extracted from videos into these BDS code words. This procedure produces a representation of the video as a histogram of BDS code words, which is denoted the bag-of-words for attribute dynamics (BoWAD). An extensive experimental evaluation reveals that this representation outperforms other state-of-the-art approaches in temporal structure modeling for complex activity recognition.

Creutzfeldt-Jakob disease (CJD) is a prion disease that causes progressive dementia and is invariably fatal. This results from the misfolding of normal cellular prion proteins into an abnormal conformation. Prompt diagnosis is essential to prevent human-to-human transmission. On MR imaging, CJD shows high T2 signal in basal ganglia and cerebral cortex, as well as progressive brain atrophy. However, diffusion weighted imaging (DWI) is more sensitive in the diagnosis by showing restricted diffusion as high signal on DWI image and low signal on ADC map. In this case report, we will discuss the clinical presentation and imaging features of CJD. We will also discuss the disease entities which can mimic CJD.

We propose to use action, scene and object concepts as semantic attributes for classification of video events in InTheWild content, such as YouTube videos. We model events using a variety of complementary semantic attribute features developed in a semantic concept space. Our contribution is to systematically demonstrate the advantages of this concept-based event representation (CBER) in applications of video event classification and understanding. Specifically, CBER has better generalization capability, which enables to recognize events with a few training examples. In addition, CBER makes it possible to recognize a novel event without training examples (i.e., zero-shot learning). We further show our proposed enhanced event model can further improve the zero-shot learning. Furthermore, CBER provides a straightforward way for event recounting/understanding. We use the TRECVID Multimedia Event Detection (MED11) open source event definitions and datasets as our test bed and show results on over 1400 hours of videos.

We present a novel method for matching ground-based query images to a georeferenced LIDAR 3D dataset acquired from an airborne platform in urban environments. We are addressing two main technical challenges: (i) different modalities between the query and the reference data (electro-optical vs. LIDAR) that impose unique challenges to the matching problem; (ii) very different viewing directions from which the query, respectively the LIDAR data were acquired. We make two main technical contributions in this paper. First, we present a method for automatically extracting features from LIDAR data that largely remain invariant to the projection in a 2D image and thus allow robust matching across modalities and change in viewpoint. Second, we describe a matching technique that finds the best 3D pose that relates the query input image to a rendered image of the 3D models. We present results of matching images to high-resolution LIDAR data covering five square kilometers over a city that demonstrate the power of the matching method proposed.

Despite recent advances, automatic blood vessel extraction from low quality retina images remains difficult. We propose an interactive approach that enables a user to efficiently obtain near perfect vessel segmentation with a few mouse clicks. Given two seed points, the approach seeks an optimal path between them by minimizing a cost function. In contrast to the Live-Vessel approach, the graph in our approach is based on the curve fragments generated with vessel tracing instead of individual pixels. This enables our approach to overcome the shortcut problem in extracting tortuous vessels and the problem of vessel interference in extracting neighboring vessels in minimal-cost path techniques, resulting in less user interaction for extracting thin and tortuous vessels from low contrast images. It also makes the approach much faster.

In image retrieval based on color, the weighted distance between color histograms of two images, represented as a quadratic form, may be defined as a match measure. However, this distance measure is computationally expensive and it operates on high dimensional features (O(N)). We propose the use of low-dimensional, simple to compute distance measures between the color distributions, and show that these are lower bounds on the histogram distance measure. Results on color histogram matching in large image databases show that prefiltering with the simpler distance measures leads to significantly less time complexity because the quadratic histogram distance is now computed on a smaller set of images. The low-dimensional distance measure can also be used for indexing into the database. >

Research on ways to extend and improve query methods for image databases is widespread. We have developed the QBIC (Query by Image Content) system to explore content-based retrieval methods. QBIC allows queries on large image and video databases based on example images, user-constructed sketches and drawings, selected color and texture patterns, camera and object motion, and other graphical information. Two key properties of QBIC are (1) its use of image and video content-computable properties of color, texture, shape and motion of images, videos and their objects-in the queries, and (2) its graphical query language, in which queries are posed by drawing, selecting and other graphical means. This article describes the QBIC system and demonstrates its query capabilities. QBIC technology is part of several IBM products. >

Multimedia event detection has drawn a lot of attention in recent years. Given a recognized event, in this paper, we conduct a pilot study of the multimedia event recounting problem, which answers the question why this video is recognized as this event, i.e. what evidences this decision is made on. In order to provide a semantic recounting of the multimedia event, we adopt a concept-based event representation for learning a discriminative event model. Then, we present a recounting approach that exactly recovers the contribution of semantic evidence to the event classification decision. This approach can be applied on any additive discriminative classifiers. The promising result is shown on the MED11 dataset that contains 15 events in thousands of YouTube like videos.

Low-level appearance as well as spatio-temporal features, appropriately quantized and aggregated into Bag-of-Words (BoW) descriptors, have been shown to be effective in many detection and recognition tasks. However, their effcacy for complex event recognition in unconstrained videos have not been systematically evaluated. In this paper, we use the NIST TRECVID Multimedia Event Detection (MED11 [1]) open source dataset, containing annotated data for 15 high-level events, as the standardized test bed for evaluating the low-level features. This dataset contains a large number of user-generated video clips. We consider 7 different low-level features, both static and dynamic, using BoW descriptors within an SVM approach for event detection. We present performance results on the 15 MED11 events for each of the features as well as their combinations using a number of early and late fusion strategies and discuss their strengths and limitations.

Matching street-level images to a database of airborne images is hard because of extreme viewpoint and illumination differences. Color/gradient distributions or local descriptors fail to match forcing us to rely on the structure of self-similarity of patterns on facades. We propose to capture this structure with a novel "scale-selective self-similarity" (S4) descriptor which is computed at each point on the facade at its inherent scale. To achieve this, we introduce a new method for scale selection which enables the extraction and segmentation of facades as well. Matching is done with a Bayesian classification of the street-view query S4 descriptors given all labeled descriptors in the bird's-eye-view database. We show experimental results on retrieval accuracy on a challenging set of publicly available imagery and compare with standard SIFT-based techniques.

We study the feasibility of solving the challenging problem of geolocalizing ground level images in urban areas with respect to a database of images captured from the air such as satellite and oblique aerial images. We observe that comprehensive aerial image databases are widely available while complete coverage of urban areas from the ground is at best spotty. As a result, localization of ground level imagery with respect to aerial collections is a technically important and practically significant problem. We exploit two key insights: (1) satellite image to oblique aerial image correspondences are used to extract building facades, and (2) building facades are matched between oblique aerial and ground images for geo-localization. Key contributions include: (1) A novel method for extracting building facades using building outlines; (2) Correspondence of building facades between oblique aerial and ground images without direct matching; and (3) Position and orientation estimation of ground images. We show results of ground image localization in a dense urban area.

Analyzing change in the 3D structure of the optic disc over time has long been recognized as central to the diagnosis of glaucoma but has been inadequately addressed by computer vision methods. Currently, clinicians examine stereo pairs from different time instants for interval changes indicative of glaucoma. Due to the clinical procedures in capturing optic disc images, these stereo pairs are usually completely uncalibrated - the camera intrinsics and extrinsics are unknown. Clinicians have to account for these unknown factors and hence their diagnoses of optic disc stability or change are subjective. Changes in the 3D structure of the optic disc are typically accompanied by changes in the 3D structure of blood vessels in that region. Therefore, change in the 3D structure of blood vessels can be used for glaucoma diagnosis. In this paper, we introduce a projective geometry based approach that reconstructs and aligns 3D blood vessel networks given two stereo pairs of optic disc images. We demonstrate that this alignment can identify regions where the vessel structure has changed. Since calibration is unavailable, the 3D structures and the alignment have a projective ambiguity, and hence, we cannot use an absolute threshold on the alignment error to automatically identify change. We have therefore developed an interactive tool that highlights regions with the largest alignment errors. This tool demonstrates the utility of our approach and also can guide clinical observers to optic disc regions where they should look for changes. We believe that our approach can serve as a platform to develop much needed novel tools for glaucoma diagnosis.

We describe a vehicle tracking algorithm using input from a network of nonoverlapping cameras. Our algorithm is based on a novel statistical formulation that uses joint kinematic and image appearance information to link local tracks of the same vehicles into global tracks with longer persistence. The algorithm can handle significant spatial separation between the cameras and is robust to challenging tracking conditions such as high traffic density, or complex road infrastructure. In these cases, traditional tracking formulations based on MHT, or JPDA algorithms, may fail to produce track associations across cameras due to the weak predictive models employed. We make several new contributions in this paper. Firstly, we model kinematic constraints between any two local tracks using road networks and transit time distributions. The transit time distributions are calculated dynamically as convolutions of normalized transit time distributions that are learned and adapted separately for individual roads. Secondly, we present a complete statistical tracker formulation, which combines kinematic and appearance likelihoods within a multi-hypothesis framework. We have extensively evaluated the algorithm proposed using a network of ground-based cameras with narrow field of view. The tracking results obtained on a large ground-truthed dataset demonstrate the effectiveness of the algorithm proposed.

We present a novel LIDAR streaming architecture for real-time, on-board processing using unmanned robots. We propose a two-level 3D data structure that allows pipelined and streaming processing of the 3D data as it arrives from a moving robot: (i) at the coarse level, the incoming 3D scans are stored in memory in a dense 3D voxel grid with a relatively large voxel size - this ensures buffering of the most recent data and the availability of sufficient 3D measurements within a specific processing volume at the next level; (ii) at the fine level, we employ a data chunking mechanism guided by the movement of the robot and a rolling dense 3D voxel grid for processing the data in the immediate vicinity of the robot, which enables reuse of previously computed features. The architecture proposed requires a very small memory footprint, minimal data copying, and allows a fast spatial access for 3D data, even at the finest resolutions. We illustrate the proposed streaming architecture on a real-time 3D structure characterization task for detecting doors and stairs in indoor environments and show qualitative results demonstrating the effectiveness of our approach.

In many man-made environments, obstacles in the path of a mobile robot can be characterized asshallow, that is, they have relatively small extent in depth compared to the distance from the camera. We present a framework for segmenting shallow structures from their background over a sequence of images. Shallowness is first quantified asaffine describability. This is embedded in a tracking system within which hypothesized model structures undergo a cycle of prediction and model-matching. Structures emerge either as shallow or nonshallow based on theiraffine trackability. Two major contributions of this work are (i) aggregate object tracking based on 3-D motion and structure constraints in constrast with traditional primitive feature tracking based on image motion heuristics, and (ii) use of temporal behavior for object segmentation and 3-D reconstruction.

Localizing blood vessels in eye images is a crucial step in the automated and objective diagnosis of eye diseases. Most previous research has focused on extracting the centerlines of vessels in large field of view images. However, for diagnosing diseases of the optic disk region, like glaucoma, small field of view images have to be analyzed. One needs to identify not only the centerlines, but also vessel widths, which vary widely in these images. We present an automatic technique for localizing vessels in small field of view images using multi-scale matched filters. We also estimate local vessel properties - width and orientation - along the length of each vessel. Furthermore, we explicitly account for highlights on thick vessels - central reflexes - which are ignored in many previous works. Qualitative and quantitative results demonstrate the efficacy of our method - e.g. vessel centers are localized with RMS and median errors of 2.11 and 1 pixels, respectively in 700×700 images.