Abstract

Localization in 3D is an important problem with wide ranging applications from autonomous navigation in robotics to location specific services on mobile devices. GPS sensors are a commercially viable option for localization, and are ubiquitous in their use, especially in portable de-vices. With the proliferation of mobile cameras however, maturing localization algorithms based on computer vision are emerging as a viable alternative. Although both vision and GPS based localization algorithms have many limita-tions and inaccuracies, there are some interesting compli-mentarities in their success/failure scenarios that justify an investigation into their joint utilization. Such investigations are further justified considering that many of the modern wearable and mobile computing devices come with sensors for both GPS and vision. In this work, we investigate approaches to reinforce GPS localization with vision algorithms and vice versa. Specif-ically, we show how noisy GPS signals can be rectified by vision based localization of images captured in the vicin-ity. Alternatively, we also show how GPS readouts might be used to disambiguate images when they are visually similar looking but belong to different places. Finally, we empiri-cally validate our solutions to show that fusing both these approaches can result in a more accurate and reliable lo-calization of videos captured with a Contour action camera, over a 600 meter long path, over 10 different days.

Abstract

In this work, we address the problem of cross-modal retrieval in presence of multi-label annotations. In particular, we introduce multi-label Canonical Correlation Analysis (ml-CCA), an extension of CCA, for learning shared subspaces taking into account high level semantic information in the form of multi-label annotations. Unlike CCA, ml-CCA does not rely on explicit pairing between modalities, instead it uses the multi-label information to establish correspondences. This results in a discriminative subspace which is better suited for cross-modal retrieval tasks. We also present Fast ml-CCA, a computationally efficient version of ml-CCA, which is able to handle large scale datasets. We show the efficacy of our approach by conducting extensive cross-modal retrieval experiments on three standard benchmark datasets. The results show that the proposed approach achieves state of the art retrieval performance on the three datasets.

Abstract

Recently, exemplar based approaches have been successfully applied for face detection in the wild. Contrary to traditional approaches that model face variations from a large and diverse set of training examples, exemplar-based approaches use a collection of discriminatively trained exemplars for detection. In this paradigm, each exemplar casts a vote using retrieval framework and generalized Hough voting, to locate the faces in the target image. The advantage of this approach is that by having a large database that covers all possible variations, faces in challenging conditions can be detected without having to learn explicit models for different variations. Current schemes, however, make an assumption of independence between the visual words, ignoring their relations in the process. They also ignore the spatial consistency of the visual words. Consequently, every exemplar word contributes equally during voting regardless of its location. In this paper, we propose a novel approach that incorporates higher order information in the voting process. We discover visual phrases that contain semantically related visual words and exploit them for detection along with the visual words. For spatial consistency, we estimate the spatial distribution of visual words and phrases from the entire database and then weigh their occurrence in exemplars. This ensures that a visual word or a phrase in an exemplar makes a major contribution only if it occurs at its semantic location, thereby suppressing the noise significantly. We perform extensive experiments on standard FDDB, AFW and G-album datasets and show significant improvement over previous exemplar …

Abstract

Social commerce is a field that is growing rapidly with the rise of Web 2.0 technologies. This paper presents a review of existing research on this topic to ensure a comprehensive understanding of social commerce. First, we explore the evolution of social commerce from its marketing origins. Next, we examine various definitions of social commerce and the motivations behind it. We also investigate its advantages and disadvantages for both businesses and customers. Then, we explore two major tools for important for social commerce: Sentiment Analysis, and Social Network Analysis. By delving into well-known research papers in Information Retrieval and Complex Networks, we seek to present a survey of current research in multifarious aspects of social commerce to the scientific research community.

Abstract

Optic nerve head (ONH) segmentation problem is of interest for automated glaucoma assessment. Although various segmentation methods have been proposed in the recent past, it is difficult to evaluate and compare the performance of individual methods due to a lack of a benchmark dataset. The assessment involves segmentation of optic disk and cup region within the ONH. In this paper, we present a comprehensive dataset of retinal images of both normal and glaucomatous eyes with manual segmentations from multiple human experts. The dataset also provides expert opinion on an image representing a normal or glaucomatous eye and on the presence of notching in an image. Several state of the art methods are assessed against this dataset using cup to disc diameter ratio (CDR), area and boundary-based evaluation measures. These are presented to aid benchmarking of new methods. A supervised, notch detection method based on the segmentation results is also proposed and its assessment results are included for benchmarking.

Abstract

Annotated data is critical for the development of many computer assisted diagnostic (CAD) algorithms. The process of manual annotation is very strenuous, time-consuming and an expensive component in CAD development. In this paper,we propose the idea of an interactive Assistive Annotation System (AAS) aimed at helping annotators by automatically marking possible regions of interest for further refinement by an annotator. We propose an unsupervised, biologically inspired method for bright lesion annotation. The performance of the proposed system has been evaluated against region-level ground truth in DiaretDB1 dataset and was found to have a sensitivity of60%at7false positives per image. Preliminary testing was also done on public datasets which do not provide any lesion level annotations. A visual assessment of the obtained results affirm a good agreement with lesions visible in images. The system with a simple modification is shown to have the potential to handle dark lesion annotation,which is a significantly more challenging problem. Thus, the proposed system is a good starting point for exploring the AAS idea for retinal images. Such systems can help extend the use of many existing datasets by enriching the image-level annotations with localised information.

Abstract

Nuclear imaging modalities like Positron emission tomography (PET) are characterized by a low SNR value due to the underlying signal generation mechanism. Given the significant role images play in current-day diagnostics, obtaining noise-free PET images is of great interest. With its higher packing density and larger and symmetrical neighbourhood,the hexagonal lattice offers a natural robustness to degradation in signal. Based on this observation, we propose an alternate solution to denoising, namely by changing the sampling lattice. We use filtered back projection for reconstruction, fol-lowed by a sparse dictionary based denoising and compare noise-free reconstruction on the Square and Hexagonal lattices. Experiments with PET phantoms (NEMA, Hoffman)and the Shepp-Logan phantom show that the improvement indenoising, post reconstruction, is not only at the qualitative but also quantitative level. The improvement in PSNR in the hexagonal lattice is on an average between 2 to 10 dB. These results establish the potential of the hexagonal lattice for re-construction from noisy data, in general.

Abstract

We propose a fast and accurate method for counting themitotic figures from histopathological slides using regenerative random forest. Our method performs automatic feature selection in an integratedmanner with classification. The proposed random forest assigns a weight to each feature (dimension) of the feature vector in a novel manner based on the importance of the feature (dimension). The forest also assigns amis classification-based penalty term to each tree in the forest. The trees are then regenerated to make a new population of trees (new forest)and only the more important features survive in the new forest. The feature vector is constructed from domain knowledge using the intensity features of nucleus, features of nuclear membrane and features of the possible stroma region surrounding the cell. The use of domain knowledge improves the classification performance. Experiments show at least 4%improvement in F-measure with an improvement in time complexity on the MITOS dataset from ICPR 2012 grand challenge

Abstract

Abnormality detection in medical images is a critical problem across image modalities and organs. Many ap-proaches to automatic abnormality detection use discrim-inative methods, based on domain knowledge, to address the problem. In this paper, we investigate the effective-ness of a generative model with no assumption of domain knowledge. We propose a method for classification of tis-sues based on Latent Factor analysis, and demonstrate iton colour retinal images with Diabetic Retinopathy-related abnormalities. A generative model based on Gaussian la-tent dictionaries is used to model various structures present at a patch level in an image. The model is used to classify a given patch into one of 5 classes: namely dark and bright lesions, neo-vascularisation (NV), plain tissue background and background with vessel. Evaluation of the proposed method was done on 3 different datasets. Same and crossdataset validation of the method yields an area under the receiver operating characteristic curve (AUC) of 0.85 for abnormality detection. The method was also modified to address a challenging problem of NV detection by posing it as a 2-class problem and the AUC for the same was found to be 0.92. This establishes the potential of LF model for abnormality detection.

Abstract

Accurate detection of optic disk and macula areof interest in automated analysis of retinal images as they are landmarks in retina and their detection aids in assessing these verity of diseases based on the locations of abnormalities relative to these landmarks. The general strategy is to design different methods to these landmarks. In contrast, we propose a novel and unified approach for Optic disk and macula detection in this paper using the Generalized Motion Pattern (GMP)[10] [19] which is derived by inducing motion to an image to smooth out unwanted information. The proposed method is unsupervised, parallelizable and handles illumination differences efficiently but assumes a fixed protocol in image acquisition. The proposed method has been tested on five public datasets and obtained results indicate comparable performance to supervised approaches for the same problem.