Abstract

Post-capture control of the focus position of an image is a useful photographic tool. Changing the focus of a single image involves the complex task of simultaneously estimating the radiance and the defocus radius of all scene points. We introduce RefocusGAN, a deblurthen-reblur approach to single image refocusing. We train conditional adversarial networks for deblurring and refocusing using wide-aperture images created from light-fields. By appropriately conditioning our networks with a focus measure, an in-focus image and a refocus control parameter δ, we are able to achieve generic free-form refocusing over a single image.

Abstract

Human body tracking typically requires specialized capture set-ups. Although pose tracking is available in consumer devices like Microsoft Kinect, it is restricted to stick figures visualizing body part detection. In this paper, we propose a method for full 3D human body shape and motion capture of arbitrary movements from the depth channel of a single Kinect, when the subject wears casual clothes. We do not use the RGB channel or an initialization procedure that requires the subject to move around in front of the camera. This makes our method applicable for arbitrary clothing textures and lighting environments, with minimal subject intervention. Our method consists of 3D surface feature detection and articulated motion tracking, which is regularized by a statistical human body model [26]. We also propose the idea of a Consensus Mesh (CMesh) which is the 3D template of a person created from a single view point. We demonstrate tracking results on challenging poses and argue that using CMesh along with statistical body models can improve tracking accuracies. Quantitative evaluation of our dense body tracking shows that our method has very little drift which is improved by the usage of CMesh.

Abstract

The ability of machine-based agents to play games in human-like fashion is considered a benchmark of progress in AI. In this paper, we introduce the first computational model aimed at Pictionary, the popular word-guessing social game. We first introduce Sketch-QA, an elementary version of Visual Question Answering task. Styled after Pictionary, Sketch-QA uses incrementally accumulated sketch stroke sequences as visual data. Notably, Sketch-QA involves asking a fixed question (" What object is being drawn?") and gathering open-ended guess-words from human guessers. To mimic Pictionary-style guessing, we propose a deep neural model which generates guess-words in response to temporally evolving human-drawn sketches. Our model even makes human-like mistakes while guessing, thus amplifying the human mimicry factor. We evaluate our model on the large-scale guess-word dataset generated via Sketch-QA task and compare with various baselines. We also conduct a Visual Turing Test to obtain human impressions of the guess-words generated by humans and our model. Experimental results demonstrate the promise of our approach for Pictionary and similarly themed games.

Abstract

Given a target image as query, person re-identification systems retrieve a ranked list of candidate matches on a per-camera basis. In deployed systems, a human operator scans these lists and labels sighted targets by touch or mouse-based selection. However, classical re-id approaches generate per-camera lists independently. Therefore, target identifications by operator in a subset of cameras cannot be utilized to improve ranking of the target in remaining set of network cameras. To address this shortcoming, we propose a novel sequential multi-camera re-id approach. The proposed approach can accommodate human operator inputs and provides early gains via a monotonic improvement in target ranking. At the heart of our approach is a fusion function which operates on deep feature representations of query and candidate matches. We formulate an optimization procedure custom-designed to incrementally improve query representation. Since existing evaluation methods cannot be directly adopted to our setting, we also propose two novel evaluation protocols. The results on two large-scale re-id datasets (Market-1501, DukeMTMC-reID) demonstrate that our multi-camera method significantly outperforms baselines and other popular feature fusion schemes. Additionally, we conduct a comparative subject-based study of human operator performance. The superior operator performance enabled by our approach makes a compelling case for its integration into deployable video-surveillance systems.

Abstract

This paper strives to answer the following question: Is it possible to recognize an intersection when seen from different road segments that constitute the intersection? An intersection or a junction typically is a meeting point of three or four road segments. Its recognition from a road segment that is transverse to or 180 degrees apart from its previous sighting is an extremely challenging and yet a very relevant problem to be addressed from the point of view of both autonomous driving as well as loop detection. This paper formulates this as a problem of video recognition and proposes a novel LSTM based Siamese style deep network for video recognition. For what is indeed a challenging problem and the limited annotated dataset available we show competitive results of recognizing intersections when approached from diverse viewpoints or road segments. Specifically, we tabulate effective recognition accuracy even …

Abstract

A system for capturing horizontal disparity stereo panorama is disclosed . The system includes a multi surface selective light reflector unit , a secondary reflector and a computing unit . The multi surface selective light reflector unit ( a ) obtains light rays from a 3D scene of outside world that are relevant to create ( i ) a left eye panorama and ( ii ) a right eye panorama and ( b ) reflects the light rays without internal reflections between the light rays . The secondary reflector ( a ) obtains the reflected light rays from the multi surface selective light reflector unit and ( b ) reflects the light rays through the viewing aperture . The computing unit captures ( i ) the reflected light rays from the secondary reflector and ( ii ) the upper part of the 3D scene from a concave lens as a warped image and processes the warped image to ( a ) the left eye panorama and ( b ) the right eye panorama .

Abstract

Purpose To analyse the expansion of radial peripapillary capillary (RPC) network with optical coherence tomography angiography (OCT-A) in normal human eyes and correlate RPC density with retinal nerve fibre layer thickness (RNFLT) at various distances from the optic nerve head (ONH) edge. Methods Fifty eyes of 50 healthy subjects underwent imaging with RTVue XR-100 Avanti OCT. OCT-A scans of Angio disc (6 9 6 mm) and Angio retina (8 9 8 mm) were combined to create a wide-field montage image of the RPC network. RPC density and RNFLT was calculated at different circle diameter around the ONH, and their correlation was measured. Results In the arcuate region, RPC was detected as far as 8.5 mm from the ONH edge, but not around the perifoveal area within 0.025 ± 0.01 mm2.The mean RPC density (0.1556 ± 0.015) and RNFLT (245.96 ± 5.79) were highest at 1.5 mm from ONH margin, and there was a trend in its decline, in a distance-dependent manner, with the least density at 8.5 mm (all P\0.0001). Highest RPC density was noted in the arcuate fibre region at all the distances. Overall mean RPC density correlated significantly (P\0.0001) with the overall mean RNFLT. Conclusions Wide-field montage OCT-A angiograms can visualize expansion of the RPC network, which is useful in obtaining information about various retinal disorders. The results obtained support the hypothesis that the RPC network could be responsible for RNFL nourishment.

Abstract

Purpose: To compare the diagnostic ability of optical coherence tomography angiography (OCT-A) derived radial peripapillary capillary (RPC) measured capillary density (CD) and inside the optic nerve head (ONH) CD measurements to differentiate between the normal and primary open angle glaucoma (POAG) eyes. Methods: AngioVue disc OCT-A images were obtained and assessed in 83 eyes of POAG patients and 74 age matched healthy eyes. RPC CD was quantitatively measured in the peripapillary area within 3.45 mm circle diameter around the ONH and inside the ONH in 8 equally divided sectors, using Bar – Selective Combination of Shifted Filter Responses method after the suppressing large vessels. Area under receiver operating characteristic (AUROC) curve was used to assess the diagnostic accuracy of the two scanning regions of CD to differentiate between the normal and POAG eyes. Results: The mean peripapillary RPC density (0.12 ± 0.03) and mean ONH CD (0.09 ± 0.03) were significantly lower in POAG eyes when compared to the normal eyes (RPC CD: 0.17 ± 0.05, p < 0.0001 and ONH CD 0.11 ± 0.02, p = 0.01 respectively). The POAG patients showed 29% reduction in the RPC CD and 19% reduction in the ONH CD when compared to the normal eyes. The AUROC for discriminating between healthy and glaucomatous eyes was 0.784 for mean RPC CD and 0.743 for the mean ONH CD. Conclusions: Diagnostic ability of OCT-A derived peripapillary CD and ONH CD was moderate for differentiating between the normal and glaucomatous eyes. Diagnostic ability of even the best peripapillary average and inferotemporal sector for RPC CD and average and superonasal sector for the ONH CD was moderate.

Abstract

Translation Quality Estimation (QE) aims to estimate the quality of an automated machine translation (MT) output without any human intervention or reference translation. With the increasing use of MT systems in various cross-lingual applications, the need and applicability of QE systems is increasing. We study existing approaches and propose multiple neural network approaches for sentence-level QE, with a focus on MT outputs in Indian languages. For this, we also introduce five new datasets for four language pairs: two for English–Gujarati, and one each for English–Hindi, English–Telugu and English–Bengali, which includes one manually post-edited dataset for English– Gujarati. These Indian languages are spoken by around 689M speakers world-wide. We compare results obtained using our proposed models with multiple state-ofthe-art systems including the winning system in the WMT17 shared task on QE and show that our proposed neural model which combines the discriminative power of carefully chosen features with Siamese Convolutional Neural Networks (CNNs) works best for all Indian language datasets.

Abstract

In order to expand their reach and increase website ad revenue, media outlets have started using clickbait techniques to lure readers to click on articles on their digital platform. Having successfully enticed the user to open the article, the article fails to satiate his curiosity serving only to boost click-through rates. Initial methods for this task were dependent on feature engineering, which varies with each dataset. Industry systems have relied on an exhaustive set of rules to get the job done. Neural networks have barely been explored to perform this task. We propose a novel approach considering different textual embeddings of a news headline and the related article. We generate sub-word level embeddings of the title using Convolutional Neural Networks and use them to train a bidirectional LSTM architecture. An attention layer allows for calculation of significance of each term towards the nature of the post. We also generate Doc2Vec embeddings of the title and article text and model how they interact, following which it is concatenated with the output of the previous component. Finally, this representation is passed through a neural network to obtain a score for the headline. We test our model over 2538 posts (having trained it on 17000 records) and achieve an accuracy of 83.49% outscoring previous state-of-the-art approaches.