Abstract

A system and method for automatically extracting contract data from electronic contracts includes an administrator module configured to provide templates for inputting document patterns and a list of contract data tags for each of a plurality of contract document types. A parser is configured to convert an electronic contract document into a contract text document and reformat the contract text document to provide a pattern for the text contract document. A pattern recognition engine is configured to determine a list of contract document types in the electronic contract by comparing and matching patterns of all known contract document types with the pattern of the contract text document. A contract data extraction engine is configured to extract contract data for each contract document type on the list.

Abstract

Precomputed Radiance Transfer (PRT) is widely used for real-time photorealistic effects. PRT disentangles the rendering equation into transfer and lighting, enabling their precomputation. Transfer accounts for the cosine-weighted visibility of points in the scene while lighting for emitted radiance from the environment. Prior art stored precomputed transfer in a tabulated manner, either in vertex or texture space. These values are fetched with interpolation at each point for shading. Vertex space methods require densely tessellated mesh vertices for high quality images. Texture space

Abstract

Biometric noise is often discarded in many biometric template protection systems. However, the noise ratio be- tween two templates encodes specific correlational proper- ties that template protection schemes can exploit. Biometric authentication usually occurs between mutually distrusting parties, which calls for privacy-preserving techniques. In this paper, we propose a novel biometric authentication pro- tocol, SIAN(Secure Iris Authentication using Noise), adapt- ing secure two-party computation and incorporating un- certainty constraints from biometric noise for security. We evaluate it on three iris datasets: MMU v1, Ubiris v1, and IITD v1, and observe a low EER degradation. The pro- posed protocol has information-theoretic security and low computational complexity, making it suitable for practical real-time applications.

Abstract

n this paper, we develop a robust 3D garment digitiza- tion solution that can generalize well on real-world fashion catalog images with cloth texture occlusions and large body pose variations. We assumed fixed topology parametric template mesh models for known types of garments (e.g., T- shirts, Trousers) and perform mapping of high-quality tex- ture from an input catalog image to UV map panels corre- sponding to the parametric mesh model of the garment. We achieve this by first predicting a sparse set of 2D landmarks on the boundary of the garments. Subsequently, we use these landmarks to perform Thin-Plate-Spline-based tex- ture transfer on UV map panels. Subsequently, we employ a deep texture inpainting network to fill the large holes (dueto view variations & self-occlusions) in TPS output to gen- erate consistent UV maps. Furthermore, to train the su- pervised deep networks for landmark prediction & texture inpainting tasks, we generated a large set of synthetic data with varying texture and lighting imaged from various views with the human present in a wide variety of poses. Addition- ally, we manually annotated a small set of fashion catalog images crawled from online fashion e-commerce platforms to finetune. We conduct thorough empirical evaluations and show impressive qualitative results of our proposed 3D gar- ment texture solution on fashion catalog images. Such 3D garment digitization helps us solve the challenging task of enabling 3D Virtual Try-on

Abstract

Fingerprint feature extraction is a task that is solved using either a global or a local representation. State-of-the-art global approaches use heavy deep learning models to process the full fingerprint image at once, which makes the corresponding approach memory intensive. On the other hand, local approaches involve minutiae based patch extraction, multiple feature extraction steps and an expensive matching stage, which make the corresponding approach time intensive. However, both these approaches provide useful and sometimes exclusive insights for solving the problem. Using both approaches together for extracting fingerprint representations is semantically useful but quite inefficient. Our convolutional transformer based approach with an in-built minutiae extractor provides a time and memory efficient solution to extract a global as well as a local representation of the fingerprint. The use of these …

Abstract

A system for generating high - resolution de - warped omni directional stereo image from captured omni - directional stereo image by correcting optical distortions using projec tion patterns is provided . The system includes a projection pattern capturing arrangement , a projector or a display , and a de - warping server . The projection pattern capturing arrangement includes one or more omnidirectional cameras to capture projection patterns from the captured omni directional stereo image from each omni - directional stereo camera . The projector or the display displays the projection patterns . The de - warping server obtain the projection pat terns and processes the projection patterns to generate high resolution de - warped omni - directional stereo image by cor recting optical distortions in the captured omni - directional stereo image and mapping the captured omni - directional stereo image and the high resolution de - warped omni directional stereo image

Abstract

Quantum sensing applications such as nitrogen vacancy based magnetometry require phase locked loops (PLL), which can synthesize microwave frequencies in a narrow band near 2.87 GHz. Moreover, it is also required that the PLLs should have low noise and low jitter for high stability and fast settling time. These requirements seek low phase noise voltage controlled oscillator (VCO) with small variation in its gain (KVCO) within the desired tuning range. In this paper, we present a technique for designing a low phase noise VCO with low KVCO and small KVCO variation. To validate the proposed technique, an LC VCO has been designed and implemented in 0.18 µm CMOS process and post layout simulation results are presented. The simulation results show that the proposed LC VCO achieves KVCO variation of ±3.82% in the frequency range of 2.75 - 2.94 GHz and exhibits a phase noise of -118.64 dBc/Hz at an offset of 1 MHz, while consuming 9 mW of power from a 1.8 V supply.

Abstract

Normative aging trends of the brain can serve as an important reference in the assessment of neurological structural disorders. Such models are typically developed from longitudinal brain image data—follow-up data of the same subject over diferent time points. In practice, obtaining such longitudinal data is difcult. We propose a method to develop an aging model for a given population, in the absence of longitudinal data, by using images from diferent subjects at diferent time points, the so-called cross-sectional data. We defne an aging model as a difeomorphic deformation on a structural template derived from the data and propose a method that develops topology preserving aging model close to natural aging. The proposed model is successfully validated on two public crosssectional datasets which provide templates constructed from diferent sets of subjects at diferent age points.

Abstract

In this paper, we explore an interesting question of what can be obtained from an 8 × 8 pixel video sequence. Surprisingly, it turns out to be quite a lot. We show that when we process this 8 × 8 video with the right set of audio and image priors, we can obtain a full-length, 256 × 256 video. We achieve this 32× scaling of an extremely low-resolution input using our novel audio-visual upsampling network. The audio prior helps to recover the elemental facial details and precise lip shapes and a single high-resolution target identity image prior provides us with rich appearance details. Our approach is an end-to-end multi-stage framework. The first stage produces a coarse intermediate output video that can be then used to animate single target identity image and generate realistic, accurate and high-quality outputs. Our approach is simple and performs exceedingly well (an 8× improvement in FID score) compared to previous super-resolution methods. We also extend our model to talking-face video compression, and show that we obtain a 3.5× improvement in terms of bits/pixel over the previous state-of-the-art. The results from our network are thoroughly analyzed through extensive ablation experiments (in the paper and supplementary material). We also provide the demo video along with code and models on our website

Abstract

In this work, we address the problem of generating speech from silent lip videos for any speaker in the wild. In stark contrast to previous works, our method (i) is not restricted to a fixed number of speakers, (ii) does not explicitly impose constraints on the domain or the vocabulary and (iii) deals with videos that are recorded in the wild as opposed to within laboratory settings. The task presents a host of challenges, with the key one being that many features of the desired target speech, like voice, pitch and linguistic content, cannot be entirely inferred from the silent face video. In order to handle these stochastic variations, we propose a new VAE-GAN