9 research outputs found
Signature Verification Approach using Fusion of Hybrid Texture Features
In this paper, a writer-dependent signature verification method is proposed.
Two different types of texture features, namely Wavelet and Local Quantized
Patterns (LQP) features, are employed to extract two kinds of transform and
statistical based information from signature images. For each writer two
separate one-class support vector machines (SVMs) corresponding to each set of
LQP and Wavelet features are trained to obtain two different authenticity
scores for a given signature. Finally, a score level classifier fusion method
is used to integrate the scores obtained from the two one-class SVMs to achieve
the verification score. In the proposed method only genuine signatures are used
to train the one-class SVMs. The proposed signature verification method has
been tested using four different publicly available datasets and the results
demonstrate the generality of the proposed method. The proposed system
outperforms other existing systems in the literature.Comment: Neural Computing and Applicatio
Generative Multiplane Neural Radiance for 3D-Aware Image Generation
We present a method to efficiently generate 3D-aware high-resolution images
that are view-consistent across multiple target views. The proposed multiplane
neural radiance model, named GMNR, consists of a novel {\alpha}-guided
view-dependent representation ({\alpha}-VdR) module for learning view-dependent
information. The {\alpha}-VdR module, faciliated by an {\alpha}-guided pixel
sampling technique, computes the view-dependent representation efficiently by
learning viewing direction and position coefficients. Moreover, we propose a
view-consistency loss to enforce photometric similarity across multiple views.
The GMNR model can generate 3D-aware high-resolution images that are
viewconsistent across multiple camera poses, while maintaining the
computational efficiency in terms of both training and inference time.
Experiments on three datasets demonstrate the effectiveness of the proposed
modules, leading to favorable results in terms of both generation quality and
inference time, compared to existing approaches. Our GMNR model generates
3D-aware images of 1024 X 1024 pixels with 17.6 FPS on a single V100. Code :
https://github.com/VIROBO-15/GMNRComment: Technical repor
Person Image Synthesis via Denoising Diffusion Model
The pose-guided person image generation task requires synthesizing
photorealistic images of humans in arbitrary poses. The existing approaches use
generative adversarial networks that do not necessarily maintain realistic
textures or need dense correspondences that struggle to handle complex
deformations and severe occlusions. In this work, we show how denoising
diffusion models can be applied for high-fidelity person image synthesis with
strong sample diversity and enhanced mode coverage of the learnt data
distribution. Our proposed Person Image Diffusion Model (PIDM) disintegrates
the complex transfer problem into a series of simpler forward-backward
denoising steps. This helps in learning plausible source-to-target
transformation trajectories that result in faithful textures and undistorted
appearance details. We introduce a 'texture diffusion module' based on
cross-attention to accurately model the correspondences between appearance and
pose information available in source and target images. Further, we propose
'disentangled classifier-free guidance' to ensure close resemblance between the
conditional inputs and the synthesized output in terms of both pose and
appearance information. Our extensive results on two large-scale benchmarks and
a user study demonstrate the photorealism of our proposed approach under
challenging scenarios. We also show how our generated images can help in
downstream tasks. Our code and models will be publicly released