Search results for "dolo"
showing 10 items of 4274 documents
Extracting Deformation-Aware Local Features by Learning to Deform
2021
Despite the advances in extracting local features achieved by handcrafted and learning-based descriptors, they are still limited by the lack of invariance to non-rigid transformations. In this paper, we present a new approach to compute features from still images that are robust to non-rigid deformations to circumvent the problem of matching deformable surfaces and objects. Our deformation-aware local descriptor, named DEAL, leverages a polar sampling and a spatial transformer warping to provide invariance to rotation, scale, and image deformations. We train the model architecture end-to-end by applying isometric non-rigid deformations to objects in a simulated environment as guidance to pr…
RationalizeRoots: Software Package for the Rationalization of Square Roots
2019
The computation of Feynman integrals often involves square roots. One way to obtain a solution in terms of multiple polylogarithms is to rationalize these square roots by a suitable variable change. We present a program that can be used to find such transformations. After an introduction to the theoretical background, we explain in detail how to use the program in practice.
Combination of Hidden Markov Random Field and Conjugate Gradient for Brain Image Segmentation
2017
Image segmentation is the process of partitioning the image into significant regions easier to analyze. Nowadays, segmentation has become a necessity in many practical medical imaging methods as locating tumors and diseases. Hidden Markov Random Field model is one of several techniques used in image segmentation. It provides an elegant way to model the segmentation process. This modeling leads to the minimization of an objective function. Conjugate Gradient algorithm (CG) is one of the best known optimization techniques. This paper proposes the use of the Conjugate Gradient algorithm (CG) for image segmentation, based on the Hidden Markov Random Field. Since derivatives are not available fo…
Visual Illusions Also Deceive Convolutional Neural Networks: Analysis and Implications
2019
Visual illusions allow researchers to devise and test new models of visual perception. Here we show that artificial neural networks trained for basic visual tasks in natural images are deceived by brightness and color illusions, having a response that is qualitatively very similar to the human achromatic and chromatic contrast sensitivity functions, and consistent with natural image statistics. We also show that, while these artificial networks are deceived by illusions, their response might be significantly different to that of humans. Our results suggest that low-level illusions appear in any system that has to perform basic visual tasks in natural environments, in line with error minimiz…
Degraded Historical Documents Images Binarization Using a Combination of Enhanced Techniques
2019
Document image binarization is the initial step and a crucial in many document analysis and recognition scheme. In fact, it is still a relevant research subject and a fundamental challenge due to its importance and influence. This paper provides an original multi-phases system that hybridizes various efficient image thresholding methods in order to get the best binarization output. First, to improve contrast in particularly defective images, the application of CLAHE algorithm is suggested and justified. We then use a cooperative technique to segment image into two separated classes. At the end, a special transformation is applied for the purpose of removing scattered noise and of correcting…
A Psychophysically Oriented Saliency Map Prediction Model
2020
Visual attention is one of the most significant characteristics for selecting and understanding the outside redundancy world. The human vision system cannot process all information simultaneously due to the visual information bottleneck. In order to reduce the redundant input of visual information, the human visual system mainly focuses on dominant parts of scenes. This is commonly known as visual saliency map prediction. This paper proposed a new psychophysical saliency prediction architecture, WECSF, inspired by multi-channel model of visual cortex functioning in humans. The model consists of opponent color channels, wavelet transform, wavelet energy map, and contrast sensitivity function…
On the Reliability of the PNU for Source Camera Identification Tasks
2020
The PNU is an essential and reliable tool to perform SCI and, during the years, became a standard de-facto for this task in the forensic field. In this paper, we show that, although strategies exist that aim to cancel, modify, replace the PNU traces in a digital camera image, it is still possible, through our experimental method, to find residual traces of the noise produced by the sensor used to shoot the photo. Furthermore, we show that is possible to inject the PNU of a different camera in a target image and trace it back to the source camera, but only under the condition that the new camera is of the same model of the original one used to take the target image. Both cameras must fall wi…
A Region-based Gauss-Newton Approach to Real-Time Monocular Multiple Object Tracking
2018
We propose an algorithm for real-time 6DOF pose tracking of rigid 3D objects using a monocular RGB camera. The key idea is to derive a region-based cost function using temporally consistent local color histograms. While such region-based cost functions are commonly optimized using first-order gradient descent techniques, we systematically derive a Gauss-Newton optimization scheme which gives rise to drastically faster convergence and highly accurate and robust tracking performance. We furthermore propose a novel complex dataset dedicated for the task of monocular object pose tracking and make it publicly available to the community. To our knowledge, it is the first to address the common and…
Deep Generative Model-Driven Multimodal Prostate Segmentation in Radiotherapy
2019
Deep learning has shown unprecedented success in a variety of applications, such as computer vision and medical image analysis. However, there is still potential to improve segmentation in multimodal images by embedding prior knowledge via learning-based shape modeling and registration to learn the modality invariant anatomical structure of organs. For example, in radiotherapy automatic prostate segmentation is essential in prostate cancer diagnosis, therapy, and post-therapy assessment from T2-weighted MR or CT images. In this paper, we present a fully automatic deep generative model-driven multimodal prostate segmentation method using convolutional neural network (DGMNet). The novelty of …
Analyzing multidimensional movement interaction with generalized cross-wavelet transform
2021
Humans are able to synchronize with musical events whilst coordinating their movements with others. Interpersonal entrainment phenomena, such as dance, involve multiple body parts and movement directions. Along with being multidimensional, dance movement interaction is plurifrequential, since it can occur at different frequencies simultaneously. Moreover, it is prone to nonstationarity, due to, for instance, displacements around the dance floor. Various methodological approaches have been adopted for the study of human entrainment, but only spectrogram-based techniques allow for an integral analysis thereof. This article proposes an alternative approach based upon the cross-wavelet transfor…