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FisheyeHDK: Hyperbolic Deformable Kernel Learning for Ultra-Wide Field-of-View Image Recognition

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Conventional convolution neural networks (CNNs) trained on narrow Field-of-View (FoV) images are the state-of-the-art approaches for object recognition tasks. Some methods proposed the adaptation of CNNs to ultra-wide FoV images by learning deformable kernels. However, they are limited by the Euclidean geometry and their accuracy degrades under strong distortions caused by fisheye projections. In this work, we demonstrate that learning the shape of convolution kernels in non-Euclidean spaces is better than existing deformable kernel methods. In particular, we propose a new approach that learns deformable kernel parameters (positions) in hyperbolic space. FisheyeHDK is a hybrid CNN architecture combining hyperbolic and Euclidean convolution layers for positions and features learning. First, we provide an intuition of hyperbolic space for wide FoV images. Using synthetic distortion profiles, we demonstrate the effectiveness of our approach. We select two datasets - Cityscapes and BDD100K 2020 - of perspective images which we transform to fisheye equivalents at different scaling factors (analog to focal lengths). Finally, we provide an experiment on data collected by a real fisheye camera. Validations and experiments show that our approach improves existing deformable kernel methods for CNN adaptation on fisheye images.


Fashion Image Search Engine - AI Summary

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Introduction Computers are able to see, hear and learn. Welcome to the future.Dave Waters In this post, I want to talk about a computer vision use case, it's called Content Based Image Retrieval or CBIR in short. In simple words, retrieving images relevant to the user needs from image databases on the basis of low-level visual features. Image Search…


La veille de la cybersécurité

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As it is subjected to machines for identification, artificial intelligence (AI) is becoming sophisticated. The greater the number of databases kept for Machine Learning models, the more thorough and nimbler your AI will be in identifying, understanding, and predicting in a variety of circumstances. It is difficult to identify or distinguish items without picture recognition. Because image recognition is critical for computer vision, we must learn more about it. Image recognition, a subset of computer vision, is the art of recognizing and interpreting photographs to identify objects, places, people, or things observable in one's natural surroundings.


What is AI Image Recognition? How Does It Work in the Digital World?

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As it is subjected to machines for identification, artificial intelligence (AI) is becoming sophisticated. The greater the number of databases kept for Machine Learning models, the more thorough and nimbler your AI will be in identifying, understanding, and predicting in a variety of circumstances. It is difficult to identify or distinguish items without picture recognition. Because image recognition is critical for computer vision, we must learn more about it. Image recognition, a subset of computer vision, is the art of recognizing and interpreting photographs to identify objects, places, people, or things observable in one's natural surroundings.


Multi-modal unsupervised brain image registration using edge maps

arXiv.org Artificial Intelligence

Diffeomorphic deformable multi-modal image registration is a challenging task which aims to bring images acquired by different modalities to the same coordinate space and at the same time to preserve the topology and the invertibility of the transformation. Recent research has focused on leveraging deep learning approaches for this task as these have been shown to achieve competitive registration accuracy while being computationally more efficient than traditional iterative registration methods. In this work, we propose a simple yet effective unsupervised deep learning-based {\em multi-modal} image registration approach that benefits from auxiliary information coming from the gradient magnitude of the image, i.e. the image edges, during the training. The intuition behind this is that image locations with a strong gradient are assumed to denote a transition of tissues, which are locations of high information value able to act as a geometry constraint. The task is similar to using segmentation maps to drive the training, but the edge maps are easier and faster to acquire and do not require annotations. We evaluate our approach in the context of registering multi-modal (T1w to T2w) magnetic resonance (MR) brain images of different subjects using three different loss functions that are said to assist multi-modal registration, showing that in all cases the auxiliary information leads to better results without compromising the runtime.


ViT -- An Image is worth 16x16 words: Transformers for Image Recognition at scale -- ICLR'21

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After the blooming success of transformers in NLP, researchers started applying them in the vision domain too, where for high-level tasks like object detection, segmentation, classification still CNN based variants are dominant. Google brain's research team jumped in again and published a paper called Vision Transformers, which you are here for reading a summary of. ViT, didn't give satisfactory results when they were trained on smaller datasets, but outperformed SOTA for object classification, by a few percentage points, when trained on large datasets. Specifically, ViTs were pretty good, when pre-trained on large datasets, and then finetuned on smaller datasets. Pretrained ViTs outperformed EfficientNet and ResNet-based SOTA networks on datasets including ImageNet, Image-Net Real, CIFAR-100, and VTAB-19.


top-5-face-and-image-recognition-jobs-in-future

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Image and face recognition platforms and solutions have been a major focus in the technology sector over the past two decades. Images and face recognition technology are used in many industries, including healthcare, security, e-commerce and security. This has resulted in remarkable progress. Experts believe this technology can perform at or even surpass human-level in many medical diagnoses and security domains. Many brands now use image recognition technology to harness the intersection of visual analytics and text to understand the industry and target audience, and to deploy visual intelligence to drive meaningful communications.


Transfer Learning for Image Recognition and Natural Language Processing - KDnuggets

#artificialintelligence

If you had the chance to read Part 1 of this article, you will remember that Transfer Learning is a machine learning method where the application of knowledge obtained from a model used in one task, can be reused as a foundation point for another task. If you did not get the chance and don't have a great understanding of Transfer Learning, give it a read it will help you understand this article much better. So let's first go through what Image Recognition is. Image Recognition is the task assigned to computer technology to be able to detect and analyse an object or a feature in an image or video. It is the major area where deep neural networks work their magic as they are designed to recognise patterns.


An Intro to AI Image Recognition and Image Generation

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Artificial intelligence, undoubtedly, is altering the ways we live, work, and even create. It enhances productivity, quality, and speed of work. Image recognition that used to be tedious work has now been performed by AI-enabled machines. The image-generating feature of artificial intelligence has opened ways for people to go in directions they have never heard of.


Top Face and Image Recognition Apps to Follow in December 2021

#artificialintelligence

With the development of technology, Image recognition has convincingly become an integral part of our life. There are diverse kinds of products and applications in the market now, intended to analyze and recognize specific objects in graphics. Biometrics is now a critical feature utilized by firms and even individuals for their security. This concept now has complete application and helps control false arrests, diagnose genetic disorders and reduce malware attacks, cybercrimes, etc. Each application varies with its performance, working methods, applications, etc. Users can choose the product based on our requirements.