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Image Classification with TensorFlow

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This article is an end-to-end example of training, testing and saving a machine learning model for image classification using the TensorFlow python package. TensorFlow is a machine learning (primarily deep learning) package developed and open-sourced by Google; when it was originally released TensorFlow was a relatively low-level package for experienced users, however in the last few years and especially since the release of TensorFlow 2.0 it is now aimed at a wider range of users. A few years ago I ran a PoC with one of our developers that looked at running TensorFlow models offline on one of our mobile applications. Whilst we found that it was possible we also encountered a few challenges that made the solution quite fiddly. Roll forward to 2020 and TensorFlow has improved a lot; the latest version has greater integration with the Keras APIs, it's being extended to cover more of the data processing pipeline and has also branched out to support for new languages, with the TensorFlow.js


Earphone tracks facial expressions, even with a face mask

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Cornell researchers have invented an earphone that can continuously track full facial expressions by observing the contour of the cheeks – and can then translate expressions into emojis or silent speech commands. With the ear-mounted device, called C-Face, users could express emotions to online collaborators without holding cameras in front of their faces – an especially useful communication tool as much of the world engages in remote work or learning. "This device is simpler, less obtrusive and more capable than any existing ear-mounted wearable technologies for tracking facial expressions," said Cheng Zhang, assistant professor of information science and senior author of "C-Face: Continuously Reconstructing Facial Expressions by Deep Learning Contours of the Face With Ear-Mounted Miniature Cameras." The paper will be presented at the Association for Computing Machinery Symposium on User Interface Software and Technology, to be held virtually Oct. 20-23. "In previous wearable technology aiming to recognize facial expressions, most solutions needed to attach sensors on the face," said Zhang, director of Cornell's SciFi Lab, "and even with so much instrumentation, they could only recognize a limited set of discrete facial expressions."


Ton Peijnenburg, Fellow HTSC and Deputy Director VDL-ETG

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AI, deep learning and algorithms such as Convolutional Neural Networks are outperforming other techniques in object detection in images or image streams. When we look at the detection of soccer balls in our soccer robots2, traditional machine vision techniques (including color segmentation and contour detection) are expensive in terms of computing power and not very robust to changes like different illumination. The Robocup initiative is struggling to move its soccer games outdoors, one of the reasons being poor sensing performance in outdoor daylight conditions. Driver assist systems for cars deal with outdoor conditions much better. When we use an off-the-shelf, less traditional neural network like YOLO3 in our lab, we can reliably and robustly detect all the balls in our field independently of their color, paint pattern, distance and illumination.


Cornell researchers created an earphone that can track facial expressions

Engadget

Researchers from Cornell University have created an earphone system that can track a wearer's facial expressions even when they're wearing a mask. C-Face can monitor cheek contours and convert the wearer's expression into an emoji. That could allow people to, for instance, convey their emotions during group calls without having to turn on their webcam. "This device is simpler, less obtrusive and more capable than any existing ear-mounted wearable technologies for tracking facial expressions," Cheng Zhang, director of Cornell's SciFi Lab and senior author of a paper on C-Face, said in a statement. "In previous wearable technology aiming to recognize facial expressions, most solutions needed to attach sensors on the face and even with so much instrumentation, they could only recognize a limited set of discrete facial expressions."


Computer Vision is Changing the Face of Sports

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Fast and accurate, that is what most sports are about. Although when computer vision has been around for many years, not many people in sports seem to be aware of its values, feasibility and applications to the real stadium and the world. Computer Vision (CV) is a subfield of artificial intelligence and machine learning that develops techniques to train computers to interpret and understand the contents inside images. Computer Vision aims to replicate parts of the complexities in the human visual system and visual perception by applying deep learning models to accurately detect and classify objects from the dynamic and varying physical world. Many types of sports are often multidimensional systems that incorporate a plethora of data points that make one team or athlete better than the other.


Face recognition and the future air travel experience

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Airports uniquely demand both a very high passenger throughput and a very high degree of security underpinned by the positive identity confirmation of those passengers. At multiple points throughout the air travel experience, traveler identity must be confirmed to meet commercial policy, physical security, or national security requirements. This uncommon set of demands has forced innovation in the form of automated identity confirmation, primarily using biometrics. For two decades, some combination of face, fingerprint, and iris recognition has been deployed in an effort to speed up identity confirmation, with the goal of creating a secure and frictionless passenger experience. Thanks to rapid advances in Artificial Intelligence and specific technologies like Deep Learning and Convolutional Neural Networks, face recognition, in particular, has dramatically improved in the last few years.


Open-Source Computer Vision Projects (With Tutorials) - The Click Reader

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If you are a student or a professional looking for various open-source computer vision projects, then, this article is here to help you. The computer vision projects listed below are categorized in an experience-wise manner. All of these projects can be implemented using Python. Face and Eyes Detection is a project that takes in a video image frame as an input and outputs the location of the eyes and face (in x-y coordinates) in that image frame. The script is fairly easy to understand and uses Haar Cascades for detecting the face and the eyes if found in the image frame.


The 5 Most Amazing Computer Vision Techniques to Learn

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At this point, computer vision is the hottest research field within deep learning. It fits in many academic subjects such as Computer science, Mathematics, Engineering, Biology, and psychology. Computer vision represents a relative understanding of visual environments. Therefore, due to its cross-domain mastery, many scientists believe the field paves the way towards Artificial General Intelligence. Recent developments in neural networks and deep learning approaches have immensely advanced the performance of state-of-the-art visual recognition systems. Let's look at what are the five primary computer vision techniques.


10 Best Python Libraries For Computer Vision Tasks

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One of the most favourite languages amongst the developers, Python is well-known for its abundance of tools and libraries available for the community. The language also provides several computer vision libraries and frameworks for developers to help them automate tasks, which includes detections and visualisations. Below here, we are listing down 10 best Python libraries that developers can use for Computer Vision. It also provides researchers with low-level components that can be mixed and matched to build new approaches. IPSDK is an image processing library in C and Python.


Computer Vision, Deep Learning, and OpenCV Courses - PyImageSearch Plus

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Truly, I think I have offered enough of my content and expertise for free. If you would like to access my centralized code repos, video tutorials, pre-configured Jupyter Notebooks in Google Colab, I have to ask that you kindly join PyImageSearch Plus. Your support of PyImageSearch Plus will not only give you a great education (which will pay off multiple times over), but you'll also be supporting myself/PyImageSearch, which allows me to produce even more high-quality content.