[unable to retrieve full-text content]Accessing the internal component of digital images using Python packages becomes more convenient to understand its properties as well as nature.
Original Post: Basic Image Data Analysis Using Numpy and OpenCV – Part 1
[unable to retrieve full-text content]The best way to go about learning object detection is to implement the algorithms by yourself, from scratch. This is exactly what we’ll do in this tutorial.
Original Post: How to Implement a YOLO (v3) Object Detector from Scratch in PyTorch: Part 1
[unable to retrieve full-text content]PyTorch Tensor Basics; Top 7 Data Science Use Cases in Finance; The Executive Guide to Data Science and Machine Learning; Data Augmentation: How to use Deep Learning when you have Limited Data
Original Post: KDnuggets™ News 18:n20, May 16: PyTorch Tensor Basics; Data Science in Finance; Executive Guide to Data Science
Posted by Vittorio Ferrari, Research Scientist, Machine PerceptionIn 2016, we introduced Open Images, a collaborative release of ~9 million images annotated with labels spanning thousands of object categories. Since its initial release, we’ve been hard at work updating and refining the dataset, in order to provide a useful resource for the computer vision community to develop new modelsToday, we are happy to announce Open Images V4, containing 15.4M bounding-boxes for 600 categories on 1.9M images, making it the largest existing dataset with object location annotations. The boxes have been largely manually drawn by professional annotators to ensure accuracy and consistency. The images are very diverse and often contain complex scenes with several objects (8 per image on average; visualizer). In conjunction with this release, we are also introducing the Open Images Challenge, a new object detection challenge to be held…
Original Post: Announcing Open Images V4 and the ECCV 2018 Open Images Challenge
Eric Christiansen, Senior Software Engineer, Google ResearchIn the fields of biology and medicine, microscopy allows researchers to observe details of cells and molecules which are unavailable to the naked eye. Transmitted light microscopy, where a biological sample is illuminated on one side and imaged, is relatively simple and well-tolerated by living cultures but produces images which can be difficult to properly assess. Fluorescence microscopy, in which biological objects of interest (such as cell nuclei) are specifically targeted with fluorescent molecules, simplifies analysis but requires complex sample preparation. With the increasing application of machine learning to the field of microscopy, including algorithms used to automatically assess the quality of images and assist pathologists diagnosing cancerous tissue, we wondered if we could develop a deep learning system that could combine the benefits of both microscopy techniques while minimizing the downsides.With “In Silico…
Original Post: Seeing More with In Silico Labeling of Microscopy Images
Posted by Matthias Grundmann, Research Scientist and Jianing Wei, Software Engineer, Google Research One of the most compelling things about smartphones today is the ability to capture a moment on the fly. With motion photos, a new camera feature available on the Pixel 2 and Pixel 2 XL phones, you no longer have to choose between a photo and a video so every photo you take captures more of the moment. When you take a photo with motion enabled, your phone also records and trims up to 3 seconds of video. Using advanced stabilization built upon technology we pioneered in Motion Stills for Android, these pictures come to life in Google Photos. Let’s take a look behind the technology that makes this possible! Motion photos on the Pixel 2 in Google Photos. With the camera frozen in place the focus…
Original Post: Behind the Motion Photos Technology in Pixel 2
Posted by Yang Song, Staff Software Engineer and Serge Belongie, Visiting Faculty, Google ResearchThanks to recent advances in deep learning, the visual recognition abilities of machines have improved dramatically, permitting the practical application of computer vision to tasks ranging from pedestrian detection for self-driving cars to expression recognition in virtual reality. One area that remains challenging for computers, however, is fine-grained and instance-level recognition. Earlier this month, we posted an instance-level landmark recognition challenge for identifying individual landmarks. Here we focus on fine-grained visual recognition, which is to distinguish species of animals and plants, car and motorcycle models, architectural styles, etc. For computers, discriminating fine-grained categories is challenging because many categories have relatively few training examples (i.e., the long tail problem), the examples that do exist often lack authoritative training labels, and there is variability in illumination, viewing angle and…
Original Post: Introducing the iNaturalist 2018 Challenge