Image Understanding

London-headquartered BeMyEye has made another acquisition, its third in a little over three years. This time the retail execution monitoring service is purchasing Russian crowdsourcing and image recognition provider Streetbee. The acquisition will see BeMyEye launch "Perfect Shelf," which will use image recognition technology to lower the cost for consumer goods companies wanting to get "objective and actionable" in-store insights. These will typically include share of shelf and planogram compliance (the specific placement of products on a store shelf). More broadly, BeMyEye offers a platform to enable companies and brands to crowdsource various in-store data.

We are going to use this existing model and build our own on top of it. This approach brings with it numerous advantages. For instance, it will save us a lot of time, some of the parameters that the Inception has already learned can be reused and we can still build a pretty accurate classifier with far less training data. This process of reusing pre-trained models on different but related tasks is known as Transfer Learning in the world of Deep Learning. First step is to download the training images for your classifier.

TLDR; This series is based on the work detecting complex policies in the following real life code story. Code for the series can be found here. Recent developments in computer vision have changed the computer vision landscape. Many scenarios that were once considered possible only in sci-fi have recently become as easy as consuming an API. In this series we are going to review a real world computer vision use case from the retail sector and are going to compare and contrast some of the different approaches and technologies available to solve the problem.

Python image recognition sounds exciting, right? However, it can also seem a bit intimidating. There's no need to be scared! This tutorial will teach you Python basics and how to use TensorFlow. Take this chance to discover how to code in Python and learn TensorFlow linear regression then apply these principles to automated Python image recognition.

Traditional color images only depict color intensities in red, green and blue channels, often making object trackers fail when a target shares similar color or texture as its surrounding environment. Alternatively, material information of targets contained in a large amount of bands of hyperspectral images (HSI) is more robust to these challenging conditions. In this paper, we conduct a comprehensive study on how HSIs can be utilized to boost object tracking from three aspects: benchmark dataset, material feature representation and material based tracking. In terms of benchmark, we construct a dataset of fully-annotated videos which contain both hyperspectral and color sequences of the same scene. We extract two types of material features from these videos. We first introduce a novel 3D spectral-spatial histogram of gradient to describe the local spectral-spatial structure in an HSI. Then an HSI is decomposed into the detailed constituent materials and associate abundances, i.e., proportions of materials at each location, to encode the underlying information on material distribution. These two types of features are embedded into correlation filters, yielding material based tracking. Experimental results on the collected benchmark dataset show the potentials and advantages of material based object tracking.

Let's first write a simple Image Recognition Model using Inception V3 and Keras The goal of the inception module is to act as a "multi-level feature extractor" by computing 1 1, 3 3, and 5 5 convolutions within the same module of the network -- the output of these filters are then stacked along the channel dimension and before being fed into the next layer in the network. The original incarnation of this architecture was called GoogLeNet, but subsequent manifestations have simply been called Inception vN where N refers to the version number put out by Google. What are we going to Detect? What does this Image say to a Computer?

Artificial Intelligence generated many possibilities which enhanced the understanding power of human. Today AI has become the foundation of the trending technologies in the market. When it comes about processing visual information AI is helping in identifying specific objects or categorizing images based on their content. Artificial Intelligence can also execute image recognition with the use of computer vision to communicate with humans. AI communications includes to understand the human gestures and then react accordingly.

The singular example of AI's progress in the last several years is how well computers can recognize something in a picture. Still, even simple tests can show how brittle such abilities really are. The latest trick to game the system comes courtesy of researchers at Auburn University in Auburn, Ala., and media titan Adobe Systems. In a paper released this week, they showed that top image-recognition neural networks easily fail if objects are moved or rotated even by slight amounts. A fire truck, for example, seen from head on, could be correctly recognized.

This project presents the results of a partnership between the Data Science for Social Good fellowship, Jakarta Smart City and Pulse Lab Jakarta to create a video analysis pipeline for the purpose of improving traffic safety in Jakarta. The pipeline transforms raw traffic video footage into databases that are ready to be used for traffic analysis. By analyzing these patterns, the city of Jakarta will better understand how human behavior and built infrastructure contribute to traffic challenges and safety risks. The results of this work should also be broadly applicable to smart city initiatives around the globe as they improve urban planning and sustainability through data science approaches.

The training method of repetitively feeding all samples into a pre-defined network for image classification has been widely adopted by current state-of-the-art. In this work, we provide a new method, which can be leveraged to train classification networks in a more efficient way. Starting with a warm-up step, we propose to continually repeat a Drop-and-Pick (DaP) learning strategy. In particular, we drop those easy samples to encourage the network to focus on studying hard ones. Meanwhile, by picking up all samples periodically during training, we aim to recall the memory of the networks to prevent catastrophic forgetting of previously learned knowledge. Our DaP learning method can recover 99.88%, 99.60%, 99.83% top-1 accuracy on ImageNet for ResNet-50, DenseNet-121, and MobileNet-V1 but only requires 75% computation in training compared to those using the classic training schedule. Furthermore, our pre-trained models are equipped with strong knowledge transferability when used for downstream tasks, especially for hard cases. Extensive experiments on object detection, instance segmentation and pose estimation can well demonstrate the effectiveness of our DaP training method.