"Image understanding (IU) is the research area concerned with the design and experimentation of computer systems that integrate explicit models of a visual problem domain with one or more methods for extracting features from images and one or more methods for matching features with models using a control structure. Given a goal, or a reason for looking at a particular scene, these systems produce descriptions of both the images and the world scenes that the images represent."
– Image Understanding, by J.K. Tsotos. In Encyclopedia of Artificial Intelligence. Stuart C. Shapiro, editor. 1987. New York: John Wiley & Sons.
Whether you're interested in learning how to apply facial recognition to video streams, building a complete deep learning pipeline for image classification, or simply want to tinker with your Raspberry Pi and add image recognition to a hobby project, you'll need to learn OpenCV somewhere along the way. The truth is that learning OpenCV used to be quite challenging. The documentation was hard to navigate. The tutorials were hard to follow and incomplete. And even some of the books were a bit tedious to work through. The good news is learning OpenCV isn't as hard as it used to be. And in fact, I'll go as far as to say studying OpenCV has become significantly easier. And to prove it to you (and help you learn OpenCV), I've put together this complete guide to learning the fundamentals of the OpenCV library using the Python programming language. Let's go ahead and get started learning the basics of OpenCV and image processing. By the end of today's blog post, you'll understand the fundamentals of OpenCV.
Yes, as the title says, it has been very usual talk among data-scientists (even you!) where a few say, TensorFlow is better and some say Keras is way good! Let's see how this thing actually works out in practice in the case of image classification. Before that let's introduce these two terms Keras and Tensorflow and help you build a powerful image classifier within 10 min! Tensorflow is the most used library to develop models in deep learning. It has been the best ever library which has been completely opted by many geeks in their daily experiments .
This is the second story in our continuing series covering the basics of artificial intelligence. While it isn't necessary to read the first article, which covers neural networks, doing so may add to your understanding of the topics covered in this one. Teaching a computer how to'see' is no small feat. You can slap a camera on a PC, but that won't give it sight. In order for a machine to actually view the world like people or animals do, it relies on computer vision and image recognition.
While several feature scoring methods are proposed to explain the output of complex machine learning models, most of them lack formal mathematical definitions. In this study, we propose a novel definition of the feature score using the maximally invariant data perturbation, which is inspired from the idea of adversarial example. In adversarial example, one seeks the smallest data perturbation that changes the model's output. In our proposed approach, we consider the opposite: we seek the maximally invariant data perturbation that does not change the model's output. In this way, we can identify important input features as the ones with small allowable data perturbations. To find the maximally invariant data perturbation, we formulate the problem as linear programming. The experiment on the image classification with VGG16 shows that the proposed method could identify relevant parts of the images effectively.
Our Brain-Inspired Computing group at IBM Research-Almaden will be presenting at the 2018 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2018) our most recent paper titled "A Low Power, High Throughput, Fully Event-Based Stereo System." The paper describes an end-to-end stereo vision system that uses exclusively spiking neural network computation and can run on neuromorphic hardware with a live streaming spiking input. Inspired by the human vision system, it uses a cluster of IBM TrueNorth chips and a pair of digital retina sensors (also known as Dynamic Vision Sensors, DVS) to extract the depth of rapidly moving objects in a scene. Our system captures scenes in 3D with low power, low latency and high throughput, which has the potential to advance the design of intelligent systems. FIGURE 1: A fully event-based stereo vision system comprised of a pair of Dynamic Vision Sensors (left) which sends their output to a cluster of TrueNorth processors (right).
I wrote this blog to wrap up my first ever public talk at PyCon Thailand 2018 and to add some more details . Advertising technology, commonly known as "Ad Tech", has been used by brands, vendors, and agencies to analyze and get insights from potential customers' activities online. In the past year, machine learning and deep learning became a major tools for Ad Tech. For example, an image recognition system is used to identify the targets from brands, products, and logos on publicly posted images. The easiest way to identify brand from images is by its logo.
Whether it is facial recognition tech that is (allegedly) able to pick a wanted criminal out of a crowd of thousands or aerial drones which use image recognition smarts to predict fights before they take place, there is no doubt that we are living through a major paradigm shift for automated surveillance technology. But this kind of tech can have more grounded, everyday applications, too -- like helping prevent shoplifters stealing goods from their local mom-and-pop corner store. That is something seemingly demonstrated by a new artificial intelligence security camera called the "A.I. Guardman," built by Japanese telecommunication company NTT East and startup Earth Eyes Corp. The camera uses a special pose detection system to identify behavior it deems to be suspicious.
Our brains are wired in a way that they can differentiate between objects, both living and non-living by simply looking at them. In fact, the recognition of objects and a situation through visualization is the fastest way to gather, as well as to relate information. This becomes a pretty big deal for computers where a vast amount of data has to be stuffed into it, before the computer can perform an operation on its own. Ironically, with each passing day, it is becoming essential for machines to identify objects through facial recognition, so that humans can take the next big step towards a more scientifically advanced social mechanism. So, what progress have we really made in that respect?
The variety of pedestrians detectors proposed in recent years has encouraged some works to fuse pedestrian detectors to achieve a more accurate detection. The intuition behind is to combine the detectors based on its spatial consensus. We propose a novel method called Content-Based Spatial Consensus (CSBC), which, in addition to relying on spatial consensus, considers the content of the detection windows to learn a weighted-fusion of pedestrian detectors. The result is a reduction in false alarms and an enhancement in the detection. In this work, we also demonstrate that there is small influence of the feature used to learn the contents of the windows of each detector, which enables our method to be efficient even employing simple features. The CSBC overcomes state-of-the-art fusion methods in the ETH dataset and in the Caltech dataset. Particularly, our method is more efficient since fewer detectors are necessary to achieve expressive results.