Over the past few months, I've been working on a fascinating project with one of the world's largest pharmaceutical companies to apply SAS Viya computer vision to help identify potential quality issues on the production line as part of the validated inspection process. As I know the application of these types of AI and ML techniques are of real interest to many high-tech manufacturing organisations as part of their Manufacturing 4.0 initiatives, I thought I'd take the to opportunity to share my experiences with a wide audience, so I hope you enjoy this blog post. For obvious reasons, I can't share specifics of the organisation or product, so please don't ask me to. But I hope you find this article interesting and informative, and if you would like to know more about the techniques then please feel free to contact me. Quality inspections are a key part of the manufacturing process, and while many of these inspections can be automated using a range of techniques, tests and measurements, some issues are still best identified by the human eye.
Image recognition typically is a process of the image processing, identifying people, patterns, logos, objects, places, colors, and shapes, the whole thing that can be sited in the image. And advanced image recognition, in this way, is a framework for employing AI and deep learning that can accomplish greater automation across identification processes. As vision and speech are two crucial human interaction elements, data science is able to imitate these human tasks using computer vision and speech recognition technologies. Even it has already started emulating and has leveraged in different fields, particularly in e-commerce amongst sectors. Advancements in machine learning and the use of high bandwidth data services are fortifying the applications of image recognition.
We propose an unsupervised method that, given a word, automatically selects non-abstract senses of that word from an online ontology and generates images depicting the corresponding entities. When faced with the task of learning a visual model based only on the name of an object, a common approach is to find images on the web that are associated with the object name, and then train a visual classifier from the search result. As words are generally polysemous, this approach can lead to relatively noisy models if many examples due to outlier senses are added to the model. We argue that images associated with an abstract word sense should be excluded when training a visual classifier to learn a model of a physical object. While image clustering can group together visually coherent sets of returned images, it can be difficult to distinguish whether an image cluster relates to a desired object or to an abstract sense of the word.
Training large-scale image recognition models is computationally expensive. This raises the question of whether there might be simple ways to improve the test performance of an already trained model without having to re-train or fine-tune it with new data. Here, we show that, surprisingly, this is indeed possible. The key observation we make is that the layers of a deep network close to the output layer contain independent, easily extractable class-relevant information that is not contained in the output layer itself. We propose to extract this extra class-relevant information using a simple key-value cache memory to improve the classification performance of the model at test time.
When we are faced with challenging image classification tasks, we often explain our reasoning by dissecting the image, and pointing out prototypical aspects of one class or another. The mounting evidence for each of the classes helps us make our final decision. In this work, we introduce a deep network architecture -- prototypical part network (ProtoPNet), that reasons in a similar way: the network dissects the image by finding prototypical parts, and combines evidence from the prototypes to make a final classification. The model thus reasons in a way that is qualitatively similar to the way ornithologists, physicians, and others would explain to people on how to solve challenging image classification tasks. The network uses only image-level labels for training without any annotations for parts of images.
Parametric spatial transformation models have been successfully applied to image registration tasks. In such models, the transformation of interest is parameterized by a fixed set of basis functions as for example B-splines. Each basis function is located on a fixed regular grid position among the image domain because the transformation of interest is not known in advance. As a consequence, not all basis functions will necessarily contribute to the final transformation which results in a non-compact representation of the transformation. For each element in the sequence, a local deformation defined by its position, shape, and weight is computed by our recurrent registration neural network.
Metric learning, aiming to learn a discriminative Mahalanobis distance matrix M that can effectively reflect the similarity between data samples, has been widely studied in various image recognition problems. Most of the existing metric learning methods input the features extracted directly from the original data in the preprocess phase. What's worse, these features usually take no consideration of the local geometrical structure of the data and the noise existed in the data, thus they may not be optimal for the subsequent metric learning task. In this paper, we integrate both feature extraction and metric learning into one joint optimization framework and propose a new bilevel distance metric learning model. Specifically, the lower level characterizes the intrinsic data structure using graph regularized sparse coefficients, while the upper level forces the data samples from the same class to be close to each other and pushes those from different classes far away.
This paper concerns the undetermined problem of estimating geometric transformation between image pairs. Recent methods introduce deep neural networks to predict the controlling parameters of hand-crafted geometric transformation models (e.g. However, the low-dimension parametric models are incapable of estimating a highly complex geometric transform with limited flexibility to model the actual geometric deformation from image pairs. To address this issue, we present an end-to-end trainable deep neural networks, named Arbitrary Continuous Geometric Transformation Networks (Arbicon-Net), to directly predict the dense displacement field for pairwise image alignment. Arbicon-Net is generalized from training data to predict the desired arbitrary continuous geometric transformation in a data-driven manner for unseen new pair of images.
Deep learning is a fascinating sub field of machine learning that creates artificially intelligent systems inspired by the structure and function of the brain. The basis of these models are bio-inspired artificial neural networks that mimic the neural connectivity of animal brains to carry out cognitive functions such as problem solving. A field with the most impressive results of neuromorphic computing is that of visual image analysis. Similar to how our brains learn to recognize objects in order to make predictions and act upon them, artificial intelligence must be shown millions of pictures before they are able to generalize them in order to make their best educated guesses for images they have never seen before. Professor Cheol Seong Hwang from the Department of Material Science and Engineering at Seoul National University and his research team have developed a method to accelerate the image recognition process by combining the inherent efficiency of resistive random access memory (ReRAM) and cross-bar array structures, two of the most commonly used hardware.
The global image recognition market was valued at USD 22,429.7 million in 2018 and is expected to reach USD xx million by 2026, growing at a CAGR of 18.4% during the forecast period. Image recognition is a method for collecting, processing, and scrutinizing images. Image recognition gathers a huge amount of data from the real world to generate symbolic or numerical information. The growth of the image recognition market is primarily driven by the increasing application of facial recognition in the financial industry and growing demand for security applications integrated with image recognition functions. Moreover, an upsurge in the usage of big data analytics across every industry vertical where image recognition plays a vital role is expected to create opportunities for the global image recognition market over the forecast period.