Computer Vision – Its Successful Application in Healthcare , Security , Transportation , Retail

Computer vision is a field of artificial intelligence that trains computers to interpret and understand the visual world. Using digital images from cameras and videos and deep learning models, machines can accurately identify and classify objects — and then react to what they “see.” Back in early 2011, Android 4 added a “geeky feature” that allowed you to unlock your phone with your face. But, the technology was so primitive and insecure, that even a picture of the person could unlock the phone. This tech took a backseat, and the fingerprint scanner drove the tech-scene for a long time. With an estimated 16 million phones sold in just the first quarter of entering the market, the iPhone X was a sensation in 2017. Flaunting the revolutionary Face ID technology, the Apple device introduced a whole new era of mobile convenience. Face recognition technology in the device was so fine-tuned that it couldn’t even be fooled by identical twins. Currently, face unlock is a standard feature among all medium to premium segment smartphones. This Face Recognition technology was possible with Computer Vision. Computer Vision is an aspect of Artificial Intelligence. It involves the extraction of data and information from visuals by computers. 1. What is Computer Vision? If you come across the saying “a picture is worth a thousand words”, you probably know by now that the human brain extracts and analyses volumes of data using visual cues. Computer Vision is a rudimentary attempt at mimicking the biological function, of which we incidentally understand very little of. To understand how deeply rooted we are to the sense of sight, 40% of our nerve fibers are linked to the retina, and 90% of information transmitted to the brain is visual. Human brains can recognize patterns, shapes, colors, shades, contours, motion, and much more. We process visuals at 60,000X faster than text! To put it lightly, humans are visual creatures. 1.1 Computer Vision And Related Fields: What Makes CV Stand Out? Computer vision is often clubbed with many other AI and ML fields that deal with aspects of visual learning. But, it’s important to understand that there are significant differences in each of the areas. CV vs Image Processing CV vs Machine Vision 1.2 What makes Computer Vision challenging? While we have come a long way in the field of AI as a whole, the fact remains that the concept of deep learning is still yet to be explored to its full depth. What this essentially means is that computers are generally good at performing specific tasks in controlled environments. However, ComputerVision, in its real sense, is when computers can grasp clues from an open, unrestricted environment where the possibility of randomness and chaos is infinite. To do this has proven to be daunting at the least. What can aid deep learning is understanding how biological vision functions, and fitting those jigsaw puzzles into the AI system to make the computer “smarter”. Brain-eye coordination is a marvel of nature. Get to know about Computer Vision Application in real life by Downloading this Ebook  Get data science certification from the World’s top Universities. Learn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career.

WHAT IS NLP? Natural Language Processing (NLP) is the amalgamation of linguistics and Machine Learning (ML). NLP attempts to parse human-human and human-computer interactions in the form of language (speech or text) to deliver actionable results. NLP is an ML application, as machines “learn” from millions of sample datasets to understand the language in its natural form. Deep diving: The layers of NLP Language simply doesn’t fit into neatly lined boxes. Put another way, parsing language using rows and columns is rather unfounded, as it is inherently ambiguous. But since the computer can only process information in rows of zeros and ones, the attempt is inevitable. An attempt that has resulted in several real-world applications! Because of this conjuncture, NLP is considered a “Core AI” technology – in that it is one of the few disciplines that is a pursuit to further the understanding of computers on how the human brain functions. 1. Input Processing: Input processing is the method of collecting or rather archiving and segmenting speech or text in its raw form, i.e. dismantling long sentences into more digestible pieces.  For text inputs: If the data isn’t available in the standard UTF-8 character sets but is embedded in the image, and OCR (Optical Character Recognition) software is used. As this technology is quite old (especially given the advances that image processing has made over the years), plenty of open-source software is available to achieve this.  For speech inputs: When it comes to speech, input processing gets slightly more complicated. An entire field, known as Speech Recognition, forms a Deep Learning subset in the NLP universe. Let’s take a small segue into how Speech-to-text is accomplished today. 2. Morphological Analysis This phase aims to derive more meaning from the tokens themselves. The processes involved here are: Predicting parts-of-speech Each token is tagged as noun, pronoun, verb, adverb, adjective, and so on. POS tagging requires the usage of predictive analytics, as many words can take different forms when used in different contexts. For example, in the sentence “The moral law is above the civil”, the word “above” acts as a preposition. In the sentence “our blessings come from above”, the same word acts as a noun. Most models simply use statistical data, including common prefixes and suffixes, to tag the token. Lemmatization/Stemming The process of lemmatization strips down a word to its root level using a language dictionary. On the other hand, stemming simply aims to remove any suffixes to derive a root word using pattern matching. Lemmatizing is far more accurate than stemming. Identifying the stop word Some words like ‘a’, ‘and’ and ‘the’ simply create distraction and confusion in the statistical process. While they may be needed to identify parts-of-speech, their use beyond that is moot. So some NLP pipelines might categorize these words as stop words. The list and number of stop words used can affect the efficiency of the entire NLP system. Decompounding In some languages (like Germanic, Scandinavian and Dravidian languages), compound words are common and require to be split into their basic parts. For example, the word “hellblau” means “light blue” and can be split into its root words for a better context. To know more about NLP and Speech Recognition Download this Ebook