Lexicon | Opporture | Page 7 Of 9

Object Detection

Object detection is a computer technology related to computer vision and image processing that offers the capability of identifying instances of semantic objects of specific classes (e.g., humans, buildings, and cars) within digital images and videos. In addition it provides localization information on the whereabouts of such objects. This procedure sets it apart from classification, which only informs about the type of object. Object detection has various applications across a range of fields, including autonomous driving, surveillance, robotics, healthcare, and more. It can be employed in numerous ways so as to ensure optimal results when utilizing artificial intelligence. Applications of Object Detection Object detection plays an essential role in enabling autonomous vehicles to navigate safely on roads by detecting and localizing pedestrians, vehicles, traffic lights and road signs. It is also widely used in surveillance systems for identifying potential threats and alerting authorities. Additionally, it is a key component in robotics, aiding perception of the environment and interaction with objects. Furthermore, object detection is highly beneficial in medical imaging applications such as cancer detection, X-rays or CT scans analysis, and MRI scans segmentation. Lastly, it can be applied in retail sectors for monitoring customers’ behavior, tracking inventory, and fighting shoplifting.

Object Tracking

Object tracking is a concept employed within Artificial Intelligence which involves locating and following one or multiple specific objects over a video sequence or set of images. This necessitates the detection of the subject object(s) in each frame, calculating its position and motion, then utilizing that data to forecast their location in upcoming frames. Object tracking is essential for tasks such as surveillance, robotics, driverless cars and augmented reality, thus making it a focal point for research in computer vision and artificial intelligence. A range of techniques are available for this purpose such as feature-based strategies, correlation filters, optical flow and deep learning-aligned approaches. The complexity of object tracking increases when the subject items are quickly moving, undergo an alteration in appearance, or become concealed by other objects on the scene. As such, developing efficient object tracking algorithms is being vigorously pursued in computer vision and AI. Applications Of Object Tracking AcrossVarious Fields: Object tracking is used in security systems to detect and monitor potential threats. It is deployed in public places such as airports, railway stations, and shopping malls. Robotics employ object tracking for navigation, object grasping and manipulation, and autonomous vehicles. In sports, it is used to track the position and movement of athletes during training and competitions in order to analyze their performance and provide feedback. Augmented reality applications use object tracking to overlay digital information onto real-world objects for gaming, advertising and education purposes. Automotive applications such as self-driving cars make use of object tracking to monitor the position and movement of other vehicles, pedestrians, and obstacles on the road. Finally, it is widely used in medical imaging to track the position and movement of organs, tissues, and tumors for cancer treatment, radiation therapy, and surgery planning.

Obsorve

Obsorve is a combination of the words “Observe” and “Absorb.” It denotes a learning technique by observing first and then absorbing the information – in simple terms, to watch and learn. In Opporture, we focus on training our AI models to Obsorve and improve the accuracy, contextual understanding, and relevance of the outcomes they generate. This reflects our unique methodology of keenly observing the requirements and content standards that apply to each industry and then absorbing and applying them to tailor our AI model training strategies. It also signifies our commitment to staying ahead in our game in this fast-paced, dynamic AI industry by continuously observing trends and absorbing insights to deliver cutting-edge AI-enabled services to our clients. How does Opporture use the Obsorve Philosophy? Content moderation Opporture’s Obsorve methodology allows us to closely observe user-generated content and other forms of content, absorb and understand the industry guidelines and apply this knowledge to identify and flag inappropriate content. Content labeling: While labeling content, Opporture’s obsorve approach makes us observe fine-grained details in every piece of content – image, video, or text and absorb this information to accurately label the details. This results in efficient content organization and retrieval. Content annotation Opporture’s obsorve mindset comes in handy for our content annotation services, where we carefully observe and absorb the context and relevance of the content to be annotated. We then use these insights to apply detailed notes or annotations to enhance the contextual understanding of AI/ML models. Content tagging When applied to our content tagging services, the obsorve philosophy helps us analyze and observe different forms of content and absorb every relevant information, including keywords, metadata, categories, etc. This results in accurate tagging and classification of content for improved content discoverability and organization. Related terms AI-Enabled Services Annotation Model

Opporture

The word Opporture was coined out of a combination of the words opportunity and future. Opporture, an AI company, stands for creating opportunities for the future. We’re a content conglomerate that’s the spine behind many successful startups. Opporture delivers AI-enabled content solutions, including content moderation, content annotation, content tagging and labeling, content consulting, distribution, and transformation. Through its host of AI model training services, Opporture aims to make AI more human and context-driven. This involves leveraging human perception to train AI algorithms to understand and mimic human behavior. What does Opporture do? Opporture empowers businesses across industry verticals with bespoke AI model training services that align with their business needs and use cases. Opporture’s core offering is pivoted on enabling businesses to leverage AI models in their content processes – from content creation to content dissemination. In short, Opporture is the content master that businesses need to help them create, transform and distribute relevant, useful, safe, and appropriate content to their consumers. Content Consulting Opporture’s experts help businesses narrow down the right niche, suitable media format, the perfect audience, and the right content for your platform. Backed by rich AI expertise and NLP (natural language processing) capabilities across 100+ languages and dialects, Opporture helps train algorithms to deliver content that aligns with audience interests. Content Creation Harnessing ML capabilities and powered by rich data insights, Opporture helps businesses extract, curate, and commission relevant content that aligns with the community or platform guidelines. With Opporture, businesses gain useful insights on what their audience wants and tailor their content strategy accordingly. Content moderation Opporture helps content moderation teams and organizations to identify and eliminate harmful or inappropriate content, thus ensuring safe digital spaces. Opporture’s AI experts also help improve the decision-making capabilities of AI algorithms by training them with accurate content insights. Content Tagging & Labeling Opporture’s AI experts identify, group, and organize content under relevant tags and labels to improve the relevance and discoverability of the content. Leveraging NLP and data mining techniques, Opporture helps add contextual information to raw, unstructured content to enhance its usability. Content annotation By annotating text, video, images, and audio content, Opporture helps calibrate algorithms used by businesses for training prediction data and derive accurate results. Opporture employs different annotation techniques, such as bounding boxes, semantic segmentation, polygon annotation, keypoint annotation, etc., to enable models to be trained with sufficient amount of data and deliver precision outcomes. Related terms Annotation Bounding Box Machine Learning Model Natural Language Processing Polygon Annotation Semantic segmentation

Panoptic Segmentation

In computer vision, the task of panoptic segmentation involves three distinct steps: separation of each object in an image into individual parts that are independent of one another, painting of separate parts with different colors for labeling, and classification of the objects. The purpose of panoptic segmentation is to unify the two tasks of object detection and semantic segmentation into one overall mechanism. Objects extracted from an image are classified into two categories: things and stuff. Things refer to objects with well-defined geometry and are countable, such as people and cars. On the other hand, stuff is characterized more by texture and material, for example, the sky or water bodies. How Is Panoptic Segmentation Used? Radiologists can use panoptic segmentation to easily recognize tumor cells in their workflows, as the algorithm enables detection of the foreground and background. Autonomous vehicles can benefit from this method’s distance-to-object estimations for better steering, braking, and acceleration decisions. Panoptic segmentation can also power features like Portrait mode, Auto-focus, and Photomanipulation in smartphones. AR applications leverage panoptic segmentation to add virtual objects to the scene in real time. Finally, it can be employed to analyze videos in real-time, such as detecting objects in security camera feeds or tracking player movements in sports footage.

Part-of-Speech Tagging

Part-of-Speech (POS) tagging is a crucial step in natural language processing (NLP) and computational linguistics. It involves figuring out and assigning parts of speech, like nouns, adjectives, or verbs, to each word in a given text. This is done by considering both the word’s definition and its context in the sentence. POS tagging helps in understanding how sentences are structured and what they mean. It’s a fundamental task in NLP. Different methods are used to get accurate tagging, such as stochastic, neural network-based, and rule-based approaches. These methods help computers comprehend the nuances of language and make sense of written text. Applications and Examples of Part-of-Speech Tagging Text-to-Speech Conversion POS tagging plays a key role in text-to-speech (TTS) systems by helping them figure out the right way to pronounce words based on their context. For example, the word ‘read’ is said differently depending on whether it’s in the past or present tense. POS tagging can spot these differences, making sure that TTS systems say words correctly in different situations. Additionally, it helps handle words that are spelled the same but sound different (homographs), making sure TTS systems pronounce them clearly based on how they fit into a sentence. Sentiment Analysis In sentiment analysis, POS tagging makes detecting and understanding opinion words easier. It accurately picks out adverbs, verbs, and adjectives – words that are often important for expressing feelings. This helps in digging deeper into the emotions and sentiments conveyed in a text. It comes in handy for things like monitoring social media, doing market research, and analyzing customer feedback, where understanding public sentiment is really important. Syntactic Parsing In the world of parsing, POS tagging is a crucial step that helps build a detailed structure of sentences. By making clear the role of each word, POS tagging allows for the creation of parse trees, which are super useful for checking grammar and analyzing how sentences are put together. This is a big deal in language learning apps, grammar-checking tools, and other NLP apps that need to really understand how language works. Information Retrieval When it comes to information retrieval systems like search engines, POS tagging steps up search accuracy by focusing on specific parts of speech. For example, giving more attention to proper nouns and nouns can help fine-tune search results, making them more relevant and precise. This is especially handy in searches where what the user is looking for is closely tied to certain key terms. Machine Translation In machine translation, POS tagging helps in getting the grammatical structure of the source language right. This understanding is crucial for creating translations in the target language that are not only grammatically correct but also make sense in context. POS tagging helps identify the roles of words and how they relate to each other in sentences, making sure the translated text is top-notch in accuracy and quality. Answers to Frequently Asked Questions What algorithms are employed for part-of-speech tagging? Commonly used algorithms for part-of-speech tagging are Maximum Entropy, neural network-based approaches like LSTM, and Hidden Markov Models. These methods help accurately categorize words in text based on their grammar. How does part-of-speech tagging contribute to sentiment analysis? POS tagging aids sentiment analysis by pinpointing word types. It helps understand sentiment by looking at how adverbs, verbs, and adjectives are used, making sentiment analysis models more accurate. What difficulties does part-of-speech tagging encounter? POS tagging encounters challenges such as dealing with homonyms, which are words with multiple meanings, and keeping up with changes in language over time. Overcoming these challenges involves using advanced algorithms and contextual analysis. Is part-of-speech tagging important for machine translation? Yes. Part-of-speech tagging is crucial for machine translation. It helps to figure out precisely the grammatical role of words in sentences. This understanding allows the translation system to keep the right sentence structure and meaning intact when going from one language to another. What advantages does part-of-speech tagging offer? Part-of-speech tagging boosts natural language processing by refining syntax analysis and understanding context better. It plays a key role in getting text translation, information retrieval, and sentiment analysis right, making it really important for AI-driven language applications. Related Terms Natural Language Processing (NLP) Sentiment Analysis Named Entity Recognition (NER)

Pixel Learning

A pixel is the smallest unit of display on a digital device that shows a picture or an image. Any digital image, video or object displayed on a computer or other digital device is composed of millions of pixels. Pixel learning denotes learning one pixel at a time. It’s a type of AI/ML approach that makes predictions at individual pixel levels within an image or a video. The pixel learning approach allows the model to analyze the input visual information pixel by pixel to offer a more fine-grained and accurate understanding of the visual content. Applications of Pixel Learning in AI/ML Image segmentation Pixel learning is most commonly used in image segmentation tasks is fields such as medical imaging, where the idea is to identify areas within an image or a video at pixel-level detail and classify them as needed. Object detection Pixel learning is particularly useful to detect finer details of objects within an image by analyzing every pixel of the image. This is especially crucial in applications such as robotics, video analytics, augmented reality, etc. Image restoration Pixel learning is used to restore or enhance images by working on several pixels at a time. This might involve activities such as denoising, inpainting, and super-resolution techniques which are critical tasks in fields like digital forensics, medical imaging, etc. Image classification Using pixel learning, machine learning algorithms can be trained to classify images (represented as matrices of pixels) into different classes. Image classification techniques are applied in applications that involve object recognition, face recognition or scene classification. Image synthesis Pixel learning techniques can be applied to generate new images or visualization by learning from individual pixel data. Tasks such as image synthesis, style transferring and generative adversarial networks (GAN) can be benefited from this technique. Autonomous vehicles When it comes to autonomous vehicles, pixel learning is applied in tasks such as lane detection, object detection, and semantic segmentation of road visuals. Machine learning algorithms can analyze pixel-level data from cameras or LiDAR sensors to recognize lane markings and detect objects and pedestrians in the vicinity. This is a critical requirement for safe autonomous driving. The term Pixel learning was invented by Opporture for the betterment of the AI industry. So please seek written permission before using this term. Related Terms Generative Adversarial Networks Machine Learning Model

Polygon Annotation

Polygon annotation is a precise and accurate process used to define the boundaries of an object or region in an image or video. It involves the use of a set of points connected by lines to form a closed shape, known as a polygon. This polygon then takes the shape of the object or region it surrounds, effectively marking objects in computer vision applications. Additionally, polygon annotation offers more detailed identifying information than other methods, such as bounding boxes or circles, as it allows for more complex shapes to be created. This can be particularly useful when working with objects or regions that possess irregular or partially occluded shapes. Polygon Use Cases: Image segmentation is one of the most common use cases of polygon annotation. In this, an image is divided into different segments. This helps in recognizing objects, classification, and processing images. Polygon annotation also helps identify the class and location of objects within an image. This is useful for working of autonomous vehicles, security systems, and robotics. In Radiology, polygon annotation identifies and labels structures and abnormalities within an image or a scan. This is useful in the diagnosis of diseases, particularly cancer. It also helps in surgical planning. Polygon annotation helps identify and label grasslands, forests, and urban areas. This assists in monitoring the environment and managing resources. Geologists use polygon annotation to create spatial data and maps that can be used in GIS applications such as urban planning, environmental analysis, and disaster management. Polygon annotation is also used in computer vision research to help with ground truth data for training and evaluating machine learning models.

Q-function

In artificial intelligence, the Q-function (short for Quality function) maps a state-action pair to a numerical value, which represents the expected total reward that an agent will receive if it takes that action in that state and thereafter acts optimally. The Q-function is primarily used in Q-learning, a reinforcement learning algorithm that uses the Q-function to arrive at an optimal policy for an agent in a given environment. During training, the agent updates the estimate of the Q-function based on the rewards it receives and the Q-values of the next state-action pairs. Once the Q-function has been learned, the agent can use it to choose actions that maximize its expected future rewards. Q-function is also referred to as state-action value function. Fields in Which Q-Function Can Be Applied Q- Function can be applied in various field like Robotics, gaming, autonomous driving, finance, healthcare and energy management. It enables robots to learn optimal control policies for performing tasks such as object manipulation, navigation, and obstacle avoidance. It is used in developing game-playing agents to play games such as chess, and poker by optimizing strategies to maximize the expected rewards. It can train autonomous vehicles to make optimal decisions based on the current state of the environment and maximizes safety and efficiency. It helps optimize optimize investment strategies and trading decisions, where the reward could be the profit or return on investment. As far a healthcare is concerned, Q-Function optimizes treatment strategies for chronic diseases, drug dosage, and clinical decision-making. Q-Function also optimizes energy consumption in buildings by controlling heating, ventilation, and air conditioning systems.

Q-learning

Q-Learning is a model-free, off-policy Reinforcement learning approach that determines the optimal course of action when presented with a given environment. This action is selected randomly and is based on the expectation of maximizing reward. Q-Learning does not require a pre-defined policy and can instead generate its own as it explores the environment. This enables the agent to take dynamic actions while operating outside of a given policy. Ultimately, this allows for efficient decision-making in any given context. What are the Uses of Q-Learning? Q-learning Helps train agents to make optimal decisions based on the current state of the environment to maximize rewards and minimize losses. Is used in the field of natural language processing to train chatbots and virtual assistants to ensure optimal responses based on the user’s query. Enables robots to learn optimal control policies for various tasks. Trains autonomous vehicles to make optimal decisions based on the current state of the environment to maximize safety and efficiency.

Recall (Sensitivity)

Recall refers to the ability of a machine learning model to identify relevant instances of a particular class or category accurately. More specifically, it is a metric that measures the proportion of actual positive instances identified by the model as positive out of all the positive instances in the dataset. Recall is also referred to as sensitivity or true positive rate. It is a crucial evaluation metric for classification models, especially when identifying all positive instances is critical, for example, in medical diagnosis or fraud detection. Let us take an example: In a binary classification problem where the goal is to predict whether an email is spam or not, recall helps to measure the proportion of all spam emails that are correctly classified as spam by the model out of all the spam emails in the dataset. A high recall value indicates that the model is good at identifying all spam emails, while a low recall value suggests that the model is missing some spam emails. Applications of Recall in AI Recall is important in medical diagnosis, where identifying positive instances (such as tumors or diseases) is critical. A high recall value allows accurate diagnoses and better patient outcomes. In fraud detection, too, recall plays a vital role. A high recall value can help prevent financial losses and improve security. Recall is a crucial metric in information retrieval systems, where the goal is to retrieve all relevant documents related to a particular query or topic. A high recall value, in this case, allows for accurate and comprehensive search results. In object detection tasks, where the target is to identify all instances of a specific object or class in an image or video, recall plays an important role. A high recall value allows for more accurate and complete object detection.

Recurrent Neural Networks (RNNs)

Recurrent neural networks, or RNNs, are a category of artificial neural networks. They are characterized by the flow of data in multiple directions between layers. RNNs can perform tasks like data sequence processing, speech recognition, and handwriting recognition by using their memory or internal state to recall past inputs. What Are the Applications of Recurrent Neural Networks? Language Modeling & Text Generation Since RNNs can predict the next word in a sequence, they are suitable for relevant and coherent text generation. This ability also makes them applicable for developing sophisticated language models in writing tools, predictive text inputs, and chatbots. Speech Recognition RNNs process audio signals to generate text from spoken languages- a vital technology behind all interactive voice-activated assistants like Alexa and Siri. The RNN-powered speech recognition enables voice assistants to comprehend and respond accurately to verbal commands. Image description generation Combining RNNs with CNNs (Convoluted Neural Networks) enables the interpretation and description of image contents. This integrated technology enables automated alt-text generation- a vital feature that helps the visually impaired with textual descriptions of images on digital platforms and websites. Video tagging & analysis RNNs are significant in content categorization and digital library retrieval. They help analyze video frames and generative descriptive tags to aid the process and improve search functionality in discovering content from large video databases. Text Summarization RNNs are particularly useful in generating document overviews, research, and news aggregation. They facilitate text summarization by condensing extensive text documents while retaining the critical contextual information in the summaries. Time series prediction RNNs’ ability to forecast future values in time-series data makes them valuable for meteorologists and financial analysts. These professionals rely on RNNS to process sequential data for weather forecasting and stock market analysis. FAQs 1. How are CNN and RNN different from each other? The main difference between CNNs and RNNs is their structure and application. Convoluted Neural Networks use convolutional layers to recognize patterns within images. Their ability to process grid-like data makes them ideal for video and image analysis. Recurrent Neural Networks, on the other hand, are suitable for text or time series. RNNs use their memory feature to make predictions based on previous inputs, making them suitable for speech recognition and text generation. 2. How do RNNs facilitate language processing? In language processing, RNNs use their sequence processing ability to facilitate several tasks like: Language modeling (prediction of the next word in a sentence) Machine translation Text generation RNNs sequentially analyze the language’s structure and context to generate coherent and relevant texts. They also translate the text from one language to another. 3. What is the primary advantage of RNNs? RNNs can process sequential data and remember previous inputs. This primary advantage helps RNNs comprehend and generate relevant texts. This ability makes RNNs indispensable in tasks involving: Speech recognition Natural Language Processing Time series data Applications relying on order and the data points’ context 4. What are the common hurdles of working with RNNs? Vanishing gradients – One of the main issues with RNNs arises when gradients become too small for the network to learn long-range sequences. On the contrary, the exploding gradients can cause large erratic gradients, resulting in unstable network behavior. Complicated sequential nature – The sequential nature of RNNs makes them computationally intensive. This drawback can complicate their training and application. 5. How has RNN technology progressed? RNN development includes: Long Short-Term Memory and Gates Recurrent Units (LSTM and GRU) to address the vanishing gradient issue and improve RNNs’ ability to capture long-term dependencies. Integration of attention mechanisms and transformer models, which facilitate various parts of the input sequence, has been successful in language translation and text summarization. Related Terms Convolutional Neural Networks Model Natural Language Processing

Category: Lexicon