We develop computer vision solutions to teach computers to see and understand the world
around them. For example, we create software that can process and analyze visual
information, such as images and videos.
We achieve this using a combination
of machine learning algorithms and software
engineering techniques. Our machine learning algorithms allow computers to learn from
data and improve their performance over time, while our software engineering expertise
enables us to develop and deploy computer vision software on a variety of devices.
Computer vision is a field of artificial intelligence (AI) that enables computers and
systems to derive meaningful information from digital images, videos, and other
visual inputs, and to take actions or make recommendations based on that information.
Computer vision works by first acquiring an image from a camera or other sensor.
The image is then processed and analyzed using various techniques, including
machine learning and deep learning. Once the image is processed, the computer
can extract useful information, such as object presence location, and movement.
We teach computers to identify and classify objects in images for better and faster recognition. Our first step is assigning an image a label from a predetermined list of categories. We evaluate an input image and provide a label that classifies the image.
We create object detection systems in computer vision to locate and identify objects in images. Our models are trained to identify objects in an image, returning the coordinates of those objects. Each initial detection receives a unique identifier, and we track moving objects in a video as they are detected.
We develop methods for dividing an image into different parts, called regions, objects, or segments. This makes images easier to understand and analyze. Image segmentation is often used to find objects in images and to identify their borders.
We create high-quality video analytics solutions to automatically derive insights from video. Our systems recognize and track items, events, and patterns in videos using computer vision and machine learning algorithms. This is a useful tool for tracking and evaluating a variety of activities, including customer behavior, traffic patterns, and security risks.
We develop content analysis tools to extract meaningful insights from visual content like pictures or videos. Understanding and interpreting the content, structure, and context of visual data requires analysis. Using computer vision techniques for content analysis enables machines to perform tasks like image segmentation, object recognition, and scene understanding.
We develop facial recognition systems to recognize or authenticate a person's identity through their face. Our systems identify a set of facial features, such as the distance between an individual’s eyes, nose shape, and jawline contour. These features are then compared to a database of known faces to find a match.
We clearly define the problem or task that the computer vision system will address. This involves understanding the requirements, goals, and constraints of the users.
We collect a diverse and representative dataset of images relevant to the problem at hand. The dataset covers different variations, angles, lighting conditions, and potential challenges that the system may encounter.
We clean and preprocess the collected data to ensure its quality and consistency. This includes tasks such as resizing images, normalizing pixel values, removing noise, and augmenting the dataset through techniques like rotation, flipping, or adding noise.
We choose an appropriate model architecture or algorithm for image classification. This can range from traditional machine learning algorithms to deep learning models such as convolutional neural networks (CNNs).
We train the selected model using the preprocessed dataset. This involves feeding the images into the model and adjusting its parameters to minimize the difference between predicted and actual labels. The training process may require multiple iterations and hyperparameter tuning to optimize performance.
We assess the performance of the trained model using evaluation metrics such as accuracy, precision, recall, or F1 score. This step helps us determine how well the model generalizes to unseen data and whether it meets the desired performance criteria.
We integrate the trained model into a production environment or application where it can be used to classify new, unseen images. This can involve creating an API or embedding the model into an existing software system.
We continuously monitor the performance of the deployed model and collect feedback to identify any issues or areas for improvement. We regularly update the model with new data or retrain it if necessary to ensure its accuracy and relevance over time.
Self-driving cars employ computer vision to identify and monitor objects on the road, including pedestrians, traffic signals, and other vehicles. The car uses this information to navigate safely.
Computer vision is used to analyze medical images such as X-rays and MRI scans. This can aid medical professionals in making timely and more accurate diagnoses of illnesses and injuries.
Computer vision is a tool used by robots to sense and communicate with their environment. For instance, a robot may use computer vision to recognize and grasp objects or to navigate through a challenging environment.
Security and surveillance systems use computer vision to identify and monitor individuals and objects of interest. For example, a computer vision-equipped security system could identify an intruder automatically and alert security staff.
Computer vision is used in retail and e-commerce to enhance customer satisfaction and operational effectiveness. For example, a retail establishment may monitor customer flow and recognize popular items, while an online retailer may use computer vision to automate product searches.
Computer vision development increases efficiency and productivity by automating various tasks. This automation frees up time for more strategic endeavors.
Leveraging automation minimises the need for manual labour, reduces errors, and optimises resource usage, leading to reduced costs.
Computer vision significantly contributes to improving safety and security through applications such as surveillance, monitoring, and anomaly detection. This helps prevent accidents, identify threats, and maintain a secure environment.
Computer vision development opens avenues for creating innovative products and services. Whether through augmented reality applications or advanced imaging solutions, businesses can introduce novel offerings that align with evolving market demands.
Self-driving cars use computer vision to employ real-time object detection and tracking, enabling them to recognize and monitor various objects nearby, such as pedestrians, vehicles, and obstacles. This capability is essential for making adaptive decisions based on the ever-changing environment.
Self-driving cars depend on computer vision algorithms for lane detection and tracking. This process involves identifying lane markings and ensuring that the vehicle stays within the designated path. Lane detection enhances the car’s ability to remain centered and navigate lanes.
Computer vision is crucial in enabling self-driving cars to recognize and interpret traffic signs, including speed limits, stop signs, and directional indicators. Accurate traffic sign recognition is essential for the vehicle to comprehend and adhere to traffic regulations, ensuring safe and compliant operation on the road.
Computer vision is employed for categorizing medical images, including X-rays, MRIs, and CT scans. This process involves training models to precisely recognize and classify specific conditions or abnormalities in the images, providing valuable assistance to healthcare professionals in diagnosis and treatment planning.
The segmentation of medical images involves dividing an image into distinct regions based on specific criteria. Computer vision techniques are applied to accurately outline structures or abnormalities within medical images. This segmentation facilitates more detailed analysis and targeted planning for medical treatments.
Computer vision plays a pivotal role in thoroughly analyzing medical images. This encompasses the extraction of meaningful information, identification of patterns, and quantification of characteristics within images.
Utilizing computer vision technologies, robots can navigate their surroundings by perceiving and interpreting visual data. This includes employing sensors and visual information to autonomously navigate through environments, avoid obstacles, and reach predefined destinations.
Computer vision is pivotal in the domain of robotic manipulation, empowering robots to use visual information for precise object handling. This involves tasks like grasping, picking up objects, and responding to the environment based on visual clues.
Computer vision is integral to the recognition of objects by robots, allowing them to identify and categorize objects in their environment. This process includes training models to recognize a variety of objects, aiding robots in comprehending their surroundings and executing tasks based on object-specific information.
Computer vision is applied for facial recognition, enabling security systems to authenticate and identify individuals based on facial features. This technology is used in access control, identity verification, and security monitoring.
Computer vision contributes to detecting intrusions by analyzing visual data to identify unauthorized access or suspicious activities within a monitored area. This involves recognizing unusual movements, breaches of defined perimeters, or other anomalies indicative of potential security breaches.
Computer vision is employed for recognizing activities, monitoring and analyzing human behavior, or specific actions in a given space. This encompasses detecting unusual or suspicious activities and ensuring a proactive response to potential security incidents.
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Some common computer vision tasks include image classification, object detection, semantic segmentation, and instance segmentation.
Getting started with computer vision development is a long journey but a fruitful one. Follow the steps mentioned below to kickstart your journey:
There are plenty of tools available, but the ones that have gained popularity are OpenCV and TensorFlow.
Computer vision has numerous applications, including self-driving cars, parking occupancy detection, traffic flow analysis, X-ray analysis, cancer detection, and much more.
