Search Results
12 results for “computer vision”
AI in Malaysian Manufacturing
Artificial intelligence adoption in Malaysian manufacturing covers predictive maintenance, computer vision quality control, demand forecasting, and supply-chain optimisation across the electronics, automotive, and food sectors.
AI in Malaysian Retail
AI in Malaysian retail encompasses the deployment of machine learning, computer vision, and natural language processing across Malaysia's retail sector, including e-commerce platforms, brick-and-mortar stores, and omnichannel retail operations.
AI in the Malaysian Palm Oil Industry
The application of artificial intelligence, computer vision, robotics, and predictive analytics to oil palm cultivation, harvesting, milling, and supply chain traceability in Malaysia.
Computer Vision
Computer vision is the field of artificial intelligence that enables machines to interpret and act upon visual information from the world — including images, video, and depth data.
Convolutional Neural Network
A convolutional neural network (CNN) is a type of deep neural network that uses convolutional layers to automatically learn spatial hierarchies of features from grid-structured data, most commonly images.
Image Segmentation
A computer vision task that partitions an image into meaningful regions by assigning a class label to every pixel, enabling pixel-level understanding of visual scenes.
Object Detection
Object detection is a computer vision task that involves identifying the location and category of one or more objects within an image or video frame, producing bounding boxes and class labels for each detected instance.
Optical Character Recognition
A computer vision technology that converts images of typed, handwritten, or printed text into machine-readable digital text, increasingly powered by deep learning and transformer-based vision models.
Pose Estimation
Pose estimation is the computer vision task of detecting and tracking the position and orientation of human bodies, hands, or objects from images or video, typically by locating keypoints such as joints.
Residual Network
A deep convolutional neural network architecture introduced by Microsoft Research in 2015 that uses skip connections to enable training of very deep networks, winning the ImageNet challenge with a top-5 error rate of 3.57%.
Segment Anything Model
The Segment Anything Model (SAM) is a foundation model from Meta AI for promptable image and video segmentation, able to isolate any object from a click, box, or mask with strong zero-shot generalisation.
YOLO (You Only Look Once)
YOLO is a family of real-time object detection models that frame detection as a single regression problem, predicting bounding boxes and class probabilities directly from an image in one network pass.