Computer Vision
Images to Insights
Visual content holds a wealth of untapped insights. We build powerful, automated solutions that efficiently extract actionable intelligence from your images and videos.
Our Expertise
Our team has deep expertise in computer vision, working with 2D images, 3D data, and video across multiple modalities—including RGB, X-ray, Ultrasound, and beyond. We develop high-performance AI solutions for multi-object detection and tracking (MOT), volumetric segmentation, knowledge distillation, and more, ensuring accuracy and efficiency across diverse applications.
We don’t just build AI—we optimize it for real-world performance. Our solutions are designed for high-throughput batch processing when scalability is key, and low-latency real-time inference for applications that require instant decision-making. We deploy across cloud, edge, and on-prem environments, ensuring flexibility and efficiency. With extensive experience in ONNX, TensorRT, and hardware-accelerated optimization, we make sure our models run fast, efficiently, and at scale—whether on high-performance cloud infrastructure or resource-constrained edge devices.
From medical imaging analysis to quality control in production lines, we specialize in transforming raw visual data into actionable insights. Our cross-industry expertise allows us to build customized AI-driven vision solutions that streamline operations, enhance decision-making, and unlock new opportunities—whether in healthcare, industrial automation, retail, or beyond.
Case Studies
Identifying Athlete Injuries in
Real-Time Video
Project Details
- Designed, developed, and deployed custom edge-capable 4K camera hardware on-site.
- Implemented a comprehensive suite of ML algorithms, including:
- Classification (EfficientNet)
- Multi-object tracking (CenterNet + ByteTrack)
- Keypoint detection (CenterNet)
- Action recognition (CSN via MMAction2)
- Custom musculoskeletal analysis (classical CV)
- Scalable AWS-based pipeline efficiently processes over 1TB/day of 4K video (30 fps) from multiple locations nationwide.
- Alerts on-site physicians within 15 minutes of athlete injury event, enabling rapid medical intervention.
Volumetric Segmentation of Scar Tissue
From 3D Ultrasound
Project Details
- Developed a comprehensive pipeline featuring specialized ultrasound preprocessing techniques paired with custom 3D CNN architectures.
- Implemented V-Net for precise volumetric segmentation tasks.
- Achieved over 75% Dice score for segmenting target volumes (scar tissue), despite targets representing less than 0.1% of total image volume.
- Model trained on 45 histology-labeled 3D ultrasound images.
- Learn more about the project background
ML Based Preprocessing for Improved
X-Ray Segmentation Accuracy
Project Details
- Achieved over 20% improvement in segmentation accuracy (DICE/IOU) through a novel CNN-based preprocessing approach.
- Developed lightweight CNN models that automatically correct image laterality and rotation, significantly simplifying the segmentation solution space.
- Successfully validated on:
- Hand PA X-rays (index metacarpal segmentation)
- Lateral lumbar spinal X-rays (L3 vertebra segmentation)
- Presented results at NVIDIA GTC 2019.
Distilling Large Models To Enable
Edge Computing
Project Details
- Consolidated multiple could-based binary classifiers into a single, streamlined edge-capable multilabel classification model
- Introduced new model format to have less parameters and a single multilabel classification head
- Trained using original data, leveraging KL-Divergence loss on class activations
- Recaptured the target activations with Pearson-R correlation of >0.95
- Deployed the optimized model on edge devices, resulting in over a 3x reduction in processing costs.