Our computer vision development services
Reduced Inspection Times for Property Inspectors with a Computer Vision Solution
- Deep learning and computer vision driven image data extraction
- GPT-based NLP chatbot for enhanced customer experience
- Improved work efficiency by 80%
- Image classification for detecting anomalies
Computer Vision-Powered Blueprint Analysis for Improved Construction Workflow
- Reduced marking errors by up to 90%
- Accurate identification of blueprint regions using edge detection
- Lowered Project Completion time by 30%
- Automated labeling and coloring aligned with construction notes
Computer vision tools and frameworks we use
Leading brands we’ve worked with
Image Preprocessing
Our team excels in designing robust preprocessing pipelines tailored to specific tasks and datasets. We ensure optimal normalization, resizing, and enhancement to improve model performance and generalization.
Feature Extraction
Leveraging a deep understanding of traditional feature extraction methods and state-of-the-art deep learning architectures, we extract meaningful features crucial for accurate object recognition, classification, and segmentation.
OCR and ICR
Leveraging advanced OCR and ICR techniques, we extract text from images and documents accurately, enabling automated data extraction and analysis in domains like finance, healthcare, and document management.
Image Generation with GANs
Our proficiency in GANs empowers us to generate synthetic data for augmenting training sets, creating diverse datasets for training robust models, and generating realistic imagery for various applications.
Image Segmentation
Our expertise in semantic and instance segmentation enables precise delineation of objects in complex scenes, facilitating applications like medical image analysis, autonomous navigation, and industrial automation.
Object Detection
With extensive experience in object detection frameworks such as Faster R-CNN, YOLO, and SSD, we develop custom solutions for real-time detection in various scenarios, from surveillance to autonomous vehicles.
Intelligent Video Analysis
With expertise in video processing and deep learning, we develop intelligent solutions for video summarization, activity recognition, and real-time object tracking, enabling actionable insights from video data.
Tracking and Labeling
Our team excels in designing scalable tracking algorithms and automated labeling pipelines, facilitating efficient data annotation and enabling high-quality labeled datasets for training and validation.
Data Augmentation
We develop sophisticated data augmentation strategies to enrich training datasets, mitigating overfitting and enhancing model robustness in the face of real-world variability.
Transfer Learning
Leveraging pre-trained models and transfer learning, we accelerate development cycles and deliver cost-effective solutions tailored to specific customer needs, even with limited labeled data.
Our computer vision development process
Requirements Gathering & Problem Definition
Data Collection and Annotation
Exploratory Data Analysis (EDA)
Model Selection and Architecture Design
Training, Validation and Evaluation
Deployment and Integration
Requirements Gathering & Problem Definition
Data Collection and Annotation
Exploratory Data Analysis (EDA)
Model Selection and Architecture Design
Training, Validation and Evaluation
Deployment and Integration
Computer vision development services
What is Computer Vision?
How do you address privacy and security concerns in computer vision solutions?
We leverage secure cloud platforms with access controls to safeguard your computer vision data throughout its lifecycle. Our team ensures compliance with data privacy regulations (GDPR, HIPAA) and prioritizes transparency with clear communication on data usage, deletion options, and user-controlled privacy features within your application.
What level of accuracy can be expected from the computer vision solution?
Our deep learning experts tailor Convolutional Neural Networks (CNNs) to your specific task, like facial recognition or object detection. Additionally, we employ advanced techniques like data augmentation, creating variations of existing data (like different pothole severities in road images), to enhance the model’s ability to handle unforeseen situations.
Rigorous validation with computer vision-specific metrics like mAP for object detection ensures exceptional performance and identifies any potential biases. This collaborative approach guarantees a computer vision solution that delivers top-notch accuracy in real-world scenarios.
How does Computer Vision Software Development separate itself from conventional image processing?
Computer vision software development goes beyond conventional image processing by focusing on semantic understanding, contextual analysis, machine learning integration, robustness to variability, diverse applications, and real-time processing needs. It extracts meaningful information from visual data, recognizes patterns, and enables intelligent decision-making, distinguishing it from basic image manipulation techniques.
Our expertise lies in building software that leverages machine learning, specifically CNNs, to make machines truly “see” by extracting meaning from images and videos. Our solutions analyze the context, not just individual pixels, and are robust to real-world variations. This, combined with real-time processing capabilities, unlocks a vast array of applications, from self-driving cars to medical analysis, empowering machines to understand and interact with the world around them.
Which industries stand to gain from Computer Vision Software Development?
We’re at the forefront of developing groundbreaking solutions to facilitate these computer vision applications. Our solutions, using computer vision capabilities, help automate the analysis of complex medical scans (advanced image analysis), optimize production lines with visual inspection systems, and create frictionless checkout experiences (object recognition).
How do you perform data annotation in computer vision projects?
We use bounding boxes to precisely define the location of the objects, or even employ segmentation masks to differentiate the object from the background scenery. Additionally, keypoint annotations can pinpoint specific features of the objects, while attribute annotations capture details like color, size, or material. The type of annotation used depends entirely on the specific task the model is designed for. By providing this rich and granular data, we ensure our models “learn” from the most accurate information available, leading to exceptional performance.
What kind of support and maintenance do you provide post-deployment?
Point of view
AI Image Validation Solutions: Automating Quality Checks
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AI Object Detection: A Revolutionary Solution for Heavy Work
As industries face increasing demands for efficiency and safety, AI object detection stands as one of the best innovations. Object detection identifies and localizes objects in images or videos. It has advanced so much due to AI and machine learning integration with...
AI for Manufacturing Quotations: Streamline Quoting, Secure Margins
Traditional quoting processes rely on manually analyzing CAD drawings, extracting material and operation details, and estimating costs and lead times. For complex products, this process can take hours or even days, slowing operations and increasing the risk of errors....
How AI Blueprint Interpretation is Changing Quotation Workflows?
Over the past year, 95% of manufacturers have invested in, or plan to invest in, AI technologies, marking a decisive shift in how design, production, and supply chain workflows are evolving. Meanwhile, traditional automation - macros, scripts, and rule-based processes...
