COMPUTER VISION

Rapid advances in Artificial Intelligence have enabled programs to process countless digital images and videos. With considerable amount of digital data being generated these days in the form of text, audio, video, and images, organizations must equip themselves competently to address the evolving demands of analytics-driven by this change.We integrate computer vision services as well as train models to identify specific places, people, and objects and categorize them to retrieve valuable information as well as analytics.

COMPUTER VISION

Rapid advances in Artificial Intelligence have enabled programs to process countless digital images and videos. With considerable amount of digital data being generated these days in the form of text, audio, video, and images, organizations must equip themselves competently to address the evolving demands of analytics-driven by this change.

We integrate computer vision services as well as train models to identify specific places, people, and objects and categorize them to retrieve valuable information as well as analytics.
Computer-Vision-3.jpg
Services
The simple camera clicks we’re so familiar with have brought about an exponential rise in the volume of digital media over the last few years. By inculcating nested object classification, pattern recognition, segmentation, detection and more, our custom-built computer vision apps and models allow businesses to reduce human effort, optimize operations and utilize this rich data to scale visual technology.

Image Segmentation

This process involves segmenting an image into multiple homogeneous regions based on certain similarity parameters, so that each region can be individually analyzed and is different from its neighboring regions. Such categorization helps in tagging people, labeling objects, face recognition, traffic control, and various other tasks.

Contextual Image Classification

Humans can separate a person or object from their surroundings by identifying boundaries and doing a comparative check with memories or records of similar entities. Computers require a certain context to classify things and form a relationship between pixelated regions. This way, signals, and noise are distinguished and pattern recognition can be performed.

Object Detection

Object detection is the first stage of intelligent image analysis. Each object consists of several distinguishable properties that the software can use for classification. This, combined with an existing library of images, allows the software to compare, learn and determine valuable techniques to locate similarities and differences and provide accurate detection results. Object detection facilitates processes like automated damage assessment for insurance claims, property maintenance, store inventory management and more.

Face Recognition

Once the software recognizes an object, in this case — a face, the image is further processed to identify the person by comparing the facial data with existing data. The applications of this technology range across industries like healthcare, traffic management, manufacturing, HR management, security and so on.
Our Process
A majority of the information humans receive is visual. We help you capitalize on this limitless amount of visual data around us by creating cutting edge computer vision solutions through a systemized process.
Acquiring Image Datasets
Acquiring Image Datasets
To initiate the process, we analyze the business goals and create a database of images extracted from multiple sources. Structured, relevant, and quality data is prepared to serve as a guideline for future comparison.
Labelling Datasets
Labelling Datasets
In image processing, labeling helps to make the database more search-friendly. Filtering similar patterns and making object comparisons become more efficient with this method. Variables like color, contour, intensity, and size are used to create labels and organize the data.
Processing the Data
Processing the Data
The labeled dataset undergoes a meticulous quality check by being tested against training data. We run a series of automated processes to enhance the images like adding or removing pixels, removing noise, sorting misclassified data, and so on.
Data Augmentation
Data Augmentation
To improve the training data, the images are modified with a variety of techniques like flipping (horizontally or vertically), cropping, blurring, zooming, and compression to train the model for more accurate image recognition results.
Understanding the Image
Understanding the Image
In the final stage, the model is able to correctly interpret and categorize the object identified. The software is now adequately trained to recognize images from new input sources. This iterative process ensures that the model continues to enhance its capabilities over time.
Acquiring Image Datasets
Acquiring Image Datasets
To initiate the process, we analyze the business goals and create a database of images extracted from multiple sources. Structured, relevant, and quality data is prepared to serve as a guideline for future comparison.
Labelling Datasets
Labelling Datasets
In image processing, labeling helps to make the database more search-friendly. Filtering similar patterns and making object comparisons become more efficient with this method. Variables like color, contour, intensity, and size are used to create labels and organize the data.
Processing the Data
Processing the Data
The labeled dataset undergoes a meticulous quality check by being tested against training data. We run a series of automated processes to enhance the images like adding or removing pixels, removing noise, sorting misclassified data, and so on.
Data Augmentation
Data Augmentation
To improve the training data, the images are modified with a variety of techniques like flipping (horizontally or vertically), cropping, blurring, zooming, and compression to train the model for more accurate image recognition results.
Understanding the Image
Understanding the Image
In the final stage, the model is able to correctly interpret and categorize the object identified. The software is now adequately trained to recognize images from new input sources. This iterative process ensures that the model continues to enhance its capabilities over time.
Tools & Methods
Cloud-and-vision
open-cv
Tesseract_OCR
keras
Torch
Tensor-flow
Cloud-and-vision
open-cv
Tesseract_OCR
keras
Torch
Tensor-flow
Case Studies
Other Services
We offer the full spectrum of services to assist enterprises in working better & achieving their goals. Take a look at our other service offerings below.
Other Services
Other Services
We offer the full spectrum of services to assist enterprises in working better & achieving their goals. Take a look at our other service offerings below.
Other Services