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Image Analysis Software Consulting and Development


Image analysis (IA) is the identification of attributes within an image via digital image processing techniques to make the IA process more accurate and cost-effective.
Since 2013, ScienceSoft helps both product companies and non-IT enterprises gain a competitive advantage by developing IA software.

Hand Your Image Analysis Project to Experts


ScienceSoft is ready to set your image analysis project in motion by providing the IA solution design, IA project estimation and roadmapping, software architecture planning, and more.

Image Analysis Core Tasks


Detect

Distinguish regions of interest for further analysis, individual objects from the background, etc.

Recognize

Label or classify objects in digital images based on one or several object classes: people, vehicles, electronic components, etc.

Identify

Recognize individual features of an object and classify it with more precision: identify individual people, specific vehicles, animal species, device models, etc.

WHY PARTNER WITH SCIENCESOFT FOR YOUR IMAGE ANALYSIS PROJECT


31 years in C++ development.

Image analysis consulting and development services since 2013.

Data science and AI services. since 1989

700+ highly skilled employees on board.

OUR DOMAIN EXPERIENCE


ScienceSoft leverages expertise in 23+ industries to build your digital image analysis project:

15 years in healthcare

15 years in retail

15 years in telecoms

12 years in professional services

15 years in banking

31 in manufacturing

8 years in insurance

13 years in marketing and advertising

5 years in education

IMAGE ANALYSIS SOLUTIONS WE OFFER


Facial recognition

Identification of a specific person’s face to provide exclusive services, identify suspects and trespassers, etc.

Emotion recognition

Assessing the level of a customer’s satisfaction to solve unique business challenges.

Grading and sorting

Object quality analysis for streamlined classifying and sorting.

Quality control (QC)

Checking for surface defects, foreign materials, discoloration, absence of components, etc.

automated visual inspection

Counting

Using an optical system to count similar objects on the production line or in a warehouse.

Computer-aided diagnosis

Reading X-ray images, CT, PET and MRI scans, ultrasound scans (including 3D and 4D), isotope scans, etc. Enhancing clinical images, measuring organ dimensions and blood flow, detecting pathological signs and suggesting a diagnosis.

Medical image analysis

Damage assessment

Identifying damage issues in complex electronic devices, vehicles, etc.

3D reconstruction

Producing 3D models from 2D data (e.g., medical scans).

Optical character recognition

Reading texts and number sequences (printed and handwritten).

Event detection

Identifying behavior anomalies and alarms in surveillance videos, counting people traversing a passage.

Organizing visual information

Indexing visual databases.

Rule-based approach

For a small amount of visual data of low variability

Excellent performance within a narrow domain.

Doesn’t require big datasets.

Performance can be easily validated.

Explicability (every decision step is clearly seen in the code).

Easy debugging.

Machine learning approach

For large datasets of unstructured data

Deals better with complex objects and tasks.

Doesn’t require explicit knowledge.

Easier scalability.

Lower operational costs.

DEVELOPMENT WORKFLOW


1

Image analysis solution design

Defining how certain business problems should be solved with IA technology. Converting high-level business needs to software features, eliciting the requirements to image quality and recognition accuracy.

2

Business case creation

Outlining IA solution alternatives, providing business case calculations – ROI and TCO.

3

Software architecture (re)design

Developing the architecture while considering all the nuances that might affect image analysis system’s performance; enhancement and optimization of the existing IA software architecture.

4

Assessment and selection of implementation options

Third-party computer vision software API integration and customization.

Developing proprietary ML-driven technology from scratch.

Developing proprietary ML-driven technology from scratch.

5

Software architecture (re)design

6

PoC and prototyping

(if required)

7

IA software development and integration

With hardware and third-party apps, IoT devices (sensors, cameras, controllers, etc.).

8

Quality assurance

Manual and automated testing.

9

IA software maintenance and support

PRICING MODELS FOR IMAGE ANALYSIS SERVICES


Time & Material

Good for projects with a well-defined and stable scope.

Applied to consulting services.

Fixed price

Good for projects with a well-defined and stable scope.

Jump-Start Your Image Analysis Project with ScienceSoft!


Develop innovative image analysis software

Entrust your IA software project to a reliable provider with 31 years of experience in custom software development.

Upgrade software with image analysis technology

We assess your software and enforce it with the latest digital image processing technologies to address your pressing IA needs.