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Envision: Intelligent, Reliable, Quality Solution
Mausumi Acharyya, Ph.D.CEO
Advenio TecnoSys (P) Ltd
Executive Summary2
Advenio TecnoSys is involved in Data and Image Analytics. We are a multidisciplinary engineering system and software development
team aimed at providing quality service and solutions to Image Processing needs. We have a highly skilled development team,
well positioned with over 40 years of collective R&D experience. We are a global firm delivering the best possible Image and Data
Analysis services in different vertical markets. Key areas we specialize:
Image Analytics
Image Forensic
HPC Solutions
Video Analytics
Data Analytics
Our Offering3
Image Analytics Video Analytics Big Data Analytics
Forensic Analysis
Image Processing & Vision Security Machine
Learning Image Forensic
ML for Big Data:
•Assessment of statistical methodology needs/ possibilities•Custom ML modeling and algorithm design•Scalability planning•Implementation architecture and integration
Security Proactive Solutions:
•Number plate recognition•Occupancy detection•Intelligent scene verification•People counting•Perimeter protection•Camera tampering alarm•Traffic flow monitoring•Panic alarm•Left object detection
Image Forensic Applications:
•Image & Video Analysis - Examination, evaluation, analysis, enhancement, authentication, comparison•Image & Video Authentication – validate authenticity•Comparative Analysis - comparisons of people, clothing, or vehicles involved in crimes or accidents, or other objects of evidence
Image Analysis Applications :
•Healthcare – Medical Images (CT, MRI, PET, X-ray etc.), microscopic images (histology, cytology) , molecular images (gene expression, microarray) data•Life Science – Bioinformatics, Biotechnology•Security – Biometrics (face, finger print, iris recognition)•Industrial Imaging – Defect Identification•Defense – Radar Images
HPC
Parallel Processing
High Performance Computing Services:
Design, Develop & Maintain Applications for large data set computation in real time using immense parallel processing capabilities of CPU/GPU, GPGPU
Research & Development4
We identify the specific cutting-edge technologies and need in healthcare domain and design and develop innovative solutions based on these needs
We offer complete productization of research output at academic level and go for licensing
We offer upgradation of existing products in absence of right resources and expertise at client’s end
Productization
Advenio
Original algorithm development as per market need Modification of existing or published solutions
Research & Development
Technical Consultation
Convert research prototype to deliverable product
Innovative Solution
Upgradation Consultation
Product development consultation
We provide a wide range of solutions and
services that help organizations to transform their
business
Knowledge Base5
Image Processing
Pattern Recognition
Machin
e Learnin
g
Computer Vision
Data
Mining
Hyperspectral &
Multispectral Re
mote
Sensing
Statistical
analysi
s
Video Processing
Mathe
matical Modeling
Soft-
Computin
g
Specialized Knowledge6
Object Recognition & Detection Shape Analysis Segmentation Texture Analysis & Synthesis Image Reconstruction Image & Video matching Image Compression & Decompression Feature extraction in spatial and frequency domain Data Fusion/Registration Pre- and Post- Processing (image corrections, noise removal,
deblurring, image enhancement etc.) Classification and Prediction Video Processing
Domain7 • Healthcare – Medical Images (CT, MRI, PET, X-ray etc.), microscopic images (histology,
cytology) , molecular images (gene expression, micro-array) data• Life Science – Bioinformatics, Biotechnology• Security – Biometrics (face, finger print, iris recognition)• Industrial Imaging – Defect/Purity Identification (Textile, Food, Consumable Items,
Civil Structure, Mechanical Systems, Steel Plant, Coal Mines and many more)• Video Processing - Number plate recognition, Occupancy detection, Intelligent scene
verification, People counting, Left object detection, Camera tampering alarm, Traffic flow monitoring etc.
• Defense – Radar Images• Agriculture – Satellite Images• Mining - Satellite Images• Climatology - Satellite Images• Social & Media – Any kind of Image Processing for Fun Applications• And Many More
8 Credible Projects
Case Studies
Texture Analysis9
Considerable knowledge and experience on Texture AnalysisDeveloped several novel algorithms for Texture Analysis
Segmentation of multi-textured images Textures which are even difficult to discriminate visually by human eye were
being discriminated by the machine vision algorithm developed Features developed were affine transformation (scale, rotation, translation)
invariant Document image (scanned documents) segmentation for separating textual and
graphics parts applicable for second generation compression Features developed were invariant to skewness, resolution, rotation etc. Satellite image segmentation for classifying different regions like: habitation,
vegetation, water bodies, man-made structures, open areas etc. Designed and developed a novel neuro-fuzzy based unsupervised classifier for
this purpose
Texture Analysis10
Input Textured image where some of the regions are not discriminable as different class even visually
Segmented image based on different textured regions
Novelty of the algorithm is that it identifies textured regions as different class which are even very difficult to identify visually by human
Input Textured imagewhere some of the regions are not discriminable as different class even visually
Segmented image based on different textured regions
Algorithm involves in-depth knowledge of Texture Analysis, Wavelet Analysis, Feature Extraction, Classification, Fuzzy Set Theory, Neural Network & Soft Computing
Document Segmentation – Second Generation Compression
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Novel algorithm developed for segmentation of textual part from graphics part which is essential for second generation compression. Here text and graphics parts were considered as two different textured regions.
Skewed image and its segmented output
Test image with document skewed and text regions with different orientations and its segmented output
Test image with non-convex and overlapping object boundaries and its segmented output
Satellite Image Classification12
Several regions were accurately segmented by the algorithm based on textures.
In the Bombay IRS image even the two different densities in the Arabian Sea were identified correctly.
In the Calcutta IRS image the open space which is a race course and even the Howrah bridge were identified correctly.
Nodule (Lesion) Detection in Chest X-ray
134 US Patents
Novel algorithms were developed for identification of nodules (lesions) in chest X-rays.
Chest X-ray data is a 2D projection image where each pixel represents a volumetric integration posing a challenge in detection and estimation of nodules andtheir characteristics.
Several algorithms were developed to reduce False Positives or Nodule-look-alikes detection.
Algorithm involves in-depth knowledge of Segmentation, Mathematical modeling, Pre- & Post Processing, Fractal Analysis, Shape Analysis, Image Analysis, Machine Learning, Statistical Analysis
Bleed (Aneurysm) Detection in Brain CT, Trauma Cases
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2 US Patents
Novel algorithms were developed for identification of subtle bleeds in brain CT images without contrast for trauma cases posing immense challengeSeveral algorithms were developed to reduce False Positives bleed-look-alikes (calcification, fresh-flowing blood, bright falx)Algorithm involves in-depth knowledge of Segmentation, Registration, Mathematical modeling, Pre- & Post Processing, Morphological Operations, Image Analysis, Machine Learning
Lie Detection in Functional MRI (fMRI) Images
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Novel approach one of the first attempts to find alternative to Polygraphs.
Novel algorithm for identifying whether someone is telling a lie or truth based on functional MRI images. A one hundred percent classification accuracy was obtained on a set of unknown data (images)
Algorithm involves exhaustive use of Registration both Rigid & Deformable and Non-linear Pattern Classification
Tuberculosis Bacilli Detection in Sputum Smears (microscopic image)
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US Patent
Algorithm identifies TB Bacilli in microscopic images of sputum smears Algorithm based on Color Image analysis, Morphology, Machine Learning
Hairline Crack Detection in Bone X-ray, CT etc
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US Patent
Algorithm involves use of exhaustive Image analysis like several feature extraction including fractal analysis, Gabor orientation mapping etc. & Machine Learning Techniques
Foreign Material and Crack Detection in Chocolate Bars (X-ray)
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Original image Crack identification
Original image Identified foreign element
Original image Identified foreign elementDefect Analysis in X-ray images of Chocolate bars
This involved an unsupervised technique to identify crack or presence of any foreign materials inside the bars.
In this analysis no reference image were used and it is completely a machine vision technique
Segmentation of stained microscopic image of Root
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Segmentation of stained microscopic images of Root
Algorithm involves different state-of-art image segmentation techniques
Pulmonary Embolism Detection in CT Angiogram
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Pulmonary embolism (PE) is a serious medical condition, characterized by the partial/complete blockage of an artery within the lungs
Novel algorithm developed for PE detection and several algorithms for reducing False Positives or PE-look-alikes
Algorithm involves use of exhaustive Image analysis & Machine Learning Techniques
Subtle Nodule Detection in Low Resolution Lung CT images
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Algorithm identified very subtle nodules which were missed by radiologists
Algorithm involves use of exhaustive Image analysis, Deformable Registration & Machine Learning Techniques
Nodule
Alzheimer Detection in Diffusion Tensor MRI (DTMRI) Images
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Diffusion tensor tractography
Algorithm based on Singular Value Decomposition, Tensor Analysis, Deformable Registration and intensive Mathematical modeling
Mammogram Segmentation and Nodule Detection23
Algorithm based on Morphological operations, Segmentation based on Active Contour and Fuzzy Sets and intensive Mathematical modeling
Mammogram segmentation and Nodule detection. It is extremely challenging to segment mammograms which are digitized from X-ray films due to presence of noise and other artifacts
Bitting Code Generation in Locksmith Key
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Locksmith Key Coding:
Mobile Application
Automatic detection, segmentation, bit edge profiling and generation of code from space and depth information
Face Authentication25
Face Authentication:
Face recognition feature extraction, authentication
Iris Authentication26
Iris Authentication:
Eye lash and other denoising and segmentation of pupil and iris region. Encoding the iris for authentication
Optical Character Recognition (OCR)
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Automated Answer Script Evaluation (Optical Character Recognition)
Algorithm involves rigid registration and Scale Invariant Feature Transform (SIFT)
Optical Character Recognition28
• Removal of “Phrase” from Text Document
Bioacoustics29
• Bird Chirp Identification and Classification
Accelerometer Data Analysis30
• Golf Swing Key Points Identification
Watermark31
• Watermarking - novel algorithm developed to embed patient information in medical images as watermark
• The uniqueness and novelty of the algorithm lies in the fact that apart from the use of embedded watermark as an information source we can watermark some extra ‘text’ information pertaining to the image. We see this as a revolutionary tool in the arsenal of a radiologist using a CAD suite, wherein he can embed certain crucial information into an image rather than relying on the DICOM header. The significant benefit would be the selective access of this information and its being free of traditional viewing platforms. Thus the watermark here acts in actual way of both conveying information as well as providing security. A failure of the watermark would mean the non-reliability of both the image as well as the additional information thereby maintaining its traditional purpose.
• Algorithm involves Wavelet Analysis, quantization, coding
• Published Patent
Satellite Image SegmentationContrast Enhancement Vegetation areas
Region classification of satellite images into different categories like vegetation, building area, shadows, road area etc
Shadow areas Rough Building Mask
Satellite Image Segmentation
Satellite Image SegmentationInitial Building Mask based on Texutre Final Buidling Areas
Liver Histology (Biopsy) Image Analysis for Automatic Detection and Grading of Liver Diseases
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Automatic Lesion detection and gradation of Liver diseases
Steatosis Lobular Inflammation
Algorithm involves novel machine learning and image processing techniques for extraction of features and classification
Histology (Microscopic Tissue Images) Image Analysis of different Organs for aiding Drug Development36
Detection of Normal and Abnormal OrgansAlgorithm involves novel machine learning and image processing techniques for extraction of features and classification
37 Identifying image tampering, alteration or forgery
Image Forensic
Passive Authentication - Patterns of original images are distinguishable due to the different imaging devices and image processing inside them. Original patterns constrained by the statistical characteristics in natural scenes, physical conditions in a scene, etc. would be altered after tampering.
• Passive forensics can be converted to a problem of pattern recognition• Solution to the problem is finding the different patterns according to the knowledge
from various imaging devices or the manipulations or the natural scene constraints, etc.
• Selected patterns with distinguishing ability are crucial for this new technology
Passive Forensic38 • Identification of image source
– Imaging devices have varying characteristics due to different • physics apparatus• image processing• parameters applied inside the imaging devices
– Lead to different patterns of the output images– Use these patterns as inherent “fingerprints” of the imaging devices to identify the
source of the image
Feature Extraction Feature Map
Pattern Extraction Pattern Map
Image X
Imaging Device
Knowledge of image acquisition model Compare
Similarities between patterns and characteristic features measured. Confidence measure for each imaging device to identify the source of the image is computed.
Passive Forensic39
• Detection of image alteration– Original images always contain some consistent characteristics
• noise distribution • light condition
• Characteristics change after image post-processing (alterations)• Some features of the altered images become more or less inconsistent• Finding the differences before and after the operations is the key of the technology
Feature Extraction Feature Map
Pattern Extraction Pattern Map
Image X
Original/ Altered Image
Knowledge of image manipulation model Compare
Extract features from image X and obtain the original/ altered patterns mainly using the knowledge of the image manipulation model. Compare the distance between the features
and the patterns to decide whether or not image X has been altered
Image Forensic40 • Fourier analysis of histograms of the images to determine if a JPEG image underwent
double compression • Localize which portions underwent double compression by analyzing difference images• Any forgery in the image or splicing of two images from different cameras can be
identified by analyzing the correlations introduced by performing color filter array (CFA) interpolation and demosaicking
• Any tampering can be measured by the local correlation of camera noise or photo-response non-uniformity noise (PRNU) and the image
• Forgery due to splicing of images require resampling which disturbs the specific correlation of the image. Estimating this correlation using expectation maximization can identify the forgery
• A common manipulation is to copy and paste portions of an image to replicate an object or conceal a person in a scene called cloning. Presence of identical (or virtually identical) regions in an image can, therefore, be used as evidence of tampering. Image similarity measure, by matching key points in the images using different techniques like scale invariant feature transform (SIFT) and random sample consensus algorithm (RANSAC) can be used to identify this cloning
Image Forensic41 • Computer graphics (CG) capable of generating highly realistic images. But CG images
are rendered under idealized models of geometry, lighting, camera optics and sensors, and their underlying statistics differ from those of photographic images. Image decomposition at different orientation, scale and spatial frequency that are localized in spatial position would capture all the statistical irregularities in case of CG images
• Creating a photo composite requires translation, scaling, rotations of portions of an image. Such image-based manipulations can change the effective intrinsic camera parameters. Differences in these estimated parameters can be used as evidence of tampering
• Shadow analysis (light travels in straight line so lines drawn on shadows and objects should intersect at one point (light source). This can be used to analyze any forgery
• Image created by splicing together individual images is difficult to exactly match the lighting effects due to directional lighting (e.g., the sun on a clear day). Differences in lighting can be a telltale sign of tampering. Direction of the light source can be estimated for different objects/people in an image, inconsistencies in the lighting direction can be used as evidence of tampering
ProductsOptiNio – Retinal abnormality detection softwareCheckCount – Automated counter of production of any item
Brief Profile of Founder & CEO43Dr. Mausumi Acharyya•Post-Doc (Medical Imaging), University of Pennsylvania, USA•PhD, Computer Science (Image Processing), Indian Statistical Institute, India•M.Tech and B.Tech (Electronics & Telecommunication), Calcutta University, India
•13+ years in IT industry (GE Global Research, Siemens, Defense Research & Development Organization)•20+ years of rich research experience in Multifarious domains •Distinguished research and development exposure in
– Signal/Image processing– Medical Image Processing– Machine Learning and Vision
• In-depth understanding and extensive knowledge of designing and development of Image, Video, Data analytics applications
• Key in successfully delivering several commercial products during her engagement with the above organizations
• Mausumi holds several US Patents and international publications to her credit
44
Contacts
Advenio TecnoSys Pvt. Ltd.STEP, IIT, Kharagpur, India
Dr. Mausumi Acharyya, CEO+918607026111, +91
9845311644
[email protected]@adveni
otecnosys.com
Thank You
45
Proprietary and Confidential
• By accepting this document the recipient agrees to keep all information permanently confidential or otherwise acquired by the receiving party from Advenio TecnoSys including all handbooks, manuals, drawings, designs, specifications, charts, diagrams etc. and any other documents or materials containing such information. The information presented in this document is for discussion purposes only and is subject to change. Advenio TecnoSys shall not be liable for errors contained herein, or for consequential damages in connection with the furnishing, performance or use of the material. No part of this document may be reproduced or transmitted in any form or by any means, for any purpose, or translated to another language without the prior written consent of Advenio TecnoSys.
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