Computer Vision Systems. Dean, Faculty of Technology Professor, Department of Technology University of Pune, Pune
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1 Improving Performance for Computer Vision Systems Dr. Aditya Abhyankar Dean, Faculty of Technology Professor, Department of Technology University of Pune, Pune
2 Homography based Hybrid Mixture Model for 3D Reconstruction True focal length gets converted into apparent focal length in digital camera In turn, the true depth information gets mapped to apparent depth information 2
3 Homography based Hybrid Mixture Model for 3D Reconstruction The reverse transformation needs to recover, that is to estimate the depth from focal length 3
4 Homography based Hybrid Mixture Model for 3D Reconstruction The projective transformation that relates the two camera views /images An invertible mapping of points or lines projective plane(p 2 ) to another plane (P 2 ) on one Also called as Collineation, aprojective transform or homography P Π Π P 1 Π 2 P Π O 1 P π = HP π O 2 4
5 Experimental Setup - Background plane P 2 P 1 P 3 Object Cam 1 FOV More than Cam 6 Cam 2 Cam 5 Cam3 Cam 4 5
6 Research Non-linear Adaptive Signal Processing Bio-informatics Biometrics Security Systems WORK Statistical Analysis of Signals Biomedical Signal Analysis Scalable Video Coders
7 Research: Biometrics Ideal and Non-Ideal Iris Recognition Fingerprint Liveness Detection Biometric Security Biometrics Iris Template Aging and Update Fingerprint Interoperability Wavelet Based Scalable Video Coders
8 Liveness Detection Algorithm Part (I) Wavelet Based Approach Part (II) Perspiration Pattern Characterization Part (III) Empirical Mode Decomposition Based Approach Part (IV) Fingerprint Ridge and Texture Analysis Approach
9 Liveness Detection Hypothesis Live fingers demonstrate a specific changing moisture pattern due to perspiration. Cadaver and spoof fingerprint images do not. Algorithm uses two fingerprint images over time Original algorithm: capacitive DC, 5-second time frame, small dataset
10 Liveness Detection Perspiration observed in live images after applying the threshold to the first difference of waveletenhanced images Cadaver Spoof Live
11 Iris Recognition
12 Iris Recognition- Segmentation Segments iris region from the rest of the eye image Performs maxima energy extraction Number of wavelet coefficients retained = 10, If iris region is not segmented correctly, it can not be used for doing personal identification
13 Iris Recognition- Segmentation Traditional iris segmentation uses model based transforms like Hough or circular canny Appropriate for on-angle images, but fails for off-axis images Goal: Develop segmentation which improve iris recognition performance, particularly for non-ideal iris images
14 Active Contour Deformation
15 ASM Segmentation
16 BWN (Bi-orthogonal Wavelet Network) The basic idea behind matching using BWNs BWN(y,w) optimized for a particular class of an iris BWN(y,w) specific for that particular class only Any other class is not well represented by the same BWN Each representation gets uniquely trained
17 Encryption Scheme (OTBT) Trained NN to generate representations Mixing Communication Channel Trained HMM Database of Biometric Representations Biometric Template Biometric Representations Encrypted Template Encrypted Template Iterative Blind Source Separation Fuzzy Matcher Acts like transformation and key No use of Raw template Continuously updated i.e. dynamic system stolen not stored Self generating dynamic keys Information No privacy threats
18 Statistical Testing The key stream generated using OTBT tested using the NIST Statistical Test Suite. (Only Reports Number of Streams Failed on average in 1000 independent tests with random keys)
19 Image Quality - Fuzzy c-means
20 CUDA!!!
21 Research Non-linear Adaptive Signal Processing Bio-informatics Biometrics Security Systems WORK Statistical Analysis of Signals Biomedical Signal Analysis Scalable Video Coders
22 Multi-Target Tracking (MTT) Multiple Human targets to be tracked Computationally complex process Applications are real-time
23 MATLAB GPU Computing MATLAB GPU Computing AcceLereYes Jacket Casting input data to Jacket s GPU data-structure To run native codes on the GPU
24 Efficient Multi-target Human Motion Tracking
25 Efficient Multi-target Human Motion Tracking
26 Efficient Multi-target Human Motion Tracking
27 Efficient Multi-target Human Motion Tracking
28 Efficient Multi-target Human Motion Tracking
29 Efficient Multi-target Human Motion Tracking
30 Efficient Multi-target Human Motion Tracking
31 Efficient Multi-target Human Motion Tracking
32 Efficient Multi-target Human Motion Tracking
33 Efficient Multi-target Human Motion Tracking
34 Efficient Multi-target Human Motion Tracking
35 Efficient Multi-target Human Motion Tracking
36 Efficient Multi-target Human Motion Tracking
37 Efficient Multi-target Human Motion Tracking
38 Efficient Multi-target Human Motion Tracking
39 Efficient Multi-target Human Motion Tracking
40 Efficient Multi-target Human Motion Tracking
41 Efficient Multi-target Human Motion Tracking
42 Efficient Multi-target Human Motion Tracking
43 Research Non-linear Adaptive Signal Processing Bio-informatics Biometrics Security Systems WORK Statistical Analysis of Signals Biomedical Signal Analysis Scalable Video Coders
44 Scalable Video Coders (SVCs) Produces bit-stream which can be decoded at different bit rates Encoder design crucial Decoding on less powerful platform possible Streaming Basketball Match Broadcast Channel User 1 ENCODER User n
45 Wavelet based SVC Spatio-temporal scalability Performs motion compensation Mesh based approach Computationally complex GPU-CUDA accelerates Effective implementation
46 Mesh Based MC
47 MATLAB GPU Computing MATLAB GPU Computing AcceLereYes Jacket Casting input data to Jacket s GPU data-structure To run native codes on the GPU
48 Results CPU Specifications GPU Specifications HP xw9400, AMD Dual core nvidia GF 9800 GX2 + AMD processor, 2.00 GHz, RAM 2 Dual Core GB CPU Time (VD-10) GPU Time (VD-10) sec sec Time Units CPU Processing Acceleration Achieved ~ 13x GPU Processing Video Depth
49 Research Non-linear Adaptive Signal Processing Bio-informatics Biometrics Security Systems WORK Statistical Analysis of Signals Biomedical Signal Analysis Scalable Video Coders
50 Research: Biometrics Ideal and Non-Ideal Iris Recognition Fingerprint Liveness Detection Biometric Security Biometrics Iris Template Aging and Update Fingerprint Interoperability Wavelet Based Scalable Video Coders
51 Research: Biometrics Fingerprint Orientations Ideal and Non-Ideal Iris Recognition Fingerprint Liveness Detection Biometric Security Biometrics Iris Template Aging and Update Fingerprint Interoperability Wavelet Based Scalable Video Coders
52 Fingerprint Orientations
53 Results CPU Specifications GPU Specifications HP xw9400, AMD Dual core nvidia GF 9800 GX2 + AMD processor, 2.00 GHz, RAM 2 Dual Core GB CPU Time (dpi-1000) GPU Time (dpi-1000) sec sec Time Units Acceleration Achieved ~ 7x CPU Processing Fingerprint Images dpi resolution GPU Processing
54 Results CPU Specifications GPU Specifications HP xw9400, AMD Dual core nvidia GF 9800 GX2 + AMD processor, 2.00 GHz, RAM 2 Dual Core GB CPU Time (dpi-1000) GPU Time (dpi-1000) sec sec Acceleration Achieved ~ 8x Time Units CPU Processing GPU Processing Facial Images dpi resolution
55 Research Non-linear Adaptive Signal Processing Bio-informatics Biometrics Security Systems WORK Statistical Analysis of Signals Biomedical Signal Analysis Scalable Video Coders
56 Results - CBIR CPU Specifications GPU Specifications HP xw9400, AMD Dual core nvidia GF 9800 GX2 + AMD processor, 2.00 GHz, RAM 2 Dual Core GB CPU Time (dpi-1000) GPU Time (dpi-1000) sec sec Time Units Acceleration Achieved ~ 10x CPU Processing Search Depth GPU Processing
57 Research Non-linear Adaptive Signal Processing Bio-informatics Biometrics Security Systems WORK Statistical Analysis of Signals Biomedical Signal Analysis Scalable Video Coders
58 Fuzzy c-means scatter plot
59 Results CPU Specifications GPU Specifications HP xw9400, AMD Dual core nvidia GF 9800 GX2 + AMD processor, 2.00 GHz, RAM 2 Dual Core GB CPU Time (Epochs-200) GPU Time (Epochs-200) sec 4.87 sec Acceleration Achieved ~ 8x Time Units CPU Processing GPU Processing Epochs
60 P1: Liveness Embedded Multimodal Biometric System Biometric System Security, Forensics LIVENESS Fusion: 1. Image Level 2. Feature Level 3. Score Level
61 P2: Adaptive DPOAE design Dual Pulse Oto-Acoustic Emmissions
62 P3: Homography based 3D voxel mapping for Rehabilitation Engineering
63 P4: Evolutionary Framework for objective diagnosis of cancer
64 P4: Evolutionary Framework for objective diagnosis of Histo-paths Image Analysis Request Flash Viewer Report Generation Protein, Cell, Tissue, Diagnostics libraries Image Analysis Server Application Internet Image Server Application With caching readers / co onnectors image Image Storage 2D Gel scanner HCS Tissue scanner
65 References (selected):
66 Questions?? Thank you!
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