Vision Based Automated Cluster Tester

Vision Based Automated Instrument Cluster Tester: An automated machine vision system which acquires the vehicle cluster (car dashboard) image using an USB interfaced webcam. The system further processes the acquired image for predefined cluster configurations using computer vision algorithms developed and implemented in C++. The extracted cluster data (readings) are displayed using VC++ (MFC) based GUI. Comparing the given inputs and acquired image data, an instrument cluster can be tested from functional point of view. [Used: VC++ (MFC), MATLAB]

Gesture Recognition using DSTW & DTW

Gesture Recognition using DSTW and DTW: Scale and Translation invariant gesture recognition based on similarity measure between query and model video sequences is implemented. Similarity measure is find out by using either simple Dynamic Time Warping (DTW) or Advanced Dynamic Space Time Warping (DSTW) algorithm. In presence of moving distractors motion and skin detection based hand detector module may fail but use of dynamic space time warping algorithm (DSTW) to find the similarity measure improves accuracy greatly over simple DTW algorithm.[Language Used: MATLAB]

Face Detector

Face detector: Developed Face detector which uses Ada-Boosting and Bootstrapping algorithms at training stage and multi-stage cascades of boosted classifiers aided with skin detection at testing stage. [Language Used: MATLAB]

Face imageFace image

Face image

Face image

Person Tracker

Person Tracker: Implemented motion estimation based person tracker which draws bounding box around person. The tracker determines center of mass of the person and calculates his/her speed from Euclidean distance between centers of mass. Furthermore, the tracker determines walking pose of the person such as Constricted Legs or Expanded Legs. [Language Used: MATLAB]

Person partially in frame:20

Speed between frame:20 and frame:10 is 0 pixels per frame

Person present in POSE 1(Extended Legs) inframe:68

Speed between frame:68 and frame:58 is 3.733631e+000 pixels per frame

Person present in POSE 1(Extended Legs) inframe:85

Speed between frame:85 and frame:75 is 3.705401e+000 pixels per framePerson present in POSE 2(Constricted Legs) inframe:77

Speed between frame:77 and frame:67 is 3.701351e+000 pixels per frame

Face Recognition using Correlation, Principal Component Analysis and Ada-boosted Classifiers

Face Recognition using Correlation, Principal Component Analysis and Ada-boosted Classifiers: A face recognition using simple 2-D cross correlation, PCA and Ada-Boosted Classifier(rectangle filter) was implemented in order to compare accuracy and speed of these methods.[Language Used: MATLAB]

original 100 eigenfaces Rectangle Filter Classifier based adaptive boosting

Bayesian Probabilistic Skin Detector

Bayesian Probabilistic Skin Detector: A simple Bayesian probabilistic skin detector using RGB histogram and Normalized RGB histogram technique is implemented. The skin detector finds its application in face detection and gesture recognition using skin detection based hand detector[Language Used: MATLAB]

Direction Identifier for maps

Direction Identifier: A direction identifier based on canny edge detector is designed and implemented to determine direction of prominent road in the given map image.[Language Used: MATLAB]

77

180

Digit Recognition

Digit Recognition: A simple 2-D normalized cross correlation based digit recognition module is implemented. The numerical digits are recognized even in presence of significant amount of noise. [Language Used: MATLAB]

test20.bmp contains digit - 2

test40.bmp contains digit - 3

test15.bmp contains digit - 2

test35.bmp contains digit - 3

test10.bmp contains digit - 2

test31.bmp contains digit - 3