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Dinesh Portfolio (showing 1 - 9 of 9 results)
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Sparse Signal Processing
Website:
Website Description:
Compression of the signal using Gabor Frame and analysis of the compressed signal with coherence, mobility.
Skill:
Opencv and MATLAB project to banner extraction from given Image.
Website:
Website Description:
Banner extraction from the images. That uses the OpenCV.
Skill:
Quadcopter simulation using MATLAB Code.
Website:
Website Description:
A quadrotor helicopter (quadcopter) is a helicopter which has four equally spaced rotors, usually arranged at the corners of a square body. With four independent rotors, the need for a swashplate mechanism is alleviated. The swashplate mechanism was needed to allow the helicopter to utilize more degrees of freedom, but the same level of control can be obtained by adding two more rotors.
Skill:
Build a system dynamics approach to the Keynes model using Simulink/Matlab.
Website:
Website Description:
Financial crises are becoming more frequent events in the world economy. The broad-based move to increased capital market liberalization over the past few decades has not only contributed to the fragility exhibited, but also managed to make contagion more likely. The need to understand the logic of financial panics is greater than ever.
Skill:
Face recognition using SVM and HOG
Website:
Website Description:
A geometric face model is formed with the detection of eyes performed using the Haar Cascade Classifier, while nose detection has been used as a reaffirmation mechanism along with the eyes. Later, HOG (Histogram of Oriented Gradients) features are extracted from large numbers of facial images to be used as part of the recognition mechanism. These HOG features are then labeled together for a face/user and a Support Vector Machine (SVM) model is trained to predict faces that are fed into the system.
Skill:
Face recognition using SVM and SURF
Website:
Website Description:
Face recognition is a fascinating research topic in recent years. Numerous methods and algorithms have been suggested by researchers. The accuracy of face recognition technique is affected by factors like variation in illumination, facial expression, scaling and perspective movement. It is important to note that Speeded-up Robust Features (SURF) extracted from a facial image are invariant to shifting, scaling and rotation. In addition to that they are also partially invariant to illumination and affine transformation. This paper suggests a facial recognition technique using SURF features and Support Vector Machine (SVM) classifier. Above techniques has been tested on Yalefaces and UMIST face databases. The results indicate that the proposed method can lead to high recognition efficiency.
Skill:
Face recognition using CNN
Website:
Website Description:
a hybrid system is presented in which a convolutional neural network (CNN) and a Logistic regression classifier (LRC) are combined. A CNN is trained to detect and recognize face images, and a LRC is used to classify the features learned by the convolutional network. Applying feature extraction using CNN to normalized data causes the system to cope with faces subject to pose and lighting variations. LRC which is a discriminative classifier is used to classify the extracted features of face images. Discriminant analysis is more efficient when the normality assumptions are satisfied. The comprehensive experiments completed on Yale face database shows improved classification rates in smaller amount of time.
Skill:
Change detection using FCM algorithm.
Website:
Website Description:
A fuzzy cluster validity index (Xie–Beni) is used to quantitatively evaluate the performance. Results are compared with those of existing Markov random field (MRF) and neural network based algorithms and found to be superior. The proposed technique is less time consuming and unlike MRF does not require any a priori knowledge of distributions of changed and unchanged pixels.
Skill:
Natural language processing using machine learning.
Website:
Website Description:
Text Classification Language Modeling Speech Recognition Caption Generation Machine Translation
Skill:
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