arXiv subjects

Naimul Khan

Publications and source records attributed to Naimul Khan.

39 records · Page 3Linked to original sources

arXiv2017

Real-Time System for Human Activity Analysis

We propose a real-time human activity analysis system, where a user's activity can be quantiatively evaluated with respect to a ground truth recording. We use two Kinects to solve the ptorblem of self-occlusion through extraction optimal joint positions using Singular Value Decomposition (SVD) and Sequential Quadratic Programming (SQP). Incremental Dynamic Time Warping (IDTW) is used to compare the user and expert (ground truth) to quantiatively score the user's performance. Furthermore, the user's performance is displayed through a visual feedback system, where colors on the skeleton represent the user's score. Our experiements use a motion capture suit as ground truth to compare our dual Kinect setup to a single Kinect. We also show that with out visual feedback method, users gain statistically significant boost to learning as opposed to watching a simple video.

cs.MM↗

arXiv2017

Towards Alzheimer's Disease Classification through Transfer Learning

Detection of Alzheimer's Disease (AD) from neuroimaging data such as MRI through machine learning have been a subject of intense research in recent years. Recent success of deep learning in computer vision have progressed such research further. However, common limitations with such algorithms are reliance on a large number of training images, and requirement of careful optimization of the architecture of deep networks. In this paper, we attempt solving these issues with transfer learning, where state-of-the-art architectures such as VGG and Inception are initialized with pre-trained weights from large benchmark datasets consisting of natural images, and the fully-connected layer is re-trained with only a small number of MRI images. We employ image entropy to select the most informative slices for training. Through experimentation on the OASIS MRI dataset, we show that with training size almost 10 times smaller than the state-of-the-art, we reach comparable or even better performance than current deep-learning based methods.

cs.CV↗

arXiv2017

A Novel Image-centric Approach Towards Direct Volume Rendering

Transfer Function (TF) generation is a fundamental problem in Direct Volume Rendering (DVR). A TF maps voxels to color and opacity values to reveal inner structures. Existing TF tools are complex and unintuitive for the users who are more likely to be medical professionals than computer scientists. In this paper, we propose a novel image-centric method for TF generation where instead of complex tools, the user directly manipulates volume data to generate DVR. The user's work is further simplified by presenting only the most informative volume slices for selection. Based on the selected parts, the voxels are classified using our novel Sparse Nonparametric Support Vector Machine classifier, which combines both local and near-global distributional information of the training data. The voxel classes are mapped to aesthetically pleasing and distinguishable color and opacity values using harmonic colors. Experimental results on several benchmark datasets and a detailed user survey show the effectiveness of the proposed method.

cs.GR↗