S.N. Omkar

Aerial Scene Understanding Using Deep Wavelet Scattering Network and Conditional Random Field

This paper presents a fast and robust architecture for scene understanding for aerial images recorded from an Unmanned Aerial Vehicle. The architecture uses Deep Wavelet Scattering Network to extract Translation and Rotation Invariant features that are then used by a Conditional Random Field to perform scene segmentation. Experiments are conducted using the proposed framework on two annotated datasets of 1277 images and 300 aerial images, introduced in the paper. An overall pixel accuracy of 81 % and 78 % is achieved for the datasets. A comparison with another similar framework is also presented.

Level Set Segmentation of Brain Matter Using a Trans-Roto-Scale Invariant High Dimensional Feature

Brain matter extraction from MR images is an essential, but tedious process performed manually by skillful medical professionals. Automation can be a potential solution to this complicated task. However, it is an ambitious task due to the irregular boundaries between the grey and white matter regions. The intensity inhomogeneity in the MR images further adds to the complexity of the problem. In this paper, we propose a high dimensional translation, rotation, and scale-invariant feature, further used by a variational framework to perform the desired segmentation. The proposed model is able to accurately segment out the brain matter. The above argument is supported by extensive experimentation and comparison with the state-of-the-art methods performed on several MRI scans taken from the McGill Brain Web.

Investigation of Wrist Extension Using Three Dimensional Digital Image Correlation

Wrist is an extremely significant and important part of our body. It is the performance index of our work. Previous research has shown that wrist flexion can lead to compression of the median nerve in the carpal tunnel, resulting in numbness, parenthesis and muscle weakness in hand. We present an effective optical approach for the measurement of strain on superficial muscles and tendons due to wrist extension, a remedy for carpal tunnel syndrome. The 3D correlation software computes the in-plane and out of plane strain using pictures taken, using 2 charge coupled device cameras, before and after extension. Strain plots obtained after comparison indicates the strain distribution in anterior compartment of the forearm muscle. The experiment is then repeated for four other participants and the trends are observed. The effect of stretching on the two important anterior components of the forearm muscles, namely the flexor pollicis longus muscle, flexor carpi ulnaris muscle is studied. This study can assist practitioners working in the field of applied anthropology to develop advanced diagnosis methodologies.

Variance Based Moving K-Means Algorithm

Clustering is a useful data exploratory method with its wide applicability in multiple fields. However, data clustering greatly relies on initialization of cluster centers that can result in large intra-cluster variance and dead centers, therefore leading to sub-optimal solutions. This paper proposes a novel variance based version of the conventional Moving K-Means (MKM) algorithm called Variance Based Moving K-Means (VMKM) that can partition data into optimal homogeneous clusters, irrespective of cluster initialization. The algorithm utilizes a novel distance metric and a unique data element selection criteria to transfer the selected elements between clusters to achieve low intra-cluster variance and subsequently avoid dead centers. Quantitative and qualitative comparison with various clustering techniques is performed on four datasets selected from image processing, bioinformatics, remote sensing and the stock market respectively. An extensive analysis highlights the superior performance of the proposed method over other techniques.