EREL Selection using Morphological Relation

This work concentrates on Extremal Regions of Extremum Level (EREL) selection. EREL is a recently proposed feature detector aiming at detecting regions from a set of extremal regions. This is a branching problem derived from segmentation of arterial wall boundaries from Intravascular Ultrasound (IVUS) images. For each IVUS frame, a set of EREL regions is generated to describe the luminal area of human coronary. Each EREL is then fitted by an ellipse to represent the luminal border. The goal is to assign the most appropriate EREL as the lumen. In this work, EREL selection carries out in two rounds. In the first round, the pattern in a set of EREL regions is analyzed and used to generate an approximate luminal region. Then, the two-dimensional (2D) correlation coefficients are computed between this approximate region and each EREL to keep the ones with tightest relevance. In the second round, a compactness measure is calculated for each EREL and its fitted ellipse to guarantee that the resulting EREL has not affected by the common artifacts such as bifurcations, shadows, and side branches. We evaluated the selected ERELs in terms of Hausdorff Distance (HD) and Jaccard Measure (JM) on the train and test set of a publicly available dataset. The results show that our selection strategy outperforms the current state-of-the-art.Comment: 6 pages, 8 figures, accepted to be published in International Conference on SMART MULTIMEDIA, 2018. The final authenticated publication is available online at https://doi.org

A Review of methods for Textureless Object Recognition

Textureless object recognition has become a significant task in Computer Vision with the advent of Robotics and its applications in manufacturing sector. It has been very challenging to get good performance because of its lack of discriminative features and reflectance properties. Hence, the approaches used for textured objects cannot be applied for textureless objects. A lot of work has been done in the last 20 years, especially in the recent 5 years after the TLess and other textureless dataset were introduced. In our research, we plan to combine image processing techniques (for feature enhancement) along with deep learning techniques (for object recognition). Here we present an overview of the various existing work in the field of textureless object recognition, which can be broadly classified into View-based, Feature-based and Shape-based. We have also added a review of few of the research papers submitted at the International Conference on Smart Multimedia, 2018. Index terms: Computer Vision, Textureless object detection, Textureless object recognition, Feature-based, Edge detection, Deep LearningComment: 25 page

Shape Detection In 2D Ultrasound Images

Ultrasound images are one of the most widely used techniques in clinical settings to analyze and detect different organs for study or diagnoses of diseases. The dependence on subjective opinions of experts such as radiologists calls for an automatic recognition and detection system that can provide an objective analysis. Previous work done on this topic is limited and can be classified by the organ of interest. Hybrid neural networks, linear and logistic regression models, 3D reconstructed models, and various machine learning techniques have been used to solve complex problems such as detection of lesions and cancer. Our project aims to use Dual Path Networks (DPN) to segment and detect shapes in ultrasound images taken from 3D printed models of the liver. Further the DPN deep architectures could be coupled with Fully Convolutional Network (FCN) to refine the results. Data denoised with various filters would be used to gauge how they fare against each other and provide the best results. Small amount of dataset works with DPNs, and hence, that should be appropriate for us as our dataset shall be limited in size. Moreover, the ultrasound scans shall need to be taken from different orientations of the scanner with respect to the organ, such that the training dataset can accurately perform segmentation and shape detection