We have designed and developed a new framework for an interactive texture segmentation technique, which we term as IT-SNAPS (Interactive Texture-Snapping System). Unlike its predecessors, IT-SNAPS can effectively aid the user in accurately segmenting images with complex texture, without placing undue burden on the user. This is made possible through the formulation of IT-SNAPS, which enables it to be user-aware, i.e., it unobtrusively elicits information from the user during the segmentation process, and hence, adapts itself on-the-fly to the boundary being segmented. In addition to generating an accurate segmentation, IT-SNAPS allows for extraction of useful information post-segmentation, which can potentially assist in the development of customized automatic segmentation algorithms. The core computational engine of IT-SNAPS has been designed to operate on NVIDIA's GPU, which enables IT-SNAPS to be rapidly-trainable, thus, greatly increasing its efficiency as a interactive delineation tool.

GPU-powered Machine Vision System for Simultaneous Quantification of Shrinkage and Stain Release on Solid and Patterned Fabrics

We have developed a machine vision system that can automatically detect and recognize multiple objects in the presence of a heavily textured and noisy background. The objects of interest in this application are shrinkage dots and stains, which, once detected and localized, can yield important functional measurements of a fabric. Termed “shrinkage” and “stain release”, these functional attributes impact the fabric pricing, and hence, objective measurements of these parameters are of significant consequence to the textile industry.

The proposed vision-based approach operates within a semi-supervised framework, wherein it is assumed that the machine vision system has the knowledge of the background texture/pattern. Using a customized combination of area-based and feature-based registration techniques, the objects of interest are enhanced with respect to the background, following which, the stains are localized using a GPU-based implementation of adaptive statistical snakes.

Integrating Internal Properties into an Interactive Segmentation Framework

Incorporating shape-based information into an ‘on-the-fly' interactive segmentation scheme is a challenging task due to the unavailability of the complete shape information during the segmentation procedure. A partial-shape registration technique in combination with shape-based level sets is employed in order to integrate internal properties, such as shape and symmetry into an interactive segmentation framework.

Morphometry of Cervical and Lumbar Vertebrae

We continue to develop an extensive tool under Matlab that is capable of segmenting and characterizing the lumbar and cervical vertebrae from digitized x-ray images of the spine. The tool is comprised of customized shape-based segmentation algorithms.

Detecting Biliary Dyskinesia from 3D Ultrasound Images

Current medical analysis of proper gallbladder function relies on the measurement of volume change over time. This project seeks to replace the current method of measurement, which requires the injection of radioactive markers for imaging, with a non-invasive 3D ultrasound approach. A 3D segmentation toolbox has been built in order to segment the gallbladder and subsequently detect biliary dyskinesia.

Part-Based Object Recognition for Aerial Image Synthesis and Analysis

Complicated objects can generally be decomposed into smaller building blocks (parts). Understanding contextual relations between these parts in modelling an object recognition system is of great importance for various applications. This project involves using the state-of-the-art, part-based algorithms, such as Conditional Random Fields and Parse Graphs, to aid in image synthesis and object recognition.

Plant Water Stress Estimation through Processing of IR and Color Images

We are developing an automated vision system that estimates plant water stress status by utilizing infrared and color images. The system will be applied and validated in a study of cotton plant water stress tolerance in the AgriLife Center in Lubbock.