Marwan A. Mattar

Automatic In Situ Identification of Plankton

Earths oceans are a soup of living micro-organisms known as plankton. As the foundation of the food chain for marine life, plankton are also an integral component of the global carbon cycle which regulates the planets temperature. In this paper, we present a technique for automatic identification of plankton using a variety of features and classification methods including ensembles. The images were obtained in situ by an instrument known as the flow cytometer and microscope (FlowCAM), that detects particles from a stream of water siphoned directly from the ocean. The images are of necessity of limited resolution, making their identification a rather difficult challenge. We expect that upon completion, our system will become a useful tool for marine biologists to assess the health of the worlds oceans.

Automatic Sign Detection and Recognition in Natural Scenes

Visually impaired individuals are unable to utilize the significant amount of information in signs. VIDI is a system for detecting and recognizing signs in the environment and voice synthesizing their contents. The wide variety of signs and unconstrained imaging conditions make the problem challenging. We detect signs using local color and texture features to classify image regions with a conditional maximum entropy model. Detected sign regions are then recognized by matching them against a known database of signs. A support vector machine classifier uses color to focus the search, and a match is found based on the correspondences of corners and their associated shape contexts. Our dataset includes images of downtown scenes with several signs exhibiting both illumination differences and projective distortions. A wide range of signs are detected and recognized including both text and symbolic information. The detection and the recognition components each perform well on their respective tasks, and initial evaluations of a complete detection and recognition system are promising.

Sign Classification using Local and Meta-Features

Our world is populated with visual information that a sighted person makes use of daily. Unfortunately, the visually impaired are deprived from such information, which limits their mobility in unconstrained environments. To help alleviate this we are developing a wearable system [1, 19] that is capable of detecting and recognizing signs in natural scenes. The system is composed of two main components, sign detection and recognition. The sign detector, uses a conditional maximum entropy model to find regions in an image that correspond to a sign. The sign recognizer matches the hypothesized sign regions with sign images in a database. The system decides if the most likely sign is correct or if the hypothesized sign region does not belong to a sign in the database. Our data sets encompass a wide range of variability including changes in lighting, orientation and viewing angle. In this paper, we present an overview of the system while while paying particular attention to the recognition component. Tested on 3,975 sign images from two different data sets, the recognition phase achieves accuracies of 99.5% with 35 distinct signs and 92.8% with 65 distinct signs.

Nonparametric curve alignment

Congealing is a flexible nonparametric data-driven framework for the joint alignment of data. It has been successfully applied to the joint alignment of binary images of digits, binary images of object silhouettes, grayscale MRI images, color images of cars and faces, and 3D brain volumes. This research enhances congealing to practically and effectively apply it to curve data. We develop a parameterized set of nonlinear transformations that allow us to apply congealing to this type of data. We present positive results on aligning synthetic and real curve data sets and conclude with a discussion on extending this work to simultaneous alignment and clustering.