CNN based lung segmentation models in absence of diverse training dataset fail to segment lung volumes in presence of severe pathologies such as large masses, scars, and tumors. To rectify this problem, we propose a multi-stage algorithm for lung volume segmentation from CT scans. The algorithm uses a 3D CNN in the first stage to obtain a coarse segmentation of the left and right lungs. In the second stage, shape correction is performed on the segmentation mask using a 3D structure correction CNN. A novel data augmentation strategy is adopted to train a 3D CNN which helps in incorporating global shape prior. Finally, the shape corrected segmentation mask is up-sampled and refined using a parallel flood-fill operation. The proposed multi-stage algorithm is robust in the presence of large nodules/tumors and does not require labeled segmentation masks for entire pathological lung volume for training. Through extensive experiments conducted on publicly available datasets such as NSCLC, LUNA, and LOLA11 we demonstrate that the proposed approach improves the recall of large juxtapleural tumor voxels by at least 15% over state-of-the-art models without sacrificing segmentation accuracy in case of normal lungs. The proposed method also meets the requirement of CAD software by performing segmentation within 5 seconds which is significantly faster than present methods.

We describe a probabilistic approach for supervised learning when we have multiple experts/annotators providing (possibly noisy) labels but no absolute gold standard. The proposed algorithm evaluates the different experts and also gives an estimate of the actual hidden labels. Experimental results indicate that the proposed method is superior to the commonly used majority voting baseline.

Automatic polyp detection is an increasingly important task in medical imaging with virtual colonoscopy [15] being widely used. In this paper, we present a 3D object detection algorithm and show its application on polyp detection from CT images. We make the following contributions: (1) The system adopts Probabilistic Boosting Tree (PBT) to probabilistically detect polyps. Integral volume and 3D Haar filters are introduced to achieve fast feature computation. (2) We give an explicit convergence rate analysis for the AdaBoost algorithm [2] and prove that the error at each step \in t+1. is tightly bounded by the previous error \in t. (3) For a 3D polyp template, a generative model is defined. Given the bound and convergence analysis, we analyze the role of "sample alignment" in the template design and devise a robust and efficient algorithm for polyp detection. The overall system has been tested on 150 volumes and the results obtained are very encouraging.

As a comprehensive problem, video enhancement includes many subset problems, including super-resolution, noise reduction, increase of dynamic range, to name a few. In this paper, we focus on a special subset problem -- scintillation removal. Scintillation is one type of image distortions caused by the propagation of visible light through unstable atmosphere. One commonly known source for disturbing atmosphere is heat. We propose an image processing method to remove scintillation in video. This method explores the temporal information redundancy in video and is based on image registration and image fusion. We examine the scintillation problem in detail and explain why we take an image registration plus fusion approach. Finally we demonstrate successful application of scintillation removal on real video clips.

This paper presents an approach to enhance the dynamic range of monochromatic and color images. The goal of this method is that of overcoming the inherent physical limitations of the acquisition process. The approach uses pattern-selective image fusion as a means to integrate information from images acquired under different shutter speeds or apertures. The result of this process is a composite in which none of the portions of the image is over or under saturated and the underlying information has been preserved. The results show the performance of this enhancement process from imagery taken from consumer-grade video camera equipment.

We use Sarnoff's next-generation video processor, the PVT-200, to demonstrate real-time algorithms for stereo processing, obstacle detection, and terrain estimation from stereo cameras mounted on a moving vehicle. Sarnoff's stereo processing and obstacle detection capabilities are currently being used in several Unmanned Ground Vehicle (UGV) programs, including MDARS-E and DEMO III. Sarnoff's terrain estimation capabilities are founded on a "model-based directed stereo" approach. We demonstrate ongoing collaborative research between Sarnoff and Universitat der Bundeswehr Munchen, where we are studying vision processing for autonomous off-road navigation as part of the AUTONAV program.

A new driving mechanism for holonomic omnidirectional mobile robots is designed, which enables 3 DOF motion control by three correspondent actuators in a decoupled manner with no redundancy. A prototype of the omnidirectional mobile robot with the driving mechanism is developed including a parallel link suspension mechanism. The kinematics of the omnidirectional mobile robot is also analyzed, and simulation for velocity control of the robot is performed by a method for velocity modulation with interpolation to achieve the given target position and velocity.

Functionality of an object defines its applicability to a task, In this paper, we introduce a representation for functionality and present a methodology for its recovery. In addition, we introduce force-shape maps as means to integrate and classify the recovered functionality, Since functionality describes an interaction, the representation for an object must include not only its intrinsic (material) but also its functional (how it is used) properties. In order to recover the functionality of an object, we have developed a formalism based on discrete event system theory and on the paradigm of active perception. This formalism allows us to express an investigating task in the form of a finite automaton for controlling and observing the interactions in our task. Our current investigation focuses on manipulatory interactions, and thus far, the functionality of piercing. We envision an expandable system having at its disposal a score of investigative procedures and through them to be able to classify and recognize an object based on its functional attributes.

The application of tools by artificial systems depends on the ability to recognize an object as having a particular function as well as on the ability to express the functional interaction. This paper focuses on the operation of piercing as means of investigating the issue of functional representation. The operation is expressed using a formalism, based on discrete event system (DES) theory and on the paradigm of active perception. The visual and tactile data acquired during piercing motor actions performed with a robotic manipulator is discussed.

In this paper we present a framework for modeling behaviors and tasks for heterogeneous robotic agents. For this purpose we have adopted a formalism from the discrete events systems (DES) theory. We distinguish two kinds of scenarios. In the first one, reactive behaviors of mobile agents directly connect observations with actions. The overall objective is to achieve controllability of the system which is composed from modular components operating in parallel. In the second one, observations are implicitly connected with actions and the objective is to design an observer for manipulatory tasks which would guarantee the task's observability. The use of the DES formalism allows one to describe complex interactions between different components in a systematic fashion and guarantee some control-theoretic properties. We demonstrate our approach by presenting examples of navigation, obstacle avoidance, piercing and picking.>