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This paper confirms the new distributional records of four rare deep-water chondrichthyans viz. sharpnose sevengill shark, Heptranchias perlo (Bonnaterre, 1788); bluntnose sixgill shark, Hexanchus griseus (Bonnaterre, 1788); bramble shark, Echinorhinus brucus (Bonnaterre, 1788); and sicklefin chimaera, Neoharriotta pinnata (Schnakenbeck, 1931) from the Andaman waters in the Indian EEZ. The above-mentioned four fish species have not been reported earlier from this region. More exploratory surveys in the region are essential to understand the diversity and distribution pattern which is essential for critical management actions. An updated checklist of chondrichthyans known from Andaman and Nicobar waters of India is also presented.

We consider the problem of group testing with sum observations and noiseless answers, in which we aim to locate multiple objects by querying the number of objects in each of a sequence of chosen sets. We study a probabilistic setting with entropy loss, in which we assume a joint Bayesian prior density on the locations of the objects and seek to choose the sets queried to minimize the expected entropy of the Bayesian posterior distribution after a fixed number of questions. We present a new non-adaptive policy, called the dyadic policy, show that it is optimal among non-adaptive policies, and is within a factor of two of optimal among adaptive policies. This policy is quick to compute, its nonadaptive nature makes it easy to parallelize, and our bounds show that it performs well even when compared with adaptive policies. We also study an adaptive greedy policy, which maximizes the one-step expected reduction in entropy, and show that it performs at least as well as the dyadic policy, offering greater query efficiency but reduced parallelism. Numerical experiments demonstrate that both procedures outperform a divide-and-conquer benchmark policy from the literature, called sequential bifurcation, and show how these procedures may be applied in a stylized computer vision problem.

Most large asteroid population discovery has been accomplished to date by Earth-based telescopes. It is speculated that most of the smaller Near Earth Objects (NEOs) that are less than 100 meters in diameter, whose impact can create substantial city-size damage, have not yet been discovered. Many asteroids cannot be detected with an Earth-based telescope given their size and/or their location with respect to the Sun. We are investigating the feasibility of deploying asteroid detection algorithms on-board a spacecraft, thereby minimizing the expense and need to downlink large collection of images. Having autonomous on-board image analysis algorithms enables the deployment of a spacecraft at approximately 0.7 AU heliocentric or Earth-Sun L1/L2 halo orbits, removing some of the challenges associated with detecting asteroids with Earth-based telescopes. We describe an image analysis algorithmic pipeline developed and targeted for on-board asteroid detection and show that its performance is consistent with deployment on flight-qualified hardware.

A variety of pixel and feature based methods have been proposed for registering multiple views of anatomy visible in studies obtained using diagnostic, minimally invasive imaging. A given registration method may outperform another depending on anatomical variations, imaging conditions, and imaging sensor performance, and it is often difficult a priori to determine the best registration method for a particular application. To address this problem, we propose a registration framework that pools the results of multiple registration methods using a decision function for validating registrations. We refer to this as meta registration. We demonstrate that our framework outperforms several individual registration methods on the task of registering multiple views of Crohn's disease lesions sampled from a Capsule Endoscopy (CE) study database. We also report on preliminary work on assessing the quality of registrations obtained, and the possibility of using such assessment in the registration framework.

This paper presents a new approach to evaluation of registration using a general discriminative learning model that is independent of the type of registration method. We select features by association of a registration with a set of metrics (pixel based, patch based and histogram based statistics) and learn a classifier that discriminates mis-registrations from correct registrations using Adaboost. Experiments on a set of wireless capsule endoscopy (CE) images and images extracted from minimally invasive surgical endoscopic video data are presented. Results show that the proposed method outperforms any single classifier.

In this paper, we describe current progress on the development of a Computer Assisted Diagnosis System (CAD) for the classification of Barrett’s esophagus and associated neoplasia. Barrett’s esophagus is a condition in which normal squamous mucosa is replaced by columnar epithelium, which is similar to the lining of the intestine. Barrett’s esophagus as a known precancerous condition leading to esophageal cancer. Diagnosis is performed via histological analysis of tissue located during endoscopic examination. We compare four different automated classification tools (SVM, KNN, and Boosting) operating on three different imaging modalities (white light, narrow-band, and acetic acid chromoendoscopy) for lesion classification. Preliminary results suggest that narrow band imaging is more effective than either of the other two modalities for disease assessment.