Joint Inference in Weakly-Annotated Image Datasets via Dense Correspondence

We present a principled framework for inferring pixel labels in weakly-annotated image datasets. Most previous, example-based approaches to computer vision rely on a large corpus of densely labeled images. However, for large, modern image datasets, such labels are expensive to obtain and are often unavailable. We establish a large-scale graphical model spanning all labeled and unlabeled images, then solve it to infer pixel labels jointly for all images in the dataset while enforcing consistent annotations over similar visual patterns. This model requires significantly less labeled data and assists in resolving ambiguities by propagating inferred annotations from images with stronger local visual evidences to images with weaker local evidences. We apply our proposed framework to two computer vision problems, namely image annotation with semantic segmentation, and object discovery and co-segmentation (segmenting multiple images containing a common object). Extensive numerical evaluations and comparisons show that our method consistently outperforms the state-of-the-art in automatic annotation and semantic labeling, while requiring significantly less labeled data. In contrast to previous co-segmentation techniques, our method manages to discover and segment objects well even in the presence of substantial amounts of noise images (images not containing the common object), as typical for datasets collected from Internet search

Confocal Laser scanning microscopy

xii.106 : 23c

Influence of Geometry on Liquid Oxygen Magnetohydrodynamincs

Magnetic fluid actuators have performed well in industrial applications, but have a limited temperature range due to the freezing point of the carrier fluid. Liquid oxygen (LOX) presents a pure, paramagnetic fluid suitable for use in a cryogenic magnetic fluid system; therefore, it is a potential solution to increasing the thermal range of magnetic fluid technology without the need for magnetic particles. The current study presents experimental work regarding the influence of geometry on the dynamics of a LOX slug in a 1.9 mm quartz tube when pulsed by a solenoid in a closed volume. A numerical analysis calculated the optimal solenoid geometry and balanced the magnetic, damping, and pressure forces to determine optimal slug lengths. Three configurations comprised the experiment: (1) a 24-gauge wire solenoid with an optimized 2.7 cm length slug, (2) a 30-gauge wire solenoid with an optimized 1.3 cm length slug, and (3) a 30-gauge wire solenoid with a nonoptimized 2.5 cm length slug. Typically, the hydrodynamic breakdown limit is calculated and used to determine the system range; however the experiment showed that the hydrodynamic breakdown limit was never reached by the slug. This implied that, instead, the system range should factor in a probabilistic risk of failure calculated as a function of the induced pressure change from its oscillations. The experimental data were also used to establish a nondimensional relationship between the maximum displacement and initial magnetic pressure on the slug. The average initial velocity of the slug was found to be proportional to the initial magnetic pressure, Mason number, and slug length. The results of this study can be used in the design and optimization of a LOX fluid system for space or low-temperature applications

Efecto de la aplicación de nanopartículas de ZnO en la translocación y acumulación de Pb en cultivos de maíz (Zea mays).

En el presente trabajo se llevó a cabo la determinación del efecto de las nanopartículas de ZnO como inhibidor de la acumulación y translocación de Pb en cultivos de maíz (Zea mays) germinados en suelos enriquecidos (127 mg/kg) y no enriquecidos (35 mg/kg) con Pb, previamente tratados con nanopartícula de ZnO (0-3200 mg/kg). A las plántulas se les determinó la biomasa empleando horno de secado; la concentración de pigmentos fotosintéticos (clorofila (a y b) y carotenos) y la actividad del antioxidante enzimático catalasa con espectrofotómetro UV/Vis; la cuantificación en sus tejidos (hoja, tallo y raíz) de Pb, Zn, Fe, Mn, Ca, Mg, K, P y S empleando espectroscopia de emisión óptica con plasma acoplado inductivamente; y la determinación de los factores de translocación y bioacumulación

Beyond Pairwise Shape Similarity Analysis

Abstract. This paper considers two major applications of shape matching algorithms: (a) query-by-example, i. e. retrieving the most similar shapes from a database and (b) finding clusters of shapes, each represented by a single prototype. Our approach goes beyond pairwise shape similarity analysis by considering the underlying structure of the shape manifold, which is estimated from the shape similarity scores between all the shapes within a database. We propose a modified mutual kNN graph as the underlying representation and demonstrate its performance for the task of shape retrieval. We further describe an efficient, unsupervised clustering method which uses the modified mutual kNN graph for initialization. Experimental evaluation proves the applicability of our method, e. g. by achieving the highest ever reported retrieval score of 93.40 % on the well known MPEG-7 database.

Boosting Chamfer Matching by Learning Chamfer Distance Normalization

Abstract. We propose a novel technique that significantly improves the performance of oriented chamfer matching on images with cluttered background. Different to other matching methods, which only measures how well a template fits to an edge map, we evaluate the score of the template in comparison to auxiliary contours, which we call normalizers. We utilize AdaBoost to learn a Normalized Oriented Chamfer Distance (NOCD). Our experimental results demonstrate that it boosts the detection rate of the oriented chamfer distance. The simplicity and ease of training of NOCD on a small number of training samples promise that it can replace chamfer distance and oriented chamfer distance in any template matching application.

Enhanced display of scientific articles using extended metadata

<p>Although the Web has transformed science publishing, scientific papers themselves are still essentially “black boxes”, with much of their content intended for human readers only. Typically, computer-readable metadata associated with an article is limited to bibliographic details. By expanding article metadata to include taxonomic names, identifiers for cited material (e.g., publications, sequences, specimens, and other data), and geographical coordinates, publishers could greatly increase the scientific value of their digital content. At the same time this will provide novel ways for users to discover and navigate through this content, beyond the relatively limited linkage provided by bibliographic citation.</p> <p>As a proof of concept, my entry in the Elsevier Grand Challenge extracted extended metadata from a set of articles from the journal Molecular Phylogeny and Evolution and used it to populate an entity-attribute-value database. A simple web interface to this database enables an enhanced display of the content of an article, including a map of localities mentioned either explicitly or implicitly (through links to geotagged data), taxonomic coverage, and both data and citation links. Metadata extraction was limited to information listed in tables in the articles, such as GenBank sequences and specimen codes. The body of the article was not used, a restriction that was deliberate to demonstrate that making extended metadata available does not require a journal’s publisher to make the full-text freely available (although this is desirable for other reasons).</p&gt