Learning to segment with image-level supervision

Deep convolutional networks have achieved the state-of-the-art for semantic image segmentation tasks. However, training these networks requires access to densely labeled images, which are known to be very expensive to obtain. On the other hand, the web provides an almost unlimited source of images annotated at the image level. How can one utilize this much larger weakly annotated set for tasks that require dense labeling? Prior work often relied on localization cues, such as saliency maps, objectness priors, bounding boxes etc., to address this challenging problem. In this paper, we propose a model that generates auxiliary labels for each image, while simultaneously forcing the output of the CNN to satisfy the mean-field constraints imposed by a conditional random field. We show that one can enforce the CRF constraints by forcing the distribution at each pixel to be close to the distribution of its neighbors. This is in stark contrast with methods that compute a recursive expansion of the mean-field distribution using a recurrent architecture and train the resultant distribution. Instead, the proposed model adds an extra loss term to the output of the CNN, and hence, is faster than recursive implementations. We achieve the state-of-the-art for weakly supervised semantic image segmentation on VOC 2012 dataset, assuming no manually labeled pixel level information is available. Furthermore, the incorporation of conditional random fields in CNN incurs little extra time during training.Comment: Published in WACV 201

Attention to attributes and objects in working memory

It has been debated on the basis of change-detection procedures whether visual working memory is limited by the number of objects, task-relevant attributes within those objects, or bindings between attributes. This debate, however, has been hampered by several limitations, including the use of conditions that vary between studies and the absence of appropriate mathematical models to estimate the number of items in working memory in different stimulus conditions. We re-examined working memory limits in two experiments with a wide array of conditions involving color and shape attributes, relying on a set of new models to fit various stimulus situations. In Experiment 2, a new procedure allowed identical retrieval conditions across different conditions of attention at encoding. The results show that multiple attributes compete for attention, but that retaining the binding between attributes is accomplished only by retaining the attributes themselves. We propose a theoretical account in which a fixed object capacity limit contains within it the possibility of the incomplete retention of object attributes, depending on the direction of attention

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 349)

This bibliography lists 149 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during April, 1991. Subject coverage includes: aerospace medicine and psychology, life support systems and controlled environments, safety equipment, exobiology and extraterrestrial life, and flight crew behavior and performance

One Time Over Another: Tom Marioni's Conceptual Art

types: Articlepublication-status: PublishedNo abstract available

Human Perception Based Color Image Segmentation

Color image segmentation is probably the most important task in image analysis and understanding. A novel Human Perception Based Color Image Segmentation System is presented in this paper. This system uses a neural network architecture. The neurons here uses a multisigmoid activation function. The multisigmoid activation function is the key for segmentation. The number of steps ie. thresholds in the multisigmoid function are dependent on the number of clusters in the image. The threshold values for detecting the clusters and their labels are found automatically from the first order derivative of histograms of saturation and intensity in the HSI color space. Here the main use of neural network is to detect the number of objects automatically from an image. It labels the objects with their mean colors. The algorithm is found to be reliable and works satisfactorily on different kinds of color images

Making power visible for museum educators : a theoretical framework for multicultural museum education

The central guiding question of this study is how can museum educators (and volunteers) effectively engage multicultural audiences, who may face langauge and socioeconomic barriers, with objects of art in museum galleries?

Usage of Infrared-Based Technologies in Forensic Sciences

Infrared (IR) radiation comprises a beam located in the electromagnetic radiation family; it arises from the thermal vibrations of radiation that have longer wavelengths than visible light, but shorter wavelengths than microwave radiation. Its wavelength is between 750 nm and 1 mm. The amount of thermal IR radiation emitted by an object is associated with the temperature of the object, the surface area of the object and the spreading of light. IR-based technologies have been demonstrated as a method of evidence identification in forensic sciences in addition to many daily uses

DNA BARCODING AS A TOOL FOR SPECIES DISCOVERY AND DOCUMENTATION IN THE SUPERFAMILY ICHNEUMONOIDEA

Changes to traditional taxonomic methods to incorporate new technologies and methods have already improved the quality of species hypotheses, but more work can be done to improve the speed of new species documentation. The mitochondrial COI DNA barcode has been successfully used to identify species with high accuracy since the early 2000s, and has been used in conjunction with morphological examinations and other DNA markers to discover and delimit new species. This thesis explores the application of DNA barcodes as the primary data for delimitation and diagnosis of new species of ichneumonoids. The genera Zelomorpha and Hemichoma are revised and 18 new species from the Área de Conservación Guanacaste in Costa Rica are diagnosed based on COI barcodes. Two additional species are described based on morphology. An illustrated morphological key and morphological diagnoses for each species are also included