SenseCam image localisation using hierarchical SURF trees

The SenseCam is a wearable camera that automatically takes photos of the wearer's activities, generating thousands of images per day. Automatically organising these images for efficient search and retrieval is a challenging task, but can be simplified by providing semantic information with each photo, such as the wearer's location during capture time. We propose a method for automatically determining the wearer's location using an annotated image database, described using SURF interest point descriptors. We show that SURF out-performs SIFT in matching SenseCam images and that matching can be done efficiently using hierarchical trees of SURF descriptors. Additionally, by re-ranking the top images using bi-directional SURF matches, location matching performance is improved further

Towards learning free naive bayes nearest neighbor-based domain adaptation

As of today, object categorization algorithms are not able to achieve the level of robustness and generality necessary to work reliably in the real world. Even the most powerful convolutional neural network we can train fails to perform satisfactorily when trained and tested on data from different databases. This issue, known as domain adaptation and/or dataset bias in the literature, is due to a distribution mismatch between data collections. Methods addressing it go from max-margin classifiers to learning how to modify the features and obtain a more robust representation. Recent work showed that by casting the problem into the image-to-class recognition framework, the domain adaptation problem is significantly alleviated [23]. Here we follow this approach, and show how a very simple, learning free Naive Bayes Nearest Neighbor (NBNN)-based domain adaptation algorithm can significantly alleviate the distribution mismatch among source and target data, especially when the number of classes and the number of sources grow. Experiments on standard benchmarks used in the literature show that our approach (a) is competitive with the current state of the art on small scale problems, and (b) achieves the current state of the art as the number of classes and sources grows, with minimal computational requirements. © Springer International Publishing Switzerland 2015

ClassCut for Unsupervised Class Segmentation

Abstract. We propose a novel method for unsupervised class segmentation on a set of images. It alternates between segmenting object instances and learning a class model. The method is based on a segmentation energy defined over all images at the same time, which can be optimized efficiently by techniques used before in interactive segmentation. Over iterations, our method progressively learns a class model by integrating observations over all images. In addition to appearance, this model captures the location and shape of the class with respect to an automatically determined coordinate frame common across images. This frame allows us to build stronger shape and location models, similar to those used in object class detection. Our method is inspired by interactive segmentation methods [1], but it is fully automatic and learns models characteristic for the object class rather than specific to one particular object/image. We experimentally demonstrate on the Caltech4, Caltech101, and Weizmann horses datasets that our method (a) transfers class knowledge across images and this improves results compared to segmenting every image independently; (b) outperforms Grabcut [1] for the task of unsupervised segmentation; (c) offers competitive performance compared to the state-of-the-art in unsupervised segmentation and in particular it outperforms the topic model [2].

UV Degradation of the Optical Properties of Acrylic for Neutrino and Dark Matter Experiments

UV-transmitting (UVT) acrylic is a commonly used light-propagating material in neutrino and dark matter detectors as it has low intrinsic radioactivity and exhibits low absorption in the detectors' light producing regions, from 350 nm to 500 nm. Degradation of optical transmittance in this region lowers light yields in the detector, which can affect energy reconstruction, resolution, and experimental sensitivities. We examine transmittance loss as a result of short- and long-term UV exposure for a variety of UVT acrylic samples from a number of acrylic manufacturers. Significant degradation peaking at 343 nm was observed in some UVT acrylics with as little as three hours of direct sunlight, while others exhibited softer degradation peaking at 310 nm over many days of exposure to sunlight. Based on their measured degradation results, safe time limits for indoor and outdoor UV exposure of UVT acrylic are formulated.Comment: 13 pages, 6 figures, 3 tables; To be submitted to Journal of Instrumentatio

Pathway to Entrepreneurship University: an Autoethnography of Entrepreneurial Research Experience

Many universities in Indonesia are currently competing to become the best Entrepreneurship University. A number of theoretical models and public policies have been formulated at the national and local levels, and attempts targeting cognitive, affective, and psychomotor changes have been made. However, so far the emphasis on business orientation is still thick as if entrepreneurship deals only with the creation of economic benefits. In addition, evaluation at the micro level appears to be rarely performed. This research uses qualitative approach with autoethnography method. The purpose of this study is to explore the experience of entrepreneurial activity in the research track in Bina Nusantara University, Indonesia. This study shows the acquisition of a number of key competencies of entrepreneurship, mainly from the viewpoint of the first person (the actor/participant, the first author), together with the research supervisor (second author) and faculty supervisor (third author). The experience is further reflected theoretically in the Discussion section of this article. The unique feature of this autoethnography is the depiction of innovative learning gained from the concrete process of falling and awake for a semester passed by the participant. There are appreciations of diversity of opportunities or channels, of the role of historicity of the self, narrative process, and altruism driving force. This study results might be useful for sharpening entrepreneurship program and curriculum especially in universities that make entrepreneurship an orientation of students and graduates that is inevitable nowadays

A Review of Rare Pion and Muon Decays

After a decade of no measurements of pion and muon rare decays, PIBETA, a new experimental program is producing its first results. We report on a new experimental study of the pion beta decay, Pi(+) -> Pi(0) e(+) Nu, the Pi(e2 gamma) radiative decay, Pi(+) -> e(+) Nu Gamma, and muon radiative decay, Mu -> e Nu Gamma. The new results represent four- to six-fold improvements in precision over the previous measurements. Excellent agreement with Standard Model predictions is observed in all channels except for one kinematic region of the Pi(e2 gamma) radiative decay involving energetic photons and lower-energy positrons.Comment: 10 pages, 6 figures, 2 tables, invited talk presented at MESON 2004, 8th Int'l. Workshop on Meson Production, Properties and Interaction, Krakow, Poland 4-8 June 200

Fluxes of microbes, organic aerosols, dust, sea-salt Na ions, non-sea-salt Ca ions, and methanesulfonate onto Greenland and Antarctic ice

Using a spectrofluorimeter with 224-nm laser excitation and six emission bands from 300 to 420 nm to measure fluorescence intensities at 0.3-mm depth intervals in ice cores, we report results of the first comparative study of concentrations of microbial cells (using the spectrum of protein-bound tryptophan (Trp) as a proxy) and of aerosols with autofluorescence spectra different from Trp (denoted "non-Trp") as a function of depth in ice cores from West Antarctica (WAIS Divide and Siple Dome) and Greenland (GISP2). The ratio of fluxes of microbial cells onto West Antarctic (WAIS Divide) versus Greenland sites is 0.13±0.06; the ratio of non-Trp aerosols onto WAIS Divide versus Greenland sites is 0.16±0.08; and the ratio of non-sea-salt Ca<sup>2+</sup> ions (a proxy for dust grains) onto WAIS Divide versus Greenland sites is 0.06±0.03. All of these are roughly comparable to the ratio of fluxes of dust onto Antarctic versus Greenland sites (0.08±0.05). By contrast to those values, which are considerably lower than unity, the ratio of fluxes of methanesulfonate (MSA) onto Antarctic versus Greenland sites is 1.9±0.4 and the ratio of sea-salt Na<sup>2+</sup> ions onto WAIS Divide versus Greenland sites is 3.0±2. These ratios are more than an order of magnitude higher than those in the first grouping. We infer that the correlation of microbes and non-Trp aerosols with non-sea-salt Ca and dust suggests a largely terrestrial rather than marine origin. The lower fluxes of microbes, non-Trp aerosols, non-sea-salt Ca and dust onto WAIS Divide ice than onto Greenland ice may be due to the smaller areas of their source regions and less favorable wind patterns for transport onto Antarctic ice than onto Greenland ice. The correlated higher relative fluxes of MSA and marine Na onto Antarctic versus Greenland ice is consistent with the view that both originate largely on or around sea ice, with the Antarctic sea ice being far more extensive than that around Greenland

Deep Discrete Hashing with Self-supervised Pairwise Labels

Hashing methods have been widely used for applications of large-scale image retrieval and classification. Non-deep hashing methods using handcrafted features have been significantly outperformed by deep hashing methods due to their better feature representation and end-to-end learning framework. However, the most striking successes in deep hashing have mostly involved discriminative models, which require labels. In this paper, we propose a novel unsupervised deep hashing method, named Deep Discrete Hashing (DDH), for large-scale image retrieval and classification. In the proposed framework, we address two main problems: 1) how to directly learn discrete binary codes? 2) how to equip the binary representation with the ability of accurate image retrieval and classification in an unsupervised way? We resolve these problems by introducing an intermediate variable and a loss function steering the learning process, which is based on the neighborhood structure in the original space. Experimental results on standard datasets (CIFAR-10, NUS-WIDE, and Oxford-17) demonstrate that our DDH significantly outperforms existing hashing methods by large margin in terms of~mAP for image retrieval and object recognition. Code is available at \url{https://github.com/htconquer/ddh}