Dangerous, antibiotic resistant bacteria have been observed with increasing frequency over the past several decades. In this review the factors that have been linked to this phenomenon are addressed. Profiles of bacterial species that are deemed to be particularly concerning at the present time are illustrated. Factors including economic impact, intrinsic and acquired drug resistance, morbidity and mortality rates, and means of infection are taken into account. Synchronously with the waxing of bacterial resistance there has been waning antibiotic development. The approaches that scientists are employing in the pursuit of new antibacterial agents are briefly described. The standings of established antibiotic classes as well as potentially emerging classes are assessed with an emphasis on molecules that have been clinically approved or are in advanced stages of development. Historical perspectives, mechanisms of action and resistance, spectrum of activity, and preeminent members of each class are discussed.

Antibiotics and Bacterial Resistance in the 21st Century

https://www.comp.nus.edu.sg/~cs4243/projects/TextDetectionSurvey.pdf

Text information extraction in images and video: a survey

This paper gives an algorithm for detecting and reading text in natural images. The algorithm is intended for use by blind and visually impaired subjects walking through city scenes. We first obtain a dataset of city images taken by blind and normally sighted subjects. From this dataset, we manually label and extract the text regions. Next we perform statistical analysis of the text regions to determine which image features are reliable indicators of text and have low entropy (i.e. feature response is similar for all text images). We obtain weak classifiers by using joint probabilities for feature responses on and off text. These weak classifiers are used as input to an AdaBoost machine learning algorithm to train a strong classifier. In practice, we trained a cascade with 4 strong classifiers containing 79 features. An adaptive binarization and extension algorithm is applied to those regions selected by the cascade classifier. Commercial OCR software is used to read the text or reject it as a non-text region. The overall algorithm has a success rate of over 90% (evaluated by complete detection and reading of the text) on the test set and the unread text is typically small and distant from the viewer.

/pdf/detecting-and-reading-text-in-natural-scenes-5g0mr0vsvj.pdf

Detecting and reading text in natural scenes

An electronic device with a display and a fingerprint sensor may authenticate a user for a respective function. While a respective function of the device is in a locked state, the device displays a graphical element on the display, the graphical element indicating a first direction of finger movement that enables unlocking of the respective function. While displaying the graphical element, the device detects an input that includes movement of a finger in the first direction over the fingerprint sensor and determines whether the input meets unlock criteria based at least in part on fingerprint information of the finger detected by the fingerprint sensor during the input. In accordance with a determination that the input meets the unlock criteria, the device unlocks the respective function; and in accordance with a determination that the input does not meet the unlock criteria, the device maintains the respective function in the locked state.

Embedded authentication systems in an electronic device

Multimedia content analysis refers to the computerized understanding of the semantic meanings of a multimedia document, such as a video sequence with an accompanying audio track. With a multimedia document, its semantics are embedded in multiple forms that are usually complimentary of each other, Therefore, it is necessary to analyze all types of data: image frames, sound tracks, texts that can be extracted from image frames, and spoken words that can be deciphered from the audio track. This usually involves segmenting the document into semantically meaningful units, classifying each unit into a predefined scene type, and indexing and summarizing the document for efficient retrieval and browsing. We review advances in using audio and visual information jointly for accomplishing the above tasks. We describe audio and visual features that can effectively characterize scene content, present selected algorithms for segmentation and classification, and review some testbed systems for video archiving and retrieval. We also describe audio and visual descriptors and description schemes that are being considered by the MPEG-7 standard for multimedia content description.

Multimedia content analysis-using both audio and visual clues

Video OCR is a technique that can greatly help to locate topics of interest in a large digital news video archive via the automatic extraction and reading of captions and annotations. News captions generally provide vital search information about the video being presented, the names of people and places or descriptions of objects. In this paper, two difficult problems of character recognition for videos are addressed: low resolution characters and extremely complex backgrounds. We apply an interpolation filter, multi-frame integration and a combination of four filters to solve these problems. Segmenting characters is done by a recognition-based segmentation method and intermediate character recognition results are used to improve the segmentation. The overall recognition results are good enough for use in news indexing. Performing video OCR on news video and combining its results with other video understanding techniques will improve the overall understanding of the news video content.

/pdf/video-ocr-for-digital-news-archive-4llo1ivh5f.pdf

Video OCR for digital news archive

The automatic extraction and recognition of news captions and annotations can be of great help locating topics of interest in digital news video libraries. To achieve this goal, we present a technique, called Video OCR (Optical Character Reader), which detects, extracts, and reads text areas in digital video data. In this paper, we address problems, describe the method by which Video OCR operates, and suggest applications for its use in digital news archives. To solve two problems of character recognition for videos, low-resolution characters and extremely complex backgrounds, we apply an interpolation filter, multiframe integration and character extraction filters. Character segmentation is performed by a recognition-based segmentation method, and intermediate character recognition results are used to improve the segmentation. We also include a method for locating text areas using text-like properties and the use of a language-based postprocessing technique to increase word recognition rates. The overall recognition results are satisfactory for use in news indexing. Performing Video OCR on news video and combining its results with other video understanding techniques will improve the overall understanding of the news video content.

Video OCR: indexing digital new libraries by recognition of superimposed captions

Feature amounts of faces of visitors photographed at a first gate are registered in a registration section in relation to entrance permission time information items each of which defines a period of time during which entrance through a second gate provided inside the first gate is permitted. At the second gate, the face of the visitor is photographed and the feature amount of the photographed face of the visitor is collated with the face feature amounts registered in the registration section. Entrance through the second gate is controlled based on the result of collation and the entrance permission time information registered in the registration section.

Entrance management apparatus and entrance management method

An automatic mail processing apparatus comprises a physical quantity detection section for detecting physical quantities of mail with a stamp, such as the weight and dimensions of the mail, a postage determining section for determining the valid postage for the mail with reference to a table in which valid charges are previously stored on the basis of the information items indicating physical quantities, and a stamp detection section for detecting the amount paid on the basis of the information on the stamp contained in the image of the mail, and a processing section for verifying the determined postage with the amount paid to detect a surplus or deficit of the amount paid, and to identify the kind of the mail, classify the mail, and compile statistics data on the mail.

Processing apparatus for mail with stamps

Apparatus and method for identifying the color image of an object. The object's image is digitized and a cut-off portion of the image containing the object is determined. The cut-off portion image data is normalized. Selected pixels of the normalized image are subjected to an averaging process to provide an averaged image containing R, G, and B color components. H, V, and C components of the averaged image data are computed, and their V, c, and d components are computed from the H, V, and C components. A color feature extractor computes from the c and d component data a parameter representative of the object to be identified. This parameter is provided to a memory controller to retrieve from a memory that stores target images one or more images categorized according to the computed parameter. A matching section matches each retrieved target image with the Vcd component data and determines which target image is most similar.

Toshio Sato

Papers

Video OCR for digital news archive

Video OCR: indexing digital new libraries by recognition of superimposed captions

Entrance management apparatus and entrance management method

Processing apparatus for mail with stamps

Image identifying apparatus