![]() |
![]() |
![]() |
![]() |
![]() |
![]() |
![]() |
![]() |
![]() |
![]() |
![]() by Staff Writers University Park PA (SPX) Feb 12, 2019
A new automated method to prepare digital photos for analysis will help wildlife researchers who depend on photographs to identify individual animals by their unique markings. A wildlife biologist from Penn State teamed up with scientists from Microsoft Azure, a cloud computing service, using machine learning technology to improve how photographs are turned into usable data for wildlife research. A paper describing the new technique appears online in the journal Ecological Informatics. "Many researchers need to identify and collect data on specific individuals in their work, for example to estimate survival, reproduction, and movement," said Derek Lee, associate research professor of biology at Penn State and principal scientist of the Wild Nature Institute. "Instead of tags and other human-applied markings that could interfere with the animal's behavior, many researchers take photographs of the animal's unique markings. We have pattern recognition software to help analyze these photos, but the photos all have to be manually prepared for analysis. Because we often have thousands of photos to go thorough, this creates a serious bottleneck." Lee uses photographs as part of a large ongoing study to understand births, deaths, and movement of more than 3,000 giraffes in East Africa. He and his team take digital photographs of each animal's unique and unchanging spot patterns to identify them throughout their lives. But before the images can be processed by pattern recognition software to identify individuals, the research team has to manually crop each photo or delineate an area of interest. Lee collaborated with scientists from Microsoft, who have provided a new image processing service to automate this essential and time-consuming process using machine learning technology deployed on the Microsoft Azure cloud. Using a computer algorithm for object detection, the Microsoft team trained a program to recognize giraffe torsos using existing photos that had been annotated by hand. The program was iteratively improved using an active learning process, where the system showed predicted cropping squares on new images to a human who could quickly verify or correct the results. These new images were then fed back into the training algorithm to further update and improve the program. The resulting system identifies the location of giraffe torsos in images with a very high accuracy, even when the giraffe is a small portion of the image or its torso is partially blocked by vegetation. "The system achieves near-perfect recognition of giraffe torsos without expensive hardware requirements like a dedicated high-powered graphics processing unit," said Lee. "It is wonderful how the Azure team automated this tedious aspect of our work. It used to take us a week to process our new images after a survey, and now it is done in minutes. This system moves us closer to fully automatic animal identification from photos." The new system will dramatically accelerate Lee's research on giraffe populations, which have rapidly declined across Africa due to habitat loss and illegal killing for meat. "Giraffe are big animals, and they cover big distances, so naturally we are using big data to learn where they are doing well, where they are not, and why, so we can protect and connect the areas important to giraffe conservation," said Lee. "We needed new tools to accomplish this, and there was a harmonic conjunction with the Azure technology that made our work possible." This process will also be useful to researchers studying other animals with unique identifying patterns - including some wild cats, elephants, salamanders, fish, penguins, and marine mammals. The system could be trained to identify and crop a photo to a region of interest specific to these species.
![]() ![]() India's 'granny' elephant dies aged 88 Thiruvananthapuram, India (AFP) Feb 7, 2019 An Asian elephant believed to be the oldest ever in captivity has died aged 88 in the southern Indian state of Kerala, officials said Thursday. Awarded the title of "Gaja Muthassi" (elephant granny), Dakshayani took part in temple rituals and processions for decades, but breathed her last on Tuesday after becoming reluctant to eat, her veterinary surgeon said. "At 3 pm, a sudden shiver passed through her large frame beginning from the head region. After a few minutes she bent her forelimbs and l ... read more
![]() |
|
The content herein, unless otherwise known to be public domain, are Copyright 1995-2024 - Space Media Network. All websites are published in Australia and are solely subject to Australian law and governed by Fair Use principals for news reporting and research purposes. AFP, UPI and IANS news wire stories are copyright Agence France-Presse, United Press International and Indo-Asia News Service. ESA news reports are copyright European Space Agency. All NASA sourced material is public domain. Additional copyrights may apply in whole or part to other bona fide parties. All articles labeled "by Staff Writers" include reports supplied to Space Media Network by industry news wires, PR agencies, corporate press officers and the like. Such articles are individually curated and edited by Space Media Network staff on the basis of the report's information value to our industry and professional readership. Advertising does not imply endorsement, agreement or approval of any opinions, statements or information provided by Space Media Network on any Web page published or hosted by Space Media Network. General Data Protection Regulation (GDPR) Statement Our advertisers use various cookies and the like to deliver the best ad banner available at one time. All network advertising suppliers have GDPR policies (Legitimate Interest) that conform with EU regulations for data collection. By using our websites you consent to cookie based advertising. If you do not agree with this then you must stop using the websites from May 25, 2018. Privacy Statement. Additional information can be found here at About Us. |