Towards computer-assisted photo-identification of humpback whales

Ranguelova, Elena; Huiskes, Mark J.; Pauwels, Eric J.

doi:10.1109/icip.2004.1421406

Cited by 16 publications

(13 citation statements)

References 2 publications

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“…Such systems usually require some level of user control, either at the input level (the user must label the features to be matched by hand) or at the output level (the user is asked to select the correct match from a ranked list of images suggested by the algorithm). Where a manual choice must be made from a ranked list of between 3 and 10 potential matches, the correct individual is contained within the image subset in 84% (Ranguelova et al 2004) to 100% of cases (e.g. Kelly 2001).…”

Section: Discussionmentioning

confidence: 99%

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Spotting the difference: towards fully-automated population monitoring of African penguins Spheniscus demersus

Sherley¹,

Burghardt²,

Barham³

et al. 2010

Endang. Species. Res.

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Placing external monitoring devices onto seabirds can have deleterious effects on welfare and performance, and even the most benign marking and identification methods return sparse population data at a huge time and effort cost. Consequently, there is growing interest in methods that minimise disturbance but still allow robust population monitoring. We have developed a computer vision system that automatically creates a unique biometric identifier for individual adult African penguins Spheniscus demersus using natural markings in the chest plumage and matches this against a population database. We tested this non-invasive system in the field at Robben Island, South Africa. False individual identifications of detected penguins occurred in less than 1 in 10 000 comparisons (n = 73 600, genuine acceptance rate = 96.7%) to known individuals. The monitoring capacity in the field was estimated to be above 13% of the birds that passed a camera (n = 1453). A significant increase in this lower bound was recorded under favourable conditions. We conclude that the system is suitable for population monitoring of this species: the demonstrated sensitivity is comparable to computer-aided animal biometric monitoring systems in the literature. A full deployment of the system would identify more penguins than is possible with a complete exploitation of the current levels of flipper banding at Robben Island. Our study illustrates the potential of fully-automated, non-invasive, complete population monitoring of wild animals.

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Section: Discussionmentioning

confidence: 99%

“…Kelly 2001). However, when such systems are asked to find the 1 correct match (with no user choice), as AnimalID is asked to do, the matching accuracy drops to a low of 60.8% (Ranguelova et al 2004) and a high of 92 to 93% (Arzoumanian et al 2005, Van Tienhoven et al 2007.…”

Section: Discussionmentioning

confidence: 99%

Spotting the difference: towards fully-automated population monitoring of African penguins Spheniscus demersus

Sherley¹,

Burghardt²,

Barham³

et al. 2010

Endang. Species. Res.

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“…Whenever animals carry individually unique visual markings and an approach for imaging these efficiently is available, biometric computer vision provides an option for non-invasive, partly or fully automated identification [9,12,14,15]. Individual great white sharks, for instance, can be fully automatically re-identified if one can photograph and match silhouettes of their dorsal fin [7], but this requires a precise (and fully automatic) extraction of boundaries as a precursor to matching.…”

Section: Introductionmentioning

confidence: 99%

Affinity Matting for Pixel-accurate Fin Shape Recovery from Great White Shark Imagery

Hughes

Burghardt

2015

Procedings of the Machine Vision of Animals and Their Behaviour Workshop 2015

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The objective of this paper is to obtain pixel-accurate reconstructions of white shark fins given automatically generated coarse pre-segmentations. Reconstruction performance is compared for affinity matting, colour matting and GrabCut against expert annotated ground truth for a test-set of 120 fin images taken in the wild. For the present domain, we find affinity matting able to most accurately recover fine shape details, whilst being robust to wide baseline trimap initialisations as needed to reconstruct prominent notches on the fin edge.

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“…Eakins et al, 1998), medicine, biomonitoring (e.g. Ranguelova et al, 2004), and interior design (discussed below).…”

Section: Introductionmentioning

confidence: 99%

Indexing, Learning and Content-Based Retrieval for Special Purpose Image Databases

Huiskes¹,

Pauwels²

2005

Advances in Computers

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C e n t r u m v o o r W i s k u n d e e n I n f o r m a t i c a PNA Probability, Networks and Algorithms Probability, Networks and AlgorithmsIndexing, learning and content-based retrieval for special purpose image databases M.J. Huiskes, E.J. Pauwels Indexing, learning and content-based retrieval for special purpose image databases ABSTRACT This chapter deals with content-based image retrieval in special purpose image databases. As image data is amassed ever more effortlessly, building efficient systems for searching and browsing of image databases becomes increasingly urgent. We provide an overview of the current state-of-the art by taking a tour along the entire "image retrieval chain" -from processing raw image data, through various methods of machine learning, to the interactive presentation of query results. As the key to building successful image retrieval systems is in the detailed and accurate description of the images, we start out with a discussion on content representation and indexing. We describe various methods to obtain image features, and also introduce the representation of content by means of MPEG-7 metadata. With regard to the search system itself, we focus particularly on interfaces and learning algorithms which facilitate relevance feedback, i.e. on systems that allow for natural interaction with the user in refining queries directly in terms of example images. To this end the literature on this subject is reviewed, and an outline is provided of the special structure of the relevance feedback learning problem. Finally we present a probabilistic approach to relevance feedback that addresses this special structure. REPORT PNA-E0423 DECEMBER 2004

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Towards computer-assisted photo-identification of humpback whales

Cited by 16 publications

References 2 publications

Spotting the difference: towards fully-automated population monitoring of African penguins Spheniscus demersus

Spotting the difference: towards fully-automated population monitoring of African penguins Spheniscus demersus

Affinity Matting for Pixel-accurate Fin Shape Recovery from Great White Shark Imagery

Indexing, Learning and Content-Based Retrieval for Special Purpose Image Databases

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