A New SVM-Based Architecture for Object Recognition in Color Underwater Images with Classification Refinement by Shape Descriptors

Gordan, Mihaela; Dancea, O.; Stoian, I.; Georgakis, Apostolos; Tsatos, O.

doi:10.1109/aqtr.2006.254654

Cited by 9 publications

(3 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The long-term potential application scenarios are wide ranging, and include:Inspection, maintenance and repairing of offshore structures, which are frequently carried out by the oil and gas industries [22]. Safe and secure exploration of inaccessible, polluting and dangerous maritime resources, including the detection of man-made objects [23,24]. Subsea collision avoidance, by using systems to identify and locate a different obstacles [25], for example in the early assessment of accident sites.Detection and identification of marine wildlife, with the aim of studying their physical environment [26].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

3D Object Recognition Based on Point Clouds in Underwater Environment with Global Descriptors: A Survey

Himri

Ridao

Gracias

2019

Sensors

View full text Add to dashboard Cite

This paper addresses the problem of object recognition from colorless 3D point clouds in underwater environments. It presents a performance comparison of state-of-the-art global descriptors, which are readily available as open source code. The studied methods are intended to assist Autonomous Underwater Vehicles (AUVs) in performing autonomous interventions in underwater Inspection, Maintenance and Repair (IMR) applications. A set of test objects were chosen as being representative of IMR applications whose shape is typically known a priori. As such, CAD models were used to create virtual views of the objects under realistic conditions of added noise and varying resolution. Extensive experiments were conducted from both virtual scans and from real data collected with an AUV equipped with a fast laser sensor developed in our research centre. The underwater testing was conducted from a moving platform, which can create deformations in the perceived shape of the objects. These effects are considerably more difficult to correct than in above-water counterparts, and therefore may affect the performance of the descriptor. Among other conclusions, the testing we conducted illustrated the importance of matching the resolution of the database scans and test scans, as this significantly impacted the performance of all descriptors except one. This paper contributes to the state-of-the-art as being the first work on the comparison and performance evaluation of methods for underwater object recognition. It is also the first effort using comparison of methods for data acquired with a free floating underwater platform.

show abstract

Section: Introductionmentioning

confidence: 99%

“…Safe and secure exploration of inaccessible, polluting and dangerous maritime resources, including the detection of man-made objects [23,24].…”

Section: Introductionmentioning

confidence: 99%

3D Object Recognition Based on Point Clouds in Underwater Environment with Global Descriptors: A Survey

Himri

Ridao

Gracias

2019

Sensors

View full text Add to dashboard Cite

show abstract

“…To solve this bottleneck problem, computational approaches for taxon detection and classification have been proposed in different contexts. Until now, a number of these are restricted to controlled environments [58] , [59] , the detection of manufactured objects [60] – [62] or designed to work specifically in the water column [58] , [63] where no sediment (i. e. background) has to be distinguished from the taxa investigated. In the majority of published cases, a single taxon or a group of similar taxa [56] , [63] – [68] is studied and taxon-customized features are utilized.…”

Section: Introductionmentioning

confidence: 99%

Semi-Automated Image Analysis for the Assessment of Megafaunal Densities at the Arctic Deep-Sea Observatory HAUSGARTEN

et al. 2012

View full text Add to dashboard Cite

Megafauna play an important role in benthic ecosystem function and are sensitive indicators of environmental change. Non-invasive monitoring of benthic communities can be accomplished by seafloor imaging. However, manual quantification of megafauna in images is labor-intensive and therefore, this organism size class is often neglected in ecosystem studies. Automated image analysis has been proposed as a possible approach to such analysis, but the heterogeneity of megafaunal communities poses a non-trivial challenge for such automated techniques. Here, the potential of a generalized object detection architecture, referred to as iSIS (intelligent Screening of underwater Image Sequences), for the quantification of a heterogenous group of megafauna taxa is investigated. The iSIS system is tuned for a particular image sequence (i.e. a transect) using a small subset of the images, in which megafauna taxa positions were previously marked by an expert. To investigate the potential of iSIS and compare its results with those obtained from human experts, a group of eight different taxa from one camera transect of seafloor images taken at the Arctic deep-sea observatory HAUSGARTEN is used. The results show that inter- and intra-observer agreements of human experts exhibit considerable variation between the species, with a similar degree of variation apparent in the automatically derived results obtained by iSIS. Whilst some taxa (e. g. Bathycrinus stalks, Kolga hyalina, small white sea anemone) were well detected by iSIS (i. e. overall Sensitivity: 87%, overall Positive Predictive Value: 67%), some taxa such as the small sea cucumber Elpidia heckeri remain challenging, for both human observers and iSIS.

show abstract

Accurate object recognition in the underwater images using learning algorithms and texture features

Srividhya

Ramya

2017

Multimed Tools Appl

View full text Add to dashboard Cite

A New SVM-Based Architecture for Object Recognition in Color Underwater Images with Classification Refinement by Shape Descriptors

Cited by 9 publications

References 11 publications

3D Object Recognition Based on Point Clouds in Underwater Environment with Global Descriptors: A Survey

3D Object Recognition Based on Point Clouds in Underwater Environment with Global Descriptors: A Survey

Semi-Automated Image Analysis for the Assessment of Megafaunal Densities at the Arctic Deep-Sea Observatory HAUSGARTEN

Accurate object recognition in the underwater images using learning algorithms and texture features

Contact Info

Product

Resources

About