Object Detection at Object Detection at Different Resolutions in Different Resolutions in Archaeological Sidescan Archaeological Sidescan

Sonar ImagesSonar Images

Louis Atallah and Changjing ShangLouis Atallah and Changjing ShangInstitute of InformaticsInstitute of Informatics

The British University in Dubai-The University of The British University in Dubai-The University of EdinburghEdinburgh

latallah and shang @inf.ed.ac.uklatallah and shang @inf.ed.ac.uk

Richard BatesRichard BatesSchool of Geography and GeosciencesSchool of Geography and Geosciences

The University of St AndrewsThe University of St Andrewscrb@st-andrews.ac.ukcrb@st-andrews.ac.uk

June 2005June 2005

Talk planTalk plan

►Motivation and backgroundMotivation and background►Survey and Images usedSurvey and Images used►The scale-saliency algorithmThe scale-saliency algorithm►Object detection Object detection ►Object matchingObject matching

Motivation and BackgroundMotivation and Background

This work is part of the ‘Rapid Archaeological Site Survey and Evaluation’, which is a three-year research project funded by the Aggregates Levy Sustainability Fund (ALSF) administered by English Heritage, and based at the University of St Andrews, School of Geography and Geosciences. Partners include:

• The University of Ulster• The British University in Dubai/ The University of

Edinburgh• Wessex Archaeology • Reson Offshore.

Motivation and backgroundMotivation and background

The project involves exploring the following areas:

► The Stirling Castle (lost in a storm in 1703), located on the Goodwin Sands, a series of banks off the East Kent coast.

► Hastings Shingle Bank. Aggregate extraction already taking place. The RASSE project has identified a test site within the Hastings Shingle Bank Licence Area located approximately 15km south of Hastings.

► Placing artificial targets in a low spring tide water depth of 3m in Plymouth Sound (the area already has an artificially target, a 5m long boat, in place).

► This work is a preliminary experiment done in Belfast Lough as a part of this project aiming at differentiating between useful archaeological material and other objects.

The DatasetThe Dataset

The survey was done in Smelt Mill Bay (Belfast Lough) in July/August 2001.

A test site of material objects was placed on the seafloor. These objects were car tyres, ceramic balls, leather jackets, among other types...

Three different sidescan systems were used to survey these objects, and images obtained for these three types of sonar. These were: EdgeTech 272-TD: 100/500 kHz, Imagenex 885; 675 kHz, and Geoacoustics 159-A; 100/500 kHz.

The images used for this work are the Edgetech 272-TD images, each image containing 8 objects.

ImagesImages

Types of Objects:Types of Objects:1 & 2 car tyres.1 & 2 car tyres.3 amphora shoulder and neck3 amphora shoulder and neck4 ceramic ball4 ceramic ball5, 6 7 baskets5, 6 7 baskets8 leather jacket8 leather jacket

Any problems with theseAny problems with theseimages?images?

Typical commercial sonar imagesTypical commercial sonar imagesNoisy!Noisy!

PreprocessingPreprocessing

►The images are taken at different The images are taken at different depths. First use the sonar geometry to depths. First use the sonar geometry to correct for that.correct for that.

►De-noising images using a Wiener De-noising images using a Wiener filter…filter…

►Still, how can we locate the objects in Still, how can we locate the objects in these images?these images?

►Looking into local appearance based Looking into local appearance based feature detection…scale saliency.feature detection…scale saliency.

The scale-saliency algorithmThe scale-saliency algorithm► Salient areas in an image are areas that stand out Salient areas in an image are areas that stand out

from the background. Defined also as areas with local from the background. Defined also as areas with local unpredictability or complexity.unpredictability or complexity.

► First, we start by calculating the local Shannon entropy First, we start by calculating the local Shannon entropy over a range of scales : H(s,x). However, there might over a range of scales : H(s,x). However, there might be several peaks over several scales. s refers to the be several peaks over several scales. s refers to the radius of the circle used to calculate the scale saliency radius of the circle used to calculate the scale saliency of a certain point.of a certain point.

► We can weight the entropy function with W(s,x) We can weight the entropy function with W(s,x) describing the change of the magnitude at the scale describing the change of the magnitude at the scale peak points.peak points.

► The weighted scale-saliency can be used to detect the The weighted scale-saliency can be used to detect the range of most important scales for a certain pixel.range of most important scales for a certain pixel.

► That’s per pixel, how do we find the objects in an That’s per pixel, how do we find the objects in an image?image?

Scale saliency - finding Scale saliency - finding objectsobjects

► Clustering algorithm using KNN to group neighbouring Clustering algorithm using KNN to group neighbouring pixels to form individual salient regions, using a pixels to form individual salient regions, using a threshold T to remove the least salient features.threshold T to remove the least salient features.

T=3.5

T=4.5

T=4.9

Detected ObjectsDetected Objects

ResultsResults

►Varying the parameters of the method Varying the parameters of the method (T, the number of scales), ideally using (T, the number of scales), ideally using a large training set.a large training set.

►Results are very encouraging, the Results are very encouraging, the method detects the object parts of method detects the object parts of almost all objects (success rates of almost all objects (success rates of more than 90%).more than 90%).

►The method is also robust to intensity, The method is also robust to intensity, rotation and contrast which vary in a rotation and contrast which vary in a real survey. real survey.

Object ClassificationObject Classification

► Using a subset of images for training. First find the Using a subset of images for training. First find the salient areas and label the object parts from 1-9 (8 salient areas and label the object parts from 1-9 (8 object types and 1 for background).object types and 1 for background).

► Obtaining features for each part including: location, Obtaining features for each part including: location, entropy, saliency over scale and weighted saliency. entropy, saliency over scale and weighted saliency. Add also a normalised histogram of the area Add also a normalised histogram of the area selected.selected.

► Feature size=38, use PCA to lower dimensions.Feature size=38, use PCA to lower dimensions.► PCA dimension=11 for best classification rates.PCA dimension=11 for best classification rates.► The rates vary according to training/testing set The rates vary according to training/testing set

selectionselection► In general, promising results with misclassification In general, promising results with misclassification

rates as low as 3.6% in some cases.rates as low as 3.6% in some cases.

Conclusions and Future workConclusions and Future work

►Scale saliency can be used as a method Scale saliency can be used as a method to locate objects in large surveys.to locate objects in large surveys.

►Object matching using scale saliency.Object matching using scale saliency.►Future work on differentiating between Future work on differentiating between

archaeological material and other archaeological material and other underwater objectsunderwater objects

►Observing objects over a certain period, Observing objects over a certain period, matching with changing surroundings.matching with changing surroundings.

►Applications to wreck detection.Applications to wreck detection.