PCB Recognition using Local Features for Recycling Purposes

Christopher Pramerdorfer and Martin Kampel

Computer Vision Lab, Vienna University of Technology, Favoritenstr. 9/183-2, Vienna, Austria

Keywords:

Interest Points, Descriptors, Local Features, Instance Recognition, PCB Recognition, Evaluation.

Abstract:

We present a method for detecting and classifying Printed Circuit Boards (PCBs) in waste streams for recycling

purposes. Our method employs local feature matching and geometric veriﬁcation to achieve a high open-set

recognition performance under practical conditions. In order to assess the suitability of different local features

in this context, we perform a comprehensive evaluation of established (SIFT, SURF) and recent (ORB, BRISK,

FREAK, AKAZE) keypoint detectors and descriptors in terms of established performance measures. The

results show that SIFT and SURF are outperformed by recent alternatives, and that most descriptors beneﬁt

from color information in the form of opponent color space. The presented method achieves a recognition rate

of up to 100% and is robust with respect to PCB damage, as veriﬁed using a comprehensive public dataset.

1 INTRODUCTION

Chemical elements such as gallium, indium, and rare-

earth elements are required for the production of elec-

tronics like integrated circuits, photovoltaics, and ﬂat

panel displays (Moss et al., 2011). In recent years the

demand for these elements has been rising faster than

the supply, and, for certain elements, has already sur-

passed it (Moss et al., 2011). Increasing the produc-

tion capacity is not possible without limitations due

to the geographical concentration of the supply and

trade restrictions, for example (Moss et al., 2011).

For this reason, reclaiming these chemical elements

via recycling is important in order to overcome sup-

ply bottlenecks and to assure a sustainable production

of electronics that demand these elements.

This paper focuses on the optical recognition of

Printed Circuit Boards (PCBs) in waste streams for

recycling purposes. PCBs are a common electronics

waste and, depending on the mounted components,

contain gallium and other valuable elements (Moss

et al., 2011). The purpose of PCB recognition in

waste streams is to detect and classify speciﬁc PCBs

that are known to contain such elements, which are

then separated and recycled individually depending

on the particular type. This corresponds to an open-

set instance recognition problem; the task is to detect

and classify known target objects reliably while re-

jecting unknown objects.

To the knowledge of the authors, optical PCB

recognition in waste streams for recycling purposes

is an application that has not been explored so far. A

related application is the optical inspection of PCBs in

order to detect manufacturing defects (Moganti et al.,

1996; Guerra and Villalobos, 2001). Methods for de-

tecting individual PCB components (surface-mounted

devices, through-hole components) for recycling pur-

poses are presented in (Herchenbach et al., 2013; Li

et al., 2013). (Koch et al., 2013) describe a method for

generating 3D models of PCBs via laser triangulation.

These methods operate at the component level

rather than the PCB level or, in case of (Koch et al.,

2013), are designed for PCBs in general. In conse-

quence, they are inadequate for use in recycling sys-

tems that process speciﬁc PCBs as a whole.

To this end, we present a method for detecting and

classifying PCBs in waste streams via image analy-

sis.

Our method is designed for use in a speciﬁc

recycling appliance, which is detailed in Section 2.

This entails distinctive operating conditions, namely

(i) target objects with a characteristic and similar ap-

pearance, (ii) constant illumination, motion blur, and

image noise, and (iii) the absence of signiﬁcant cam-

era viewpoint changes apart from in-plane rotation. In

order to cope with these conditions, our method em-

ploys object representations based on local features

(local image descriptors computed at interest point lo-

This work is supported by the European Union under

grant FP7-NMP (Project reference: 309620). However, this

paper reﬂects only the authors’ views and the European

Community is not liable for any use that may be made of

the information contained herein.

Pramerdorfer C. and Kampel M..

PCB Recognition using Local Features for Recycling Purposes.

DOI: 10.5220/0005289200710078

In Proceedings of the 10th International Conference on Computer Vision Theory and Applications (VISAPP-2015), pages 71-78

ISBN: 978-989-758-091-8

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

Figure 1: Reference frames obtained via preprocessing.

cations) that are invariant to in-plane rotation and ro-

bust to small perspective distortions and image noise.

Another advantage of such representations is that they

are stable with respect to dust and partially damaged

or broken PCBs due to their part-based nature.

Following the success of SIFT (Lowe, 1999), sev-

eral local feature extractors with different characteris-

tics have been proposed. In order to be able to select

a suitable feature extractor for a given task, the per-

formance characteristics of the different alternatives

must be known. To this end, performance evaluations

of keypoint detectors and descriptor extractors have

been carried out. (Mikolajczyk et al., 2005) presented

a standard dataset for this purpose, the so-called Ox-

ford dataset, and used it to compare afﬁne region de-

tectors. (Mikolajczyk and Schmid, 2005) utilize this

dataset to compare descriptors. (Moreels and Perona,

2007) and (Aanæs et al., 2012) evaluate different key-

points and descriptors on nonplanar objects. (Heinly

et al., 2012) compare different combinations of recent

keypoint detectors and descriptor extractors on two

datasets, including the Oxford dataset.

While the datasets used in these evaluations cover

a broad range of photometric and geometric image

transformations, they do not correspond to the afore-

mentioned operating conditions. For instance, the

Oxford dataset does not contain test cases for motion

blur and pure in-plane rotation. On the other hand, it

does include test cases that do not occur in the con-

text of our application, such as signiﬁcant viewpoint

and lighting changes. Furthermore, the appearance

characteristics of the depicted objects differ; the test

datasets contain natural scenes and different kinds of

objects, whereas PCBs all have a distinctive, struc-

tured appearance due their component-based compo-

sition (Figure 1). For these reasons, the results re-

ported in these evaluations are inadequate for assess-

ing the suitability of different local features in the dis-

cussed PCB recognition context.

A general limitation of these evaluations is that

they do not cover recent developments such as

FREAK and AKAZE, and that they do not study the

effect of utilizing color information despite the posi-

tive results with SIFT (Van De Sande et al., 2010).

For these reasons, we carry out a comprehensive

evaluation of local features in a PCB recognition con-

text in terms of established performance measures.

The results show that recent binary features outper-

form the established features SIFT and SURF, and

that most features beneﬁt from utilizing color infor-

mation in the form of opponent color space. On this

basis, we select ORB features for PCB recognition,

and show that our method achieves a recognition rate

of up to 100% while being robust to PCB damage.

This paper is organized as follows. Section 2 de-

scribes the PCB recognition setup and our recognition

method. Section 3 discusses different local feature ex-

tractors, details the evaluation protocol, and presents

the evaluation results. The recognition performance

of our method is analyzed in Section 4. Conclusions

are drawn in Section 5.

2 PCB RECOGNITION

The proposed recognition method is used in an appli-

ance for recognizing speciﬁc PCBs in waste streams

in real-time. As such, the input is a live stream from

an IP camera (1280x960 px resolution, approx. 50

pixels per inch) . The appliance includes a black

conveyor belt that carries the waste stream at a con-

stant velocity of about 0.2 m/s. The camera is located

above the conveyor belt, oriented such that the image

plane and the conveyor belt are parallel to each other.

The appliance provides constant illumination by po-

larized light, which, in conjunction with a polariza-

tion ﬁlter in front of the camera, suppresses specular

reﬂections. Figure 2 illustrates this setup.

PCB recognition is accomplished in two steps.

First, individual objects in the waste stream are de-

tected and tracked over time, in order to be able to

extract a suitable reference image for each object. We

refer to this step as preprocessing. Subsequently, each

reference image is analyzed by means of local feature

matching and geometric veriﬁcation.

VISAPP2015-InternationalConferenceonComputerVisionTheoryandApplications

Figure 2: Image acquisition setup.

2.1 Preprocessing

Individual objects are detected via background sub-

traction. We employ a unimodal background model,

which is sufﬁcient considering the stable illumination

and conveyor belt appearance. More precisely, we

model the intensity distribution of the conveyor belt

as a single Gaussian for each pixel and learn the pa-

rameters from M frames during system initialization,

ˆµ

x,y

∑

m=1

x,y

− ˆµ

x,y

)

. (1)

x,y

denotes the pixel value at position (x, y) in the

mth frame after smoothing via linear ﬁltering. At run-

time, each pixel of the current frame F

x,y

is classi-

ﬁed as background if F

x,y

− ˆµ

x,y

< 3

x,y

, otherwise as

foreground. Afterwards, small holes and noise are re-

moved via morphological closing and opening.

The resulting binary foreground mask is subject to

connected component analysis. Components whose

size (number of pixels) is too small to represent PCBs

are discarded. The remaining components are tracked

over time on a frame-by-frame basis, with each com-

ponent being represented by its centroid location. The

goal is thus to ﬁnd, in each frame, the optimal asso-

ciation between C components and T tracks. We for-

mulate this task as an energy minimization problem,

with the energy between a component and a track be-

ing the Euclidean distance between the correspond-

ing centroid locations. Track centroid locations are

estimated from previous observations via Kalman ﬁl-

tering (Kalman, 1960). We ensure that C = T by in-

troducing dummy tracks and components if required

(Papadimitriou and Steiglitz, 1982), which enables us

to ﬁnd the optimal association efﬁciently using the

Hungarian algorithm (Kuhn, 1955).

The tracking information allows us to select a suit-

able reference frame for each observed PCB candi-

date; we select the frame in which the distance be-

tween the camera principal point and the component

centroid location is minimal. This ensures a similar

viewpoint between observed PCB images and mini-

mizes perspective distortions. Figure 1 shows refer-

ence frames obtained this way. After correcting for

lens distortions, each reference image is processed in

an open-set recognition context.

2.2 PCB Recognition

PCBs are recognized by extracting local features from

the input reference image, which are then matched

to the features of each PCB that should be recog-

nized. These features are stored in a database together

with metadata that facilitate recycling. Features are

matched using the descriptor distance ratio test pro-

posed in (Lowe, 2004).

A key characteristic of our recognition method is

that it utilizes the fact that PCBs are ﬂat and that the

camera position is stable to perform geometric veri-

ﬁcation; the feature matches are used to estimate the

homography H that describes the mapping between

both feature sets. RANSAC is used for robustness

with respect to erroneous matches, and all matches

that do not agree with H are discarded (a match agrees

with H if both feature locations are close under H).

The number of agreeing matches is used as the sim-

ilarity measure. Furthermore, H is tested for plausi-

bility; due to the preprocessing step, two images of

the same PCB are, in approximation, related by an in-

plane rotation, which implies det(H) ≈ 1. If this is

not the case, a similarity of 0 is assumed.

On this basis, recognition is performed by clas-

sifying the input image as the PCB with the highest

similarity. This applies unless this similarity is below

a threshold, in which case the image is rejected.

We do not employ techniques used in large-scale

image recognition such as bag of words (Sivic and

Zisserman, 2003). Our recognition method is used

with small databases (less than 100 PCBs), which, in

conjunction with features that can be matched efﬁ-

ciently, ensures real-time analysis. By not resorting

to these techniques, we avoid the associated perfor-

mance decrease due to the incurred information loss.

3 FEATURE EVALUATION

Our PCB recognition method supports arbitrary local

features. To obtain information on the performance

characteristics of different features in the discussed

context, we compare different candidates in terms of

precision vs. recall and descriptor matching score,

two established performance measures. To study the

effect of color information, this comparison is carried

out on grayscale images as well as in opponent color

PCBRecognitionusingLocalFeaturesforRecyclingPurposes

Table 1: Key characteristics of the analyzed keypoint-descriptor pairings (II: intensity invariance, RI: rotation invariance, SI:

scale invariance, AI: afﬁne invariance, ES: feature extraction speed, MS: feature matching speed, BD: binary descriptor, DS:

descriptor size). Speed rankings are based on (Heinly et al., 2012; Alcantarilla et al., 2013; Alahi et al., 2012).

Keypoints Descriptors Reference II RI SI AI ES MS BD DS

SIFT SIFT (Lowe, 2004) Y Y Y N 6 5 N 128 Bytes

SURF SURF (Bay et al., 2006) Y Y Y N 5 4 N 64 Floats

SURF FREAK (Alahi et al., 2012) Y Y Y N 4 3 Y 512 Bits

ORB ORB (Rublee et al., 2011) Y Y Y N 1 1 Y 256 Bits

BRISK BRISK (Leutenegger et al., 2011) Y Y Y N 2 3 Y 512 Bits

AKAZE AKAZE (Alcantarilla et al., 2013) Y Y Y N 3 2 Y 488 Bits

space. Table 1 summarizes the analyzed features re-

spectively keypoint-descriptor pairings and their key

characteristics. For brevity, we henceforth refer to

these features by their descriptor names (e.g. FREAK

instead of SURF-FREAK).

All features are tested with default parameters as

stated in the corresponding publications, with the ex-

ception of keypoint detector thresholds. These thresh-

olds are selected such as to limit the number of de-

tected keypoints to 500 in order to mitigate the effect

of the number of keypoints on performance scores

(Mikolajczyk et al., 2005). The Euclidean and Ham-

ming distance is used for matching real and binary

descriptors, respectively. We use OpenCV (version

2.4.9) implementations of all features except BRISK

and AKAZE. For BRISK we resort to the code pro-

vided by the author (Leutenegger et al., 2011) due to

a performance-degrading bug in recent OpenCV ver-

sions. As AKAZE is not part of OpenCV at the time

of writing, we use the implementation provided by the

authors (Alcantarilla et al., 2013), adapted to support

opponent color space.

For evaluation we employ a dataset consisting of

six reference images for each of 25 PCBs in ran-

dom orientations, obtained as discussed in Section

2.1. As such, the dataset tests the feature perfor-

mance in presence of constant illumination, motion

blur, image noise, and with a focus on in-plane ro-

tation. The depicted PCBs originate from a waste

stream in a recycling facility. The dataset is thus rep-

resentative in terms of both the depicted PCBs and

their condition (e.g. dust and damages). Figure 1

shows example images. The dataset is publicly avail-

able at http://www.caa.tuwien.ac.at/cvl/research/pcb-

ip-dataset/index.html. We manually annotate key-

points in all images (only points on the boards them-

selves, not on mounted components). For each PCB,

we select one image as the reference and use the anno-

tations to compute ground-truth homographies to the

remaing images.

We note that the test PCBs are not perfectly pla-

nar. As such, the relation between images of the same

PCB cannot be precisely described by a homography

over the whole domain. While this circumstance im-

pacts established performance measures that depend

on ground-truth homographies, it affects all tested

features alike and thus does not favor certain features.

3.1 Precision vs. Recall

Precision and recall are established performance mea-

sures that encode the number of correct and incor-

rect feature matches between two images. We calcu-

late these measures as in (Mikolajczyk and Schmid,

2005). Two features are matches if the distance be-

tween their descriptors is below t

. If the region over-

lap between the corresponding keypoints (the ratio

between the intersection and the union of their regions

after scale normalization) after applying the ground-

truth homography is larger than t

= 0.5, the match

is deemed correct. On this basis, the precision is

calculated as the share of correct matches among all

matches. The recall is the fraction between the num-

ber of correct matches and the number of keypoint

correspondences in terms of region overlap. We vary

to generate 1−precision vs. recall graphs. The re-

ported values are averages over all images.

As shown in Figure 3, FREAK performs best over

the whole precision range, with AKAZE being ranked

second. Both features have a clear performance ad-

vantage over the competitors in the high-precision

range. SIFT performs worst because it extracts sev-

eral descriptors per keypoint if multiple dominant

keypoint orientations are found, which impacts the re-

call (Leutenegger et al., 2011).

All features except SURF and AKAZE beneﬁt

from opponent color space (Figure 4). FREAK,

which again performs best over the whole domain,

improves by 5-10% on average. BRISK beneﬁts the

most from opponent color space, with gains between

10% and 15%. SIFT shows moderate performance

gains in the high-precision range, whereas the perfor-

mance of AKAZE decreases signiﬁcantly.

VISAPP2015-InternationalConferenceonComputerVisionTheoryandApplications

●

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00

1 − Precision

Recall

Method

●

AKAZE−AKAZE

BRISK−BRISK

ORB−ORB

SIFT−SIFT

SURF−FREAK

SURF−SURF

Figure 3: 1−Precision vs. recall (grayscale images).

●

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00

1 − Precision

Recall

Method

●

AKAZE−AKAZE

BRISK−BRISK

ORB−ORB

SIFT−SIFT

SURF−FREAK

SURF−SURF

Figure 4: 1−Precision vs. recall (opponent color space).

3.2 Descriptor Matching Score

A common strategy for matching features between

two images is the descriptor distance ratio test, which

matches two features f

, f

if f

is the nearest neigh-

bor of f

in terms of descriptor distance and if the dis-

tance ratio between f

and the ﬁrst and second near-

est neighbors, respectively, is below t

= 0.8 (Lowe,

2004). This strategy effectively suppresses incorrect

matches while preserving correct matches. In order

to evaluate the feature performance in this regard, we

use this strategy to obtain matches between all image

pairs, and compute the descriptor matching score as

the fraction of matches that agree with the ground-

truth homography ((Heinly et al., 2012) refer to this

measure as the precision). A match is in agreement if

the corresponding keypoint locations are within t

= 5

pixels distance from each other after applying the

ground-truth homography. We set t

comparatively

large (a common value in the literature appears to be

= 2.5) to compensate for the fact that the test ob-

jects are not perfectly planar. The reported values are

again averages over all images.

Figure 5 illustrates that all binary features exhibit

similar matching scores on grayscale images, and that

these scores are 7-10% higher than those of SIFT and

SURF. AKAZE performs best in this experiment as it

yields the largest number of correct matches, followed

by FREAK and ORB. BRISK achieves the high-

est matching score, which is consistent with (Heinly

et al., 2012), but returns the lowest number of correct

matches among all binary features. The results also

show a large variation in the number of matches and

the matching score due to the differences in test object

appearance. BRISK is most stable in this regard.

●

0.6

0.7

0.8

0.9

1.0

100 150 200 250 300

Matches

Matching Score

Method

●

AKAZE−AKAZE

BRISK−BRISK

ORB−ORB

SIFT−SIFT

SURF−FREAK

SURF−SURF

Figure 5: Average number of matches and matching scores

(grayscale images). Bars mark ±1 standard deviations.

Utilizing opponent color space improves the

matching score by 3-5% (Figure 6). The exception

is SIFT with an improvement of 10%, which renders

it competitive to the binary features. The winner in

terms of matching score is again BRISK. Opponent

color space decreases the number of matches in all

cases, with ORB, FREAK, and BRISK being the most

stable in this regard. AKAZE again returns the largest

number of correct matches, followed by FREAK and

ORB. SURF performs worse than the competitors.

3.3 Discussion and Feature Selection

The feature evaluation results show that the estab-

lished features SIFT and SURF are outperformed by

more recent alternatives with binary descriptors in

most experiments. The performance ranking depends

on the feature matching strategy; FREAK achieves

better results than the competition with threshold-

based matching, whereas AKAZE performs best in

case of distance-ratio-based matching.

As the results are obtained from a dataset with dis-

tinctive characteristics (Section 1), they agree with the

PCBRecognitionusingLocalFeaturesforRecyclingPurposes

●

0.6

0.7

0.8

0.9

1.0

100 150 200 250 300

Matches

Matching Score

Method

●

AKAZE−AKAZE

BRISK−BRISK

ORB−ORB

SIFT−SIFT

SURF−FREAK

SURF−SURF

Figure 6: Average number of matches and matching scores

(opponent color space). Bars mark ±1 standard deviations.

literature only partially. For example, (Alahi et al.,

2012) also ﬁnd that FREAK outperforms BRISK,

SIFT, and SURF with threshold-based matching, but

the ranking differs. SIFT is reported to perform fa-

vorably to both BRISK, ORB, and SURF under pure

in-plane rotations both in terms of precision vs. re-

call and descriptor matching score (Leutenegger et al.,

2011; Heinly et al., 2012), which contrasts to our ﬁnd-

ings. As discussed in Section 1, this is attributed to

the different appearance characteristics of the test ob-

jects. The disagreement between previous and our re-

sults highlights the importance of feature evaluations

that accurately capture the operating conditions.

Furthermore, the results show that most descrip-

tors, particularly FREAK, BRISK, ORB, and SIFT,

beneﬁt from opponent color space, which was origi-

nally conceived for SIFT (Van De Sande et al., 2010).

We therefore put forward to use opponent color space

with all these descriptors if a high matching perfor-

mance is paramount, unless the computational over-

head is a limiting factor; employing opponent color

space instead of grayscale images increases the fea-

ture extraction complexity and descriptor size by a

factor of 3. The performance of AKAZE regresses in

opponent color space with threshold-based matching.

We will investigate this issue in the future.

On the basis of these results, we select ORB fea-

tures for use with our recognition method. These fea-

tures achieve a competitive performance in all exper-

iments while being the most efﬁcient to compute and

match (Table 1). Computing 500 ORB features in a

test image takes around 18ms on a PC with an Intel i7

CPU, and feature matching takes only about 7ms.

4 EXPERIMENTAL RESULTS

We assess the performance of the proposed PCB

recognition method using a dataset consisting of 480

PCB images (six images for each of 80 PCBs). The

PCBs originate from a recycling facility and the im-

ages were obtained as described in Section 3.

For evaluation purposes, we randomly select 25

images of different PCBs for the database and pro-

cess the 455 remaining images as described in Section

2.2 (matches are rejected if | det(H) − 1| > 0.5). We

use ORB features and compare results obtained using

grayscale images to those using opponent color space.

In practice, waste PCBs are often partially dam-

aged or broken. In order to investigate the robust-

ness with respect to broken PCBs, we set a fraction

of z PCB pixels to zero before applying our recog-

nition method to simulate missing PCB pieces. This

is accomplished on a per-row basis to ensure that the

missing fraction constitutes a contiguous area; we it-

eratively set consecutive image rows to zero until the

number of visible PCB pixels decreases below 1 − z

times the original number. We repeat the test de-

scribed above for z = {0, 0.1, 0.2, . . . , 0.9}.

For each z, we calculate the the overall error rate

(ERR) as the fraction of images that are classiﬁed cor-

rectly, regardless of whether the depicted PCBs exist

in the database. Furthermore, we compute the false

classiﬁcation rate (FCR) as the fraction of images that

are represented in the database but assigned to an in-

correct class, the false rejection rate (FRR) as the frac-

tion of images that are rejected even though they are

represented in the database, and the false accept rate

(FAR) as the fraction of images that are classiﬁed as

in the database even tough this is not the case.

Figure 7 summarizes the experimental results.

With z = 0 (i.e. with intact PCBs), no errors are ob-

served; all target PCBs are recognized correctly and

all other PCBs are rejected. Increasing z increases

only the FRR (which in turn affects the ERR); both

the FCR and FAR remain zero in all tests. With

z = 0.2 and z = 0.5 (i.e. with 20% respectively 50%

missing data), the FRR is 3% and 19%, respectively.

The results obtained using grayscale images are al-

most identical to those obtained using opponent color

space over the whole domain of z.

The results conﬁrm the suitability of the proposed

method for PCB recognition in a recycling context.

The method achieves a recognition rate of 100% with

intact PCBs and is robust with respect to broken

PCBs; even with 50% missing data (which corre-

sponds to a PCB that was broken in half), 80% of

target PCBs are detected and classiﬁed correctly. Due

to homography veriﬁcation, the method is remarkably

VISAPP2015-InternationalConferenceonComputerVisionTheoryandApplications

robust in terms of classiﬁcation errors; both the FCR

and FAR are zero even with 90% missing data. This

is important in the discussed context as it ensures that

selective recycling lines are not contaminated.

●

100

0.00 0.25 0.50 0.75

Fraction of Missing Data

Error Percentage

Score

●

ERR

FCR

FRR

FAR

Figure 7: Recognition performance of the proposed

method, using ORB features. Continuous lines denote re-

sults obtained using grayscale images, while dashed lines

represent results obtained using opponent color space.

5 CONCLUSIONS

We have presented a method for recognizing speciﬁc

PCBs in waste streams via local feature matching and

geometric veriﬁcation. The method achieves an open-

set recognition rate of up to 100% on a comprehen-

sive test dataset while being robust with respect to

broken PCBs. It is a key component in a recycling

appliance designed for reclaiming valuable chemical

elements and thus contributes to overcoming supply

bottlenecks and to sustainable electronics production.

Furthermore, we have performed a comprehen-

sive evaluation of local features in a new applica-

tion context, namely with respect to PCB recogni-

tion. The evaluation results show that ORB, BRISK,

FREAK, and AKAZE outperform SIFT and SURF

in this context. The differences between our ﬁnd-

ings and previous results highlight the need for task-

speciﬁc test datasets. We contribute to the body of

available datasets by providing an extensive, freely

available dataset consisting of PCB images.

Moreover, we have demonstrated that utilizing

color information in the form of opponent color space

is beneﬁcial not only to SIFT, but also to ORB,

BRISK, and FREAK.

REFERENCES

Aanæs, H., Dahl, A. L., and Pedersen, K. S. (2012). Inter-

esting interest points. International Journal of Com-

puter Vision, 97(1):18–35.

Alahi, A., Ortiz, R., and Vandergheynst, P. (2012). FREAK:

Fast Retina Keypoint. In IEEE Conference on Com-

puter Vision and Pattern Recognition, pages 510–517.

Alcantarilla, P. F., Nuevo, J., and Bartoli, A. (2013). Fast

Explicit Diffusion for Accelerated Features in Non-

linear Scale Spaces. In British Machine Vision Con-

ference, pages 13.1–13.11.

Bay, H., Tuytelaars, T., and Van Gool, L. (2006). SURF:

Speeded up robust features. In European Conference

on Computer Vision, pages 404–417. Springer.

Guerra, E. and Villalobos, J. (2001). A three-dimensional

automated visual inspection system for SMT assem-

bly. Computers & industrial engineering, 40(1):175–

190.

Heinly, J., Dunn, E., and Frahm, J.-M. (2012). Comparative

evaluation of binary features. In European Conference

on Computer Vision, pages 759–773. Springer.

Herchenbach, D., Li, W., and Breier, M. (2013). Segmen-

tation and classiﬁcation of THCs on PCBAs. In IEEE

International Conference on Industrial Informatics,

pages 59–64.

Kalman, R. E. (1960). A New Approach to Linear Filtering

and Prediction Problems. Transactions of the ASME–

Journal of Basic Engineering, 82(D):35–45.

Koch, T., Breier, M., and Li, W. (2013). Heightmap genera-

tion for printed circuit boards (PCB) using laser trian-

gulation for pre-processing optimization in industrial

recycling applications. In IEEE International Confer-

ence on Industrial Informatics, pages 48–53.

Kuhn, H. W. (1955). The Hungarian method for the assign-

ment problem. Naval Research Logistics Quarterly,

2(1):83–97.

Leutenegger, S., Chli, M., and Siegwart, R. Y. (2011).

BRISK: Binary robust invariant scalable keypoints. In

IEEE International Conference on Computer Vision,

pages 2548–2555.

Li, W., Esders, B., and Breier, M. (2013). SMD segmen-

tation for automated PCB recycling. In IEEE Inter-

national Conference on Industrial Informatics, pages

65–70.

Lowe, D. G. (1999). Object recognition from local scale-

invariant features. In IEEE International Conference

on Computer Vision, volume 2, pages 1150–1157.

Lowe, D. G. (2004). Distinctive image features from scale-

invariant keypoints. International Journal of Com-

puter Vision, 60(2):91–110.

Mikolajczyk, K. and Schmid, C. (2005). A perfor-

mance evaluation of local descriptors. IEEE Trans-

actions on Pattern Analysis and Machine Intelligence,

27(10):1615–1630.

Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A.,

Matas, J., Schaffalitzky, F., Kadir, T., and Van Gool, L.

(2005). A comparison of afﬁne region detectors. Inter-

national Journal of Computer Vision, 65(1-2):43–72.

PCBRecognitionusingLocalFeaturesforRecyclingPurposes

Moganti, M., Ercal, F., Dagli, C. H., and Tsunekawa, S.

(1996). Automatic PCB inspection algorithms: a

survey. Computer vision and image understanding,

63(2):287–313.

Moreels, P. and Perona, P. (2007). Evaluation of features

detectors and descriptors based on 3d objects. Inter-

national Journal of Computer Vision, 73(3):263–284.

Moss, R., Tzimas, E., Kara, H., Willis, P., and Kooroshy, J.

(2011). Critical Metals in Strategic Energy Technolo-

gies. JRC-scientiﬁc and strategic reports.

Papadimitriou, C. and Steiglitz, K. (1982). Combinatorial

Optimization: Algorithm and Complexity. Prentice

Hall.

Rublee, E., Rabaud, V., Konolige, K., and Bradski, G.

(2011). ORB: an efﬁcient alternative to SIFT or

SURF. In IEEE International Conference on Com-

puter Vision, pages 2564–2571.

Sivic, J. and Zisserman, A. (2003). Video Google: A text

retrieval approach to object matching in videos. In

IEEE International Conference on Computer Vision,

pages 1470–1477.

Van De Sande, K. E., Gevers, T., and Snoek, C. G. (2010).

Evaluating color descriptors for object and scene

recognition. IEEE Transactions on Pattern Analysis

and Machine Intelligence, 32(9):1582–1596.

VISAPP2015-InternationalConferenceonComputerVisionTheoryandApplications