scholarly journals Normalized Cross Correlation Template Matching for Oil Palm Tree Counting from UAV image

2021 ◽  
Vol 2107 (1) ◽  
pp. 012036
Author(s):  
Sharifah Nurul Husna Syed Hanapi ◽  
S A A Shukor ◽  
Jalal Johari
Keyword(s):  
Oil Palm ◽  
Template Matching ◽  
Cross Correlation ◽  
Accuracy Assessment ◽  
Sliding Window ◽  
Palm Tree ◽  
Vegetation Monitoring ◽  
Effective Measure ◽  
Normalized Cross Correlation ◽  
Uav Image

Abstract Tree crown detection and counting from remote sensing data such as images from Unmanned Aerial Vehicle (UAV) shows significant role in this modern era for vegetation monitoring. Since the data processing would depends on raw data available and for this case the RGB data, thus a suitable method such as template matching is presented. Normalized cross correlation is widely used as an effective measure in similarity between template image and the source or testing images. This paper focuses on six (6) steps involved in the overall process which are: (1) image acquisition, (2) template optimisation, (3) normalized cross correlation, (4) sliding window, (5) matched image and counting, and (6) accuracy assessment. Normalized cross correlation and sliding window techniques proposed for this work resulted in 80% to 89% F-measure values. This result indicates that UAV image data with appropriate image processing method/s have the potential to provide vital information for oil palm tree counting. This would be beneficial in plantation management to estimate yield and productivity. However, there are still rooms for improvement to achieve better results.

scholarly journals Registration of FA and T1-Weighted MRI Data of Healthy Human Brain Based on Template Matching and Normalized Cross-Correlation

2013 ◽  
Vol 26 (4) ◽  
pp. 774-785 ◽  
Author(s):  
Milos Malinsky ◽  
Roman Peter ◽  
Erlend Hodneland ◽  
Astri J. Lundervold ◽  
Arvid Lundervold ◽  
...  
Keyword(s):  
Human Brain ◽  
Template Matching ◽  
Cross Correlation ◽  
Healthy Human ◽  
Normalized Cross Correlation

A Speed-Up Way of Template Matching Using Normalized Cross-Correlation

2013 ◽  
Vol 313-314 ◽  
pp. 1188-1191 ◽  
Author(s):  
Fang Chen ◽  
Cun Ji Zhang ◽  
Bin Wen Zhao ◽  
Jin Fei Shi
Keyword(s):  
Template Matching ◽  
Cross Correlation ◽  
Correlation Method ◽  
Block Matching ◽  
Conventional Approach ◽  
Projection Algorithm ◽  
Experimental Result ◽  
Gray Scale ◽  
Normalized Cross Correlation ◽  
Speed Up

One classic algorithm usedin template matching is normalized cross correlation method. It often achieveshigh precision. But it does not meet speed requirements for time-criticalapplications. To solve that issue, a speed-up way of template matching isproposed. The fast matching way bases on pyramid hierarchical searchingalgorithm. It adopts two template matching methods to match images, which baseon rough matching proceeds local matching precision. Firstly, the coarsematching is performed based on gray-scale projection algorithm. Secondly, theprecise matching is made based on several small block matching. The new way iscompared to conventional approach without pyramid hierarchical searching byexperiments. Experimental result demonstrates that the proposed way efficientlyimproves the speed of template matching and the precision is unchanged.

scholarly journals Interpretation of Ground Penetrating Radar Dataset using Normalised Cross-Correlation Technique

2019 ◽  
Vol 9 (1) ◽  
pp. 3505-3509
Keyword(s):  
Ground Penetrating Radar ◽  
Template Matching ◽  
Cross Correlation ◽  
Accuracy Assessment ◽  
Current Method ◽  
Soil Condition ◽  
Correlation Technique ◽  
Processing Parameter ◽  
Buried Pipe ◽  
Ground Penetrating

Ground Penetrating Radar (GPR) is one of the latest non-destructive geophysical technology and most widely used in detecting underground utilities. GPR can detect both metal and non-metal, however, it is unable to identify the type of underground utility object. Many researchers come out with their techniques to interpret the GPR image. The current method requires experience in interpretation. Thus, in this study, a new method to detect underground utility utilizing the Normalised Cross-Correlation (NCC) template matching technique is proposed. This technique will reduce the dependency on experts to interpret the radargram, less time consuming and eventually save cost. Upon detection, the accuracy of the system is assessed. From the accuracy assessment performed, it is shown that the system provides accurate detection results for both, depth and pipe size. The Root Mean Square Error (RMSE) for the buried pipe depth obtained by using the proposed system is 0.110 m, whereas the highest percentage match obtained is 91.34%, the remaining 8.66% mismatched might be due to the soil condition, velocity or processing parameter that affected the radargram. Based on the assessment, the developed system seems capable to detect the subsurface utility if the radar image and template image used is acquired using the same antenna frequency, point interval, and similar GPR instrument

scholarly journals Identifikasi Ciri Garis Telapak Tangan Berbasis Template Matching dan Metode K-Nearest Neighbor

MIND Journal ◽  
2019 ◽  
Vol 3 (2) ◽  
pp. 25-35
Author(s):  
Asep Nana Hermana ◽  
Irma Amelia Dewi ◽  
Irwan Susanto
Keyword(s):  
Template Matching ◽  
Cross Correlation ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Normalized Cross Correlation

Telapak tangan merupakan ciri unik yang dimiliki oleh manusia yang dapat digunakan pada sistem identifikasi. Proses template matching membutuhkan perhitungan pencocokan untuk menentukan bagian kecil gambar yang memiliki nilai terbesar dikarenakan semakin besar nilai maka tingkat kecocokan semakin tinggi. Sehingga untuk pencocokan dibutuhkan perhitungan normalized cross correlation dengan perhitungan konvolusi yang setiap bagian pixel akan dilakukan pencocokan, diawali dari pixel bagian pojok kiri atas hingga pojok kanan bawah dan akan mendapatkan nilai pencocokan terbesar.Setelah mendapat nilai terbesar dilakukan k-nearest neighbor yang merupakan pengelompokan berdasarkan jarak dan untuk menentukan jarak k digunakan perhitungan euclidien distance. Selanjutnya pengelompokan berdasarkan voting terbanyak yang dimulai dari nilai jarak ketetanggaan terkecil hingga terbesar. Tingkat akurasi pengujian dari 30 sampel telapak tangan didapatkan presentase sebesar 86,67% teridentifikasi benar dan 13,33% salah.

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