Fundamentals of Digital Image Processing

Solomon, Christopher J.; Breckon, Toby P.

doi:10.1002/9780470689776

Cited by 326 publications

(224 citation statements)

References 0 publications

Supporting

Mentioning

198

Contrasting

Unclassified

Order By: Relevance

“…projection as opposed to a perspective projection at the sensor level [63], scale invariance at the feature level is a significantly lessor issue as the size of an object does not vary with its distance to the sensor (i.e. measurements can be recovered from CT-scan imagery in realworld values).…”

Section: Discussionmentioning

confidence: 99%

Object classification in 3D baggage security computed tomography imagery using visual codebooks

Flitton

Mouton

Breckon

2015

Pattern Recognition

View full text Add to dashboard Cite

Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-pro t purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. AbstractWe investigate the performance of a Bag of (Visual) Words ( Optimal performance is achieved using the DH and DGH descriptors in conjunction with an uncertainty assignment methodology. Successful detection rates in excess of 97% for handguns and 89% for bottles and false-positive rates of approximately 2-3% are achieved. We demonstrate that the underlying imaging modality and the irrelevance of illumination and scale invariance within the transmission imagery context considered here, result in the favourable performance of simpler density histogram descriptors (DH, DGH) over 3D extensions of the well-established SIFT and RIFT feature descriptor approaches.

show abstract

Section: Discussionmentioning

confidence: 99%

Object classification in 3D baggage security computed tomography imagery using visual codebooks

Flitton

Mouton

Breckon

2015

Pattern Recognition

View full text Add to dashboard Cite

show abstract

“…Dalam pemrosesan citra, hasil yang diharapkan diperoleh berdasarkan bentuk atau struktur citra asal. Lebih lanjut dikatakan bahwa morfologi senantiasa berkaitan erat dengan proses ketetanggaan (neighborhood) yang terbentuk dari blok nilai biner, satu dan nol [9] . Lebih lanjut lagi, proses morfologi suatu citra merupakan kumpulan operasi nonlinear yang berkaitan dengan bentuk atau morfologi dalam suatu citra [10].…”

Section: Image Morphologyunclassified

Kendali Lampu Lalu Lintas dengan Deteksi Kendaraan Menggunakan Metode Blob Detection

Hidayati¹

2017

Jurnal Nasional Teknik Elektro dan Teknologi Informasi (JNTETI)

View full text Add to dashboard Cite

Abstract-Traffic jam is a major traffic problem often found in big cities of Indonesia. It is because the number of vehicles increases annually. Therefore, a simulation to detect the number of vehicles in every lane of traffic is needed to monitor the traffic. Traffic control is also required in order to reduce traffic jam. This paper develops a vehicle detection and counting system using image processing. Detection is carried out using image segmentation which is processed by object filtering and blob extraction. Morphological operators are employed for blob extraction. Testing is conducted using a video obtained from the ATCS Bandung. The video is taken at the Laswi -A. Yani intersection Bandung. Software prototypes are created in C++, using Windows Forms Application as a programming library for Windows and Open CV for image processing module. The result shows that blob detection method can give good results if there is no intersection between blobs of each car. The performance is poor when this method is used for heavy traffic conditions, where the cars are close to each other. The performance level of sensitivity is 91.67%, precision is 61.11%, specificity is 80.55%, f-Measure is 73.33%, and accuracy is 83.33%. The accuracy for vehicles detection on sunny condition is 82.11% and reduced by 76.50% on rainy condition. This method works better in quiet condition, with accuracy of 83.07%, and is reduced by 67.70% in crowded condition. The average processing time is 0.042 seconds when using video, and 0.033 seconds using real camera. Kata kunci--deteksi kendaraan, pengolahan citra digital, blob detection, morfologi, OpenCV I. PENDAHULUAN Pada setiap tahun, peningkatan jumlah kendaraan semakin bertambah. Hal ini berakibat munculnya masalah lalu lintas kendaraan, misalnya kemacetan lalu lintas, kecelakaan lalu lintas, polusi udara, dan sebagainya. Kemacetan lalu lintas telah menjadi masalah signifikan. Beberapa hal yang telah dilakukan untuk menangani masalah kemacetan misalnya awal infrastruktur transportasi, lebar trotoar, dan pelebaran jalan. Namun, hal ini belum mampu mengurangi kemacetan kota, sehingga banyak peneliti tertarik melakukan penelitian terhadap sistem transportasi cerdas (intelligent transportation systems, ITS), seperti memprediksi arus lalu lintas pada pemantauan kegiatan di persimpangan lalu lintas untuk mendeteksi kemacetan. Dalam hal ini diperlukan peningkatan pengawasan lalu lintas pada deteksi kendaraan. Deteksi kendaraan secara otomatis dalam data video surveillance merupakah bagian dari computer vision. Intisari-Deteksi kendaraan dan penghitungan kendaraan adalah hal penting dalam komputasi kemacetan lalu lintas di jalan raya. Sistem yang diusulkan di sini bertujuan untuk memberikan data jumlah kendaraan pada persimpangan jalan raya. Tujuan utama makalah ini adalah untuk mengembangkan metode deteksi kendaraan secara otomatis dan menghitung kendaraan pada simpang empat lalu lintas. Sebuah sistem diimplementasikan untuk mendeteksi dan menghitung objek secara dinamis dan statis. Pemantauan ja...

show abstract

“…З іншого боку, в останній час значного поширення отримали методи обробки зорових зображень [20][21][22][23] та відеопотоків [24,25], сучасних інтелектуальних методів розпізнавання образів [26][27][28], тощо, які на-дають можливості «витягнути» з навіть пошкодже-ного зображення суттєву інформацію, необхідну для діагностики поточного стану, відтворення або реін-жинірингу БКМ. Залишається лише не до кінця роз-робленою проблема відображення складної структури на зорове зображення, тобто структурна візуалізація складних систем [29,30].…”

Section: аналіз літературних даних і постановка проблемиunclassified

Transformation of the structure of complex technical systems with partially unusable elements to the visual image

Нестеренко

Становський

Toropenko

et al. 2015

EEJET

View full text Add to dashboard Cite

Fundamentals of Digital Image Processing

Cited by 326 publications

References 0 publications

Object classification in 3D baggage security computed tomography imagery using visual codebooks

Object classification in 3D baggage security computed tomography imagery using visual codebooks

Kendali Lampu Lalu Lintas dengan Deteksi Kendaraan Menggunakan Metode Blob Detection

Transformation of the structure of complex technical systems with partially unusable elements to the visual image

Contact Info

Product

Resources

About