Detection of Airport Runway Edges using Line Detection Techniques

Airport runway detection is a vital aspect for both military and commercial applications. An algorithm to extract runway edges based on edge detection and line detection techniques is discussed. The runway images are initially enhanced by dilation, thresholding and edge detection. Based on some unique characteristics like the runway being gray with two white lines indicating the runway boundaries, long and continuous edges of the runway are considered to be straight lines. The straight lines are detected using Convolution operators pertaining to vertical, 45° or -45° lines. Hough Transform is then applied to fit only the pair of lines corresponding to the runway boundaries in certain orientations. The test results prove that combination of Convolution and Hough transform is very competent in detecting runway edges accurately

An Extension to Hough Transform Based on Gradient Orientation

The Hough transform is one of the most common methods for line detection. In this paper we propose a novel extension of the regular Hough transform. The proposed extension combines the extension of the accumulator space and the local gradient orientation resulting in clutter reduction and yielding more prominent peaks, thus enabling better line identification. We demonstrate benefits in applications such as visual quality inspection and rectangle detection.Comment: Part of the Proceedings of the Croatian Computer Vision Workshop, CCVW 2015, Year

Spatiogram features to characterize pearls in paintings

Objective characterization of jewels in paintings, especially pearls, has been a long lasting challenge for art historians. The way an artist painted pearls reflects his ability to observing nature and his knowledge of contemporary optical theory. Moreover, the painterly execution may also be considered as an individual characteristic useful in distinguishing hands. In this work, we propose a set of image analysis techniques to analyze and measure spatial characteristics of the digital images of pearls, all relying on the so called spatiogram image representation. Our experimental results demonstrate good correlation between the new metrics and the visually observed image features, and also capture the degree of realism of the visual appearance in the painting. In that sense, these results set the basis in creating a practical tool for art historical attribution and give strong motivation for further investigations in this direction

PENERAPAN OBJECT TRACKING DENGAN METODE ADAPTIVE PARTICLE FILTER UNTUK PELACAKAN BOLA PADA PERMAINAN

Data pergerakan bola dapat dimanfaatkan sebagai panduan untuk mengamati kejadian-kejadian pada pertandingan tenis yang telah berlangsung. Namun, untuk mendapatkan data pergerakan bola dari video pertandingan rentan terjadi kesalahan dalam pendeteksian objek, sehingga data yang dihasilkan terdapat noise. Berdasarkan alasan tesebut, penulis melakukan mining terhadap video pertandingan bola tenis dengan pendekatan object tracking, sehingga kesalahan deteksi ketika mendeteksi bola dapat dikurangi. Pendekatan tersebut diwujudkan dengan merancang model pelacakan bola dengan metode circle hough transform untuk mendeteksi lingkaran, kemudian dilanjutkan dengan metode pelacakan adaptive particle filter yang berfungsi untuk menghilangkan noise yang dihasilkan ketika melakukan deteksi lingkaran. Model tersebut dijalankan melalui proses-proses yang diantaranya adalah segmentasi citra, deteksi lingkaran, pelacakan objek dan diakhiri dengan koreksi lintasan. Model yang dirancang kemudian diimplementasikan pada bahasa pemrograman Phyton dan library OpenCV. Tahap terakhir dalam penelitian ini adalah melakukan eksperimen, eksperimen ini bertujuan untuk mendapatkan parameter masukan terbaik pada perangkat lunak, sehingga dapat diketahui efektifitas dari model yang telah diimplementasikan. Hasil eksperimen menunjukan bahwa video dengan jenis siaran pada lapangan hard court outdoor menghasilkan keluaran terbaik dengan rata-rata error sebesar 0,344, sedangkan hasil pengujian pada parameter lainnya harus disesuaikan dengan jenis video masukan agar mendapat error minimal.----------Ball movement data can be utilized as a guide for observing the events on the tennis matches that has lasted. However, the movement of the ball to get the data from the video game of the vulnerable object detection in error, so that the resulting data there is noise. Based on the reasons are, the author does mining against video game tennis ball with object tracking approach, so the error detection when it detects the ball can be reduced. The approach embodied by designing a model tracking ball with hough transform for circle method to detect circles, then proceed with adaptive particle filter tracking method that serves to eliminate noise generated when the detection loop. The model is run through processes such as image segmentation, object tracking, circle detection and end with correction trajectory. Model designed then implemented in the programming language Python and OpenCV library. The last stage in this research is doing experiments, this experiment aims to get the best input parameters in the software, so it can be known to the effectiveness of the model that has been implemented. Experimental results show that the type of video broadcast on an outdoor hard court field produce the best output with an average error of 0.344, whereas the test results on the other parameters must be adjusted to the type of video input so that it gets the error minimal