Improved Stroke Detection at Early Stages Using Haar Wavelets and Laplacian Pyramid

Stroke merupakan pembunuh nomor tiga di dunia, namun hanya sedikit metode tentang deteksi dini. Oleh karena itu dibutuhkan metode untuk mendeteksi hal tersebut. Penelitian ini mengusulkan sebuah metode gabungan untuk mendeteksi dua jenis stroke secara simultan. Haar wavelets untuk mendeteksi stroke hemoragik dan Laplacian pyramid untuk mendeteksi stroke iskemik. Tahapan dalam penelitian ini terdiri dari pra proses tahap 1 dan 2, Haar wavelets, Laplacian pyramid, dan perbaikan kualitas citra. Pra proses adalah menghilangkan bagian tulang tengkorak, reduksi derau, perbaikan kontras, dan menghilangkan bagian selain citra otak. Kemudian dilakukan perbaikan citra. Selanjutnya Haar wavelet digunakan untuk ekstraksi daerah hemoragik sedangkan Laplacian pyramid untuk ekstraksi daerah iskemik. Tahapan terakhir adalah menghitung fitur Grey Level Cooccurrence Matrix (GLCM) sebagai fitur untuk proses klasifikasi. Hasil visualisasi diproses lanjut untuk ekstrasi fitur menggunakan GLCM dengan 12 fitur dan kemudian GLCM dengan 4 fitur. Untuk proses klasifikasi digunakan SVM dan KNN, sedangkan pengukuran performa menggunakan akurasi. Jumlah data hemoragik dan iskemik adalah 45 citra yang dibagi menjadi 2 bagian, 28 citra untuk pengujian dan 17 citra untuk pelatihan. Hasil akhir menunjukkan akurasi tertinggi yang dicapai menggunakan SVM adalah 82% dan KNN adalah 88%

Visual detection of blemishes in potatoes using minimalist boosted classifiers

This paper introduces novel methods for detecting blemishes in potatoes using machine vision. After segmentation of the potato from the background, a pixel-wise classifier is trained to detect blemishes using features extracted from the image. A very large set of candidate features, based on statistical information relating to the colour and texture of the region surrounding a given pixel, is first extracted. Then an adaptive boosting algorithm (AdaBoost) is used to automatically select the best features for discriminating between blemishes and non-blemishes. With this approach, different features can be selected for different potato varieties, while also handling the natural variation in fresh produce due to different seasons, lighting conditions, etc. The results show that the method is able to build ``minimalist'' classifiers that optimise detection performance at low computational cost. In experiments, blemish detectors were trained for both white and red potato varieties, achieving 89.6\% and 89.5\% accuracy, respectively

Background suppressing Gabor energy filtering

In the field of facial emotion recognition, early research advanced with the use of Gabor filters. However, these filters lack generalization and result in undesirably large feature vector size. In recent work, more attention has been given to other local appearance features. Two desired characteristics in a facial appearance feature are generalization capability, and the compactness of representation. In this paper, we propose a novel texture feature inspired by Gabor energy filters, called background suppressing Gabor energy filtering. The feature has a generalization component that removes background texture. It has a reduced feature vector size due to maximal representation and soft orientation histograms, and it is awhite box representation. We demonstrate improved performance on the non-trivial Audio/Visual Emotion Challenge 2012 grand-challenge dataset by a factor of 7.17 over the Gabor filter on the development set. We also demonstrate applicability of our approach beyond facial emotion recognition which yields improved classification rate over the Gabor filter for four bioimaging datasets by an average of 8.22%

A contour matching approach for accurate NOAA-AVHRR image navigation

Although different methods for NOAA AVHRR image navigation have already been established, the multitemporal and multi-satellite character of most studies requires automatic and accurate methods for navigation of satellite images. In the proposed method, a simple Kepplerian orbital model for the NOAA satellites is considered as reference model, and mean orbital elements are given as input to the model from ephemeris data. In order to correct the errors caused by these simplifications, errors resulting from inaccuracies in the positioning of the satellite and failures in the satellite internal clock, an automatic global contour matching approach has been adopted. First, the sensed image is preprocessed to obtain a gradient energy map of the reliable areas (sea-land contours) using a cloud detection algorithm and a morphological gradient operator. An initial estimation of the reliable contour positions is automatically obtained. The final positions of the contours are obtained by means of an iterative local minimization procedure that allows a contour to converge on an area of high image energy (edge). Global transformation parameters are estimated based on the initial and final positions of all reliable contour points. Finally, the performance of this approach is assessed using NOAA 14 AVHRR images from different geographic areas.Postprint (published version

Particle Detection Algorithms for Complex Plasmas

In complex plasmas, the behavior of freely floating micrometer sized particles is studied. The particles can be directly visualized and recorded by digital video cameras. To analyze the dynamics of single particles, reliable algorithms are required to accurately determine their positions to sub-pixel accuracy from the recorded images. Typically, straightforward algorithms are used for this task. Here, we combine the algorithms with common techniques for image processing. We study several algorithms and pre- and post-processing methods, and we investigate the impact of the choice of threshold parameters, including an automatic threshold detection. The results quantitatively show that each algorithm and method has its own advantage, often depending on the problem at hand. This knowledge is applicable not only to complex plasmas, but useful for any kind of comparable image-based particle tracking, e.g. in the field of colloids or granular matter

Antlia Dwarf Galaxy: Distance, quantitative morphology and recent formation history via statistical field correction

We apply a statistical field correction technique originally designed to determine membership of high redshift galaxy clusters to Hubble Space Telescope imaging of the Antlia Dwarf Galaxy; a galaxy at the very edge of the Local Group. Using the tip of the red giant branch standard candle method coupled with a simple Sobel edge detection filter we find a new distance to Antlia of 1.31 +/- 0.03 Mpc. For the first time for a Local Group Member, we compute the concentration, asymmetry and clumpiness (CAS) quantitative morphology parameters for Antlia from the distribution of resolved stars in the HST/ACS field, corrected with a new method for contaminants and complement these parameters with the Gini coefficient (G) and the second order moment of the brightest 20 per cent of the flux (M_20). We show that it is a classic dwarf elliptical (C = 2.0, A = 0.063, S = 0.077, G = 0.39 and M_20 = -1.17 in the F814W band), but has an appreciable blue stellar population at its core, confirming on-going star-formation. The values of asymmetry and clumpiness, as well as Gini and M_20 are consistent with an undisturbed galaxy. Although our analysis suggests that Antlia may not be tidally influenced by NGC 3109 it does not necessarily preclude such interaction.Comment: Accepted for publication in MNRA

Pixelated detectors and improved efficiency for magnetic imaging in STEM differential phase contrast

The application of differential phase contrast imaging to the study of polycrystalline magnetic thin films and nanostructures has been hampered by the strong diffraction contrast resulting from the granular structure of the materials. In this paper we demonstrate how a pixelated detector has been used to detect the bright field disk in aberration corrected scanning transmission electron microscopy (STEM) and subsequent processing of the acquired data allows efficient enhancement of the magnetic contrast in the resulting images. Initial results from a charged coupled device (CCD) camera demonstrate the highly efficient nature of this improvement over previous methods. Further hardware development with the use of a direct radiation detector, the Medipix3, also shows the possibilities where the reduction in collection time is more than an order of magnitude compared to the CCD. We show that this allows subpixel measurement of the beam deflection due to the magnetic induction. While the detection and processing is data intensive we have demonstrated highly efficient DPC imaging whereby pixel by pixel interpretation of the induction variation is realised with great potential for nanomagnetic imaging