Learning Non-Homogenous Textures and the Unlearning Problem with Application to Drusen Detection in Retinal Images

In this work we present a novel approach for learning non- homogenous textures without facing the unlearning problem. Our learning method mimics the human behavior of selective learning in the sense of fast memory renewal. We perform probabilistic boosting and structural similarity clustering for fast selective learning in a large knowledge domain acquired over different time steps. Applied to non- homogenous texture discrimination, our learning method is the first approach that deals with the unlearning problem applied to the task of drusen segmentation in retinal imagery, which itself is a challenging problem due to high variability of non-homogenous texture appearance. We present preliminary results

Active Contours and Image Segmentation: The Current State Of the Art

Image segmentation is a fundamental task in image analysis responsible for partitioning an image into multiple sub-regions based on a desired feature. Active contours have been widely used as attractive image segmentation methods because they always produce sub-regions with continuous boundaries, while the kernel-based edge detection methods, e.g. Sobel edge detectors, often produce discontinuous boundaries. The use of level set theory has provided more flexibility and convenience in the implementation of active contours. However, traditional edge-based active contour models have been applicable to only relatively simple images whose sub-regions are uniform without internal edges. Here in this paper we attempt to brief the taxonomy and current state of the art in Image segmentation and usage of Active Contours

A least-squares implicit RBF-FD closest point method and applications to PDEs on moving surfaces

The closest point method (Ruuth and Merriman, J. Comput. Phys. 227(3):1943-1961, [2008]) is an embedding method developed to solve a variety of partial differential equations (PDEs) on smooth surfaces, using a closest point representation of the surface and standard Cartesian grid methods in the embedding space. Recently, a closest point method with explicit time-stepping was proposed that uses finite differences derived from radial basis functions (RBF-FD). Here, we propose a least-squares implicit formulation of the closest point method to impose the constant-along-normal extension of the solution on the surface into the embedding space. Our proposed method is particularly flexible with respect to the choice of the computational grid in the embedding space. In particular, we may compute over a computational tube that contains problematic nodes. This fact enables us to combine the proposed method with the grid based particle method (Leung and Zhao, J. Comput. Phys. 228(8):2993-3024, [2009]) to obtain a numerical method for approximating PDEs on moving surfaces. We present a number of examples to illustrate the numerical convergence properties of our proposed method. Experiments for advection-diffusion equations and Cahn-Hilliard equations that are strongly coupled to the velocity of the surface are also presented

Nonlocal similarity image filtering

Abstract. We exploit the recurrence of structures at different locations, orientations and scales in an image to perform denoising. While previous methods based on “nonlocal filtering ” identify corresponding patches only up to translations, we consider more general similarity transformations. Due to the additional computational burden, we break the problem down into two steps: First, we extract similarity invariant descriptors at each pixel location; second, we search for similar patches by matching descriptors. The descriptors used are inspired by scale-invariant feature transform (SIFT), whereas the similarity search is solved via the minimization of a cost function adapted from local denoising methods. Our method compares favorably with existing denoising algorithms as tested on several datasets.

ناحيه بندی فانتوم بيولوژيکی رشته اعصاب نخاع موش از روی تصاوير تشديد مغناطِيسی تانسور انتشار با روش نمو جبهه آماری غيرپارامتريک

زمينه و هدف مد لسازی آمارگان تانسور در ناحيه مورد علاقه می باشد سفيد را ب هصورت صحيح مدل نم يکند : مشکل عمده در اکثر کارهای پيشين ناحيه بندی تصاوير تانسور انتشار، استفاده از رويه پارامتريک جهت. اين نوع مد لسازی، آمارگان تانسورها در کلا فهای فيبری ماده. روش بررسی تصوير تانسور انتشار استفاده م يشود ناحيه بندی فانتوم بيولوژيکی رشته اعصاب نخاع موش استفاده می شود : در مطالعه حاضر از تخمين چگالی پارزن با هسته گوسی ب همنظور تعريف آمارگان در يک ناحيه مورد نظر از. اين تخمين در چارچوب الگوريتم ناحی هبندی نمو جبهه آماری غيرپارامتريک به منظور. يافت هها بالاتری از روش پارامتريک می انجامد م يباشد : آزمای شهای عددی نشان داد نمو سطح آماری غيرپارامتريک با متريک اقليدسی به نتايج ناحی هبندی با کيفيت. در ضمن مشخص شد علاوه بر متريک، مدل سازی آماری ناحيه نيز در کيفيت. s ناحی هبندی مؤثر م ی باشد. در ادامه، نتايج ما نشان داد مهم ترين بخش تخمين چگالی هسته، انتخاب پهنای باند نتيجه گيری عل يرغم هزينه محاسباتی بالای روش غيرپارامتريک، اين روش انتخاب مناسبی می باشد : درصورت يکه استفاده از مد لسازی پارامتريک به نتايج بخ شبندی مورد نظر در کاربرد خاص منجر نشود