Examinando por Autor "Miranda, Sergio L."
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- PublicaciónSólo datosComparative Analysis of Detectors and Feature Descriptors for Multispectral Image Matching in Rice Crops(Plants, 2021-08-28) Forero, Manuel G.; Mambuscay, Claudia L.; Monroy, María F.; Miranda, Sergio L.; Méndez, Dehyro; Valencia, Milton Orlando; Gomez Selvaraj, MichaelPrecision agriculture has greatly benefited from advances in machine vision and image processing techniques. The use of feature descriptors and detectors allows to find distinctive keypoints in an image and the use of this approach for agronomical applications has become a widespread field of study. By combining near infrared (NIR) images, acquired with a modified Nikon D80 camera, and visible spectrum (VIS) images, acquired with a Nikon D300s, a proper crop identification could be obtained. Still, the use of different sensors brings an image matching challenge due to the difference between cameras and the possible distortions from each imaging technique. The aim of this paper is to compare the performance of several feature descriptors and detectors by comparing near infrared and visual spectral bands in rice crop images. Therefore, a group of 20 different scenes with different cameras and growth stages in a rice crop were evaluated. Thus, red, green, blue (RGB) and L, a, b (CIE L*a*b*) channels were extracted from VIS images in order to compare the matches obtained between each of them and the corresponding NIR image. The BRISK, SURF, SIFT, ORB, KAZE, and AKAZE methods were implemented, which act as descriptors and detectors. Additionally, a combination was made between the FAST algorithm for the detection of keypoints with the BRIEF, BRISK, and FREAK methods for features description. BF and FLANN matching methods were used. The algorithms were implemented in Python using OpenCV library. The green channel presented the highest number of correct matches in all methods. In turn, the method that presented the highest performance both in time and in the number of correct matches was the combination of the FAST feature detector and the BRISK descriptor.
- PublicaciónSólo datosComparative analysis of inpainting techniques based on sparse models and isophote comparison(Proceedings of SPIE - The International Society for Optical Engineering, 2020-08-24) Forero, Manuel G.; Miranda, Sergio L.; Pinilla, María J.One of the most commonly used image processing tasks in photo editing is to improve the quality of the picture when it contains scratches, smudges or any unwanted object or drawing. The restoration method used to remove them is inpainting, which consists of filling in the areas, where the unwanted information is found, in an imperceptible way. Inpainting has been used since ancient times, where the concept of removing or replacing an object in an image has been developed through the painting of photographs. Recently, new inpainting techniques have emerged based on sparse models that offer a solution to this problem. A sparse model is a system of linear equations that involves the use of a dictionary together with a vector α to make the reconstruction or improvement of the images. Orthogonal Matching Pursuit (OMP) and K-SVD are the techniques used to obtain the dictionary and the vector α. These inpainting techniques provide fairly realistic results but have not been evaluated against other techniques. Therefore, in this work we compare the results obtained with sparse modelling against those obtained with two other techniques, the first one based on bilinear interpolation and the second one, called Isophote continuation, initially identifies the area to be reconstructed, then from the adjacent neighbours creates new layers within the region to be reconstructed and repeats the process until the area to be restored is completely filled. Initially, the results of the techniques were visually contrasted with the original images. Then the difference between the original and the resulting image was calculated taking into account only the areas of interest to find the number of non-zero pixels and the root mean square error (RMSE). The techniques based on dispersed models based generated good results as well as Inpainting by Continuation of Isophotes.
- PublicaciónSólo datosEvaluation of segmentation techniques for cell tracking in confocal microscopy images(Proceedings of SPIE - The International Society for Optical Engineering, 2021-08-01) Forero, Manuel G.; Rodriguez, Luis H.; Miranda, Sergio L.In different biological studies, such as cell regeneration studies, cell tracking over time is required. Thus, in these studies, the evolution of an amputated limb of the crustacean Parhyale hawaiensis is tracked using 4D confocal microscopy images. However, given the high number of images, noise level and number of cells make the manual cell tracking process a complex, cumbersome and difficult task. The tracking process using image processing techniques generally includes three stages: image enhancement, segmentation and cell identification. A tool made for this purpose, as a plugin of the ImageJ program is TrackMate, commonly used by biologists, which includes for segmentation the Laplacian of Gaussian (LoG) and Difference of Gaussians (DoG) edge detectors. To provide even more powerful detectors, the filtering methods based on the second derivative of Deriche and Shen and Castan were implemented and included in TrackMate. These four methods were evaluated for cell detection in images of Parhyale hawaiensis, finding that the Deriche and, Shen and Castan filters detected an appreciable number of false positives, due to sensitivity to noise and because the same cell was counted multiple times. As for the LoG and DoG methods, they presented the best results, being very similar because the DoG is basically an approximation of the LoG, finding that the DoG method slightly outperformed the LoG.
- PublicaciónSólo datosImprovement of the Turajlić Method for the Estimation of Gaussian Noise in Images(Lecture notes in computer sciences, 2020-06-24) Forero, Manuel G.; Miranda, Sergio L.; Jacanamejoy-Jamioy, CarlosGaussian noise estimation is an important step in some of the more recently developed noise removal methods. This is a difficult task and although several estimation techniques have been proposed recently, they generally do not produce good results. In a previous comparative study, among several noise estimation techniques, a method proposed in 2017 by Turajlić was found to give the best results. Although acceptable, they are still far from ideal. Therefore, several changes to this method are introduced in this paper to improve the estimation. Tests on monochromatic images contaminated with different levels of Gaussian noise showed that the modified method produces a significant improvement in the estimation of Gaussian noise, over 35%, at a slightly higher computational cost.
- PublicaciónSólo datosInpainting method based on variational calculus and sparse matrices(Proceedings of SPIE - The International Society for Optical Engineering, 2021-08-01) Forero, Manuel G.; Navarro, Andrés F.; Miranda, Sergio L.Photo restoration is one of the most popular tasks in digital image processing, required when an image has stains, scratches or any unwanted object. Inpainting is the name given to this type of method, which is based on modifying the areas, where the unwanted information is, in an imperceptible way. The concept of Inpainting was born in the early twentieth century, due to the need to replace or remove an object from a photograph, this was possible through manual brushstrokes of an editor or painter. From the above and the theory of Poisson's image editing, a new technique based on variational calculus and the use of sparse matrices is developed. In this technique, a functional is proposed, which is subsequently minimized, thus achieving that the union between the filled region and the image to be repaired is visually imperceptible. The results obtained were compared with those of the bilinear interpolation, isophote, Orthogonal Matching Pursuit (OMP) and KSVD techniques, the latter two being techniques based on sparse models. Then, the difference between the original and the resulting image was calculated considering only the areas of interest to find the number of distinct pixels and the root mean square error (RMSE). The proposed method presents better results than bilinear interpolation, Orthogonal Matching Pursuit and K-SVD, and very similar to those obtained with the Isotope technique.