The micro and small garment industries use traditional molds based on drawings on paper for the cutting of the fabric. This process is performed manually at the discretion of the operator, generating material loss during the cutting process. To make this task more efficient and reduce losses, this paper presents a technique for editing and vectorization of physical molds using digital image processing techniques, allowing the edition, modification or multiplication of the selected mold. For this purpose, a simple, low-cost device was developed to take photographs of the molds and an automatic method for contour detection and vectorization of textile molds was realized. Three edge detection methods, Sobel, Canny - Deriche and morphological gradient, were compared. Then, the Harris corner detection method was used, achieving a better detection, reducing the number of false corners, by using the image in gray levels as the input of the detector. The shapes of the contours between the corners were approximated by cubic splines, obtaining an analytical representation of each mold, being used to manipulate the size and position to place it in a better way on the fabric, achieving a significant reduction in fabric losses. The developed low-cost application thus allows the approximation of the models by vectorial representation, allowing their manipulation in an easy way and with a low consumption of computational resources without losing important information of the molds. The molds can thus be moved, rotated and scaled to accommodate them within the available fabric space.
Images provided by various remote sensing satellites are multispectral, low resolution, and panchromatic, high resolution, which are fused, enlarging the low resolution images to make them the same size as the panchromatic ones. Panchromatic images have good spatial resolution but only one spectral band and multispectral images typically have four or eight bands but are four times lower in spatial resolution than a panchromatic image. Image fusion of this type seeks to combine the best feature of the high spatial panchromatic image with the low spatial multispectral image to obtain an image with high spatial and spectral resolution. Several techniques have been developed to perform this fusion however the techniques with low computational resource consumption are EIHS Algorithm, Brovey Algorithm, Averaging Algorithm. To compare them, in this work, natural color photographs are taken, from which high resolution monochromatic and lower resolution chromatic images are obtained to emulate the real situation. The low resolution color images obtained were interpolated using three satellite image interpolation techniques. The fusion techniques were evaluated, obtaining the quantitative spectral and spatial ERGAS indices and the RMSE. The EIHS and Brovey techniques were found to produce artifacts because the color component values can fall above or below the representation interval [0,255]. After correcting this issue, it was found that the EIHS and Brovey methods, in that order, produced the lowest RMSE, followed by the averaging method. Since this result proved to be inconsistent with that obtained with the mean ERGAS, a new normalized mean ERGAS that gives a better indication of fusion quality, matching the result given by the RMSE, was proposed to be used instead.
Citrus Aurantifolia swingle is grown on the northern coast of Peru for domestic consumption and export. This is an indispensable ingredient due to its high level of acidity for the preparation of fish ceviche, the traditional dish of Peruvian gastronomy. Lemons are classified according to their color in yellow, green and pinton (green lemons already showing a hint of yellow), since the yellow ones are for national consumption, while the other two types are for export. This selection is done manually. This process is time consuming and additionally lemons are frequently misclassified due to lack of concentration, exhaustion and experience of the worker, affecting the quality of the product sold in domestic and foreign markets. Therefore, this paper introduces a new method for the automatic classification of Citrus Aurantifolia, which comprises three stages: acquisition, image processing, feature extraction, and classification. A mechanical prototype for image acquisition in a controlled environment and a software for the classification of lemons were developed. A new segmentation method was implemented, which makes use only of the information obtained from the blue channel. From the segmented images we obtained the color characteristics, selecting the best descriptors in the RGB and CIELAB spaces, finding that the red channel allows the best accuracy. Two classification models were used, SVM and KNN, obtaining an accuracy of 99.04% with the K-NN.
Nowadays, new security and protection systems for citizens are being developed, since criminals have found techniques to violate those already known, such as those based on fingerprints, facial recognition, iris and voice. Thus, using biometric data, new systems are being developed that are more secure, infallible and fast to identify each person, making it impossible to impersonate them, as has happened with other methods. Recently new identification methods have been proposed based on hand geometry and palmprint based on texture techniques for the identification of hand characteristics such as ridges, edges, points, and textures. Following this trend, this paper presents a method based on the detection of the palm print, acquired by contact, through the use of a scanner. For this purpose, the image is segmented to detect the silhouette of the hand and delimit the working area, achieving greater speed in identification. The images are then used as input to a convolutional neural network VGG 16 for learning and identification of subjects, achieving 100% accuracy.
Earth observation satellites provide multispectral images that are characterized by good spectral quality but low spatial quality. They also provide panchromatic images that, on the contrary, are characterized by good spatial quality but low spectral quality. Therefore, it is important to merge both images to obtain a single one that contains complementary information and can be used in land resource studies, surface geology, water management, forests, urban development, agriculture, and others. For this reason, it is important to evaluate the techniques used for the fusion of multispectral and panchromatic images: EIHS, Brovey and Averaging. Therefore, in this work these three techniques are evaluated, using the quantitative indices: spectral ERGAS and spatial ERGAS. In this way, the quality of the resulting fused images can be measured. Natural images were used to make the evaluation. The results show, on the one hand, that the best spectral quality is obtained with the Averaging algorithm, followed by the Brovey and, thirdly, by the EIHS. On the other hand, the best spatial quality was obtained with the EIHS algorithm, followed by the Brovey and then by the Averaging algorithm. It was also found that by averaging the values obtained in both evaluations that the best quality of fusion is obtained with the Averaging algorithm, followed by the Brovey and finally by the EIHS.
The avocado is a fruit that grows in tropical and subtropical areas, very popular in the markets due to its great nutritional qualities and medicinal properties. The avocado is a plant of great commercial interest for Peru and Colombia, countries that export this fruit. This tree is affected by a wide variety of diseases reducing its production, even causing the death of the plant. The most frequent disease of the avocado tree in the production zone of Peru is caused by the fungus Lasiodiplodia Theobromae, which is characterized in its initial stage by producing a chancre around the stems and branches of the tree. Detection is commonly made by manual inspection of the plants by an expert, which makes it difficult to detect the fungus in extensive plantations. Therefore, in this work we present a semi-automatic method for the detection of this disease based on image processing and machine learning techniques. For this purpose, an acquisition protocol was defined. The identification of the disease was performed by taking as input pre-processed images of the tree branches. A learning technique was evaluated, based on a shallow CNN, obtaining 93% accuracy.
The main effects of soil degradation include loss of nutrients, desertification, salinization, deterioration of soil structure, wind and water erosion, and pollution. Soil salinity is an environmental hazard present worldwide, especially in arid and semi-arid areas, which occurs mainly due to irrigation and other intensified agricultural activities. Therefore, the measurement of soil degradation in areas of low vegetation is of great importance in Peru. Two commonly used methods for estimating soil salinity are based on a measurement of electrical conductivity. Although on one hand, one of these methods is quite accurate, it requires many field samples and laboratory tests, which makes it quite expensive and impractical to measure large areas of the Peruvian coast. On the other hand, the second method is based on relative conductivity measurements in situ, being less accurate, but equally very expensive when measuring very large areas. For this reason, the use of multispectral imaging has been proposed for this purpose, using linear regression techniques. Following this trend in this work, the different descriptors used for the estimation were studied, comparing the correlations between the salinity indices and the soil samples, and two estimators based on SVM and PLSR were used to verify if the estimation improved. The PSWIR band, followed by the red one, was found to have the highest correlation and the indices based on the combination of these bands provide the best estimate with the classifiers evaluated.
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