Edge detection algorithm based on quantum superposition principle and photons arrival probability

The detection of object edges in images is a crucial step employed in a vast amount of computer vision applications, for which a series of different algorithms has been developed in the last decades. This paper proposes a new edge detection method based on quantum information, which is achieved in two main steps: (i) an image enhancement stage that employs the quantum superposition law and (ii) an edge detection stage based on the probability of photon arrival to the camera sensor. The proposed method has been tested on synthetic and real images devoted to agriculture applications, where Fram & Deutsh criterion has been adopted to evaluate its performance. The results show that the proposed method gives better results in terms of detection quality and computation time compared to classical edge detection algorithms such as Sobel, Kayyali, Canny and a more recent algorithm based on Shannon entropy

Algorithm for Iris recognition based on contourlet Transform and Entropy

The iris is one of the most secure biometric information that is widely employed in authentication systems. In this paper we present a method for iris recognition based on the Contourlet Transform and Entropy which entails i) the detection and segmentation of the iris, ii) its normalization, iii) the application of the Contourlet Transform, iv) the generation of the iris descriptor, and v) the matching between the query iris and those in the database. The proposed method has been tested with images taken from the popular CASIA-V4 and UBIRIS.v1 datasets and compared against four other iris recognition algorithms. The results show a higher true positive rate with a reduced computation time

Edge detection algorithm for omnidirectional images, based on superposition laws on Blach’s sphere and quantum entropy

This paper presents an edge detection algorithm for omnidirectional images based on superposition law on Bloch’s sphere and quantum local entropy. Omnidirectional vision system has become an essential tool in computer vision, duo to its large field of view. However, classical image processing algorithms are not suitable to be applied directly in this type of images without taking into account the spatial information around each pixel. To show the performance of the proposed method, a set of experimentation was done on synthetic and real images devoted to agriculture applications. Later, Fram & Deutsh criterion has been adopted to evaluate its performance against three algorithms proposed on the literature and developed for omnidirectional images. The results show a good performance of the proposed method in term of edge quality, edge community and sensibility to noise

Edge detection algorithm for omnidirectional images, based on superposition laws on Blach's sphere and quantum entropy

This paper presents an edge detection algorithm for omnidirectional images based on superposition law onBloch's sphere and quantum local entropy. Omnidirectional vision system has become an essential tool incomputer vision, duo to its large field of view. However, classical image processing algorithms are not suitable to be applied directly in this type of images without taking into account the spatial information around each pixel. To show the performance of the proposed method, a set of experimentation was done on synthetic and real images devoted to agriculture applications. Later, Fram & Deutsh criterion has been adopted to evaluate its performance against three algorithms proposed on the literature and developed for omnidirectional images. The results show a good performance of the proposed method in term of edge quality, edge community and sensibility to noise

An algorithm for crops segmentation in UAV images based on U-Net CNN model: Application to Sugarbeets plants

In recent years, Digital Agriculture (DA) has been widely developed using new technologies and computer vision technics. Drones and Machine learning have proved their efficiency in the optimization of the agricultural management. In this paper we propose an algorithm based on U-Net CNN Model to crops segmentation in UAV images. The algorithm patches the input images into several 256×256 sub-images before creating a mask (ground-truth) that will be fed into a U-Net Model for training. A set of experimentation has been done on real UAV images of Sugerbeets crops, where the mean intersection over Union (MIoU) and the Segmentation accuracy (SA) metrics are adopted to evaluate its performances against other algorithms used in the literature. The proposed algorithm show a good segmentation accuracy compared to three well-known algorithms for UAV image segmentation