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.

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, due to its large field of view. However, classical image processing algorithms are not suitable to be applied directly on 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 in the literature and developed for omnidirectional images. The results show a better performance in term of edge quality, edge community and sensibility to noise.

The iris is one of the most secure biometric types of information that is widely employed in authentication systems. In this paper we present a method for iris recognition based on the Contourlet Transform and Shannon 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 other six recent iris recognition algorithms using the statistics EER, AUC and ARR. The results show a higher performance of the proposed method with a reduced computation time.

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