Abstract-This paper presents a novel method for image similarity measure, where a hand-drawn rough black and white sketch is compared with an existing data base of full color images (art works and photographs). The proposed system creates ambient intelligence in terms of the evaluation of nonprecise, easy to input sketched information. The system can then provide the user with options of either retrieving similar images in the database or ranking the quality of the sketch against a given standard, i.e., the original image model. Alternatively, the inherent pattern-matching capability of the system can be utilized to allow detection of distortion in any given real time-image sequences in vision-driven ambient intelligence applications. The proposed method can cope with images containing several complex objects in an inhomogeneous background. Two abstract images are obtained using strong edges of the model image and the morphologically thinned outline of the sketched image. The angular-spatial distribution of pixels in the abstract images is then employed to extract new compact and effective features using the Fourier transform. The extracted features are rotation and scale invariant and robust against translation. Experimental results from seven different approaches confirm the efficacy of the proposed method in both the retrieval performance and the time required for feature extraction and search.

The authors present a novel approach for image representation based on geometric distribution of edge pixels. Object segmentation is not needed, therefore the input image may consist of several complex objects. For an efficient description of an arbitrary edge image, the edge map is divided into M/spl times/N angular radial partitions and local features are extracted for these partitions. The entire image is then described as a set of spatially distributed invariant feature descriptors using the magnitude of the Fourier transform. The approach is scale-and rotation-invariant and tolerates small translations and erosions. The extracted features are characterised by their compactness and fast extraction/matching time. They exhibit significant improvement in retrieval performance using the average normalised modified retrieval rank (ANMRR) measure. Experimental results, using an image database initiated from a movie, confirm the supremacy of the proposed method. Edge image description using angular radial partitioning A. Chalechale, A. Mertins and G. Naghdy Abstract: The authors present a novel approach for image representation based on geometric distribution of edge pixels. Object segmentation is not needed, therefore the input image may consist of several complex objects. For an efficient description of an arbitrary edge image, the edge map is divided into M £ N angular radial partitions and local features are extracted for these partitions. The entire image is then described as a set of spatially distributed invariant feature descriptors using the magnitude of the Fourier transform. The approach is scale-and rotationinvariant and tolerates small translations and erosions. The extracted features are characterised by their compactness and fast extraction/matching time. They exhibit significant improvement in retrieval performance using the average normalised modified retrieval rank (ANMRR) measure. Experimental results, using an image database initiated from a movie, confirm the supremacy of the proposed method. Disciplines Physical Sciences and Mathematics

The diagnosis of cancer is mainly performed by visual analysis of the pathologists, through examining the morphology of the tissue slices and the spatial arrangement of the cells. If the microscopic image of a specimen is not stained, it will look colorless and textured. Therefore, chemical staining is required to create contrast and help identify specific tissue components. During tissue preparation due to differences in chemicals, scanners, cutting thicknesses, and laboratory protocols, similar tissues are usually varied significantly in appearance. This diversity in staining, in addition to Interpretive disparity among pathologists more is one of the main challenges in designing robust and flexible systems for automated analysis. To address the staining color variations, several methods for normalizing stain have been proposed. In our proposed method, a Stain-to-Stain Translation (STST) approach is used to stain normalization for Hematoxylin and Eosin (H&E) stained histopathology images, which learns not only the specific color distribution but also the preserves corresponding histopathological pattern. We perform the process of translation based on the "pix2pix" framework, which uses the conditional generator adversarial networks (cGANs). Our approach showed excellent results, both mathematically and experimentally against the state of the art methods. We have made the source code publicly available 1 .

With the rapid development of the internet of things (IoT) devices and applications, the necessity to provide these devices with high processing capabilities appears to run the applications more quickly and smoothly. Though the manufacturing companies try to provide IoT devices with the best technologies, some drawbacks related to run some sophisticated applications like virtual reality and smart healthcare-based are still there. To overcome these drawbacks, a hybrid fog-cloud offloading (HFCO) is introduced, where the tasks associated with the complex applications are offloaded to the cloud servers to be executed and sent back the results to the corresponding applications. In the HFCO, when an IoT node generates a high-requirement processing task that cannot handle itself, it must decide to offload the task to the cloud server or to the nearby fog nodes. The decision depends on the conditions of the task requirements and the nearby fog nodes. Considering many fog nodes and many IoT nodes that need to offload their tasks, the problem is to select the best fog node to offload each task. In this paper, we propose a novel solution to the problem, where the IoT node has the choice to offload tasks to the best fog node or to the cloud based on the requirements of the applications and the conditions of the nearby fog nodes. In addition, fog nodes can offload tasks to each other or to the cloud to balance the load and improve the current conditions allowing the tasks to be executed more efficiently. The problem is formulated as a Markov Decision Process (MDP). Besides, a Q-learning-based algorithm is presented to solve the model and select the optimal offload policy. Numerical simulation results show that the proposed approach has superiority over other methods regarding reducing delay, executing more tasks, and balance the load.

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