InnerSpec: Technical Report

Abstract

In this report we describe "InnerSpec", an approach for symmetric object detection that is based both on the computation of a symmetry measure for each pixel and on gradient information analysis. The symmetry value is obtained as the energy balance of the even-odd decomposition of an oriented square patch with respect to its central axis. Such an operation is akin to the computation of a row-wise convolution in the midpoint. The candidate symmetry axes are then identified through the localization of peaks along the direction perpendicular to each considered angle. These axes are finally evaluated by computing the image gradient in their neighborhood, in particular checking whether the gradient information displays specular characteristics.

Minimal Information Exchange for Secure Image Hash-Based Geometric Transformations Estimation

“…The experimental dataset is a collection of 75 grayscale, 512⇥512 images (a subset is depicted in Fig. 8), 15 of which are taken from well-known image repositories such as [40] and the others are a selection of outdoor images from the dataset employed for [39]. They have been chosen to represent a well balanced representation of the potential applications of the proposed techniques.…”

“…11 shows a statistical evaluation of the hash bits. To achieve statistical significance, we have enlarged the dataset using all of the 200 images used in [39], that include a variety of indoor and outdoor natural pictures. Fig.…”

SymmSLIC: Symmetry Aware Superpixel Segmentation

“…We use the detected pairs mirror symmetric pixels in the Section III-A to detect the symmetry axes of the reflective symmetric objects present in the input image. We represent the detected pairs of mirror symmetric pixels as the collection of sets {P i } k i=1 such that each set P i contains pairs of mirror [62], [63], [64], [65], [36], [35], AND PROPOSED METHOD ON THE IMAGES OF THE DATASET [58]. and is perpendicular to the vector x j − x j .…”

“…8, we show the detected symmetry axes on few images from the dataset [58]. We compare our method using F-score with the methods [62], [63], [64], [65], [36], [35], for single symmetry axis detection on the dataset in [58]. In the TABLE I, we show the F -score for all the methods.…”