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In this paper, we analyse first-person-view videos to develop a personalized user authentication mechanism. Our proposed algorithm generates provisional image-based passwords which benefit a variety of purposes such as unlocking a mobile device or fallback authentication. First, representative frames are extracted from the egocentric videos. Then, they are split into distinguishable segments before...
Long videos captured by consumers are typically tied to some of the most important moments of their lives, yet ironically are often the least frequently watched. The time required to initially retrieve and watch sections can be daunting. In this work we propose novel techniques for summarizing and annotating long videos. Existing video summarization techniques focus exclusively on identifying keyframes...
Graph-based segmentation is gaining popularity among the many approaches in performing image segmentation, primarily due to its ability in reflecting global image properties. The most fundamental challenge in segmentation algorithm is to precisely define the volumetric extent of some object, which may be represented by the union of multiple regions. We developed a unified framework for volumetric...
Segmentation of text on traffic panels has shown great importance. Detection and recognition of traffic panels text is still a challenge in computer vision due to its different types and hug asymmetric information. This paper proposed a method to detect roadside traffic panels and extract the information on them. In the first stage, blue, white and green color segmentation and structure classification...
We have analyzed the problems associated with matching regions among the pair of images over the large set of overlapping regions. It is being studied that, matching images by using regions having unstructured association can be a serious problem. In this research we propose a linear formulation technique, which is matching simultaneously, so that the matched area can have color similarity histogram,...
According to the real condition of the substation inspection robot provided a vision-based navigation control method for substation inspection robot can be run in the complex road environment, with strong anti-interference, implementation of simple, good stability and high precision in this paper. Reasonably plan the inspection paths, enabling robot to full check each device in substation. Robots...
In this paper, a novel semantic segmentation model based on aggregated features and contextual information is proposed. Given an RGB-D image, we train a support vector machine (SVM) to predict initial labels using aggregated features, and then optimize the predicted results using contextual information. For aggregated features, the local features on regions are extracted to capture visual appearance...
Land cover classification is a task that requires methods capable of learning high-level features while dealing with high volume of data. Overcoming these challenges, Convolutional Networks (ConvNets) can learn specific and adaptable features depending on the data while, at the same time, learn classifiers. In this work, we propose a novel technique to automatically perform pixel-wise land cover classification...
In human interaction, understanding human behaviors is a challenging problem in todays world. Action recognition has become a very important topic in detecting the emotional activity with many fundamental applications, such as in robotics, video surveillance, human-computer interaction. In this paper, we are proposing a system that uses semantic rules to define emotional activities. First, we apply...
We introduce a novel bags-of-features framework based on relative position descriptors, modeling both spatial relations and shape information between the pairwise structural subparts of objects. First, we propose a hierarchical approach for the decomposition of complex objects into structural subparts, as well as their description using the concept of Force Histogram Decomposition (FHD). Then, an...
Exhaustive scanning is a popular scheme for the detection of visual objects in images. In this paper, we propose a polyline-driven detection scheme with an application to stop sign detection. Given an input image, we first extract basic polylines, including line segments and 2-piece polylines, from its edge image. Line segments are then used to generate a set of hypothesis boxes, i.e., a space of...
We consider the problem of object figure-ground segmentation when the object categories are not available during training (i.e. zero-shot). During training, we learn standard segmentation models for a handful of object categories (called “source objects”) using existing semantic segmentation datasets. During testing, we are given images of objects (called “target objects”) that are unseen during training...
Region-based Image Retrieval (RBIR), which bases itself on image segmentation rather than global features or key-point-based local features, is a branch of Content-based Image Retrieval. This paper proposes a novel RBIR-oriented image segmentation algorithm named Edge Integrated Minimum Spanning Tree (EI-MST). The difference between EI-MST and the traditional MST-based methods is that EI-MST generates...
Interactive image segmentation is a popular and challenging task. User interactions, e.g., setting seeds or specifying bounding box, play a critical role in determining the performance of all interactive segmentation approaches. However, most methods focus on improving segmentation performance by integrating higher level information; and to the best of our knowledge, no work has been done to improve...
Topic models (e.g., pLSA, LDA, SLDA) have been widely used for segmenting imagery. These models are confined to crisp segmentation. Yet, there are many images in which some regions cannot be assigned a crisp label (e.g., transition regions between a foggy sky and the ground or between sand and water at a beach). In these cases, a visual word is best represented with partial memberships across multiple...
We present a novel technique for pollen identification from sets of multifocal image sequences obtained from optical microscopy. Our algorithm analyzes the visual texture of pollen grains for each focal image, and performs identification using a fast sequence-matching algorithm. Although we develop a pollen-recognition protocol, the method is applicable to other microscopy object-recognition tasks...
This poster presents the problem of 3D contact measurements from two co-registered volumetric images (z-stacks). The 3D contact measurement consists of (a) segmenting an object of interest in each z-stack, (b) computing the relative spatial positions of the detected objects to detect contacts, (c) validating the accuracy of segmentation, and (d) visually verifying correct contact detection. The 3D...
In this paper, we present PaTSI, a tool for analyzing evolutions of objects in time series of satellite images. This tool is a plugin integrated in the KNIME Analytics Platform. PaTSI is a workflow composed of several nodes assembled together to form a whole KDD process (data selection, pre-processing, image segmentation, pattern mining and visualization). Input data consists of a time series of satellite...
Diffusion-based salient region detection has recently received intense research attention. In this paper, we propose a salient region detection method based on the foreground and background propagation with manifold ranking. By considering the spatial variance of superpixel clusters, foreground and background seed regions are extracted preliminarily. Then, in order to produce a pixel-accurate saliency...
Reversible data hiding is a kind of information hiding technique that can exactly recover the original image through data hiding and extraction. It can be potentially used in the medical and military applications. In the literature, by using the maximum inter-class square error to separate the background and foreground, the principal gray-scale values in the segmented background can be identified...
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