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Millions of video surveillance cameras distribute around the world, and capture tremendous number of video data endlessly. Video browsing by frame is time consuming and inefficient, since needless information is abundant in the raw videos. Video synopsis is an effective way to solve this problem by producing a short video abstraction, while keeping the essential activities of the original video. However,...
The paper introduces a novel approach to place representation for robot localization and mapping. It uses classical invariance theory while proposing an adaptive kernel to omnidirectional images and exploiting only the main significant visual information in the images. The approach is validated in real world robot exploration and localization and compared to color histograms.
In this paper, we propose a fully automatic approach for person-independent 3D facial expression recognition. In order to extract discriminative expression features, each aligned 3D facial surface is compactly represented as multiple global histograms of local normal patterns from multiple normal components and multiple binary encoding scales, namely Multi-Scale Local Normal Patterns (MS-LNPs). 3D...
This paper describes a method of tracking multiple persons with occlusions using stereo. We previously developed an accurate and stable tracking method using overlapping silhouette templates which considers how persons overlap in the image. It realized a fast tracking by using an approximated likelihood map based on kernel density estimation. The method, however, treated only two overlapping persons...
Edge detection is an important image processing technique used in face recognition. In this paper we propose an edge detection algorithm suitable for extracting edge maps from facial images under noisy conditions. We employ several improvised techniques like composite gradient variation measures, weighted wide convolution kernels and dual maxima detection. This helps in removing spurious edge points...
The representative samples can be pictured as the skeleton of a point cloud. We learn a discrete distribution defined over all samples, so that these skeleton points have large probabilities and the outliers have probabilities close to zero. The basic assumption is that any observation is generated from a nearby skeleton point. The learning objective is to minimize the communication cost from a random...
Local Binary Patterns (LBPs) and Covariance Matrices (CovMs) are two popular kinds of texture descriptors. However, local correlation brought by LBPs and global correlation brought by CovMs could not be directly combined to achieve enhanced discriminative power. This paper develops a powerful descriptor, named COV-LBP. Firstly, we propose a variant of LBPs on Euclidean space, named the LBP Difference...
Recognizing collective human activities has gained attention. Collective activities are such as queueing in a line, talking together and waiting by an intersection. It is often hard to differentiate between these activities only by the appearance of the individual. Hence, recent works exploit the contextual information of other people nearby. However, these works do not take enough care of the spacial...
Nearest-Neighbor based Image Classification (N-NIC) has drawn considerable attention in the past several years because it does not require classifier training. Similar to an orderless Bag-of-Feature image representation, the traditional NNIC ignores global geometric correspondence. In this paper, we present a technique to exploit the global geometric correspondence in a nearest neighbor classifier...
This paper presents a method for recognizing aerial image categories based on matching graphlets(i.e., small connected subgraphs) extracted from aerial images. By constructing a Region Adjacency Graph (RAG) to encode the geometric property and the color distribution of each aerial image, we cast aerial image category recognition as RAG-to-RAG matching. Based on graph theory, RAG-to-RAG matching is...
In this work1, we propose a novel approach for image categorization, which we will refer to as Bag-of-Scenes (BoS). It is based on the association of Sparse coding (Sc) and pooling techniques applied to histograms of multi-scale Local Binary Patterns (LBP) and its improved variant. This approach can be considered as a 2-layer hierarchical architecture. The first layer, encodes general local patch's...
In recent work we have shown how to use the von Neumann entropy to construct a Jensen-Shannon kernel on graphs. The kernel is defined as the difference in entropies between a product graph and the separate graphs being compared. To develop this graph kernel further, in this paper we explore how to render the computation of the Jensen-Shannon kernel more efficient by using the information functionals...
This paper provides a generic framework of component analysis (CA) methods introducing a new expression for scatter matrices and Gram matrices, called Generalized Pairwise Expression (GPE). This expression is quite compact but highly powerful: The framework includes not only (1) the standard CA methods but also (2) several regularization techniques, (3) weighted extensions, (4) some clustering methods,...
This paper presents a flexible method for singleframe hand gesture recognition by fusing information from color and depth images. Existing methods usually focus on designing intuitive features for color and depth images. On the contrary, our method first extracts common patch-level features, and fuses them by means of kernel descriptors. Linear SVM is then adopted to predict the class label efficiently...
We present an efficient method to compute similarity between graph nodes by comparing their neighborhood structures rather than proximity. The key is to use a hash for avoiding expensive subgraph comparison. Experiments show that the proposed algorithm performs well in semi-supervised node classification.
Blind image deblurring, aiming at obtaining the sharp image from blurred one, is a widely existing problem in image processing. Traditional image deblurring methods always use the deconvolution method to remove the blur kernel's effect, however, deconvolution is so sensitive to noise that inevitable artifacts always exist in the deblurring results, even though regularity terms are introduced as constraints...
This paper presents a method for the computation of polar harmonic transforms that is fast and efficient. The method is based on the inherent recurrence relations among harmonic functions that are used in the definitions of the radial and angular kernels of the transforms. The employment of these relations leads to recursive strategies for fast computation of harmonic function-based kernels. Polar...
A method for detecting a vanishing point in structured images is presented. The method relies on the detection of line segments from an edge map by representing clusters of edge points by the long axes of highly eccentric ellipses. The extracted lines provide a set of candidate vanishing points computed by their intersections, which are assigned weights proportional to the lengths of the line segments...
Semantic interpretation and understanding of images is an important goal of visual recognition research and offers a large variety of possible applications. One step towards this goal is semantic segmentation, which aims for automatic labeling of image regions and pixels with category names. Since usual images contain several millions of pixel, the use of kernel-based methods for the task of semantic...
Action recognition is an important computer vision problem that has many applications including video indexing and retrieval, event detection, and video summarization. In this paper, we propose to apply the Fisher kernel paradigm to action recognition. The Fisher kernel framework combines the strengths of generative and discriminative models. In this approach, given the trajectories extracted from...
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