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This paper proposes a novel image inpainting method to remove undesired objects in an image. Conventionally, missing regions are filled in using similar textures in an image as exemplars. However, unnatural textures are often generated due to the paucity of available samples. In this study, we take into account symmetric transformation of texture patterns to increase exemplars. To generate plausible...
Edges are caused by several imaging cues such as shadow, material and illumination transitions. Classification methods have been proposed which are solely based on photometric information, ignoring geometry to classify the physical nature of edges in images. In this paper, the aim is to present a novel strategy to handle both photometric and geometric information for edge classification. Photometric...
We propose a two phase line segmentation method for handwritten pre-modern densely written manuscripts. The proposed method combines the robustness of projection based methods with the flexibility of graph based methods. The result are cut-outs of the image containing each text line. Overlapping characters, help lines and degradation can create foreground elements spanning several lines that are hard...
This paper presents an approach for computing global distance metrics that minimize the k-NN leave-one-out (LOO) error. The approach optimizes an energy function that corresponds to a smoothened version of the k-NN LOO error. The generalization of the proposed approach is further improved by controlling the k parameter through a heuristic. Evaluation of the proposed approach on several public datasets...
Superpixel segmentation has become a popular preprocessing step in computer vision with a great variety of existing algorithms. Almost all algorithms claim to compute compact superpixels, but no one showed how to measure compactness and no one investigated the implications. In this paper, we propose a novel metric to measure superpixel compactness. With this metric, we show that there is a trade-off...
We propose a novel framework for the analysis of myocar-dial transit of contrast agent on cardiac first-pass magnetic resonance imaging (FP-MRI). Significant shape changes of the left ventricle (LV) due to heart contraction and respiratory motion limit accurate quantification of perfusion parameters on time series data. To account for the rigid and nonrigid deformations of the heart, we developed...
Combining several binary image operators, each one based on different windows, has proven to be an effective way to produce operators with better performance than designing single operators based on one window only. To facilitate the combination task that so far is done manually, we propose a genetic algorithm (GA) based approach. It consists of the definition of a collection of candidate windows...
Finding mean of matrices becomes increasingly important in modern signal processing problems that involve matrix-valued images. In this paper, we define the mean for a set of symmetric positive definite (SPD) matrices based on information-theoretic divergences as the unique minimizer of the averaged divergences, and compare it with the means computed using the Rieman-nian and Log-Euclidean metrics...
This paper proposes a novel regression method based on distance metric learning for human age estimation. We take age estimation as a problem of distance-based ordinal regression, in which the facial aging trend can be discovered by a learned distance metric. Through the learned distance metric, we hope that both the ordinal information of different age groups and the local geometry structure of the...
Video-based biometric systems are becoming feasible thanks to advancement in both algorithms and computation platforms. Such systems have many advantages: improved robustness to spoof attack, performance gain thanks to variance reduction, and increased data quality/resolution, among others. We investigate a discriminative video-based score-level fusion mechanism, which enables an existing biometric...
Several citizen service databases such as, police, national citizen identity, passport and vehicle registration, store both biographical and biometric information containing huge number of records. Achieving scalability and high accuracy for a 1:N person identification task on these databases is a huge challenge. In this work, we propose to use complementary information present in the biographical...
In this paper, a method named histogram intersection metric learning from scene tracks is proposed for automatic organizing people in videos. We make the following contributions: (i) learning histogram intersection distance instead of Mahalanobis distance for widely used face features; (ii) learning the metric from scene tracks without manually labeling any examples, which enables learning across...
The key frame extraction is designed for obtaining a (very) compressed set of video frames that summarizes the essential content of a video sequence. In this paper, a well-known information theoretic measure, the Jensen-Rényi divergence (JRD), is studied to estimate the frame-by-frame distance between consecutive video images, for segmenting shots/subshots and for choosing key frames. Our new key...
We present a new approach to compute the graph edit distance between two attributed graphs which is based on a formal connection between the graph edit distance problem and that of finding a dominant set in an auxiliary edge-weighted “association” graph. Experiments performed on various data sets show that with the proposed approach we are able to improve on state-of-the-art algorithms.
Tracking individuals in video sequences, especially in crowded scenes, is still a challenging research topic in the area of pattern recognition and computer vision. However, current single camera tracking approaches are mostly based on visual features only. The novelty of the approach proposed in this paper is the integration of evidences from a crowd simulation algorithm into a pure vision based...
This paper focuses on producing fast and accurate co-segmentation to a pair of images that is scalable and able to apply multimodal features. We present a general solution for this purpose and specifically propose a noniterative and fully unsupervised method using pointwise color and regional covariance features for image co-segmentation. The scalability and generality of our method mainly attribute...
We present a method of discriminating between authentic and forged signatures using 2-D geometric warping. After an initial coarse-alignment step, we use an automatic morphing correspondence algorithm to compute 2-D geometric warps that align the strokes of a questioned signature with those of known reference examples. We use distance maps to compute a difference metric, and then either accept the...
Discriminative approaches to human pose estimation have became popular in recent years. These approaches face a big challenge: Similar inputs might correspond to very dissimilar poses. This property misleads the mapping functions which rely on the Euclidean distances in the input space. In this paper, we use the distances between the labels of the training data to learn a metric and map the input...
This paper studies the problem of end-to-end windows mining directly from detection output. Traditional object detection systems approach this problem in an ad-hoc manner, say, Non-Maximum Suppression (NMS). Beyond NMS, multi-class context modeling has been explored thoroughly recent years. But all these methods put their emphasis on eliminating false positive windows rather than improving recall...
This paper investigates the problem of gait-based gender classification in unconstrained environments. Different from existing human gait analysis and recognition methods which assume that humans walk in controlled environments, we aim to recognize human gender from uncontrolled gaits in which people can walk freely and the walking direction of human gaits may be time-varying in a singe video clip...
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