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An application of artificial vision and artificial neural networks techniques in face recognition, is presented. In order to do that, a set of images (frontal face photos) with different lighting conditions, gestures, accessories and distances is used. A stepwise algorithm allows to achieve a satisfactory results, obtaining the correct identification of images inside and outside the data set.
In this work, we introduce a highly efficient algorithm to address the nonnegative matrix underapproximation (NMU) problem, i.e., nonnegative matrix factorization (NMF) with an additional underapproximation constraint. NMU results are interesting as, compared to traditional NMF, they present additional sparsity and part-based behavior, explaining unique data features. To show these features in practice,...
Palm Vein Identification(PVI) systems have been attracting interests from academia, industry, and governments for their advantages such as identification accuracy and relative low costs. However, low cost Infrared (IR) camera sensors produce noisy images which degrades the robustness of these systems. This paper proposes a new PVI system that uses a mirror based stereo camera setup to increase the...
In the recent years, human activity recognition is considered to be very important in video analysis researches due to the extensive requests from numerous users in different fields, such as public area surveillance, entertainment, human machine interaction and healthcare systems. In this work, we present a comparative study of two space-time interest points' detectors. These two detectors are evaluated...
In this paper, we present an improved version of MOBIL descriptor [1] (Improved MOments based BInary differences for Local description), which introduces two main contributions. The first one is the use of geometric information for the binary test instead of the classical intensity binary test, to get more precision in the description step. The second one is to attribute two bits for each test, to...
Local feature descriptor plays a fundamental role in many visual tasks, and its rotation invariance is a key issue for many recognition and detection problems. This paper proposes a novel rotation invariant descriptor by ordinal pyramid pooling of local Fourier transform features based on their radial gradient orientations. Since both the low-level feature and pooling strategy are rotation invariant,...
In this paper, we address the problem of interactive image segmentation which segments an image based on user-supplied scribbles. For this purpose, we propose a novel framework that provides consistent performance robust to the location of input seeds. Most of the existing methods, especially random walk-based approaches, strongly depend on initial seed positions, which differ from one user to another...
Effective recognition of objects calls for the appropriate selection of feature descriptor. In this paper, we generalize the "extended local ternary patterns" (ELTP) to form a novel and compact set of features named center-symmetric extended local ternary patterns (CS-ELTP). The newly defined CS-ELTP follows a simplified encoding procedure and has a lower dimension for a fixed neighborhood...
The problem of viewpoint changes is an important issue in the study of human action recognition. In this paper, we propose the use of spatial features in a spatiotemporal self-similarity matrix (SSM) based on action recognition that is robust in viewpoint changes from depth sequences. The spatial features represent a discriminative density of 3D point clouds in a 3D grid. We construct the spatiotemporal...
As an increasing number of digital images are generated, a demand for an efficient and effective image retrieval mechanisms grows. In this work, we present a new skeleton-based shape retrieval algorithm, which starts by drawing circles of increasing radius around skeleton points. Since each skeleton corresponds to the center of a maximally inscribed circle, this process results in circles that are...
Natural image matting is a useful and challenging task when processing image or editing video. It aims at solving the problem of accurately extracting the foreground object of arbitrary shape from an image by use of user-provided extra information, such as trimap. In this paper, we present a new sampling criterion based on random search for image matting. This improved random search algorithm can...
Many algorithms have been proposed to solve the problem of matching feature points in two or more images using geometric assumptions to increase the robustness of the matching. However, these assumptions do not always hold, in particular, few methods address the problem of reliable matching in cases where it is unknown whether the images have any corresponding areas or objects in the first place....
In this paper, we propose an improved SIFT algorithm based on FAST corner detection. We first estimate the level of the pyramid by the size of the image. Then, at each level, we estimate the threshold by the image content. By the threshold, we implement the FAST corner detection. At each level, the FAST corner detection, generation of SIFT descriptors and coordinate mapping are implemented by Wagner's...
RANSAC and RANSAC-like algorithm are most employed for the robust computation of relations from a number of potential matches in the field of computer vision, such as stereo matching, image retrieval, mosaic and elsewhere. There have been a number of recent efforts that aim to increase the efficiency of the standard RANSAC algorithm. This paper presents a novel optimal solution of the RANSAC algorithm...
Reliable and effective matching of visual descriptors is a key step for many vision applications, e.g. image retrieval. In this paper, we propose to integrate the Hausdorff distance matching together with our pairing algorithm, in order to obtain a robust while computationally efficient process of matching feature descriptors for image-to-image querying in standards datasets. For this purpose, Scale...
In this paper, we propose a new descriptor which is computed by comparing invariant cross color channels of pairs of points in the local patch. To efficiently obtain the sampled pairs of points, a galaxy sampling pattern is proposed. As shown in the experiments, our descriptor using invariant cross color channels and the galaxy sampling can achieve the best performance in most cases with slight computation...
In this paper, we propose a novel binary-based cost computation and aggregation approach for stereo matching problem. The cost volume is constructed through bitwise operations on a series of binary strings. Then this approach is combined with traditional winner-take-all strategy, resulting in a new local stereo matching algorithm called binary stereo matching (BSM). Since core algorithm of BSM is...
Local Binary Descriptors (LBDs) are good at matching image parts, but how much information is actually carried? Surprisingly, this question is usually ignored and replaced by a comparison of matching performances. In this paper, we directly address it by trying to reconstruct plausible images from different LBDs such as BRIEF [4] and FREAK [1]. Using an inverse problem framework, we show that this...
Most of conventional object matching methods are based on comparing the local features, which are too computational demanding to achieve realtime performance on object detection in videos. Recently, Dominant Orientation Templates (DOT) method was proposed to make online feature detection and comparison feasible. However, it still suffers the problem of fragility due to the noise and partial occlusions...
Traditional detect method could not deal well with the problem of paper zooming. This paper introduces scale invariant feature transform to the paper detection system. The hardware of paper detection system consists of digital signal processor and complex programmable logic device. The hardware can accomplish image acquisition and processing. The software of this system use SIFT method to detect the...
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