Efficient dominant point detection based on discrete curve structure

International audienceIn this paper, we investigate the problem of dominant point detection on digital curves which consists in finding points with local maximum curvature. Thanks to previous studies of the decomposition of curves into sequence of discrete structures [5–7], namely maximal blurred segments of width [13], an initial algorithm has been proposed in [14] to detect dominant points. However, an heuristic strategy is used to identify the dominant points. We now propose a modified algorithm without heuristics but a simple measure of angle. In addition, an application of polygonal simplification is as well proposed to reduce the number of detected dominant points by associating a weight to each of them. The experimental results demonstrate the e and robustness of the proposed method

Challenges in video based object detection in maritime scenario using computer vision

This paper discusses the technical challenges in maritime image processing and machine vision problems for video streams generated by cameras. Even well documented problems of horizon detection and registration of frames in a video are very challenging in maritime scenarios. More advanced problems of background subtraction and object detection in video streams are very challenging. Challenges arising from the dynamic nature of the background, unavailability of static cues, presence of small objects at distant backgrounds, illumination effects, all contribute to the challenges as discussed here

A conceptual design tool: Sketch and fuzzy logic based system

A real time sketch and fuzzy logic based prototype system for conceptual design has been developed. This system comprises four phases. In the first one, the system accepts the input of on-line free-hand sketches, and segments them into meaningful parts by using fuzzy knowledge to detect corners and inflection points on the sketched curves. The fuzzy knowledge is applied to capture user’s drawing intention in terms of sketching position, direction, speed and acceleration. During the second phase, each segmented sub-part (curve) can be classified and identified as one of the following 2D primitives: straight lines, circles, circular arcs, ellipses, elliptical arcs or B-spline curves. Then, 2D topology information (connectivity, unitary constraints and pairwise constraints) is extracted dynamically from the identified 2D primitives. From the extracted information, a more accurate 2D geometry can be built up by a 2D geometric constraint solver. The 2D topology and geometry information is then employed to further interpretation of a 3D geometry. The system can not only accept sketched input, but also users’ interactive input of 2D and 3D primitives. This makes it friendly and easier to use, in comparison with ‘sketched input only’, or ‘interactive input only’ systems. Finally, examples are given to illustrate the system

Social Fingerprinting: detection of spambot groups through DNA-inspired behavioral modeling

Spambot detection in online social networks is a long-lasting challenge involving the study and design of detection techniques capable of efficiently identifying ever-evolving spammers. Recently, a new wave of social spambots has emerged, with advanced human-like characteristics that allow them to go undetected even by current state-of-the-art algorithms. In this paper, we show that efficient spambots detection can be achieved via an in-depth analysis of their collective behaviors exploiting the digital DNA technique for modeling the behaviors of social network users. Inspired by its biological counterpart, in the digital DNA representation the behavioral lifetime of a digital account is encoded in a sequence of characters. Then, we define a similarity measure for such digital DNA sequences. We build upon digital DNA and the similarity between groups of users to characterize both genuine accounts and spambots. Leveraging such characterization, we design the Social Fingerprinting technique, which is able to discriminate among spambots and genuine accounts in both a supervised and an unsupervised fashion. We finally evaluate the effectiveness of Social Fingerprinting and we compare it with three state-of-the-art detection algorithms. Among the peculiarities of our approach is the possibility to apply off-the-shelf DNA analysis techniques to study online users behaviors and to efficiently rely on a limited number of lightweight account characteristics

Follow-up Observations of the Second and Third Known Pulsating Hot DQ White Dwarfs

We present follow-up time-series photometric observations that confirm and extend the results of the significant discovery made by Barlow et al.(2008) that the Hot DQ white dwarfs SDSS J220029.08-074121.5 and SDSS J234843.30-094245.3 are luminosity variable. These are the second and third known members of a new class of pulsating white dwarfs, after the prototype SDSS J142625.71+575218.3 (Montgomery et al. 2008). We find that the light curve of SDSS J220029.08-074121.5 is dominated by an oscillation at 654.397+-0.056 s, and that the light pulse folded on that period is highly nonlinear due to the presence of the first and second harmonic of the main pulsation. We also present evidence for the possible detection of two additional pulsation modes with low amplitudes and periods of 577.576+-0.226 s and 254.732+-0.048 s in that star. Likewise, we find that the light curve of SDSS J234843.30-094245.3 is dominated by a pulsation with a period of 1044.168+-0.012 s, but with no sign of harmonic components. A new oscillation, with a low amplitude and a period of 416.919+-0.004 s, is also probably detected in that second star. We argue, on the basis of the very different folded pulse shapes, that SDSS J220029.08-074121.5 is likely magnetic, while SDSS J234843.30-094245.3 is probably not.Comment: 12 pages, 19 figures, accepted for publication in Ap

Shape matching by curve modelling and alignment

Automatic information retrieval in the eld of shape recognition has been widely covered by many research elds. Various techniques have been developed using different approaches such as intensity-based, modelbased and shape-based methods. Whichever is the way to represent the objects in images, a recognition method should be robust in the presence of scale change, translation and rotation. In this paper we present a new recognition method based on a curve alignment technique, for planar image contours. The method consists of various phases including extracting outlines of images, detecting signicant points and aligning curves. The dominant points can be manually or automatically detected. The matching phase uses the idea of calculating the overlapping indices between shapes as similarity measures. To evaluate the effectiveness of the algorithm, two databases of 216 and 99 images have been used. A performance analysis and comparison is provided by precision-recall curves