413,820 research outputs found

    Semantic Part Segmentation using Compositional Model combining Shape and Appearance

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    In this paper, we study the problem of semantic part segmentation for animals. This is more challenging than standard object detection, object segmentation and pose estimation tasks because semantic parts of animals often have similar appearance and highly varying shapes. To tackle these challenges, we build a mixture of compositional models to represent the object boundary and the boundaries of semantic parts. And we incorporate edge, appearance, and semantic part cues into the compositional model. Given part-level segmentation annotation, we develop a novel algorithm to learn a mixture of compositional models under various poses and viewpoints for certain animal classes. Furthermore, a linear complexity algorithm is offered for efficient inference of the compositional model using dynamic programming. We evaluate our method for horse and cow using a newly annotated dataset on Pascal VOC 2010 which has pixelwise part labels. Experimental results demonstrate the effectiveness of our method

    Collaborative Computation in Self-Organizing Particle Systems

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    Many forms of programmable matter have been proposed for various tasks. We use an abstract model of self-organizing particle systems for programmable matter which could be used for a variety of applications, including smart paint and coating materials for engineering or programmable cells for medical uses. Previous research using this model has focused on shape formation and other spatial configuration problems (e.g., coating and compression). In this work we study foundational computational tasks that exceed the capabilities of the individual constant size memory of a particle, such as implementing a counter and matrix-vector multiplication. These tasks represent new ways to use these self-organizing systems, which, in conjunction with previous shape and configuration work, make the systems useful for a wider variety of tasks. They can also leverage the distributed and dynamic nature of the self-organizing system to be more efficient and adaptable than on traditional linear computing hardware. Finally, we demonstrate applications of similar types of computations with self-organizing systems to image processing, with implementations of image color transformation and edge detection algorithms

    Automatic Detection and Rectification of Paper Receipts on Smartphones

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    We describe the development of a real-time smartphone app that allows the user to digitize paper receipts in a novel way by "waving" their phone over the receipts and letting the app automatically detect and rectify the receipts for subsequent text recognition. We show that traditional computer vision algorithms for edge and corner detection do not robustly detect the non-linear and discontinuous edges and corners of a typical paper receipt in real-world settings. This is particularly the case when the colors of the receipt and background are similar, or where other interfering rectangular objects are present. Inaccurate detection of a receipt's corner positions then results in distorted images when using an affine projective transformation to rectify the perspective. We propose an innovative solution to receipt corner detection by treating each of the four corners as a unique "object", and training a Single Shot Detection MobileNet object detection model. We use a small amount of real data and a large amount of automatically generated synthetic data that is designed to be similar to real-world imaging scenarios. We show that our proposed method robustly detects the four corners of a receipt, giving a receipt detection accuracy of 85.3% on real-world data, compared to only 36.9% with a traditional edge detection-based approach. Our method works even when the color of the receipt is virtually indistinguishable from the background. Moreover, our method is trained to detect only the corners of the central target receipt and implicitly learns to ignore other receipts, and other rectangular objects. Including synthetic data allows us to train an even better model. These factors are a major advantage over traditional edge detection-based approaches, allowing us to deliver a much better experience to the user

    Low level feature detection in human vision

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    Influential models of edge detection have generally supposed that an edge is detected at peaks in the 1st derivative of the luminance profile, or at zero-crossings in the 2nd derivative. However, when presented with blurred triangle-wave images, observers consistently marked edges not at these locations, but at peaks in the 3rd derivative. This new phenomenon, termed ‘Mach edges’ persisted when a luminance ramp was added to the blurred triangle-wave. Modelling of these Mach edge detection data required the addition of a physiologically plausible filter, prior to the 3rd derivative computation. A viable alternative model was examined, on the basis of data obtained with short-duration, high spatial-frequency stimuli. Detection and feature-making methods were used to examine the perception of Mach bands in an image set that spanned a range of Mach band detectabilities. A scale-space model that computed edge and bar features in parallel provided a better fit to the data than 4 competing models that combined information across scale in a different manner, or computed edge or bar features at a single scale. The perception of luminance bars was examined in 2 experiments. Data for one image-set suggested a simple rule for perception of a small Gaussian bar on a larger inverted Gaussian bar background. In previous research, discriminability (d’) has typically been reported to be a power function of contrast, where the exponent (p) is 2 to 3. However, using bar, grating, and Gaussian edge stimuli, with several methodologies, values of p were obtained that ranged from 1 to 1.7 across 6 experiments. This novel finding was explained by appealing to low stimulus uncertainty, or a near-linear transducer

    Third-derivative filters predict edge locations in spatial vision

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    Edge detection is crucial in visual processing. Previous computational and psychophysical models have often used peaks in the gradient or zero-crossings in the 2nd derivative to signal edges. We tested these approaches using a stimulus that has no such features. Its luminance profile was a triangle wave, blurred by a rectangular function. Subjects marked the position and polarity of perceived edges. For all blur widths tested, observers marked edges at or near 3rd derivative maxima, even though these were not 1st derivative maxima or 2nd derivative zero-crossings, at any scale. These results are predicted by a new nonlinear model based on 3rd derivative filtering. As a critical test, we added a ramp of variable slope to the blurred triangle-wave luminance profile. The ramp has no effect on the (linear) 2nd or higher derivatives, but the nonlinear model predicts a shift from seeing two edges to seeing one edge as the ramp gradient increases. Results of two experiments confirmed such a shift, thus supporting the new model. [Supported by the Engineering and Physical Sciences Research Council]

    Deteksi Wajah Manusia Pada Citra Berwarna Dengan Informasi Warna Kulit dan Support Vector Machines

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    ABSTRAKSI: Pada penulisan Tugas Akhir ini, penulis mengangkat permasalahan face detection pada citra berwarna dengan menggunakan informasi warna kulit dan metoda klasifikasi Support Vector Machines. Pada face detection ini yang proses pertama dilakukan adalah melakukan proses segmentasi warna kulit dan dilanjutkan melakukan binary processing sehingga didapatkan kandidat-kandidat yang selanjutnya pada tahap kedua dilakukan deteksi oleh SVM dengan menggunakan metoda kernel linear. Untuk dapat mendeteksi wajah, SVM diberikan pelatihan dengan menggunakan dataset berupa sekumpulan data wajah dan non-wajah sehingga nantinya SVM memiliki kemampuan untuk dapat mengklasifikasikan objek wajah dan objek non-wajah. Metoda edge detection pada proses pendeteksian wajah digunakan untuk membantu memisahkan objek wajah yang salang berhimpitanHasil yang didapatkan dari proses pendeteksian wajah ini menunjukan model warna RGB memiliki tingkat keakuratan yang lebih tinggi dibandingkan dengan model warna HSV.Kata Kunci : face detection, support vector machines, edge detectionABSTRACT: In this final project is talking about face detection in color image by using skin color information and support vector machine classification method. In face detection process, the first thing to do is doing the skin color segmentation and then use binary processing, so the candidates can be gotten. Then do the detection by using SVM. To detect the face, datasets that contain many faces and non-faces are given as input to SVM then with testing data, SVM can classify between face and non-face object. Edge detection method in face detection problem used for help separate face object.From the experiment result in face detection problem, RGB color model give better result in accuracy than HSV color model.Keyword: face detection, support vector machines, edge detectio

    Network community detection with edge classifiers trained on LFR graphs

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    Abstract. Graphs generated using the Lancichinetti-Fortunato-Radicchi (LFR) model are widely used for assessing the performance of network community detection algorithms. This paper investigates an laternative use of LFR graphs: as training data for learning classifiers that discriminate between edges that are ‘within ’ a community and ‘between ’ network communities. The LFR generator has a parameter that controls the extent to which communities are mixed, and hence harder to detect. We show experimentally that a linear edge-wise weighted support vector machine classifier trained on a graph with more mixed communities also works well when tested on easier graph instances, while it achieves mixed performance on real-life networks, with a tendency towards finding many communities.
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