- Read more about FACIAL ANALYSIS IN THE WILD WITH LSTM NETWORKS
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- Read more about FACIAL ANALYSIS IN THE WILD WITH LSTM NETWORKS
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Current object segmentation algorithms are based on the hypothesis that one has access to a very large amount of data. In this paper, we aim to segment objects using only tiny datasets. To this extent, we propose a new automatic part-based object segmentation algorithm for non-deformable and semi-deformable objects in natural backgrounds. We have developed a novel shape descriptor which models the local boundaries of an object's part. This shape descriptor is used in a bag-of-words approach for object detection.
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- Read more about WEAKLY SUPERVISED OBJECT LOCALIZATION WITH DEEP CONVOLUTIONAL NEURAL NETWORK BASED ON SPATIAL PYRAMID SALIENCY MAP
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- Read more about JOINT DEMOSAICING AND DENOISING OF NOISY BAYER IMAGES WITH ADMM
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- Read more about JOINT WEBER-BASED ROTATION INVARIANT UNIFORM LOCAL TERNARY PATTERN FOR CLASSIFICATION OF PULMONARY EMPHYSEMA IN CT IMAGES
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In this paper, we present a novel image representation approach for classifying emphysema in computed tomography (CT) images of the lung. Our proposed method extends rotation invariant uniform local binary pattern (RIULBP) and local ternary pattern (LTP), which are extensively used in a variety of computer vision applications, into rotation invariant uniform local ternary pattern (RIULTP) with a human perception principle: Weber’s law.
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- Read more about LUCKY DCT AGGREGATION FOR CAMERA SHAKE REMOVAL
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We consider the task of removing the effect of camera shake during a long exposure. Technically, this is a blind deconvolution problem in which both the image and the motion blur have to be jointly inferred. Several algorithms have been proposed till date for removing camera shake that work with one or more images. However, most of these algorithms are computationally expensive and hence cannot be used in real-time.
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In this paper, we propose a novel object proposal generation scheme by formulating a graph-based salient edge classification framework that utilizes the edge context. In the proposed method, we construct a Bayesian probabilistic edge map to assign a saliency value to the edgelets by exploiting low level edge features. A Conditional Random Field is then learned to effectively combine these features for edge classification with object/non-object label. We propose an objectness score for the generated windows by analyzing the salient edge density inside the bounding box.
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- Read more about MULTISPECTRAL FOCAL STACK ACQUISITION USING A CHROMATIC ABERRATION ENLARGED CAMERA
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Capturing more information, e.g. geometry and material, using optical cameras can greatly help the perception and understanding of complex scenes. This paper proposes a novel method to capture the spectral and light field information simultaneously. By using a delicately designed chromatic aberration enlarged camera, the spectral-varying slices at different depths of the scene can be easily captured.
icip oral.pdf
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