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Multi-task feature learning aims to identify the shared features among tasks to improve generalization. Recent works have shown that the non-convex learning model often returns a better solution than the convex alternatives. Thus a non-convex model based on the capped-1, 1 regularization was proposed in [1], and the corresponding efficient multi-stage multi-task feature learning algorithm (MSMTFL)...
Classifier fusion is a well-studied problem in which decisions from multiple classifiers are combined at the score, rank, or decision level to obtain better results than a single classifier. Subsequently, various techniques for combining classifiers at each of these levels have been proposed in the literature. Many popular methods entail scaling and normalizing the scores obtained by each classifier...
Action recognition based on human skeleton structure represents nowadays a prosper research field. This is mainly due to the recent advances in terms of capture technologies and skeleton extraction algorithms. In this context, we observed that 3D skeleton-based actions share several properties with handwritten symbols since they both result from a human performance. We accordingly hypothesize that...
Automatically recognising facial emotions has drawn increasing attention in computer vision. Facial landmark based methods are one of the most widely used approaches to perform this task. However, these approaches do not provide good performance. Thus, researchers usually tend to combine more information such as textural and audio information to increase the recognition rate. In this paper we propose...
Augmented Reality (AR) is an active and exciting topic aiming to create intuitive computer interface by blending reality and virtual reality. One challenge of AR is to align virtual data with the environment. Typically, one uses a marker-based approach such as a thick-bordered black and white 2D marker which allows one to recover the relative pose (location and orientation) of a camera in real time...
In this paper, we propose a novel approach to creating clean line drawing from a scribbled sketch automatically. The main problem is determining which strokes of a scribbled sketch should be merged. We use a machine learning approach to solve this problem. Our method can automatically generate training data by comparing scribbled sketches with manually drawn line drawings without using annotations...
Despite of having no explicit shape model, multi-atlas approaches to image segmentation have proved to be a top-performer for several diverse datasets and imaging modalities. In this paper, we show how one can directly incorporate shape regularization into the multi-atlas framework. Unlike traditional methods, our proposed approach does not rely on label fusion on the voxel level. Instead, each registered...
Touchless communication is a new field for commanding electronic devices. This method is highlighted when hygiene is a special issue. Automated hand gesture recognition needs processing of hand images. Many research works have tried to cope with this recognition problem. Complexity and high computational costs are important drawbacks that make real-time execution of these algorithms difficult. In...
The analysis of human gait is more and more investigated due to its large panel of potential applications in various domains, like rehabilitation, deficiency diagnosis, surveillance and movement optimization. In addition, the release of depth sensors offers new opportunities to achieve gait analysis in a non-intrusive context. In this paper, we propose a gait analysis method from depth sequences by...
With the availability of the recent human skeleton extraction algorithm introduced by Shotton et al. [1], an interest for skeleton-based action recognition methods has been renewed. Despite the importance of the low-latency aspect in applications, it can be noted that the majority of recent approaches has not been evaluated in terms of computational cost. In this paper, a novel fast and accurate human...
This paper presents a sparse representation based image inpainting method using local patch analysis and geometric structure based feature extraction. In local patch analysis, we approximate the target region by weighted average of some local patches which are frequently occurred within a neighborhood. Local patch statistics is applied to find the most relevant neighbors for each target patch. Further...
With suggested computational post-processing workflow for correcting optical distortions, the Fresnel lens can finally be used in lightweight and inexpensive computer vision sensors. Common methods for image enhancement do not comprehensively address the blurring artifacts caused by strong chromatic aberrations in images produced by a simple Fresnel optical system. To deliver image quality acceptable...
A genetic programming (GP)-based framework to learn the effective feature representation for image dehazing is proposed in this work. In GP, an individual program is randomly generated and genetically evolved to achieve the desired goal. To make GP estimate haze in an input image, a set of operators and operands is designed, each of which is a primitive of a GP program. Specifically, we provide four...
Correspondence problems are very challenging due to the complexity of real-world scenes. Some hypergraph matching methods have been proposed for improving the recall of the solution, but the numerous outliers are brought since the precision is rarely considered. To solve this issue, we propose a sub-hypergraph matching method, which is robust with better integration of geometric information and reduces...
Low Light Level Images (LLLIs) are captured with exceptionally low brightness and low contrast, and cannot be enhanced satisfactorily with ordinary methods. In this paper, we propose a LLLI enhancement method using coupled dictionary learning. During the training stage, a pair of dictionaries and a linear mapping function are learned simultaneously. The dictionary pair aims to describe the raw LLLIs...
We address and compare two new frameworks for neural network (NN) computing-based feature enhanced (FE) fusion of remote sensing (RS) imagery acquired with different coherent radar sensing modalities. Both approaches exploit aggregation of the descriptive experiment design regularization (DEDR) based and the theoretical informatics inspired maximum entropy (ME) regularization paradigms for iterative...
Cosparse analysis model has shown its superior performance in image reconstruction. However, this analysis frame has not been exploited yet for hyperspectral image restoration task. An analysis operator learning method called GOAL (GeOmetric Analysis operator Learning) is applied for hyperspectral image. Considering the correlation of the hyperspectral bands, the hyperspectral images were cropped...
Human action recognition from videos has wide applicability and receives significant interests. In this work, to better identify spatio-temporal characteristics, we propose a novel 3D extension of Gradient Location and Orientation Histograms, which provides discriminative local features representing not only the gradient orientation, but also their relative locations. We further propose a human action...
In this article we present CoSC, a generic framework for collaborative segmentation and classification. The framework is guided by both radiometric homogeneity based criteria and implicit semantic criteria to segment and extract the objects of a given thematic class. We present a proof-of-concept case-study and show that CoSC is able to reach higher confidence for object classification and results...
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