In this paper, we use sparse modeling for processing phase-shifting interferometry measurements. The proposed approach takes into full consideration the Poissonian (photon counting) measurements. In this way we are targeting at optimal sparse reconstruction both phase and magnitude taking into consideration all details of the observation formation. Many images (and signals) admit sparse represe...

Finding maximally sparse representations from overcomplete feature dictionaries frequently involves minimizing a cost function composed of a likelihood (or data fit) term and a prior (or penalty function) that favors sparsity. While typically the prior is factorial, here we examine non-factorial alternatives that have a number of desirable properties relevant to sparse estimation and are easily...

In scientific and commercial fields associated with modern agriculture, the categorization of different rice types and determination of its quality is very important. Various image processing algorithms are applied in recent years to detect different agricultural products. The problem of rice classification and quality detection in this paper is presented based on model learning concepts includ...

Facial expression recognition is an interesting and challenging subject in signal processing and artificial intelligence. In this paper, a new method of facial expression recognition based on the sparse representation classifier (SRC) is presented. Two typical appearance facial features, i.e., local binary patterns (LBP) and Gabor wavelets representations are extracted to evaluate the performan...

This paper presents a novel and efficient framework for human action recognition based on modeling the motion of human body-parts. Intuitively, a collective understanding of human body-part movements can lead to better understanding and representation of any human action. In this paper, we propose a generative representation of the motion of human body-parts to learn and classify human actions....

A novel and robust approach for content based speech/nonspeech audio classification is proposed based on sparse representation (SR) features and Gaussian process classifiers (GPCs). The projections of the noise robust sparse representations for audio signals computed by 1 L -norm minimization are used as features. GPCs are used to learn and predict audio categories. Compare to the difficulties ...

Convolutional sparse coding methods focus on building representations of time signals as sparse and linear combinations of shifted patterns. These techniques have proven to be useful when dealing with signals (such as ECG or images) which are composed of several characteristic patterns ([1, 2, 3]). For this type of signals, the shapes and positions of these templates are crucial for their study...

This report documents the program and the outcomes of Dagstuhl Seminar 11051 “Sparse Representations and Efficient Sensing of Data”. The scope of the seminar was twofold. First, we wanted to elaborate the state of the art in the field of sparse data representation and corresponding efficient data sensing methods. Second, we planned to explore and analyze the impact of methods in computational s...

In text management tasks, the dimensionality reduction becomes necessary to computation and interpretability of the results generated by machine learning algorithms. This paper describes a feature extraction method called semantic mapping. Semantic mapping, sparse random mapping and PCA are applied to self-organization of document collections using self-organizing map (SOM). The behaviors of th...

human action recognition is an important problem in computer vision. one of the methods that are recently used is sparse coding. conventional sparse coding algorithms learn dictionaries and codes in an unsupervised manner and neglect class information that is available in the training set. but in this paper for solving this problem, we use a discriminative sparse code based on multi-manifolds. ...