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It is a simple task for humans to visually identify objects. However, computer-based image recognition remains challenging. In this paper we describe an approach for image recognition with specific focus on automated recognition of plants and flowers. The approach taken utilizes deep learning capabilities and unlike other approaches that focus on static images for feature classification, we utilize...
In this paper, we combine Histogram of Oriented Gradients (HOG) and Local Binary Pattern (LBP) in order to detect the cranium and its components; namely, the brain, eyes and mouth. Furthermore, Deformable Part Model (DPM) algorithm is paired with the AdaBoost for training and classification. We use a CT/PET database acquired from the National Biomedical Imaging Archive (NBIA) in order to train and...
In this paper, we propose a method to recognize human behavior by combining motion history images (MHI) and non-negative matrix factorization (NMF). The MHI preserves the temporal information of a behavior by holding the temporal motion appearance. Then, NMF is applied to extract the middle-level features of the moving object. The experimental results show that the proposed scheme can achieve robust...
Currently, most of the human detection methods are based on low-level features. In this paper, we proposed a middle-level feature generation method based on non-negative matrix factorization (NMF) for human detection. We also proposed an improvement scheme to guarantee that a better middle-level feature can be achieved. The proposed scheme can be applied to a complex background and the experimental...
Automatic plant identification via computer vision techniques has been greatly important for a number of professionals, such as environmental protectors, land managers, and foresters. In this paper, we conduct a comparative study on leaf image recognition and propose a novel learning-based leaf image recognition technique via sparse representation (or sparse coding) for automatic plant identification...
Nowadays, many countries have established spontaneous reporting systems (SRSs) to facilitate postmarketing surveillance of listed drugs and collect enough data for detecting unknown adverse drug reactions. Due to data in SRSs coming from different sources of reporters, there heralds the problem of duplicate reporting; even a small amount of duplicate records would bias the detection results. Although...
In order to have a rich representation for human action, we propose to combine two complementary features so that a human posture can be characterized in more details. In particular, the distance signal feature and the width feature are combined in an effective way to enhance each other's discriminating capability. The resulting feature vector is quantized into mid-level features using k-means clustering...
In this paper, a new robust image hashing scheme for image authentication via dictionary-based sparse representation of images is proposed. For image hash extraction, we create an over-complete dictionary containing the prototype image atoms to build the hash for an image, where each image patch can be represented by sparse linear combinations of these atoms. The major contribution is to formulate...
For financial institutions, the ability to predict or forecast business failures is crucial, as incorrect decisions can have direct financial consequences. Bankruptcy prediction and credit scoring are the two major research problems in the accounting and finance domain. In the literature, a number of models have been developed to predict whether borrowers are in danger of bankruptcy and whether they...
With regard to the high dimensional and small sample facial feature, this paper introduced the Kernel and Canonical Correlation Analysis (CCA) into the Locality Preserving Projections (LPP) algorithm and proposed a new face recognition algorithm based on the Kernel Base Locality Preserving Canonical Correlation Analysis (KLPCCA) with the derivation process. According to this algorithm, first use CCA...
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