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Monitoring and classification of human activity has been an active area of research for the past few years due to the increasing demands in healthcare sector. Quick aid for falls in elderly persons and detecting emergency situations are few leading cause of such interest. In this paper, a human activity recognition system based on motion patterns on a smartphone is proposed for classification of activities...
Steganalysis is a process by which we can detect the secret message i.e. hidden by using various Steganography algorithms. There are various universal Steganalysis methods and features based Steganalysis is one of them. In this paper we have used three different Steganographic methods, NsF5, JP Hide & Seek and PQ for hiding the secret information within images. We have used four embedding rates:...
Sketch recognition has important law enforcement applications in detecting and apprehending suspects. Compared to hand drawn sketches, software generated composite sketches are faster to create and require lesser skill sets as well as bring consistency in sketch generation. While sketch generation is one side of the problem, recognizing composite sketches with digital images is another side. This...
In this paper, we propose the optimal parameters of local binary patterns such as type of pattern, size of blocks in the feature space and distance measure for face recognition using a genetic algorithm. The genetic algorithm is able to optimize all these parameters quickly and to improve the recognition accuracy. We provide a comparative study of three types of local binary patterns (LBP, LGP and...
In this paper we present a physical structure detection method for historical handwritten document images. We considered layout analysis as a pixel labeling problem. By classifying each pixel as either periphery, background, text block, or decoration, we achieve high quality segmentation without any assumption of specific topologies and shapes. Various color and texture features such as color variance,...
Classification of the content of a scanned document as either printed or handwritten is typically tackled as a segmentation problem of pages into text lines or words. However these methods are not applicable on documents where handwritten annotations overlay printed text. In this paper we propose to treat the task as a pixel classification task, i.e., To classify individual foreground pixels into...
The state-of-the-art writer identification systems use a variety of different features and techniques in order to identify the writer of the handwritten text. In this paper several statistical and model based features are presented. Specifically, an improvement of a statistical feature, the edge hinge distribution, is attempted. Furthermore, the combination of this feature with a model-based feature...
We present an approach for on-line recognition of handwritten math symbols using adaptations of off-line features and synthetic data generation. We compare the performance of our approach using four different classification methods: AdaBoost. M1 with C4.5 decision trees, Random Forests and Support-Vector Machines with linear and Gaussian kernels. Despite the fact that timing information can be extracted...
Determining the individuality of handwriting in ancient manuscripts is an important aspect of the manuscript analysis process. Automatic identification of writers in historical manuscripts can support historians to gain insights into manuscripts with missing metadata such as writer name, period, and origin. In this paper writer classification and retrieval approaches for multi-page documents in the...
Recognition of offline musical symbols can aid in automatic retrieval of a particular piece of musical notation from a digital repository. Though some work on on-line Musical symbol notations exists, little work has been done on off-line recognition of the symbols. This article proposes a system for offline isolated musical symbol recognition. Efficacy of a texture analysis based feature extraction...
Writer identification from musical scores is a challenging task. A few pieces of work on writer identification in musical sheets have been published in the literature but to the best of our knowledge all these work were performed after removal of staff lines from the musical scores. In this paper we propose a symbol-independent writer identification framework using HMM in music score without removing...
Script identification is an important area in handwriting document image analysis field. The script identification at word level on documents written in multiple scripts is an open challenge for the scientific community and a real concern in countries with multiple official languages, e. G. The country like India. Such documents usually contain two scripts: the most of the document are written in...
A digital forensics examiner often has to deal with large amounts of multimedia content during an investigation. One important part of such an investigation is to identify illegal material like pictures containing child pornography. Robust image hashing is an effective technique to help identifying known illegal images even after the original images were modified by applying various image processing...
This paper describes the use of a novel A path-planning algorithm for performing line segmentation of handwritten documents. The novelty of the proposed approach lies in the use of a smart combination of simple soft cost functions that allows an artificial agent to compute paths separating the upper and lower text fields. The use of soft cost functions enables the agent to compute near-optimal separating...
Security of fingerprint authentication systems remains threatened by the presentation of spoof artifacts. Most current mitigation approaches rely upon the fingerprint liveness detection as the main anti-spoofing mechanisms. However, liveness detection algorithms are not robust to sensor variations. In other words, typical liveness detection algorithms need to be retrained and adapted to each and every...
Validation is highly important in parallel application simulations with a large number of parameters, a process that can vary depending on the structure of the simulator and the granularity of the models used. Common practice involves calculating the percentage error between the projected and the real execution time of a benchmark program. However, this coarse-grained approach often suffers from a...
Indirect immunofluorescence imaging is employed to identify antinuclear antibodies in HEp-2 cells which founds the basis for diagnosing autoimmune diseases and other important pathological conditions involving the immune system. Six categories of HEp-2 cells are generally considered, namely homogeneous, fine speckled, coarse speckled, nucleolar, cyto-plasmic, and centromere cells. Typically, this...
Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. In this paper, we present two unsupervised spike sorting algorithms based on discriminative subspace learning. The first algorithm simultaneously learns the discriminative feature subspace and performs clustering. It uses histogram of features in the most discriminative projection...
Automated motor unit (MU) decomposition algorithms of surface electromyogram (EMG) have been developed recently. However, a routine estimate of the decomposition accuracy is still lacking. The objective of this preliminary study was to examine the statistics of the inter-spike intervals (ISIs) of the identified MUs as a measure of the decomposition accuracy, such that the ISI analysis can be used...
A slight variance in dataset quality or baseline model accuracy can affect the level of confidence at which DSM project results are calculated. General measurement and verification (M&V) guidelines mitigate this inherent variance by reporting a conservative result. However, it remains important to quantify the magnitude of variance to ensure that the conservative approach does not adversely affect...
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