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With the widespread use of time-lapse data to understand cellular function, there is a need for tools which facilitate high-throughput analysis of data. We present a system for automated segmentation and mitotic phase labelling based on a wide margin discriminative Semi-Markov Model. This work takes the novel approach of using temporal features evaluated over the whole of the mitotic phases rather...
This paper presents an approach to identify a weapon from a single image using a weapon ontology. Ontological nodes selected by experts store convex hull (CH) sequences for their descendants, whereas the ontological leafs are labeled with object boundary sequences. The latter are generated from object boundary vertices, while the CH sequences are generated from objects' CHs. The object's boundary...
Nowadays, the new technologies of image processing and Human Machine Interaction (HMI) are mostly linked to the scientific domains of research (medicine, transport, etc). Indeed, the emergence of the tactile interfaces as well as the gestural interfaces allows increasing the physical world which surrounds us with the digital information. It also allows us to use natural hand gestures to interact with...
We are interested in this work to optimize the algorithmic complexity of Markovian segmentation of brain tissues in MRI by the Bootstrap sampling. The introduction of this resampling allows to create the independence conditions which gives a better convergence of the mixture identification algorithm. A comparative study is made with the non-bootstrapped version in the mean of the misclassification...
Character Recognition is a process of understanding a human readable text document by machines. Today many researchers in the academia and industry are interested in this direction. This paper describes a novel method of Character Recognition. The main objective of this is to use the Radon Transform and Principal Component Analysis to obtain a set of invariant features, on basis of which a character...
The purpose of this research is to improve the recognition rate of online Arabic handwriting recognition using HMM (Hidden Markov Model). Delayed strokes are removed from the online Arabic word to avoid the difficulty and the confusion caused by the delayed strokes in the recognition process. A new technique for extracting offline features by dividing the image into non-uniform horizontal segments...
Infinite Hidden Markov Random Fields have been proposed for image segmentation as a solution to the problem of automatically determining the number of regions in an image; however, the model does not maintain identity of segmented regions among multiple images. In order to identify segmented regions in images, we developed Hierarchical Dirichlet Process Markov Random Fields. Our model maintains global...
In this work, we propose a novel multilingual word spotting framework based on Hidden Markov Models that works on corpus of multilingual handwritten documents and documents that contain more than one handwritten script. The system deals with large multilingual vocabularies without need for word or character segmentation. A keyword is represented by concatenating its character models. We propose and...
In this paper, we introduce a hand gesture recognition system to recognize the alphabets of Indian Sign Language. In our proposed system there are 4 modules: real time hand tracking, hand segmentation, feature extraction and gesture recognition. Camshift method and Hue, Saturation, Intensity (HSV) color model are used for hand tracking and segmentation. For gesture recognition, Genetic Algorithm is...
Automatic image annotation methods require a quality training image dataset, from which annotations for target images are obtained. At present, the main problem with these methods is their low effectiveness and scalability if a large-scale training dataset is used. Current methods use only global image features for search. We proposed a method to obtain annotations for target images, which is based...
This paper introduces a vision-based continuous sign language recognition (CSR) system. This CSR system can differentiate the signs in vocabulary and the non-signs. First, the continuous sign language is segmented into isolated sign segments. Then, the sign segment which can be interpreted by Product-HMMs (pHMM) is a sign, otherwise it is a non-sign. In the experiments, we test 40 signs from Taiwanese...
This paper describes the development of a computer-based serious game to enable older individuals to practice Tai Chi at home on their own. The player plays the game by imitating Tai Chi movements presented by a virtual instructor on the screen. The proposed system is decomposed into two modules. The first module is the game design, i.e., the process of recording an instructor training Tai Chi. Acquired...
A novel level set method based on on-line discriminative appearance modeling (DAMLSM) is presented for contour tracking. In contrast with traditional level set models which emphasize the intensity consistent segmentation and consider no priors, the proposed DAMLSM takes the context of tracking into account and use a discriminative patch based target model to guide the curve evolution. By modeling...
This paper presents a conditional random field (CRF) model for aligning online handwritten Chinese/Japanese text lines (character strings) with the corresponding transcripts. The CRF model is defined on a lattice which contains all possible segmentation hypotheses. The feature functions characterize the shape and context dependences of characters, including the scores of character recognition and...
In this paper, we present a segmentation-free word spotting method that is able to deal with heterogeneous document image collections. We propose a patch-based framework where patches are represented by a bag-of-visual-words model powered by SIFT descriptors. A later refinement of the feature vectors is performed by applying the latent semantic indexing technique. The proposed method performs well...
Based on the study of image segmentation algorithm, this paper presents one kind of improved image segmentation algorithm by comprehensive using the Otsu method and the method based on fuzzy theory seeking image segment threshold. The method uses Otsu method, obtained by pre-segmentation threshold value; then the image is divided into target and background. At last we use fuzzy partition method for...
Ruling lines are commonly used to help people write neatly on paper. In document image analysis, however, they create challenges for handwriting recognition and writer identification. In this paper, we model ruling line detection as a multi-line linear regression problem and then derive a globally optimal solution giving the Least Square Error. We demonstrate the efficacy of the technique on both...
Automatic separation of signatures from a document page involves difficult challenges due to the free-flow nature of handwriting, overlapping/touching of signature parts with printed text, noise, etc. In this paper, we have proposed a novel approach for the segmentation of signatures from machine printed signed documents. The algorithm first locates the signature block in the document using word level...
We introduce quantization feature functions to represent continuous or large range discrete data into the symbolic CRF data representation. We show that doing this convertion in a simple way allows the CRF to automaticaly select discriminative features to achieve best performance. This system is evaluated on a segmentation task of degraded newspapers archives. The results obtained show the ability...
The grapheme codebook is a high-performing technique for offline writer identification. This paper considers whether the de facto standards for initial grapheme extraction are optimal for both modern and historical datasets. We examine the construction and representation of the graphemes that comprise the codebook, testing three segmentation methods and two grapheme size normalisation methods on two...
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