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Estimating speaker's physical parameters like height, weight and shoulder size can assist in voice forensics by providing additional knowledge about the speaker. In this work, statistics of the components of background GMM are employed as features in estimating the physical parameters. These features improved the performance of height and shoulder size estimation as compared to our earlier attempt...
Consider a face image data set from clients of a company and the problem of building a face recognition system from it. Video cameras can be used to acquire several images per client in order to maximize the robustness of the system. However, as the data set grows huge, the accuracy of the system might be seriously compromised since the number of negative samples for each user is increasing. We propose...
This paper presents a multiple classifier system (MCS) to identify plants species based on the texture and shape features extracted from leaf images. A diverse pool of SVM and Neural Network classifiers is trained on four different feature sets, namely, Local Binary Pattern (LBP), Histogram of Gradients (HOG), Speed of Robust Features (SURF) and Zernike Moments (ZM). Then, a static classifier selection...
We propose a novel and principled hybrid CNN+CRF model for stereo estimation. Our model allows to exploit the advantages of both, convolutional neural networks (CNNs) and conditional random fields (CRFs) in an unified approach. The CNNs compute expressive features for matching and distinctive color edges, which in turn are used to compute the unary and binary costs of the CRF. For inference, we apply...
In this paper, we aim to address the issue that semi-supervised learning is prone to be influenced by the quality and quantity of initial seeds. In order to expand the initial labeled data, we select credible samples from unlabeled data by a proposed bilateral latent information miner. The miner can extract information from unlabeled data for both positive and negative class respectively. Then we...
Protein secondary structure prediction is an important problem in bioinformatics. For this task, a method based on the SVM-PSSM Classifier combined by sequence feature (SF) is proposed in this paper. Protein sequence data is represented by a hybrid formation which combines the Position-Specific Scoring Matrix (PSSM) with the Hydrophobicity Sequence Feature (HSF), and the Structural Sequence Feature(SSF)...
The performance of the myoelectric pattern recognition system sharply decreases when working in various limb positions. The issue can be solved by cumbersome training procedure that can anticipate all possible future situations. However, this procedure will sacrifice the comfort of the user. In addition, many unpredictable scenarios may be met in the future. This paper proposed a new adaptive myoelectric...
A query image based scene/image retrieval system is a system that analyzes the properties of a query image and identifies the class in which the image belongs and retrieves a number of images which are most alike and relevant to the query image. A scene/image classifier provides the first stage for this system. Scene classification is the process that analyzes the properties of various image features...
Object classification is an important task within the field of computer vision. It is the process of labeling objects into predefined and semantically meaningful categories using trained datasets. A classification is made using a segment of image which is actually a single pixel or a group of pixels which is called a classification unit. Many researchers are working in this area to improve the accuracy...
In this paper, we propose a unified classification framework for 3D urban point clouds. First of all, an efficient segmentation approach is utilized to segment 3D point clouds. For comparison, we employed two recently developed point clouds segmentation approaches. The first one is a region-growing-based segmentation algorithm by using robust saliency features and another one is a hierarchical-clustering-based...
Advancements in Sonar image capture have opened the door to powerful classification schemes for automatic target recognition (ATR). Recent work has particularly seen the application of sparse reconstruction-based classification (SRC) to sonar ATR, which provides compelling accuracy rates even in the presence of noise and blur. However, existing sparsity based sonar ATR techniques assume that the test...
An object often has many distinct manifestations in computer vision, which brings a great challenge to utilizing more comprehensive information. Inspired by some biological researches about edge sensitivity and global structure priority, our key insight is to establish unified transfer classification network with shared contour information. Combining two convolutional networks with three cascaded...
This paper aims at investigating the best feature selection method for optimized and automated machine learning based detection of malarial parasite in wholeslide images of peripheral blood smears. We do this by extracting samples from the wholeslide images and performing feature extraction. A host of feature selection methods are used to judge the performance of the Support Vector Machine as a binary...
Feature extraction methods have an important role in image classification. In this paper, a hybrid texture feature descriptor is proposed by utilizing the attributes of two complementary features, PRICoLBP and LPQ. PRICoLBP performs well in the case of geometric and photometric variations however it does not properly express the local texture of an image, while LPQ method performs well for the local...
Image classification is a crucial task in Computer Vision. Feature detection represents a key component of the image classification process, which aims at detecting a set of important features that have the potential to facilitate the classification task. In this paper, we propose a Genetic Programming (GP) approach to image feature detection. The proposed method uses the Speeded Up Robust Features...
Performing sentiment analysis of tweets by training a classifier is a challenging and complex task, requiring that the classifier can correctly and reliably identify the emotional polarity of a tweet. Poor data quality, due to class imbalance or mislabeled instances, may negatively impact classification performance. Ensemble learning techniques combine multiple models in an attempt to improve classification...
Recognizing the face of target individuals in a watch-list is among the most challenging applications in video surveillance, especially when enrollment is based on one reference still facial image. Besides the limited representativeness of facial models used for matching, the appearance of faces captured in videos varies due to changes in illumination, pose, scales, etc., and to camera inter-operability...
This paper investigates the use of the Oriented Fast, Rotated Brief (ORB) method to automatically detect the most significant broadcast view associated with cricket broadcasts: the Bowler Run-up Sequence (BRS) for cricket highlight generation. This method is computationally less expensive than other methods proposed for BRS detection. It is shown here that only a single frame is required for training...
There are several papers about pseudo dynamic methods used in signature authentication. Recently, the gray scale features local binary pattern(LBP) originate from texture analysis has been widely used in signature verification system with advantage of robustness to illumination change. The major problem of LBP is its sensitivity to noise, hence many solutions has been applied to solve this problem...
‘Circle’ and ‘arrow’ traffic lights are both common at intersections in urban road environment. However, existing purely vision based systems are only focus on either ‘circle’ or ‘arrow’ traffic light recognition, which limits their real-world application. In this paper, A novel robust and real-time traffic light recognition system based on hierarchical vision architecture is carefully designed. The...
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