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This article develops a geometric framework for detecting targets, in the form of regions of interest, from certain sonar imagery. The main idea is to extract level sets from voxel images and compute local geometric features of the resulting surfaces. Examples include Gaussian and principal curvatures, radial distances, patch areas etc. These features are then compressed into histograms, or estimated...
Kernel descriptors have been proven to outperform existing histogram based local descriptors as such descriptors are extracted from the match kernels which measure similarities between image patches using different pixel attributes (gradient, colour or LBP pattern). The extraction of kernel descriptors does not require coarse quantization of pixel attributes. Instead, each pixel equally participates...
Pairwise protein structure comparison has taken significant scientific research effort in last two decades. Even though it all started with alignment-based comparison methods, recently there are several non-alignment based methods that have shown good potential. One such approach is based on shape descriptors. These methods use histograms or vectors to represent the molecular shapes. They have shown...
Atherosclerosis is a disease responsible for millions of deaths each year, primarily due to heart attack and stroke. Magnetic resonance (MR) imaging is a non-invasive method that can be used to analyze the carotid artery and detect signs of atherosclerosis. Most MR methods acquire high contrast, static images. These methods, however, are sensitive to artifacts from cardiac motion, produce time-averaged...
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...
This work proposes an off-line handwritten signature identification system using the Histogram of Symbolic Representation (HSR). The HSR is considered as one-class classifier which has the ability to generate a model for each writer using only its own reference signatures. This method allows also modeling the writing style of each writer by taking into account the variability of signatures. To evaluate...
We present an approach to learning features that represent the local geometry around a point in an unstructured point cloud. Such features play a central role in geometric registration, which supports diverse applications in robotics and 3D vision. Current state-of-the-art local features for unstructured point clouds have been manually crafted and none combines the desirable properties of precision,...
Aiming at the problem that the reading speed of pointer instruments is slow and the precision is low, this paper proposes a new automatic reading algorithm. The paper improves the algorithm in instrument center recognition, pointer recognition and 0 scale line recognition. First, round-degree is used to recognize the instrument center. Then the algorithm removes the interferential images outside the...
We present hierarchical multi-feature classification (HMC) system for multiclass fruit recognition problem. Our approach to HMC exploits the advantages of combining multimodal features and the fruit hierarchy property. In the construction of hybrid features, we take the advantage of using color feature in the fruit recognition problem and combine it with 3D shape feature of depth channel of RGBD (Red,...
Recent studies show that eyebrows can be used as a biometric or soft biometric for recognition. In some scenarios such as partially occluded or covered faces, they can be used for recognition. In this paper, we study eyebrow recognition using texture-based features. We apply features which have not been used before for eyebrow recognition such as 3-patch local binary pattern and WLD (Weber local descriptor)...
Diagnostic technology for coke-making plants under operation for many decades is a pressing concern in the domestic steel industry. This paper describes a new approach for monitoring carbon deposition on high-temperature coking chamber walls. Texture analysis based on gray-level co-occurrence matrices and other texture features was applied to thermal images. As a result, brick surface, solid carbon...
Human beings have inseparably attached biofield with physical body. This is now universally accepted. Scientific experiments have conclusively proved that the state of mind, i.e. the thought process, is responsible for generating different characteristics of biophotons. Detection, measurement, and even two dimensional imaging of ultraweak biophotons emission have been reported. With rapid progress...
Nature has surrounded us with lot of plants having medicinal values. But most of the time, we don't realize the importance and benefits of the plant and we just ignore it. In other cases, though we know names of the plants with medicinal values, it becomes difficult to identify the plant even if it is naturally grown in our backyard. And hence, a system is developed which would provide a solution...
This paper addresses the problem of pedestrian detection in high-density crowd images, characterized by strong homogeneity and clutter. We propose an evidential fusion algorithm which is able to exploit multiple detectors based on different gradient, texture and orientation descriptors. The evidential framework allows us to model the spatial imprecision arising from each of the detectors. A first...
In this paper, we propose a two-step textural feature extraction method, which utilizes the feature learning ability of Convolutional Neural Networks (CNN) to extract a set of low level primitive filter kernels, and then generalizes the discriminative power by forming a histogram based descriptor. The proposed method is applied to a practical medical diagnosis problem of classifying different stages...
Despite many proposed solutions, multi-object tracking remains a challenging problem in complex situations involving partial occlusions and non-uniform and abrupt illumination changes. Considering modular systems, the tracking performance strongly depends on the consistency of the different blocks relatively to error features. In this work, using the Belief Function framework, we take into account...
This paper proposes a novel shape feature extractor named Contour-SIFT along with a matching method that computes the similarity between two set of proposed descriptors. It allows a shape to be recognized based on automatically located outstanding local features on its contour, which are extracted from 1-D signal representations of different smoothing scales. The algorithm describes each local feature...
Fragment reconstruction aims to restore broken images and documents via matching spatial adjacent fragments. As the existing solutions in the literature still remain problematic, we present a novel feature descriptor, Normal Direction Local Binary Pattern (termed as ND-LBP), for document/image fragment matching. ND-LBP is based on the conventional LBP descriptor, however, it outstands LBP by introducing...
The article deals with the current situation of histogram use. The overwhelming majority of ELINT systems apply histogram depiction in their analytic displays. A significant majority of distinguished radar signals experts use this advantageous function. This contribution shows a breakthrough approach in histogram description via a Gaussian function. Specific sections are aimed at the creation of a...
Place recognition is widely used in the loop closure detection in SLAM. The current approach to place recognition is based on RGB images, but there are relatively few place recognition studies using a point cloud. This study presents the place recognition method based on the surface graph. The proposed method clusters the surfaces in the point cloud and recognizes a place through a surface descriptor...
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