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This paper studies various different ways of representing plant leaves in different feature space to recognize them accurately and efficiently in changing scenario and dimension. Here, leaves are represented as: (i) statistical features — relative sub-image sparse coefficient (RSSC), (ii) shape features — angle view projection (AVP), (iii) multi-resolution features — Gabor Wavelet transform combined...
Person re-identification is a challenge in video-based surveillance where the goal is to identify the same person in different camera views. In recent years, many algorithms have been proposed that approach this problem by designing suitable feature representations for images of persons or by training appropriate distance metrics that learn to distinguish between images of different persons. Aggregating...
Interest point detection is one of the key technologies in image processing and target recognition. This paper presents a new method for detecting interest points in digital images and computer vision problems based on complex network theory. We associate a directed and weighted complex network model to each image and then we propose three different algorithms to locate these key points based on three...
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...
This paper designs an algorithm for the moving object recognition based on support vector machine (SVM) in order to identify and classify the moving objects accurately. In view of the advantages of support vector machine in small sample, nonlinear, and high dimensional pattern recognition, a classifier is constructed based on support vector machine (SVM) is constructed. A feature vector is presented...
The identification of a writer of a handwriting image is very useful for applications in forensic and historic document analysis. Writer identification methods retrieve the closest image within a list of samples of different writers to a query sample. In automatic writer verification the system takes an automatic decision if two handwriting images were written by the same person. In recent years,...
Fish Species classification is of great utility to marine biologists for the understanding of underwater ecology and fish behavior as well as to keep a log of endangered species, which assists in fisheries management. Traditional methods being either tedious or too computationally intensive lead to the requirement of an automated method of analysis and counting. Generally, the classification of underwater...
In this work, a fast shape searching face alignment (F-SSFA) algorithm based accelerator is proposed to achieve real-time processing. Firstly, a learning based low-dimensional SURF feature is introduced to reduce the computation cost in the cascaded regression. Then the Euclidean distance and shape affine transformation are utilized to accelerate the shape searching procedure. F-SSFA therefore greatly...
Temporal contour shapes are closely linked to the narrative structure of multimedia content and provide important reference points in content-based multimedia timeline analysis. In this paper, multimedia timeline is extracted from content as time varying video and audio signal features. A temporal contour representation is implemented based on sequential pattern discovery algorithm for modeling the...
Due to image quality related issues, classification of plankton images, particularly of those collected in situ, strongly relies on shape features. Thus, image segmentation is a critical step in the classification pipeline. In general, the segmentation algorithm that leads to the best overall classification accuracy does not necessarily imply best classification accuracy with respect to each of the...
The hypothesis that bent radio sources are supposed to be found in rich, massive galaxy clusters and the avalibility of huge amount of data from radio surveys have fueled our motivation to use Machine Learning (ML) to identify bent radio sources and as such use them as tracers for galaxy clusters. The shapelet analysis allowed us to decompose radio images into 256 features that could be fed into the...
Detection of human beings in a complex background environment is a great challenge in the area of computer vision. For such a difficult task, most of the time no single feature algorithm is rich enough to capture all the relevant information available in the image. To improve the detection accuracy we propose a new descriptor that fuses the local phase information, image gradient, and texture features...
Marine dunes are bedforms that, because of their locations and dimensions, can infer with human activities at sea. New MultiBeam Echo-sounder Systems (MBES) enable to collect always more accurate and denser data revealing the diversity of shape, size and dynamics of these features with finer details. Until now, research on dunes have mostly consisted in describing or modeling their morphology and...
Automated prostate diagnoses and treatments have gained much attention due to the high mortality rate of prostate cancer. In particular, unsupervised (automatic) prostate segmentation is an active and challenging research. Most conventional works usually utilize handcrafted (low-level) features for prostate segmentation; however they often fail to extract the intrinsic structure of the prostate, especially...
In classification, a large number of features often make it difficult to select appropriate classification features. In such situations, feature selection or dimensionality reduction methods play an important role in classification. ReliefF algorithm is one of the most successful filtering feature selection methods. In this paper, some shortcomings of the ReliefF algorithm are improved, on the problem...
In this paper, we propose a low complexity method for detection and tracking of potholes in video sequences taken by a camera placed inside a moving car. The region of interest for the detection of the potholes is selected as the image area where the road is observed with the highest resolution. A threshold-based algorithm generates a set of candidate regions. For each region the following features...
The sparsity and the problem of curse of dimensionality of high dimensional data make traditional clustering algorithms such as K-Means, DBSCAN (Density-Based Spatial Clustering of Applications with Noise) result in low quality clusters and increase the time complexity exponentially. Many Projected Clustering algorithms have been proposed to deal with noisy High Dimensional Data. However, most of...
While great success has been demonstrated in numerous tracking algorithm, some challenging problems still remain such as motion, shape deformation and occlusion. In this paper, the proposed algorithm can work robustly to overcome the occlusion and fast movement in real -- world scenarios. A discriminative model based on the Gaussian superpixel model is constructed to descript the change of the target...
The problems of accuracy and computational complexity in extracting image features(color, texture and shape) in traditional Image retrieval algorithm result in a bigger error of image retrieval result and the lower efficiency of retrieval. A Content-Based Multi-Feature Comprehensively Weighting Video-Image Retrieval Algorithm is proposed to settle the problem. The essence of the algorithm is, set...
In this study we analyzed a series of LiDAR point clouds acquired over Taijiang district (part of Fujian province, China). The objective was to detect and extract water surface area from individual LiDAR point cloud, in a parallel means. To this end, interactive visualization of fine-grained data, global cluster algorithms, and statistical investigation were applied. We first rasterized point clouds...
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