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Real-time obstacle detection is a key component of autonomous vehicles. In this context, low obstacles are particularly challenging, as they are often discarded by traditional algorithms. Curb detection methods that can potentially be suitable for the problem usually target roads with clearly defined curbs and sidewalks. We propose a real-time algorithm for the detection of low obstacles (including,...
In this paper, we propose a scene clustering algorithm which uses straight line features. Scenes are represented as nodes in the graph, and each connectivity between nodes is calculated by a pre-trained vocabulary tree. By applying a spectral clustering algorithm to the constructed graph, the scenes are partitioned into k groups where k is determined by the proposed estimation method. Instead of using...
Recent studies show that it is feasible to use electrical signals from Electro-encephalography (EEG) to control devices or prostheses, these signals are provided by the body and can be measured on the scalp to determine the intent of the person when it is observing a visual stimulus frequency range detectable by the human eye. This group of signals are very susceptible to noise due to voltage levels...
In image classification tasks, the image is rarely represented as only a collection of raw pixels. Myriad alternative representations, from Gaussian kernels to bags-of-words to layers of a convolutional neural network, have been proposed both to decrease the dimensionality of the task and, more importantly, to move into a space which better facilitates classification. This work explores several methods...
Assessment of clusters is needed for effective speech data clustering. Traditional speech clustering methods produce the clustering results without a prior knowledge of number of speakers (or clusters). This is one of the key issues and is addressed in proposed work by visual approaches. A visual approach delivers the results in visual images that are useful for assessing the number of clusters by...
The development of location-based applications raises a new challenge to manage and visualize large amounts of geo-tags presented on a web map. The visualization of the geo-tags often leads to a clutter problem, especially in web-mapping systems. We present a new clustering method to reduce the amount of visual clutter. A split smart swap strategy, which has the advantage that it can be applied to...
In this paper, we propose a novel method to extract keyframes from motion capture data for people to better visualize and understand the content of the motion. It first applies a Butterworth filter to remove the noise in the motion capture data, then carries out principal component analysis (PCA) to reduce the dimension. By detecting the zero-crossing points of the velocity in the principal components,...
The disadvantages of BOW (Bag of words model) for image classification include the large amount of data in generating a codebook by clustering, redundant code words that may affect the classification results and so on. The process of BOW for the classification can be improved through the Laplace weights to improved fuzzy C means algorithm, and obtaining codebook with more ability to distinguish between...
Traditional methods for image retrieval used metadata associated with images, commonly known as keywords. These methods empowered many World Wide Web (WWW) search engines and achieved reasonable amount of accuracy. A data base shape, color, texture of content based image retrieval (CBIR) and classification algorithm is based on the K-means clustering is proposed in this paper. The algorithm is found...
Image segmentation is a key preprocessing step for object recognition and has a profound effect on the subsequent classification and recognition. Visual spatial clustering based segmentation is a commonly used method in image segmentation, which clusters pixels using visual descriptors by space similarity measure. It can achieve good results in simple image segmentation with less noise. This paper...
Herein, we explore both a new supervised and unsupervised technique for dimensionality reduction or multispectral sensor design via band group selection in hyperspectral imaging. Specifically, we investigate two algorithms, one based on the improved visual assessment of clustering tendency (iVAT) and the other based on the automatic extraction of “blocklike” structure in a dissimilarity matrix (CLODD...
The Johnson-Lindenstrauss (JL) lemma, with known probability, sets a lower bound q0 on the dimension for which a random projection of p-dimensional vector data is guaranteed to be within (1±ε) of being an isometry in a randomly projected downspace. We study several ways to identify a “good” rogue random projection when the target downspace has dimensions below the JL limit. The tools used towards...
Unsupervised transfer learning has attracted a lot of attention in the big data era, due to its capability of extracting knowledge from large-scale unlabeled samples in multiple data domains. Existing unsupervised transfer learning methods mainly focus on learning a common latent space for source and target domains, while the data representation and subspace structure in target domain are usually...
Salient object detection in hyperspectral imagery has drawn people's attention in recent years. Some detection methods which focus on extending Itti's visual saliency model into spectral domain have been proposed. However, these methods are sensitive to high-contrast edges and cannot preserve boundary of salient object well. To address these shortcomings, we propose a region-based spectral gradient...
Satellite image time series (SITS) analysis is attracting more researchers recently because SITS have the advantage of fully capturing the dynamic changes of land cover and SITS data is becoming increasingly available. As an unsupervised classification method, clustering gains more importance due to frequent updates of labeled data or training samples are too expensive. When discussing SITS clustering,...
This paper presents a new Bag-of-Features model (BoF) to enhance the efficiency of automatic image annotation. Since the traditional BoF ignores the semantic of its vocabularies, it cannot be seen as descriptive representation of images in many image applications. To handle this critical limitation, firstly, we propose the RGB compressive texton. By using compressive sensing theory, the image can...
A measurement method for the evaluation of the image complexity based on SIFT&K-means algorithm, namely the estimation of the mismatch between the target and the interesting points has been introduced in our previous research. Based on this method, we have made some improvements to calculate the image complexity of images with different memory targets. The improved algorithm SIFT&AIM&K-means...
This paper proposes a graph-based Web video search reranking method through consistency analysis using spectral clustering. Graph-based reranking is effective for refining text-based video search results. Generally, this approach constructs a graph where the vertices are videos and the edges reflect their pairwise similarities. A lot of reranking methods are built based on a scheme which regularizes...
Manifold distances are very effective tools for visual object recognition. However, most of the traditional manifold distances between images are based on the pixel-level comparison and thus easily affected by image rotations and translations. In this paper, we propose a new manifold distance to model the dissimilarities between visual objects based on the Complex Wavelet Structural Similarity (CW-SSIM)...
Key frame selection is important to dense 3D reconstruction, especially for unordered image sets. A novel method for key frame selection from unordered image sets is proposed based Distance Depedent Chinese Restaurant Process (DDCRP). First, a bag-of-features word package is constructed to describe each image in a document-like manner, which can be dealt with by the DDCRP model. Second, the overlapping...
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